i

MESOSCALE MODELING OF

ATMOSPHERIC PROCESSES IN INDIA

D. V. BHASKAR RAO

SOMESHWAR DAS

P. SANJEEVA RAO

Science and Engineering Research Council

DEPARTMENT OF SCIENCE & TECHNOLOGY

Ministry of Science & Technology

Government of India

2008

DISCLAIMER: The contents published in this report are based on the information provided by the

contributors. The editors and the Department of Science and Technology are not responsible for the opinionsexpressed and conclusions drawn. The geographical boundaries shown in this report do not necessarilycorrespond to the political boundaries.

CITATION: Bhaskar Rao, D.V., Someshwar Das and P. Sanjeeva Rao (2008): “Mesoscale Modeling of

Atmospheric Processes in India”, Published by Department of Science and Technology, Government ofIndia, pp 113.

Printed by the Director, Andhra University Press & PublicationsVisakhapatnam, Andhra Pradesh, India

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~CMI~I'-111 ~, ~ ~ 11T1t, ~ ~-110 016GOVERNMENT OF INDIA

MINISTRY OF SCIENCE & TECHNOLOGY

DEPARTMENT OF SCIENCE & TECHNOLOGYTechnology Bhavan, New Mehrauli Road, New Delhi-110 016

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~~ctDr. T. RAMASAMI

SECRETARY

~~

Foreword

Mesoscale high impact weather events like tropical cyclones,thunderstorms, western disturbances, fog, etc occur every year over differentparts of the country in all seasons with disastrous consequences. Till last decadeforecasting of such high impact mesoscale hydro-climatic disastrous events wasbased on empirical methods. Since 1995, mesoscale dynamical models havebeen adopted in India, some in research mode and others for operationalweather prediction. Considerable efforts were put by the Department of Scienceand Technology in imparting training to young scientists and creatinginfrastructure facilities at various universities in adopting these dynamicalmodels under Indian conditions. Now, several research groups in India usemesoscale models with multiple nesting techniques applying variable horizontalresolutions to resolve smaller scale motions such that the interactions betweenthe synoptic scale and mesoscale are accounted for.

The present document is an outcome of the deliberations at the'National Workshop on Mesoscale Modeling of Atmospheric Processes"held at Andhra University, Visakhapatnam during January 2007. It describes, inbrief, the status of dynamical modeling of the mesoscale atmospheric processesin India and also provides the scientific issues to undertake future researchtowards improving high impact weather prediction capabilities. I congratulateProf DV Bhaskara Rao and his associates for their efforts in bringing thispublication which serves as a reference for future research endeavors by theMinistry of Earth Sciences and the Department of Science and Technology.

~~~Dated: 4th July 2008

Tel 011-26510068,011-26511439 . I-ax : Ull-LOtloJ847, 011-26862418 .E-mail: dstsec@nic.in

PREFACE

The geographical region of India experiences different types of weather phenomena, predominantly

of tropical nature and others with subtropical characteristics. Scientific understanding towards accurate pre-

diction of these weather systems is very important due to their socio-economic impact. Rapid changes in the

public life with industrialization, economic growth, rapid transport facilities etc, necessitated the need for

mesoscale weather prediction with few kilometers of spatial scale and few hours of time scale. The mesoscale

weather phenomena which affect the Indian subcontinent are tropical cyclones, severe local storms, western

disturbances, mid-tropospheric cyclones, off shore vortices and fog. The super cyclone which crossed the

Orissa coast on 29 October 1999 was noted to be the most intense tropical cyclone on record and caused an

estimated loss of 12,500 crores in Indian Rupees. Due to their annual occurrence and the devastating nature,

tropical cyclones have been the focus of continued research for meteorologists in India and elsewhere due to

their global nature. Severe weather storms which include thunderstorm, hail storm, mesoscale convective

systems and squall lines occur over most parts of the country. These severe storms are known to cause

destruction due to heavy rainfall, gusty winds, sometimes producing flash floods inundating the low lying

regions. During the last few years the occurrence of heavy precipitation events (often exceeding 25 cm/day),

were experienced over many urban locations causing disruption of public life and resulting in economic loss.

An example is the unprecedented rainfall that occurred over Mumbai city on 26 July 2005 with a rainfall

amount of 94 cm within a span of few hours causing enormous economic losses estimated at 5000 crores in

Indian rupees. Heavy rainfall events were also observed in Chennai, Bangalore, Hyderabad, Ahmedabad and

Visakhapatnam during 2005. Fog is another weather hazard known to cause decrease of visibility and so

effect train and aircraft services. The number of the occurrences and the duration of fog have shown increase

during the last few years, attributed to increase of air pollution, and thus increased vulnerability. Similarly

avalanches occur in the mountain region of northern states due to occurrence of heavy snowfall associated

with transient mesoscale weather systems.

Understanding and prediction of the above mentioned mesoscale weather phenomena are important

for effective planning and implementation of mitigation measures. Development of atmospheric models suit-

able for mesoscale weather prediction has taken place during the last decade and a few mesoscale models are

presently available. The most commonly used mesoscale models by the research groups in India are National

Centre for Atmospheric Research MM5 and Weather Research and Forecast (WRF) models due to their

availability and versatility for the study of the different mesoscale weather phenomena.

The Department of Science and Technology (DST), Government of India recognized the need for

better understanding of the atmospheric processes associated with the mesoscale weather phenomena over

the Indian region and initiated action to promote research on the development and validation of mesoscale

models. This is being done through funding research projects, organizing training programmes and special

national workshops. During the last five years, DST supported various programmes such as “Advanced

training school on tropical cyclones”, Andhra University, Visakhapatnam during 29 November-24 Decem-

ber, 2004; ‘Brain storming session on Orissa Super Cyclone’, India Meteorological Department, New Delhi

during 21-22 March, 2005; “SERC School on Aviation Meteorology with special reference to Thunderstorm

and its Modeling”, AFAC, Coimbatore during 9-28 May 2005; ‘National Workshop on Dynamics and

Simulation of Extreme Rainfall with special reference to Mumbai Heavy rainfall events’, CMMACS, Banga-

lore during 15-17 March 2006; “Advanced Training Program and Tutorial on WRF Modelling System”, IIT

Delhi, New Delhi during 18 February-1 March 2006; Training programme on HWRF, IIT Delhi, New Delhi

during January 2007. DST has also supported to bring out two special issues of the Mausam journal, one on

‘Arabian Sea Monsoon Experiment (ARMEX)’ (Mausam, Vol.56,1, January 2005) and another on ‘Orissa

Super Cyclone of 1999’ (Mausam, vol.57,1, January 2006).

DST has supported a “National Workshop on Mesoscale Modeling of Atmospheric Processes” at

Andhra University, Visakhapatnam during 29-31 January 2007 to have an assessment of the present status of

research on mesoscale modeling of various atmospheric processes. This documentation is a sequel to the

workshop taken up by Prof. D.V. Bhaskar Rao, Andhra University and Dr. Someshwar Das, NCMRWF.

Almost all the national organisations and academic institutions engaged in atmospheric modeling research

have provided their inputs for preparation of this status document. The presentation of this report is divided

into six sections to provide the background of the atmospheric processes affecting the Indian regions; de-

scription of the systems at mesoscale overview of mesoscale models, mesoscale data assimilation, national

status and recommendations to improve the understanding toward prediction of mesoscale atmospheric pro-

cesses affecting the Indian region.

ix

CONTENTS

Page

1. Introduction 3

2. Mesoscale Atmospheric Systems affecting Indian Region 7

2.1 Tropical cyclones 9

2.2 Thunderstorms 14

2.3 Western Disturbances 17

2.4 Cold waves 18

2.5 Fog 20

2.6 Avalanches 20

2.7 Mid-tropospheric cyclones 21

2.8 Off-shore vortices 21

3. Mesoscale Atmospheric Models and their Applications 23

3.1 Overview of Mesoscale Models 25

3.2 Severe Weather Forecasting 31

3.3 Cyclone Track and Intensity Prediction 33

3.4 Thunderstorms and Cloudbursts 35

3.5 Fog Forecasting 42

3.6 Mountain Weather Forecasting 44

3.7 Seasonal Forecasting/ Downscaling 46

3.8 Air pollution/ Nuclear Emergency Response system 49

3.9 Cloud-resolving Scale Simulations 50

3.10 Cloud Seeding Experiments 52

x

4. Mesoscale Land-Ocean-Atmosphere Data Assimilation 55

4.1 Observations Nudging 57

4.2 Variational Assimilation 59

4.3 Assimilation of Satellite Observations 60

4.4 Assimilation of Doppler Radar Observations 62

5. National Status on Mesoscale Atmospheric Process Modelling 67

5.1 India Meteorological Department 70

5.2 National Centre for Medium Range Weather Forecasting 72

5.3 Indian Institute of Tropical Meteorology 73

5.4 Centre for Mathematical Modeling and Computer Simulation 75

5.5 Space Physical Laboratory 79

5.6 Indira Gandhi Centre for Atomic Research 82

5.7 Snow and Avalanche Study Establishment 82

5.8 Indian Air Force 83

5.9 Indian institute of Technology-Delhi 85

5.10 Indian institute of Technology-Kharagpur 89

5.11 Andhra University 89

5.12 Jadavpur University 94

6. Recommendations 95

References 103

Acronyms 109

List of Contributors and Institutions 113

1

CHAPTER 1

INTRODUCTION

2

3

Introduction

Mesoscale atmospheric systems refer to those which are smaller than synoptic scale (~1000 km)

and larger than the cumulus scale (~ 1 km). They are categorised into meso-α (200-2000 km),

meso-β (20-200 km) and meso-γ (2-20 km) as shown in Table-1.1 following Fujita (1986). Mesoscale

models are designed for the simulation and prediction of mesoscale weather systems. Such models remain

important for an operational numerical weather prediction centre because they can be run at very high

resolution with a wide variety of options for the parameterization of physical processes and to be run with

nested grid for optimized computational resources. The global models do not have such privileges as they

are very expensive to run at high resolutions. Moreover, at finer resolution the mesoscale models are also

capable of assimilating large amount of high resolution observations available from present day satellites

and Doppler radars.

Table -1.1: Classification of Mesoscale weather systems (Fujita, 1986)

NomenclatureDimensions

Typical FeatureSpatial Time

Mesoscale-alpha 200 - 2000 km 6 hrs - 2 days Jet stream, tropical cyclones,weak anticyclones

Mesoscale-beta 20 - 200 km 30 mins - 6 hrs Local wind fields, mountain winds,land/sea breeze, mesoscale convectivecomplexes (MCCs), large thunderstorms

Mesoscale-gamma 2 - 20 km 3 - 30 mins Most thunderstorms, large cumulus,extremely large tornadoes

The mesoscale models can be configured to run from global to cloud resolving scale for simulation of

transient thunderstorms and cloud clusters. They can also be used for a wide variety of applications such as

cloud-radiation interaction, cloud-cloud interaction, transport of heat, moisture & momentum, pollution (Srinivas

et al. 2006), precipitation, interaction with surface fluxes, topographic effects (Das et al. 2003a, Das 2005)

and, troposphere-stratosphere interaction. Since the early 1990s several important changes took place in

mesoscale modeling. First was the introduction of nonhydrostatic dynamics into mesoscale models (e.g.,

Dudhia 1993). The nonhydrostatic mesoscale models can be run at cloud resolving resolutions (~1 km)

4

without the restrictions of the hydrostatic assumption. This facilitates the application of these models to a wide

range of scientific problems. For example, mesoscale models can be used to explicitly simulate convection

and its interaction with the large scale weather systems in a realistic way (Kuo and Wang 1996). However, the

use of high-resolution mesoscale models for real-time Numerical Weather Prediction requires a tremendous

amount of computational resources. For example, to increase the grid model resolution (both horizontally and

vertically) by a factor of two would require an increase in computational resources by a factor of 16

(Kuo, 2003).

In India, mesoscale models are being used by many academic and operational organizations for differ-

ent applications ranging from research to real-time weather forecasting. For example, at National Centre for

Medium Range Weather Forecast mesoscale models such as MM5, ETA, WRF and RSM are run on real-

time basis for forecasting mesoscale weather systems (Das, 2002a, 2002b; Rajagopal and Iyengar, 2002,

2005; Mohandas and Rajagopal, 2005). Similarly, the mesoscale models are run at different institutions like

IMD (which runs LAM and MM5; Hatwar et al., 2005; Roy Bhowmik et al., 2006a; Rama Rao et al.,

2005), IIT-Delhi (which runs MM5 and WRF; Mohanty et al., 2004; Mandal et al., 2003, 2006; Routray et

al., 2006), Andhra University (which runs MM5 and an axi-symmetric cyclone model; Bhaskar Rao et al.,

2004; Bhaskar Rao and Prasad, 2005, 2006), IITM-Pune (which runs MM5, ARPS and RAMS;

Mukhopadhyay et al., 2005; Vaidya, 2007), C-MMACS (which runs a variable resolution LMD-GCM;

Goswami and Patra, 2004), IAF (which runs MM5 and WRF; Arora and Nandi, 2006), IGCAR (which

runs MM5; Srinivas et al., 2006), SASE (which runs MM5; Srinivasan et al., 2005), SPL (which runs MM5,

ARPS and HRM; Radhika et al., 2006, 2007), IIT-Kharagpur (which runs MM5; Sandeep et al., 2006;

Xavier et al., 2006), Jadavapur University (which runs MM5) and Cochin University (which runs MM5).

Besides running the model, some of the institutions (NCMRWF and C-MMACS) also run mesoscale data

assimilation based on 3DVAR/ 4DVAR.

Realizing the importance of extreme weather events and their socio-economic impact, the Government

India (through DST) and the Indian Space Research Organization [ISRO]) has launched nationally coordi-

nated major field programs named STORM (Severe Thunderstorm Observations and Regional Modeling)

and PRWONAM (Prediction of Regional Weather using Observational Meso-Network and Atmospheric

Modelling) to boost the research on mesoscale modeling and data assimilation. The STORM is a comprehen-

sive observational and modeling effort to improve the understanding and prediction of the severe thunder-

storms (STORM, 2005). Pilot field experiments were conducted during April-May of 2006 and 2007 with a

plan for the main experiments during the years 2009 and 2010. In 2007, the Pilot experiment was extended

to cover the north-eastern India as the severe thunderstorms affect this region also during April-May. Simi-

larly, the PRWONAM, has been taken up as a multi-institutional project to advance the knowledge of mesos-

cale to regional scale influences on the initiation, evolution and sustenance of regional/ local weather systems

and to understand how the predictive skill is improved by denser observational network, data assimilation and

use of suitable models and improved model physics. A mesoscale field experiment was conducted during

5

2006-2007 over southern peninsular Indian region covering SHAR-Kalpakkam-Bangalore areas and Indian

Ocean on either sides to make measurements with high-density network of stations.

In this document, we shall present in detail the status of the mesoscale models being used in India.

Section 2 presents discussion of the mesoscale atmospheric systems affecting the Indian region. In section 3,

we present a detailed discussion on the different applications of the mesoscale models including real-time

weather forecasting, cyclone track prediction, mountain weather forecasting, seasonal prediction, air pollu-

tion studies and cloud resolving simulations. Section 4 presents a discussion on mesoscale atmospheric data

assimilation. The national versus international status is discussed in section 5 and finally, recommendations are

given in section 6.

7

CHAPTER 2

MESOSCALE ATMOSPHERIC SYSTEMS AFFECTINGTHE INDIAN REGION

8

9

Introduction

The atmospheric systems that affect the Indian region are known to be tropical cyclones, severe local

storms, western disturbances, mid-tropospheric cyclones, off-shore vortices and fog. Each of these systems

has different spatial and time scale and also region of occurrence. All these systems except fog bring rain and/

or snow, many times useful for water resources but some of them are hazardous. Understanding and prediction

of these systems is very important for planners and administrators as well for general public. A description of

the characteristics of the different systems is provided in the following sections.

2.1 Tropical Cyclones

Tropical cyclones are known to be the most devastating of all natural disasters over the world. Tropical

cyclones form over warm ocean regions and intensify under favorable atmospheric conditions and decay after

landfall. The annual frequency of tropical cyclones over the north Indian ocean is 6, which is only about 6% of

the global number, but they are known to cause more damage and destruction than other parts of the world.

India has a long coastline of length nearly 8000 km and owing to major developmental activities. Location of

industries and consequent density of population along the coastal region, the devastation of the cyclones is

more over India. The devastation is mainly due to high winds, torrential rain and associated storm surge.

While the effects of high winds and storm surges are concentrated with in a few kilometers, the heavy rainfall

associated with a tropical cyclone, affects hundreds of kilometers area from the coast. The shallow waters of

the Bay of Bengal, the low flat coastal terrain and the funneling shape of the coastline lead to devastating

losses of life and property due to the generation of storm surge. It is also observed that 90% of the North

Indian Ocean (NIO) cyclonic systems form over Bay of Bengal (BOB) where as 10% form over Arabian

Sea. Tropical cyclone genesis is highly seasonal in the NIO with the primary maximum in the post-monsoon

season i.e. during the months of October, November and December and the secondary maximum during pre-

monsoon months of April and May.

2.1.1 Mechanics of tropical cyclones

Structurally, a tropical cyclone is a large, rotating system of clouds, wind and thunderstorms. The

primary source of energy is the release of the heat of condensation from water vapor condensing at high

altitudes, the heat ultimately derived from the sun. Therefore, a tropical cyclone can be thought of as a giant

vertical heat engine supported by mechanics driven by physical forces such as the rotation and gravity of the

earth. This condensation leads to higher wind speeds, as a tiny fraction of the released energy is converted

10

into mechanical energy; the faster winds and lower pressure associated with them in turn cause increased

surface evaporation and thus even more condensation. Much of the released energy drives updrafts that

increase the height of the storm clouds, speeding up condensation. This gives rise to factors that provide the

system with enough energy to be self-sufficient and cause a positive feedback loop where it can draw more

energy as long as the source of heat, warm water, remains. Factors such as a continued lack of equilibrium in

air mass distribution would also give supporting energy to the cyclone. The rotation of the earth causes the

system to spin, an effect known as the Coriolis effect, giving it a cyclonic characteristic and affecting the

trajectory of the storm.

The factors to form a tropical cyclone include a pre-existing atmospheric disturbance, warm tropical

oceans, moisture, and relatively light winds aloft. If the right conditions persist and allow it to create a feedback

loop by maximizing the energy intake possible, for example, such as high winds to increase the rate of

evaporation, they can combine to produce the violent winds, incredible waves, torrential rains, and floods

associated with this phenomenon.

The passage of a tropical cyclone over the ocean can cause the upper ocean to cool substantially,

which can influence subsequent cyclone development. Tropical cyclones cool the ocean by acting like “heat

engines” that transfer heat from the ocean surface to the atmosphere through evaporation. Cooling is also

caused by upwelling of cold water from below. Additional cooling may come from cold water from raindrops

that remain on the ocean surface for a time. Cloud cover may also play a role in cooling the ocean by shielding

the ocean surface from direct sunlight before and slightly after the storm passage. All these effects can combine

to produce a dramatic drop in sea surface temperature over a large area in just a few days.

While the most obvious motion of clouds is toward the center, tropical cyclones also develop an upper-

level (high-altitude) outward flow of clouds. These originate from air that has released its moisture and is expelled

at high altitude through the “chimney” of the storm engine. This outflow produces high, thin cirrus clouds that

spiral away from the center. The high cirrus clouds may be the first signs of an approaching tropical cyclone.

2.1.2 Formation

The formation of tropical cyclones is the topic of extensive ongoing research and is still not fully

understood. Six general factors are necessary to make tropical cyclone formation possible, although tropical

cyclones may occasionally form without meeting these conditions:

1. Water temperatures of at least 26.5 °C (80°F) down to a depth of at least 50 m (150 feet).

Waters of this temperature cause the overlying atmosphere to be provide latent heat energy to the

atmosphere thus increasing its instability and favouring sustained convection and thunderstorms.

2. Rapid cooling with height. This allows the release of latent heat, which is the source of energy in a

tropical cyclone.

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3. High humidity, especially in the lower-to-mid troposphere. This helps deep convection and condi-

tions are more favourable for disturbances to develop.

4. Low wind shear. When wind shear is high, the convection in a cyclone or disturbance will be

disrupted, blowing the system apart.

5. Distance from the equator. This allows the Coriolis force to deflect winds blowing towards the low

pressure center, causing a circulation. The minimum distance is 500 km (310 miles) or about 5

degrees from the equator.

6. A pre-existing system of disturbed weather. The system must have some sort of circulation as well

as a low pressure center.

Most tropical cyclones form in a narrow convergence region between the trade winds of the two

hemispheres, called Inter Tropical Convergence Zone (ITCZ) which is also a region of thunderstorm activity.

Band of thunderstorm activity called the Intertropical discontinuity, also called the ITCZ. Most of these systems

form between 10 and 30 degrees of the equator and 87% form within 20 degrees of it. Because the Coriolis

force initiates and maintains tropical cyclone rotation, tropical cyclones rarely form or move within about 5

degrees of the equator, where the Coriolis effect is negligible.

2.1.3 Movement and track

Although tropical cyclones are large systems generating enormous energy, their movements over the

earth’s surface are controlled by large-scale winds—the streams in the earth’s atmosphere. The path of

motion is referred to as a tropical cyclone’s track, depends on the earth’s rotation. This earths acceleration

causes cyclonic systems to turn towards the poles in the absence of strong steering currents (i.e.) in the north,

the northern part of the cyclone has winds to the west, and the Coriolis force pulls them slightly north. The

southern part is pulled south, but since it is closer to the equator, the Coriolis force is a bit weaker.] Thus,

tropical cyclones in the Northern Hemisphere, which commonly move west in the beginning, normally turn

north (and then usually towards east).

2.1.4 Interaction with high and low pressure systems

When a tropical cyclone moves into higher latitude, its general track around a high-pressure area can

be deflected significantly by winds moving toward a low-pressure area. Such a track direction change is

termed recurve.

2.1.5 Landfall

Officially, “landfall” is when a storm’s center (the center of the eye, not its edge) reaches land. Naturally,

storm conditions are experienced on the coast and inland well before landfall. In fact, for a storm moving

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inland, the landfall area experiences half the storm before the actual landfall. For emergency preparedness,

actions should be timed from when a certain wind speed will reach land, not from when landfall will occur.

2.1.6 Dissipation

A tropical cyclone can cease to have tropical characteristics in several ways:

• It moves over land, thus depriving it of the warm water it needs to power itself, and quickly loses

strength. Most strong storms lose their strength very rapidly after landfall and become disorga-

nized areas of low pressure within a day or two. There is, however, a chance they could regener-

ate if they manage to get back over open warm water. If a storm is over mountains for even a short

time, it can rapidly lose its structure. However, many storm fatalities occur in mountainous terrain,

as the dying storm unleashes torrential rainfall which can lead to deadly floods.

• It remains in the same area of ocean for too long, drawing heat off of the ocean surface until it

becomes too cool to support the storm. Without warm surface water, the storm cannot survive.

• It can be weak enough to be consumed by another area of low pressure, disrupting it and joining

to become a large area of non-cyclonic thunderstorms. (Such systems however, can strengthen

the non-tropical system as a whole).

2.1.7 Physical structure

A strong tropical cyclone consists of the following components.

• Surface Low: All tropical cyclones rotate around an area of low atmospheric pressure near the

Earth’s surface. The pressures recorded at the centers of tropical cyclones are among the lowest

that occur on Earth’s surface at sea level.

• Warm core: Tropical cyclones are characterized and driven by the release of large amounts of

latent heat of condensation as moist air is carried upwards and its water vapor condenses. This

heat is distributed vertically, around the center of the storm. Thus, at any given altitude the environ-

ment inside the cyclone is warmer than its outer surroundings.

• Central Dense Overcast (CDO): The central dense overcast is the shield of cirrus clouds pro-

duced by the eyewall thunderstorms. Typically, these are the highest and coldest clouds in the

cyclone.

• Eye: A strong tropical cyclone will harbor an area of sinking air at the center of circulation. Weather

in the eye is normally calm and free of clouds (however, the sea may be extremely violent). The

eye is normally circular in shape, and may range in size from 3 km to 320 km (2 miles to 200 miles)

in diameter. In weaker cyclones, the CDO covers the circulation center, resulting in no visible eye.

13

• Eyewall: A band around the eye of greatest wind speed, where clouds reach highest and precipi-

tation is heaviest. The heaviest wind damage occurs where a hurricane’s eyewall passes over land.

• Rainbands: Bands of showers and thunderstorms that spiral cyclonically toward the storm

center. High wind gusts and heavy downpours often occur in individual rainbands, with rela-

tively calm weather between bands. Tornadoes often form in the rainbands of landfalling tropi-

cal cyclones.

• Outflow: The upper levels of a tropical cyclone feature winds headed away from the center of the

storm with an anti-cyclonic rotation. Winds at the surface are strongly cyclonic, weaken with

height, and eventually reverse themselves. Tropical cyclones owe this unique characteristic to the

warm core at the center of the storm.

2.1.8 Storm Surge

Storm surges are produced when these tropical cyclones pass over the continental shelf. Winds associated

with tropical cyclones are the main driving force for accumulation of water on the shoreline, which in turn,

results in a sudden and substantial rise in sea level. This abnormal rise in sea level above the astronomical tide,

which reaches a maximum on the coast, normally at the time of landfall of the cyclone, is called storm surge.

Storm surges are atmospherically forced oscillations of the water level in a coastal or inland water body, in the

period range of a few hours to a few days, depending upon the speed of the cyclone. These come under the

classification of long gravity waves, which are often 100 km wide with amplitude at times higher than 5m. The

intensity of the cyclone determines the power of the storm surges, the more intense the storm, the stronger the

waves. If the occurrence of storm surge coincides with normal astronomical tide the total rise in the water level

may be spectacular. Storm surges cause heavy loss of life and property, damage coastal structures, harbours,

oil rigs and other residential complexes close to the coast. Most of the world’s greatest human disasters

associated with tropical cyclones have been directly attributed to the coastal flooding associated with storm

surges. The destruction associated with storm surges rank them as one of the foremost natural disasters and

some times even surpassing earthquakes. Coastal flooding associated with storm surges is a serious concern

along 7500 km of Indian coastline. About 60 % of all deaths due to storm surges have occurred in the low-

lying coastal areas of the countries bordering the Bay of Bengal and the adjoining Arabian Sea.

East coast of India is frequently affected by storm surges. Most vulnerable portions of the east coast

are Orissa, West Bengal and south Andhra coast. Though the frequency of occurrence of cyclones in the Bay

of Bengal is more compared with the Arabian Sea, there are several records of severe cyclonic storms in

Gujarat and north Maharashtra region. Some of the severe cyclones have also passed over the Gulf of

Cambay and the Gulf of Kutch regions. It is possible that the number of causalities could be reduced if the

surge can be predicted 24 hours in advance through effective warnings in the threatened area. The prediction

should be such that there is distinction between the dangerous and less harmful surges as people cannot be

14

evacuated from the exposed areas for every approaching storm. At present, statistical and numerical models

are being used for the prediction of storm surges to the coastal regions of Indian subcontinent.

2.2 Thunderstorms

2.2.1 Introduction

Thunderstorm may be defined as a violent, short lived atmospheric disturbance associated with deep

convective clouds, heavy rain with or without hail, strong gusty winds along with lightning and thunder.

Thunderstorms form due to severe heating at the surface when layers of warm moist air rise as strong updraft

and the moisture condenses due to adiabatic expansion to produce deep cumulonimbus cloud which eventually

precipitate. Each of these updrafts will have associated downdraft due to evaporative cooling from falling

precipitation and the downdrafts are observed as strong cool winds in the wake of the storm.

Thunderstorm is a mesoscale system with a spatial scale of a few kilometers and time scale of less than

an hour to several hours. During the pre-monsoon season of April and May north India gets severe

thunderstorms. In this season, Gangetic West Bengal and surrounding areas experience severe thunderstorms

(Norwesters) called locally as Kal Baisaki and northwest India gets convective dust-storms locally called as

Andhi. Severe thunderstorms cause lot of damage to property and crops and human and animal fatalities due

to the strong surface wind squalls, large hail and occasional tornadoes accompanying them. They pose serious

hazards to aviation activities.

Electrical charges accumulate on cloud particles (water droplets and ice) and lightning discharges occur

when the accumulated electric charge becomes sufficiently large. Lightning heats the air it passes through so

intensely and quickly that shock waves are produced; and these shock waves are heard as claps and rolls of

thunder. On some occasions, severe thunderstorms are accompanied by swirling vortices to form as tornadoes.

2.2.2 Physical characteristics

Aircraft and radar measurements show that a single thunderstorm cell extends to an altitude of 8,000 to

10,000 metres (26,000 to 33,000 feet) and lasts about 30 minutes. An isolated storm usually contains several

cells in different stages of evolution and lasts about an hour. A large storm can be many tens of kilometers in

diameter with a top that extends to altitudes above 18 km (10 miles), and its duration can be many hours.

2.2.3 Formation

A thunderstorm is known to develop when the atmosphere becomes “unstable to vertical motion”.

Such an instability can arise whenever relatively warm, light air lies below cooler, heavier air. Under such

conditions the cooler air tends to sink, displacing the warmer air upward. If sufficiently large volume of air

rises, an updraft (a strong current of rising air) will be produced. If the updraft is moist, the water will

15

condense and form clouds; condensation in turn will release latent heat energy, further fueling upward air

motion and increasing the instability.

Once upward air motions are initiated in an unstable atmosphere, rising parcels of warm air accelerate

as they rise through their cooler surroundings because they are more buoyant. This motion can set up a pattern

of convection wherein heat and moisture are transported upward and cooler and drier air is transported

downward. Areas of the atmosphere where vertical motion is relatively strong are called cells, and when they

carry air to the upper troposphere (the lowest layer of the atmosphere), they are called deep cells.

Thunderstorms develop when deep cells of moist convection become organized and merge, and then produce

precipitation and ultimately lightning and thunder.

Upward motions can be initiated in a variety of ways in the atmosphere. A common mechanism is by

the heating of a land surface and the adjacent layers of air by insolation. If the surface heating is sufficient,

temperatures of the lowest layers of air will rise faster than upper layers, and the air will become unstable. The

ability of the ground to heat up quickly is why most thunderstorms form over land rather than oceans. Mountains

can also trigger upward atmospheric motion by acting as topographic barriers that force winds to rise. Mountains

also act as high-level sources of heat and instability when their surfaces are heated by the Sun.

2.2.4 Structure

The huge clouds associated with thunderstorms typically start as isolated cumulus clouds that develop

vertically as towers. If there is enough instability and moisture and the winds are favourable, the heat released

by condensation will further enhance the buoyancy of the rising air mass. The cumulus clouds will grow and

merge with other cells to form a cumulus congestus cloud extending even higher into the atmosphere (6

kilometers or more above the surface). Ultimately, a cumulonimbus cloud will form, with its characteristic

anvil-shaped top, billowing sides, and dark base. Cumulonimbus clouds typically produce large amounts of

precipitation. Thunderstorms are classified as local, frontal, or orographic (mountain-initiated) thunderstorms

following their origin.

2.2.5 Isolated thunderstorms

Isolated thunderstorms tend to occur where there are light winds that do not change much with height

and where there is abundant moisture at low and middle levels of the atmosphere (i.e.) from near the surface

of the ground up to around 10 km in altitude. These storms are sometimes called air-mass or local thunderstorms.

They are mostly vertical in structure, are relatively short-lived, and usually do not produce violent weather at

the ground. Such storms are composed of one or more convective cells, each of which goes through a well-

defined life cycle. Early in the development of a cell, the air motion is mostly upward, not as a steady, uniform

stream but as one that is composed of a series of rising eddies. Cloud and precipitation particles form and

grow as the cell grows. When the accumulated load of water and ice becomes excessive, a downdraft starts.

16

The downward motion is enhanced when the cloud particles evaporate and cool the air—almost the reverse

of the process in an updraft. At maturity, the cell contains both updrafts and downdrafts in close proximity. In

its later stages, the downdraft spreads throughout the cell and diminishes in intensity as precipitation falls from

the cloud. Isolated thunderstorms contain one or more convective cells in different stages of evolution. Frequently,

the downdrafts and associated outflows from a storm trigger new convective cells nearby, resulting in the

formation of a multiple-cell thunderstorm.

Solar heating is an important factor in triggering local, isolated thunderstorms. Most such storms occur

in the late afternoon and early evening, when surface temperatures are highest.

2.2.6 Multiple-cell thunderstorms and mesoscale convective systems

Violent weather at the ground is usually produced by organized multiple-cell storms, squall lines, or a

supercell. All of these tend to be associated with a mesoscale disturbance (a weather system of intermediate

size with 10 to 1,000 km horizontal extent). Multiple-cell storms have several updrafts and downdrafts in

close proximity to one another. They occur in clusters of cells at various stages of development moving

together as a group. Within the cluster one cell dominates for a time before weakening, and then another cell

repeats the cycle. In squall lines, thunderstorms form in an organized line and create a single, continuous gust

front (the leading edge of a storm’s outflow from its downdraft). Supercell storms have one intense updraft

and downdraft as discussed in more detail later.

Sometimes the development of a mesoscale atmospheric disturbance causes thunderstorms to develop

over a region of hundreds of kilometres in diameter. Examples of such disturbances include frontal wave

cyclones and low-pressure troughs at upper levels of the atmosphere. The resulting pattern of storms is called

a mesoscale convective system (MCS). Severe multiple-cell thunderstorms and supercell storms are frequently

associated with MCSs. Precipitation produced by these systems typically include rainfall from convective

clouds and from stratiform clouds (cloud layers with a large horizontal extent).

Thunderstorms can be triggered by a cold front that moves into moist, unstable air. Sometimes squall

lines develop in the warm air mass, tens to hundreds of kilometres ahead of a cold front. The tendency of pre-

frontal storms to be more or less aligned parallel to the front indicates that they are initiated by atmospheric

disturbances caused by the front. In the tropics, the northeast trade winds meet the southeast trades near the

Equator, and the resulting ITCZ is characterized by air that is both moist and unstable. Thunderstorms and

MCSs appear in great abundance in the ITCZ and they play an important role in the transport of heat to upper

levels of the atmosphere and to higher latitudes.

2.2.7 Supercell storms

When environmental winds are favourable, the updraft and downdraft of a storm become organized

and twist around and reinforce each other. The result is a long-lived supercell storm. These storms are the

17

most intense type of thunderstorm. Updraft speeds in supercell storms can exceed 40 metres per second and

are capable of suspending hailstones as large as grapefruit. Supercells can last for two to six hours. These are

the most likely storms that produce spectacular wind and hail damage as well as powerful tornadoes.

2.3 Western Disturbances

A western disturbance (WD) is defined as an eastward-moving extra-tropical upper air trough in the

subtropical westerlies, often extending down to the lower atmospheric level over the north Indian latitudes

during the winter months. Sometimes, these are observed as closed cyclonic circulations at the sea-level.

WDs are one of the most important weather systems that cause adverse weather conditions over northwest

India and particularly over the western Himalayan region. Analysis of synoptic charts shows that a WD

originates usually over the Mediterranean Sea/Black Sea area as an extra-tropical frontal system, but its

frontal characteristics are lost while moving eastward towards India across Afghanistan/Pakistan. Even then

an intense WD is capable of producing widespread heavy snowfall over the western Himalayan region and

rains over northern plains for a day or two and may lead to snow avalanches.

Satellite studies indicate that the secondaries of extra-tropical depressions move northeastward from

the eastern Mediterranean and are confined to the latitudinal belt 25 N to 35 N and that the frequency of WDs

abruptly decreases from winter to the pre-monsoon season. The WDs are also observed to move across

north India even in the hot weather period of April and May.

Western disturbances are recognized as the cloud and precipitation systems that move from west to

east over northern India during the winter season. These disturbances approach from the west in contrast to

most of the rain systems that approach from the east during the monsoon season. These are low pressure

systems which move across the extreme north of the country or sometimes even further to the north. They

may be seen on the surface chart or in the lower troposphere as a closed low or even a trough. Usually there

is a trough to the west in the mid or upper troposphere. As the western disturbances come over longitudes of

Indo-Pakistan area, an induced low sometimes forms over Sind and west Pakistan. If at this stage the upper

trough extends well to the south, the induced low may become active. The induced low is generally shallow

and its circulation does not extend as high as the main disturbance.

WDs are usually associated with recognizable 24 hour pressure changes-fall of pressure ahead of the

disturbances and rise in the rear. The pressure gradient associated with the high in the rear of the western

disturbances is sometimes strong and may extend over north and central Arabian sea causing moderate to

strong winds. Rise in the minimum temperatures is also an indicator of approaching of WD.

Sometime it is possible that there can be more than one low level system over the Indian area with a

diffuse isobaric configuration. Even otherwise, surface isobaric configuration associated with WD is generally

quite weak and isobars may have to drawn at 1 mb interval, so that weaker systems are not missed. When

there is more than one low present, each low causes its own belt of weather.

18

Without the appearance of a low either at the surface or at the levels below 1.0 km, weather does not

occur over India to the south of 30 N latitude However to the north of 30 N, weather can occur due to

disturbance seen mainly in the upper air or due to the system moving to the north of India. The converse may

not always be true i.e. even when a low or trough has appeared on the chart, weather may not occur in some

cases.

The intensification of the seasonal high over east Asia, its more southerly location and its extension as a

pronounced ridge southwestward into Iran and west Pakistan appear to inhibit active WD over India. So

also depressions in the Arabian sea and the Bay of Bengal are seen to inhibit the WD.

The activity of the WD is closely related to the position of the low level circulation in relation to the

upper level systems (such as trough or wind maxima). Thus there is need to recognize the systems in the lower

or upper troposphere and follow their sequences. It is far easier to trace the upper trough and locate the

favourable region of development on the charts, than the wind maxima.

The existence of surface low to the north of India as well as the intensity and the latitudinal extent of the

upper air trough can be followed more systematically with the extended charts and hence a better understanding

of the behaviour of the western disturbance is possible to some extent. Still the behaviour of the upper air

systems themselves is a matter not clearly understood-for example, their intensification or weakening and their

amplification or relaxation.

In the middle latitudes during periods of low index circulation westerly troughs may be expected to

develop large amplitude and affect lower latitudes. On such occasions we may perhaps anticipate more

induced lows over the Indian sub-continent and the extension of weather over large areas. During high index

type circulation, upper air troughs may affect only the extreme north of the country and may also move away

quickly.

As to their relation with monsoon rainfall, monsoon onset over NW India is delayed (or advanced) by

increase (or decrease) in the number of WDs in April. In the Himalayan region of India, monsoon current

progresses from east to west. But the WDs move across north India from west to east, with consequent rise

in pressure and cold pool of air in the rear. Though WDs activate monsoon in certain areas of NW India, it is

not clear whether the visit of pre-monsoon WDs across north India has any impact on the progress of

forthcoming monsoon current towards NW India and its activity.

2.4 Cold waves

Another feature associated with WD is the cold wave. According to the IMD, ‘moderate cold wave’

and ‘severe cold wave’ are noted when the minimum temperatures drop by 6-70 C and 80 C below their

normal. No absolute minimum temperature limit is included in the definition of the cold wave which is always

with reference to the normal minimum temperatures for the stations in the area. It is often noticed that when the

19

minimum temperatures fall sufficiently, there is also a drop in the maximum temperatures; the range through

which the maximum temperatures drop may, however, not be the same as of minimum temperatures. Ground

frost may also occur in association with cold wave.

Severe cold waves occur in northwest India, west Uttar Pradesh, Madhya Pradesh, Gujarat state,

Madhya Maharashtra and Vidarbha between December and April, although the number of occasions are less

in December and April. Occasionally they also extend eastwards to east Uttar Pradesh, Bihar, Orissa and

West Bengal and southwards to Marathwada, Telangana, rayalseema and North Interior Mysore. During the

cold waves, the minimum temperatures have dropped as much as 10-120 C below normal and over Jammu

and Kashmir of 15 to 200 C below normal. A spell of cold wave over northern India generally lasts about 4 to

5 days and in exceptional cases upto ten days.

Although it is usual to associate cold waves with active western disturbances moving across India and

West Pakistan, it is found that on some occasions when the temperatures are already below normal, even a

small drop in the temperature in the rear of a weak disturbance may bring the temperature down sufficiently to

be classified as a cold wave. In some rare instances, disturbances to the north of the country also causes cold

wave, when the high in the rear of the disturbance extends to Indo-Pakistan sub-continent.

The lowest temperature in the cold wave is usually reached on the second night of the cold spell. Since

cold waves occur in the rear of WD where pressure gradient is somewhat strong, the resulting strong winds

add to human discomfort. The pressure gradient sometimes extends over north and central Arabian sea also.

Generally the winds below 1.0 km are closely related to the onset and progress of cold waves. The wind at

0.9 km has been found to be very useful in advecting the 100 C and 70 C minimum temperature isotherms

during the subsequent 24 hrs. The winds at higher levels do not seem to have any significance in forecasting

the cold wave.

Over Gujarat, Madhya Maharashtra, Vidarbha etc. a more northerly component for the upper winds

(0.3 to 0.9 km) is favourable for a cold wave than a northeasterly or easterly turning around the anticyclone

over northwest India. Cold waves are associated with large drops in surface dew points, suggesting the

incursion of extremely dry air.

On days of cold waves the changes in the dry bulb at a station with the advancement of cold wave is

much less than the changes in the dew point (i.e.) the dry bulb temperatures during the cold wave days are not

very different (or in fact even warmer) from the normal values but the dew point temperatures are very much

lower than the normal values, suggestive of the greater role played by the lack of moisture in the lowering of

temperature. During the period of cold waves the morning ascents show considerable stability in the lower

level, sometimes extending from the ground to as high as 800-750 mb level.

20

2.5 Fog

Fog is a localized phenomenon, but certain synoptic situations become favourable for the occurrence

of fog over fairly large areas. The most important synoptic situation under which fog occurs over large areas,

is the western disturbance. In the rear and sometimes ahead of WD, the low level moisture, wind field and

stability conditions become favourable for the occurrence of radiation fog. If rain falls in the evening or night

and immediately after the rain the sky clears up and strong winds do not set in, there is good chance for thick

fog on the next morning. Often fog occurs on the subsequent one or two mornings also. Such situations occur

in the immediate rear of a western disturbance. Along the coastal areas of Orissa and West Bengal fog

generally occurs ahead of the western disturbance, when a shallow current of moist air is drawn over these

areas under the influence of a WD further to the west. Occasionally such conditions also occur over Bihar and

Uttar Pradesh when an easterly stream in the very low levels extends over these areas under the influence of

a WD over northwest India.

When the low level moisture content is quite large, the fog may persist for a long period and may be

accompanied or followed by very low and thick stratiform clouds. On some occasions, thick fog and stratus

cloud have persisted up to forenoon or even mid-day.

2.6 Avalanches

In the high mountain regions, such as Himalayas, avalanches are known to be hazardous killing people,

causing disruption to movement etc. An avalanche is the downward descent of a large mass of snow on a

slope with a high velocity and force. The downward motion may be in the form of gliding or sliding along the

slope like a rock fall, flowing along the slope like a fluid or whirling through air like a hurricane. A typical

avalanche site can be divided into three zones, viz. (a) formation zone, with a slope angle of 30–50 degree, (b)

middle zone, with a slope angle of 15–30 degree and (c) run out zone, with a slope angle of less than 15

degree. Generally, an avalanche starts at formation zone, attains a maximum speed at middle zone and comes

to halt at run out zone by depositing the snow mass, which came down along with the avalanche.

Snow avalanches occur during winter months in the snowbound belt of Western and Central Himalaya

and to some extent in Eastern Himalaya. Indian Himalaya stretches from east to west for about 2500 km

across 72 E to 96 E long. and 26 N to 37 N lat. For the Himalayan belt, the main mountain system is divided

into three principle zones which have marked orographic features. These are the Great Himalaya, Lesser

Himalaya and the Outer Himalaya. The Himalaya have about 43,000 sq. km of permanent ice bound area. In

India, Jammu and Kashmir (J&K), Himachal Pradesh (HP), Uttaranchal and part of Sikkim are the avalanche

prone states.

Conventionally avalanche forecasting is being done by assessing the snow pack stability using

contributory factors approach. These factors are terrain, snow pack and weather. The terrain factor is a

fixed parameter for any given location whereas snow and weather factors are varying, which have to be

21

analysed in detail. Some of the snow pack factors considered are snow depth, nature of snow surface,

existence of weakness within snow pack, bond between new and old snow pack, etc. Qualitative and site-

wise prediction of avalanches as well as integrated avalanche forecast models require precise weather

forecast. Ideally speaking, the numerical forecast of the following weather elements up to 7 km altitude at

least 24 hours in advance, preferably 3 days in advance, is required for the purpose of avalanche forecasting

or for the use of avalanche forecast models. The important weather elements are: (a) Precipitation amount

and type (b) Wind speed and direction (c) Temperature (d) Radiation (e) Relative humidity (RH) and

surface atmospheric pressure. The numerical forecast of the above parameters is possible through nested

modeling approach of mesoscale modeling. Numerical forecasts for this region are presently available at

10 km resolution, but avalanche forecasting demands weather forecast preferably at 2 km or higher grid

resolution. Srinivasan et al (2005) provided an over view of the mountain weather forecasting as necessary

for the prediction of avalanches.

2.7 Mid-tropospheric cyclones

It was observed that very heavy rainfall over western India, especially over the northern parts of

Maharashtra, Saurashtra-Kutch and Gujarat was associated with cyclonic vortices that were confined

to the middle atmosphere. They appear as circular vortices between 3 and 6 km, with their largest

amplitude near 600 hPa (4 km). The dimensions of these vortices are roughly of the order of 300 km in

the horizontal direction and about 3 km in the vertical direction. A peculiar feature of these vortices is

that they are only confined to the middle troposphere and are not visible at the surface. The structure of

the mid-tropospheric cyclonic vortex reveals a core of warm air above the middle level and a slightly

cold core below that level. Unlike the Bay of Bengal depressions, they show very little movement and

appear to remain quasi-stationary for many days. The formation of these mid-tropospheric cyclones is

often responsible for heavy rain on the northern sectors of the west coast of India. Rainfall amounts of

20 cm in twenty four hours are not uncommon.

The formation of a mid Tropospheric cyclone could be anticipated when the winds in the lower level are

stronger than those at the upper levels of the atmosphere, which indicate unstable atmospheric conditions that

has some resemblance to a mid-tropospheric vortex.

2.8 Off-shore vortices

During the monsoon, we often observe spells of very heavy rain along the west coast of India. Apart

from mid-tropospheric cyclones, these spells of heavy rain are associated with off-shore vortices. It is to be

noted that the west coast has an orographic barrier in the form of the western Ghats, which have an average

altitude between 1.0 and 1.5 km. These mountains run in a north-south direction, and are approximately 1000

km in length and 200 km in breadth. When the monsoon winds strike the mountains, on many occasions they

do not have enough energy to climb over the western Ghats and tend to be deflected round the mountain and

22

the return current forms an off-shore vortex. These vortices have very small linear dimensions. Their diameter

is only of the order of 100 km and their presence is often detected by a weak easterly wind at coastal stations.

Notwithstanding their small dimension, they are capable of generating spells of very heavy rain lasting for 2-3

days during the monsoon season.

23

CHAPTER 3

AN OVERVIEW OF MESOSCALE MODELSAND THEIR APPLICATIONS

24

25

3.1 Overview of Mesoscale Models

In this section a brief description of the different meoscale atmospheric models (MM5, ETA, RSM,

WRF, RAMS, ARPS, HRM and LAM) run at different institutions in India are described.

3.1.1 The MM5 Model

The MM5 model is a non-hydrostatic, terrain-following sigma coordinate model developed at National

Center for Atmospheric Research [NCAR], USA (NCAR, 2003; Dudhia, 2003, Grell et al., 1994). The

model is used in different configurations depending on the requirements at different institutions. For example,

the model (version 3.6) is run at triple nested domains (90, 30 and 10 Km resolutions) over the Indian region

on real time basis at NCMRWF using the boundary conditions from its operational global spectral model,

which has a horizontal resolution of T80 and a vertical resolution of 18 sigma levels. There are 23 vertical

sigma levels in the MM5. A summary of dynamics and different physical processes parameterized in the

model is presented in Table 3.1A. Figure 3.1 illustrates the domains of the MM5 model used at NCMRWF.

The middle domain of 30 Km resolution covers all over India and neighbourhood. An inner domain at 10 Km

resolution was initially placed over Jammu and Kashmir region to cater the need of Avalanch studies. Further,

the inner domain at 10 km resolution was shifted over the central Himalayas and a fourth domain was configured

over the west Bengal region to cater the need of the STORM project.

3.1.2 The ETA Model

The ETA model is a hydrostatic mesoscale weather forecast model with an accurate treatment of

complex topography using ETa vertical coordinate and step like mountains, which eliminates the errors in the

computation of pressure gradient force over steeply sloped terrain present in sigma coordinate system developed

at the National Centre for Environmental Prediction (NCEP), USA. The version used here follows that

described by Black (1994) and Mesinger (1996). The model employs a semi-staggered Arakawa E-grid in

which wind points are adjacent to the mass points, configured in rotated spherical coordinates. The mesoscale

ETa model is run operationally at NCMRWF with a horizontal grid spacing of 48 and 22 km with 38 vertical

levels, having layer depths that range from 20 m in the planetary boundary layer to 2 km at 50 mb (details are

given in Table 3.1A). The model top is at 25 hPa. Split explicit time differencing is used with a time step of

120 seconds. Spatial differencing is done with a conserving Arakawa type scheme. The model’s step mountains

are derived using the official United States Geological Survey (USGS) topographical data. It uses analyzed

SST from NCEP for its surface boundary conditions.

26

Figure 3.1: Domains of the MM5, ETA, RSM and WRF models used at NCMRWF.

Shadings indicate topography in km.

3.1.3 The Regional Spectral Model (RSM)

The RSM used at NCMRWF is the latest version of NCEP RSM97 (Juang and Kanamitsu, 1994,

Juang et.al., 1997). It works on the philosophy of “Perturbation method”. The RSM is one-way nested with

the T80 global model. The nesting strategy used by RSM is domain and spectral nesting, which is

characteristically different from the lateral boundary nesting strategy of conventional regional models. RSM

allows global model forecast fields to be used over the entire domain and not just in the lateral boundary zone.

The difference of the regional model fields from the base fields are called “Perturbations”, which are converted

to wave space for the purpose of using semi-implicit time integration. The base fields are the time-dependent

global model forecasts. The nesting is done in such a way that the perturbation may be non-zero inside the

regional domain but zero outside of it. Perturbation signifies all other features that could not be predicted by

the global model but can be resolved over the regional domain by the regional model forecasts. The physics

and the non-linear dynamics are computed in grid space only with full regional model fields. The basis functions

for spectral conversion are double sine-cosine series with 72 waves along zonal and 70 waves along the

meridional direction. The domain is approximately 52 E – 109 E and 6 S – 45 N and covers the whole of

India and nearby oceanic regions. The time step for RSM is 5 minutes and the nesting period is 6 hours. It

uses analyzed SST from NCEP for its surface boundary conditions.

RSM predicts the relatively smaller perturbations superimposed over the previously predicted large-

scale components by the global model, which was run prior to it. Hence the errors introduced in the perturbation

due to the lateral boundary will remain small which enables a longer period time integration compared to the

27

conventional grid point regional models. The lateral boundary is relaxed towards the global model values

using Tatsumi’s boundary relaxation scheme. It is more logical for the perturbation values to approach zero

along the boundary. Table 3.1A summarizes the main features of the model.

Table 3.1A: Summary of the Mesoscale Atmospheric Models

3.1.4 The WRF Model

The Weather Research and Forecasting (WRF) Model is a next-generation mesocale numerical weather

prediction system designed to serve both operational forecasting and atmospheric process modelling research

needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system,

and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable

for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Applications

of WRF include basic research and operational numerical weather prediction (NWP), data assimilation and

Grid MM5 ETA RSM WRF

Horizontal Resolution

90/30/10 km. 48/22 km. 50 km. 36 km.

Vertical Levels 23 38 18 31

Topography USGS Silhouette Step US Navy USGS

Dynamics

Time Integration Semi Implicit Semi Implicit Semi Implicit Semi Implicit

Time Steps 270s/90s/30s 120s(48km.) / 60s(22 km.)

300 s 180 s

Vertical Differencing

Arakawa’s Energy

Conserving Scheme

Euler backward scheme

Arakawa’s Energy Conserv

Scheme

Arakawa’s Energy Conserving Scheme

Time Filtering Robert’s Method Asselin filter Robert’s Method

Horizontal Diffusion

4th order for inner domains.

2nd order for the coarser domains

Modified Euler backward with Janic advection

4th order 2nd order over Quasi-pressure, surface, scale

selective

Physics

Convection

Grell Betts-Miller- Janjic

SAS scheme & Tiedke scheme

Kain-Fritsch

PBL MRF (Non Local

closure)

Mellor – Yamada level 2.5

Turbulent Exchange

Non Local closure

YSU Scheme

Cloud Microphysics

Simple Ice (Dudhia)

Explicit Cloud Water/Ice Prediction

Slingo’s Scheme / Grid

Scale Rain

WSM 3-Class Simple Ice

Radiation Simple Cooling GFDL GFDL RRTM (LW) Dudhia (SW)

Gravity Wave Drag

No No Yes No

Land Surface Processes

Five Layer Soil Model

NOAH Pan & Mahrt Thermal Diffusion

28

parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-

ocean coupling, and idealized simulations (i.e., boundary-layer eddies, convection, baroclinic waves). There

are two dynamics solvers in the WRF system: the Advanced Research WRF (ARW) solver (originally referred

to as the Eulerian mass or “em”) developed primarily at NCAR, and the NMM (Nonhydrostatic Mesoscale

Model) solver developed at NCEP. The ARW system consists of the ARW dynamics solver with other

components of the WRF system needed to produce a simulation.

This model is being continuously developed in USA incorporating the best features of the recent generation

mesoscale models and utilizing recent knowledge. At NCMRWF, the WRF-ARW system is run operationally,

but the H-WRF (Hurricane WRF developed at NCEP) is being tested. Details of the dynamics and physics

options used at NCMRWF are listed in the Table 3.1. Presently, the model is run on a single domain covering

the Indian region at 36 km horizontal resolution and 31 vertical levels.

3.1.5 The Advanced Regional Prediction System (ARPS)

Advanced Regional Prediction System (ARPS) was developed at the Center for Analysis and Prediction

of Storms at University of Oklahoma, USA. The original goal of ARPS was to serve as a prototype system

for storm scale numerical weather prediction. It includes a data ingest, quality control, and objective analysis

package known as ADAS (ARPS Data Analysis System), a single-Doppler radar parameter retrieval and

assimilation system known as ARPSDAS (ARPS Data Assimilation System, of which ADAS is a component),

the prediction model and a post-processing package (http://www.caps.ou.edu/ARPS/arpsoverview.html).

ARPS is a three-dimensional, non-hydrostatic, fully compressible model with Boussinesq option in

generalized terrain-following coordinates that has been designed to run on a variety of computing platforms

ranging from single-processor scalar workstations to massively parallel scalar and scalar-vector processors.

The model uses Arakawa C-grid with equal-spacing in the horizontal and user-specified stretching in the

vertical. It has Polar stereographic, Lambert conformal, and Mercator map projection options.

It has a comprehensive package for physical parameterization schemes that include subgrid scale

turbulence options of Smagorinsky-Lilly diagnostic first-order closure, 1.5-order turbulent kinetic energy

formulation, and Germano dynamic closure. The model also provides options for isotropic and anisotropic

turbulence based upon grid aspect ratio. The PBL scheme is based on TKE scheme. The Cloud Microphysics

options include Kessler warm-rain, Lin-Tao 3-category ice, and Schultz simplified ice NEM parameterizations.

The Cumulus Parameterization options include Kuo and Kain-Fritsch schemes separately or in combination

with other microphysics options. The surface layer parameterization includes surface momentum, heat, and

moisture fluxes based on bulk aerodynamic drag laws as well as stability-dependent formulations. The soil

model includes two-layer diffusive soil model with surface energy budget equations. Options are provided for

multiple soil types in a single grid cell. The longwave and shortwave radiation schemes consist of full long- and

short-wave radiation capabilities including cloud interaction, cloud shadowing, and terrain gradient effects.

Table 3.1B provides a summary of the model features.

29

Table 3.1B: Summary of the Models, continued.,

Grid ARPS RAMS HRM LAM

Horizontal Resolution

50/25 km 48/16/4 km 75 km

Vertical Levels 25 36 20 16

Topography Terrain data 5 min

USGS 30” (Silhouette)

US Navy

Dynamics

Time Integration Semi-implicit Hybrid (leap frog for vector, forward for

scalars)

Semi Implicit

Time Steps 90/30 sec 90/30/10 sec 600 sec

Vertical Differencing

4th order 4th order Centered for all except humidity, which is forward

Time Filtering Asselin filter Tripoli (1992)

Horizontal Diffusion

-- 4th order 2nd order

Physics

Convection Kain-Fritsch KUO/ Kain-Fritsch

Tiedtke (1989)

Modified Kuo (Krishnamuti et al., 1983)

PBL 1.5 TKE Deardorff (1980)

Mellor-Yamada (1974) 2.5 TKE

Mellor-Yamada

(1974) 2.5 TKE

(Louis, 1979)

Cloud Microphysics

Lin etal., (1983) five prognostic hydrometeor Walko etal.

(1995), Meyer etal., (1997)

Kesseler and Lin et al.,

(1983)

Based on threshold relative humidity

Radiation NASA GSFC (Simplified radiation)

Harrington (1999)

Ritter and Geleyn (1992)

Long Wave (Harshavardan and

Corsetti, 1984); Short wave (Lacis and Hansen,

1974)

Gravity Wave Drag

No No No No

Land Surface Processes

Noilhan and Planton (1989)

LEAF-2 (Walko etal., 2000)

Dickinson, 84 Sellers

(1994)

(Businger et al. 1971)

30

3.1.6 The Regional Atmospheric Modelling System (RAMS)

The Regional Atmospheric Modeling System (RAMS) is a highly versatile limited area model developed

at Colorado State University, USA for simulating and forecasting meteorological phenomena (http://

rams.atmos.colostate.edu/rams-description.html). Its major components are: (1) an atmospheric model which

performs the actual simulations, (2) a data analysis package which prepares initial data for the atmospheric

model from observed meteorological data and, (3) a post-processing model visualization and analysis package

which interfaces atmospheric model output with a variety of visualization software utilities.

The atmospheric model is constructed around the full set of primitive dynamical equations which gov-

ern atmospheric motions, and supplements these equations with optional parameterizations for turbulent

diffusion, solar and terrestrial radiation, moist processes including the formation and interaction of clouds and

precipitating liquid and ice hydrometeors, sensible and latent heat exchange between the atmosphere, mul-

tiple soil layers, a vegetation canopy, surface water, the kinematic effects of terrain, and cumulus convection.

RAMS may be configured to cover an area as large as a planetary hemisphere for simulating mesoscale and

large scale atmospheric systems. There is no lower limit to the domain size or to the mesh cell size of the

model’s finite difference grid: microscale phenomena such as tornadoes and boundary layer eddies, as well as

sub-microscale turbulent flow over buildings and in a wind tunnel, have been simulated with this code. Two-

way interactive grid nesting in RAMS allows local fine mesh grids to resolve compact atmospheric systems

such as thunderstorms, while simultaneously modeling the large scale environment of the systems on a coarser

grid. Table 3.1B provides the summary of model features.

3.1.7 The High Resolution Model (HRM)

The High Resolution Model (HRM) is a flexible tool for numerical weather prediction, developed by

the Deutscher Wetterdienst (DWD) of Germany (http://www.iac.ethz.ch/en/groups/schaer/climmod/chrm/

index.html). The HRM is a grid point model based on Arakawa-C grid and hybrid vertical coordinate system

with 20 layers in the vertical. It has Kessler-type grid-scale cloud microphysics as updated by Lin et al.

(1983), Mass flux convection scheme after Tiedtke (1989), vertical diffusion for the surface layer and, a 1.5-

order TKE scheme after Mellor and Yamada (1974). Radiation scheme includes Ritter and Geleyn (1992)

based on the solution of the delta-two-stream version of the radiative transfer equation at all wavelengths,

with full cloud-radiation feedback. It has two-layer extended force-restore soil model including snow and

interception storage. Land surface parameterization is adapted from Dickinson (1984) and driven by ISLSCP

(Sellers et al. 1994) data.

The HRM has been run in an operational mode at SPL, since September 2002. The latest version of

HRM has been customized and made operational. The model is run by downloading initial and lateral boundary

information from DWD site. Accurate forecasting of meteorological parameters over SHAR is very important,

particularly during the period of satellite launch. During the launch, SPL has been regularly providing forecast

31

bulletins of several fields and profiles of meteorological parameters over the SHAR region valid for 48 hours.

Table 3.1B provides a summary of the model features.

3.1.8 The Limited Area Model (LAM)

The limited Area Model (LAM) is a grid point model from the FSU, USA adapted at IMD. It is a

multi-level, semi-Lagrangian, semi-implicit primitive equation regional model on an Arakawa-C grid. The

model employs a semi-Lagrangian advective scheme combined with semi-implicit time integration scheme

and includes detailed physics and initialization processes. The model includes a number of physical processes

such as cumulus convection (modified Kuo-Krishnamurti et al 1983), shallow convection (Tiedke 1984),

large scale condensation, atmospheric boundary layer (Monin-Obukhov formulation of surface layers with

stability dependent vertical diffusion in mixed layer), radiation (Harshvardan et al 1987; Lacis and Hansen

1974) and envelope orography. The orography is smoothened by a nine point smoother to prevent instability

due to steep gradients of terrain over the Himalayan region. The other features of the model include time

dependent lateral boundary conditions and dynamic normal mode initialization. Further details of the model

formulation can be found in Krishnamurti et al (1989). The analysis forecast system consists of real-time data

decoding, multivariate optimum interpolation analysis covering 300S to 600N; 00 to 1500E (RMC region).

Table 3.1B provides a summary of the model features.

Presently, the model is run at horizontal resolution of 0.5º x 0.5º lat./long with 18 sigma levels in the

vertical using the initial and boundary conditions obtained from NCMRWF/ T80 global spectral model, twice

daily initialized with 00UTC and 12UTC observations. The model is run up to 48 hours.

3.2 Severe Weather Forecasting

Forecasting of mesoscale convective systems in a tropical country like India continues to be one

of the difficult areas in NWP due to complex issues which involve: impact of orography, treatment of

synoptically induced mesoscale processes and lack of good quality mesoscale observations, particularly

over the ocean. During the last two decades, weather forecasting all over the world has greatly benefited

from the guidance provided by NWP. The significant improvements in accuracy and reliability of NWP

products were due to advances in numerical techniques, explosive growth in computer power and by

the phenomenal increase in satellite-based soundings. However limitations remain in the prediction of

severe weather events which have a very short life, but still cause extensive damage. A recent example

occurred on 26th July 2005, Mumbai, the commercial capital of the country, received unprecedented

heavy rainfall with its suburb Santa Cruz recording 94.4 cm of rainfall with in a span of 24 hours. There

were reports of even heavier rainfall of 104 cm near Vihar Lake. This torrential rain disrupted the life in

the metropolis, caused large number of deaths and according to early estimates resulted in a loss of

about 15,000 Crore Indian Rupees.

32

Many institutions in the country (NCMRWF, IIT, IITM, C-MMACS, AU) carried out post-mortem

analysis of the Mumbai rainfall event (Bohra et al., 2006; Goswami 2006). Figure 3.2 illustrates the time

series of observed rainfall at Santacruiz and Colaba on 26 July 2005. It was reported that the rain occurred

with lot of thunder activity. It was also suggested that an offshore vortex was the possible underlying cause.

Figure 3.2: Time series of observed rainfall on 26th July over Santacruz and Colaba

Table – 3.2

Mean track errors in km (no. of forecast experiments)

24hr 48 hr 72 hr

Without bogus vortex 112 (35) 146 (35) 226 (27)

With bogus vortex 89 (10) 143 (10) 166 (10)

It was, however, not supported by observations. None of the NWP models operational in India and

abroad could predict the unprecedented rainfall reported at Mumbai (Sikka and Rao, 2007). Experiments

revealed that the best rainfall simulations were obtained when the MM5 model was run at 10 km resolutions

using the initial and boundary conditions from the UKMO model having superior analysis based on radiance

assimilation (Bohra et al., 2006). Figure 3.3 shows the simulated rainfall maxima exceeding 80 cm from this

experiment, though the location is somewhat to the west of Mumbai. The NWP guidance in this case was of

limited help, if any. It revealed that the use of very high resolution global and regional models with advanced

data assimilation techniques (4-D VAR), could significantly enhance the usefulness of NWP guidance. Study

of Goswami (2006) indicated that along with resolution, the geographical coverage and the size of the domain

0.9 19.3

400.1

667.7

768.8

885 896

944.2

0.7 726.5 26.5 33.1 33.1

57.1 73.4

0

100

200

300

400

500

600

700

800

900

1000

0830-

1100

1130-

1430

1430-

1730

1730-

2030

2030-

2330

2330-

0230

0230-

0530

0530-

0830

Time in IST

Cu

mu

lati

ve

Ra

infa

ll

SANTACURZ CLOABACOLABA

33

also play critical roles in the simulation of a mesoscale event. The best simulation was found for domains that

were not the largest but that covered significant part of the equatorial ocean. While this conclusion is likely to

change based on event location, their study shows that the choice of the meso-scale domain is a non-trivial but

critical input for improved mesoscale simulation and forecasting, and needs to be determined through a

comprehensive calibration experiment.

Figure 3.3: Same as Figure 16, but with a higher resolution(10km)

3.3 Cyclone track and intensity prediction

The Bay of Bengal tropical cyclones are among the costliest and deadliest of the natural hazards in

Indian sub-continent. It has significant socio-economic impact on the countries bordering the Bay of Bengal,

in particular, India, Bangladesh and Mayanmar. Timely and reasonably accurate prediction of track and intensity

of these storms can save lot of lives and reduce damage to properties and therefore, is of utmost importance.

The genesis and movement of the Bay of Bengal tropical cyclones are unique in nature compared to other

regions. An idealized axi-symmetric model was used first time in India to understand the evolution and structure

of the Bay of Bengal tropical cyclone (Bhaskar Rao, 1987). With the availability of mesoscale models, many

studies have been carried out in India on the numerical simulation of tropical cyclones. Of particular interest

is the recently carried out exercise in which several Indian institutions participated in the inter-comparison of

simulations of the Orissa super cyclone of 1999 (Mohanty et al., 2004; Bhaskar Rao and Hari Prasad, 2006;

Ashrit et al. 2006; Mandal et al., 2006, Trivedi et al., 2006).

This cyclone was named as “Super Cyclone” ( hereafter referred to as OSC99) for the first time by

MD as this cyclone is the most severe cyclone of the last century with an estimated central surface pressure of

912 hPa and maximum wind of 140 knots. Due to its unprecedented maximum intensity, it was a challenge to

modelling community to examine the capability of mesoscale models for its successful simulation. Large number

of numerical experiments was performed to examine the role of horizontal resolution and the choice of different

parameterization schemes, especially of cumulus convection, planetary boundary layer and explicit moisture

processes. Bhaskar Rao and Hari Prasad (2006) carried out numerical experiments to study the sensitivity of

parameterization schemes of convection, planetary boundary layer and explicit moisture. The model simulated

34

movement and the development of the OSC-99 during its evolution from a low pressure system to a super

cyclone were studied using the MM5 model. Their results showed that the best simulation of track and the

intensity of the cyclone was obtained when the combination of Kain-Fritsch2 for cumulus convection, Mellor-

Yamada for planetary boundary layer and Mixed Phase scheme for explicit moisture was used in the model at

10 km resolution. Figures 3.4 and 3.5 illustrate the simulated tracks and intensity by using different combinations

of microphysical processes. The model could simulate a steep pressure fall of 55 hPa in 36 hours as compared

to 86 hPa of the IMD reports. They could simulate strong low-level convergence within 150 km radius;

upper-level divergence over a wider region; warm core, dry and subsidence motions at the center within the

50 km radius; strong upward motion in the 50-100 km radius throughout the troposphere indicating the

distinct characteristics of a mature cyclonic storm. The model predicted rainfall distribution and intensity

agreed with the observations. The model could simulate asymmetrical distribution of the rainfall, with maximum

exceeding 40 cm/day, located towards the left of the storm track before landfall and towards right after the

landfall. Their results showed that precipitation from cloud resolvable processes contributes for most of the

rainfall associated with the cyclone where as sub-grid scale convection contributes in the outer environment

Figure 3.4: Track positions for the experiments for all explicit moisture schemes in combination withKain-Fristch2 for convection and MRF for PBL processes along with IMD estimates.

Figure 3.5: Time variation of model simulated Central Sea level Pressure (hPa) for the experiments with differentexplicit moisture schemes in combination with KF2 for convection and MY for PBL along with IMD estimates.

890

910

930

950

970

990

1010

48 54 60 66 72 78 84 90 96 102 108 114 120

Tim e (hr)

CS

P (

hP

a)

IM D

K F2+MY+SI

K F2+MY+M P

K F2+MY+W R

K F2+MY+GM

35

Experiments with bogus vortex (synthetic vortex) have been conducted at NCMRWF, IIT-D and AU

using the MM5 model. The mean error statistics are shown in Table 3.2. Results indicate that the incorporation

of bogus vortex improves the forecast errors by about 20%. Further, experiments have also been conducted

at IIT-D, NCMRWF, AU , IITM and C-MMACS with the Advance Research version of the model WRF-

ARW developed by NCAR and the Hurricane HWRF model developed by NCEP. Both the models have

been tested with the recent tropical cyclones MALA and OGNI over the Bay of Bengal. The models simulated

track and intensity of the storms are compared with the other operational model forecasts viz. IMD and

UKMO. The forecast skills of these models are found to be very good. The models are being extensively

tested for other severe cyclonic storms over the Bay of Bengal during the period 1995-1999.

Table – 3.2

Mean track errors in km (no. of forecast experiments)

24hr 48 hr 72 hr

Without bogus vortex 112 (35) 146 (35) 226 (27)

With bogus vortex 89 (10) 143 (10) 166 (10)

NCMRWF has initiated coordinating a Forecast Demonstration Project (FDP) on land falling Tropical

Cyclones. It is an attempt at a comprehensive evaluation of various models in the country for tropical cyclone

forecasting. Under its multi-scale validation, C-MMACS has carried out hindcast evaluation for more than

twenty cyclones over the Indian seas for different years and season.

3.4 Thunderstorms and Cloudbursts

Severe thunderstorms form over the Eastern and Northeastern parts of India, i.e., Gangetic West

Bengal, Jharkhand, Orissa, Assam and parts of Bihar during the pre-monsoon months (April-May). These

storms are known as “Nor’wester” as they move from Northwest to Southeast. They are locally called

“Kalbaishakhi”. Strong heating of landmass during mid-day initiates convection over Chhotanagpur Plateau,

which moves southeast and gets intensified by mixing with warm moist air mass. The Nor’westers produce

heavy rain showers, lightning, thunder, hail-storms, dust-storms, surface wind squalls, down-bursts and

tornadoes. They create lot of damage to properties and crops and cause loss of life through strong surface

wind squalls, large hail, lightning and occasional tornadoes accompanying them. Realizing the importance of

these extreme weather events the Government of India, initiated a national coordinated programme known as

STORM.

During the STORM PILOT experiments (2006 and 2007), synoptic scale meteorological environment

was monitored over an area covering the region 17-27 N, 80-90 E (spread over a quadrilateral Patna-

Guwahati-Kolkata-Bhubneshwar-Ranchi-Patna), using regular network of IMD observatories consisting of

several Surface/ Radiosonde/ Radar/ Satellite and additional meso-network of automatic weather stations

(AWS) and Radiosondes provided by Indian Army. Several Nor’westers formed during the pre-monsoon

36

season (April-May) as summarized by Mohanty et al (2006). Das et al. (2006a) studied 10 cases of

thunderstorms that formed over the West Bengal and surrounding regions during 2005 and 2006. Fig. 3.6

illustrates the simulated convective available potential energy (CAPE) for 6 cases of 2006. The inner quadrilateral

of the intensive observation area of the STORM marked by Asansol-Murshidabad-Kolkata-Digha-Asansol

is also depicted in the diagrams. The CAPE values have been presented for 12 hours prior to the simulated

time of the thunderstorms in order to show the maximum buildup of convective energy for the formation of the

Nor’westers. It is known that the thunderstorms decay after the CAPE decrease. The diagrams indicate that

the magnitudes of the CAPE ranged from 1600 to 3500 JKg-1. They decreased after the storms dissipated.

Study of the composite characteristics of the simulated results by Das et al. (2006a) indicated that the

Nor’westers are triggered generally when the CAPE increases above 1500 J Kg-1. The total cloud

hydrometeors inside the Nor’westers can reach up to 600-800 mg Kg-1.

Maximum values are obtained generally at upper levels indicating a dominance of glaciated particles in

the anvil of the Nor’westers. Maximum values of the updrafts have speeds of 3 to 4 m s-1. The updrafts can

extend up to 8-9 km altitudes. The downdrafts have magnitudes of about 0.4 – 0.5 m s-1. Studies on

thunderstorms have been carried out at IITM using GAME reanalysis data using the RAMS model.

Mukhopadhyaya et al. (2003) studied Nor’westers over Kolkata region using Doppler Radar

observations.Arora and Nandi (2006) attempted to analyze the characteristic features of thunderstorm activity

that occurred on 12 May 2005 over Coimbatore in south India using nested high resolution MM5 model

(down to 2.5 km).

37

Figure 3.6: CAPE (J Kg-1) simulated by the model on (a) 00Z19 April, (b) 00Z25 April,

(c) 00Z 26 April, (d) 12Z 9 May, (e) 00Z14 May, (f) 00Z16May 2006.

38

(a) (b)

(c) (d)

(e) (f)

Figure 3.7: Composite X–Z Cross Section for Equivalent Potential Temperature, Pressure Perturbation, Total Precipi-tation Mixing Ratio and Circulation Vector at (a) 1300, (b) 1330, (c) 1400, (d) 1430, (e) 1500, (f) 1530 local time

39

The composite picture of cross sectional analysis of Equivalent Potential Temperature, Pressure

Perturbation, Total Precipitation Mixing Ratio and three-dimensional Circulation Vectors are shown in Figure

3.7 at 30 minutes intervals.

The vertical profile of equivalent potential temperature indicates the stability/ instability in the atmosphere,

whereas, the total precipitation mixing ratio shows the presence of moisture in the vertical column. Pressure

perturbation is quite important and significant in case of any significant convective activity, which suggests the

presence of low perturbation pressure ahead and high pressure to the rear of a moving thunderstorm.

The three-dimensional circulation vector indicates the scale of vertical motion compared to horizontal

motion, as well as, the extent of updrafts and downdrafts. The analysis of these composite pictures indicates

that during the period of study (i.e., between 0600 and 1800 UTC), more than one convective cell developed,

moved, intensified and dissipated. Mesoscale vortices, zones of confluence or lines of discontinuity could be

clearly identified and their movement was predicted with improved resolution. In fact, these features are

responsible for occurrence of mesoscale disturbances, like thunderstorms. Their results indicated that vertical

motion reached more than 10 m/sec in 2.25 km resolution (agreeable with theoretical considerations). The

IAF also conducts an annual exercise named ‘Meghgarjan’ over operational bases of the IAF in various

geographical locations in India. The purpose of the exercise is to validate concept of centralized forecasting

units, as well as, to test reliability of NWP forecasts and utilization of NWP products issued / generated by the

Air Force Centre for NWP. The overall aim of the exercise is to augment and integrate all available observational

inputs to provide improved weather watch in space and time and thereby enhance reliability, accuracy and

lead time of warnings issued for convective activity by the Met sections of Air force.

Cloudburst, also known as rain gush or rain gust is a sudden heavy downpour over a small region. It is

among the least well-known and understood type of mesoscale systems. Rate of rainfall equal to or greater

than 100 mm per hour featuring high-intensity rainfall over a short period, strong winds and lightning are

associated with a cloud burst. A remarkably localized phenomenon affecting an area not exceeding 20-30

km2, cloudbursts in India occur when monsoon clouds associated with low-pressure area travel northward

from the Bay of Bengal across the Indo-Gangetic plains onto the Himalayas and “burst” in heavy downpours

(75-100 mm per hour). It represents cumulonimbus convection in conditions of marked moist thermondynamic

instability and deep, rapid dynamic lifting by steep orography.

Cloudburst events over remote and unpopulated hilly areas often go unreported. The states of Himachal

Pradesh and Uttaranchal are the most affected due to the steep topography. Most of the damage to property,

communication systems and human causalities result from the flash floods and landslides that accompany

cloudbursts. Prediction of cloudbursts is challenging and requires high-resolution numerical models and

mesoscale observations, high-performance computers and doppler weather radars. Societal impact could be

markedly reduced if high-resolution measurements (~10 km) of atmospheric parameters and vertical profiles

are provided through mesonet observations such as AWS, Radiosonde/ Rawinsonde (RS/RW), and doppler

40

weather radars. Also, education and training of local administrators to give short-notice warnings would

greatly help disaster mitigation.

Cloudburst events occur at the meso-gamma (2-20 km) scale as defined by Orlanski (1975) and may

be difficult to distinguish from thunderstorm. Das et al. (2003b) studied a cloudburst event that occurred in the

Fig 3.8: Observed (TRMM) location of (a) intense rainfall and vertical cross-section ofhydrometeors (b) RNW, (c) CLW, (d) snow, and (e) ice during a cloudburst event on

16th July 2003 (00:27 IST).

(a) TRMM (00:27 IST)

Figure 3.8: Observed (TRMM) location of (a) intense rainfall and vertical cross-section of hydrometeors (b) RNW,(c) CLW, (d) snow, and (e) ice during a cloudburst event on 16th July 2003 (00:27 IST).

41

Himalayan region at Shillagarh village (Himanchal Pradesh) in the early hours of 16 July 2003. The storm

lasted for less than half an hour, followed by flash floods that affected hundreds of people. Analysis of the

numerically simulated cloudburst in the foothills of the Himalayas showed that the low-level convergence of

south-easterlies and north-westerlies along the foothills coupled with vertical shear in wind and orographic

uplifting lead to a short-lived, intensely precipitating convective storm (cloudburst) steered by the mean flow

and traveling away from the foothills.

Figure 3.8 shows the cloud hydrometeors observed by TRMM corresponding to the satellite pass

made at 00:27 IST, close to the cloudburst event. Figure 3.8a shows the instantaneous rainfall rate (mm/h)

and the horizontal line indicates the axis of the vertical cross-section. The shading indicates the outline of the

cloud. The measurements clearly indicate a deep cloud system, having a liquid-phase at lower altitudes and

ice-phase at higher altitudes. Water content is mainly in the form of rainwater and snow. Cloud liquid-water

content is an order smaller, and ice content is two orders smaller.

(a) Stage-1: 06:30 LT Seperate Cells (b) Stage-2: 08:30 LT Merger cells anddownpour

(c) Stage-3: 12:30 LT Dissipation

Figure 3.9. Conceptual model of a cloudburst (a) separate cells (b) merger of cells anddownpour (c) Stage-3: 12:30 LT Dissipation

42

Comparison between the simulated vertical structure of hydrometeors and TRMM observations showed

that MM5 overestimates the hydrometeor content. Based on the numerical simulations, Das et al (2006)

made a conceptual model of the cloudburst based on the development of the vertical shear, vertical motion

and the moisture distribution. The conceptual model of the cloudburst is shown in Figure 3.9, which illustrates

three stages in the lifecycle of the cloudburst. In the first stage, the two convective cells are separate and drift

towards each other as part of the mean flow (Figure 3.9a). Isolated heavy rain occurs during this stage. In the

second stage (Figure 3.9b), the two convective cells merge. Intensification follows due to strong wind shear

and intense vertical motion. Heavy downpour and formation of the anvil also occurs. The storm moves rapidly

southward due to strong steering flow. The third stage (Figure 3.9c) is one of dissipation in which the two

merged cells form one single large cell, which drifts westward and the cloudburst ceases.

3.5 Fog Forecasting

Fog is defined as an obscurity near the surface layer of the atmosphere which is caused by a suspension

of water droplets and is associated with visibility less than 1000 m. It affects many sections of the society.

However effective fog forecasting with sufficient accuracy in resolution in forecasts of onset, duration and

intensity (visibility) is still a major scientific challenge. Occurrence of fog during late night or early morning over

the northern plains of India is common in winter (December and January). It generally starts from the month of

October and continues till February. The impact and significance of fog ranges from disruption in aviation

services, surface transportation and results to serious accidents caused in part by poor visibility.

More than 675 aircrafts on an average depart and arrive daily from Indira Gandhi International Airport, New

Delhi (Jenamani, 2007), with the maximum number at night and morning hours when chances of dense fog are

high during the peak winter months of December and January. Owing to this, many flights are either cancelled

or diverted. It also causes a delay or stop in air traffic both locally and nationwide, resulting substantial

monetary loss to the commercial airlines. Agriculture, train services and surface transport are also severely

affected due to dense fog over the region. The thick blanket of fog remains till the afternoon and sometimes

shows no sign of abating for a few consecutive days. Day temperature also remains below 15°C.

The prediction of fog remains a difficult problem due to its dependence on micro-physical processes at

mesoscale and synoptic scale. Despite considerable progress in the field of NWP, fog prediction by present

operational NWP models remains a challenging problem. This is because of interaction of micro-physical and

mesoscale processes with the boundary layer which, itself is influenced by the prevailing synoptic regime.

Therefore, until recently most of the fog forecasting models were based on statistical methods (Madan et al.,

2000; Roy Bhowmik, 2004).

In order to study Fog occurrence and its prediction over Delhi metropolitan region, a pilot experiment

was conducted by the IAF by setting up temporary observatories in and around Delhi during January 2005.

Intensive observations were collected at half hourly intervals during the period. The method of prediction was

based on the well known fact that Hydro-lapse provides useful information on occurrence of Fog and the

43

Hydro-lapse can be accomplished with reasonably good success by using a value called the ‘Cross-over

Temperature’. The ‘Cross-over Temperature’ was defined as the lowest dew point temperature observed

during the warmest part of the day. Conceptually, this represents the dew point temperature of the air at about

200 ft above ground level, since this is when the layer is the most mixed and uniform. For the purpose of

obtaining forecast temperature, MM5 was run for 36 hours with two-way nesting of domains with horizontal

resolution as 9 km and 3 km respectively and output was saved at half-hour interval. Forecast parameters

comprised of minimum and maximum temperatures, time series of temperature, dew point depression and

rainfall. After running the model, dew point depression at the lowest level (950 hPa) was plotted as time series

of model output to verify whether meso-scale model resolves different parameters at that high temporal and

spatial resolution. The results were found to be satisfactory. However, prediction of dissipation of Fog could

not be done with much degree of confidence due to various limitations of the model.

Based on a combination of dynamical (meso-scale) forecasting and an equation for visibility, C-MMACS

has developed a fog forecasting technique in terms of onset, duration and visibility 24 hours in advance. For

the period November 2005 to January 2007, hourly forecasts of fog (visibility) over Delhi were generated 24

hours in advance. The meso-scale model used was MM5. The model was interfaced to a visibility model.

They examined and evaluated two types of fog and visibility models: in the first (diagnostic) model, formation

of fog was only governed by thresholds in large-scale meteorological variables like wind and humidity. In the

second (prognostic) model, a dynamical equation that allows persistence and dynamic dissipation described

the evolution of fog and related change in visibility. For the 31-day evaluation period, the average errors in

onset, duration and visibility were 2 hours, 1 hour and 165 meters, respectively for the 4:00-12:00 hour

period. For the 13:00-22:00 hour period, the corresponding errors were smaller.

Das et al. (2007c) made an attempt to simulate fog over Delhi using the high resolution observations

collected during fog PILOT experiment of IAF in a mesoscale model (MM5). They simulated hourly visibility

and different flight regulation categories (defined on the basis of cloud ceiling and visibility) using the MM5

model at 1 km resolution. They conducted several numerical experiments using different cloud microphysics,

boundary layer parameterization schemes and four dimensional data assimilation. Figure 3.10 illustrates the

simulated flight regulations during formation, peak and dissipating stages of fog over Delhi. The results are

very encouraging.

44

Figure 3.10: Simulated flight regulation categories during initiation, peak anddissipation phases of fog over Delhi during 16-17 January 2005.

3.6 Mountain Weather Forecasting

Severe weather has more calamitous effect in the mountainous regions as the terrain is complex,

development is poor and economy is fragile. Such weather systems occurring on small spatial-temporal scale

invites application of models with fine grid resolution and observations from radars and satellites besides the

conventional observations for forecasting and disaster mitigation (Das et al., 2003a). Northwest India and the

Himalayan region are particularly prone to vagaries of severe weather claiming casualties every year. This

region is influenced by WD, which severely affect life over the Himalayas, by inducing wide-spread rainfall,

Initiation: 21UTC Dense Fog: 00UTC

DDeennssee FFoogg:: 0033UUTTCC Dissipation: 06UTC

45

heavy snowfall, squall winds, hail and severe cold waves. Snow avalanches and land slides result on account

of gale winds and heavy rain / snowfall. The ground frost during winter season affects agriculture.

Owing to its unique geographical position, the Himalayan mountain range influences the weather and

climate in various ways. It acts as a strong barrier for the circulation pattern, as a heat source in summer and

heat sink in winter. Its varying snow cover and vegetation affects the monsoon rainfall. The surface boundary

conditions of the Himalayas determine the performance of the monsoon rainfall, which has immense impact on

water resources and agricultural production. Every year several people are killed and properties amounting to

millions of Rupees are lost due to hazards related to weather over the mountains. Advance warning of adverse

weather systems can prevent loss of valuable lives and properties. Recently, there has been many attempts on

mountain weather forecasting using mesoscale models (Das, 2005; Srinivasan et al., 2005; Rajagopal and

Iyengar, 2005). Das (2005) conducted numerical experiments using MM5 model by placing four inner domains

at 10 km resolutions over Northwest Himalayas, Central Himalayas, Northeast Himalayas and the Western

Ghat mountains on an experimental basis. Case studies of heavy rainfall events associated with the western

disturbance and active monsoon conditions indicate that the model has a good capability to predict rainfall

over the mountains for at least 48-72 hours in advance at high model resolutions.

Studies of Srinivasan et al. (2005) shows that the MM5 model is useful in meeting the operational

requirements specifically in the prediction of mountain weather. The model is useful in the prediction of

atmospheric systems and snow avalanches over Himachal Pradesh, Jammu and Kashmir including Siachen

area. However, the avalanche forecasting demands weather forecast information on a 1 km grid. Figure 3.11

illustrates an example of day-4 and day-5 precipitation predicted by MM5 model during 16-18 January 2002

associated with a western disturbance. Many stations in Western Himalaya reported moderate to rather

heavy snow precipitation during the period 13-18 Jan 2002. The results show that the model was able to

predict the snow storm successfully, 4-5 days in advance.

Figure 3.11: Precipitation predicted during 16-18 January 2002 (a) day-4, and(b) day-5 by the MM5c model over the Western Himalayas.

46

3.7 Seasonal Forecasting/ Downscaling

It is well established that mesoscale models have pronounced skill in reproducing regional climate

compared to global models. The long-range prediction of Indian summer monsoon rainfall is very important

for agriculture and socio-economic conditions of the sub-continent. The primary lacuna of present day GCMs

for regional climate studies is their coarse spatial resolution and simple representation of physical processes

(Giorgi et al., 1999; Houghton et al., 2001). It is very much insufficient to resolve the mesoscale atmospheric

circulations, influenced by the regional climate forcing, for example, those forced by small-scale topography

or surface heterogeneties including mountains, land-water contrast, urban effect and sub-synoptic phenomena.

Also, the computing time required for running a uniform resolution GCM over high resolution grid mesh is

more and thus has an unacceptable computational cost for the climate change simulations. The first advantage

of regional mesoscale models is the affordable fine resolution over a limited area. This allows better

representation of the surface forcing on the regional climate, and thus better scope for the regional climate

predictions. Regional climate phenomena like orographic precipitation, lake effect precipitation, etc. that

strongly responses to the changes in the global climate change are better represented by the RCMs. The

various parameterization schemes incorporated in the RCMs add to the better representation of the reality.

RCMs are able to simulate the variations in the climate due to the surface forcing better than the GCM.

Bhaskar Rao et al. (2004) conducted regional climate simulation experiment of the Indian summer

monsoon using the MM5. The model was integrated for four months starting from 1st May 1994 to study the

monsoon during the months of June, July and August. Initial and boundary conditions used are from NCEP/

NCAR reanalysis data available at 2.50 to the model resolution. The results indicated that significant features

of the monsoon circulation such as the monsoon trough, heat low over northwest India and mesoscale

precipitation patterns were well simulated. The model could simulate the march of precipitation belt

corresponding to the Arabian Sea and the Bay of Bengal branches of the monsoon system fairly well. However,

the model could not simulate the advancement over northwest India where the mean rainfall is meager.

Singh et al. (2007) studied the impact of different land-surface parameterization schemes for the simulation

of monsoon circulation during the month of July for a normal monsoon year (1998). They studied three land-

surface parameterization schemes namely, the NoaH, the Multi-layer soil model and the Pleim-Xiu schemes.

Their results indicated that typical features of the Indian summer monsoon, such as strength of the low-level

westerly jet, the cross-equatorial flow and the tropical easterly jet and the distribution of precipitation were

better simulated by NoaH scheme.

Over Indian region, the vegetation type and soil moisture undergoes rapid and significant variations

specially during the southwest monsoon period. Moreover, India has a very diverse and complex topography

throughout, influencing the climatic variation over varied temporal and spatial scales. In view of this and

because of the importance of the land surface forcing, the accuracy of land-use information is important to

obtain for accurate simulations. Recently, ISRO has generated monthly vegetation fraction from temporally

filtered 10 day NDVI composite of SPOT Vegetation data over the Indian region (Shefali et al., 2003). Das

47

et al. (2007b) made a detailed study on the simulations of the Indian summer monsoon during June, July,

August, September (JJAS) for 5 years from 1998-2002 using two different types of vegetation data. One is

the standard 25 categories of USGS vegetation fraction and the second is generated by ISRO, which was re-

aggregated/ regrouped to USGS 25 classes. Figure 3.12 illustrates the JJAS rainfall distributions simulated by

MM5 using USGS and ISRO vegetation data for a good monsoon year 1998 and their differences from

TRMM observations. Figure 3.13 presents the All-India area averaged total rainfall observed by TRMM and

those simulated by MM5 and T80 models for both good monsoon year 1998 and a bad monsoon year 2002.

Analysis of all the results shows that the ISRO vegetation has a positive effect on the forecasts. It performed

consistently better over Northeastern region and along the western coast. It also simulated JJAS rainfall better

than USGS, with lower RMSE in both good and bad monsoon years.

Figure 3.12: Mean Rainfall (cm/ day) simulated by MM5 model for JJAS-1998 for (a) USGS, (b) ISRO andthe difference (cm) of the forecast rainfall from TRMM for (c) USGS and (d) ISRO

48

All India Area Averaged Rainfall (cm) - 1998

0

5

10

15

20

25

30

35

40

45

50

June July August Sept

M onth

Rain

fall

(cm

)

TRMM

FC-USGS

FC-ISRO

T80

All India Area Averaged Rainfall (cm) - 2002

0

5

10

15

20

25

30

35

40

June July August Sept

M onth

Rain

fall

(cm

)

TRMM

FC-USGS

FC-ISRO

T80

All India Area Averaged JJAS Rainfall (cm)

0

20

40

60

80

100

120

140

160

1998 2002

M onth

Rain

fall

(cm

)

TRM M

FC-USGS

FC-ISRO

T80

Figure 3.13: Monthly (upper and middle plot) and JJAS (lower plot) area averaged total rainfall observed by TRMM andsimulated rainfall over All-India by MM5 and T80 models.

49

3.8 Air pollution/ Nuclear Emergency Response System

One of the important applications of mesoscale atmospheric models is to study the urban air pollution

transport problems. The pollution transport models require meteorological parameters as inputs at high resolution.

The MM5 has been widely used for such applications. In India, the BARC, Mumbai and the IGCAR,

Kalpakkam run the MM5 based on the boundary conditions provided by the NCMRWF operational model

and use the outputs to run their dispersion model FLEXPART. The models provides 48 hour forecast of the

local weather and plume dispersion due to hypothetical air borne releases over an area of 100 km2 around

Kalpakkam region (Srinivas et al., 2006). The daily forecasts consist of a set of important parameters like sea

level pressure, surface wind, temperature, humidity, rainfall and the plume dispersion forecast in terms of

Figure 3.14: Dispersion simulation using FLEXPART model using meteorological predictions from the MM5 model.

50

concentration and different forms of radioactive dose (Figure 3.14). Detailed validation is however required

for the intended dispersion prediction during radiological emergencies.

3.9 Cloud-resolving scale simulations

The cloud resolving models (CRM) have resolutions fine enough to represent individual cloud elements

and space-time domains large enough to encompass many clouds and their life-times (Cotton et al., 1982;

Tao and Simpson, 1984; Tao et al., 1987; Redelsperger et al, 1988). They are also known as cloud system-

resolving models (CRSMs). The CRSMs have been successfully used to simulate the local severe storms

Figure 3.15: Time mean vertical profiles of (a) cloud liquid water, CLW (b) rain water, RNW (c) ICE (d) SNOW(e) graupel, GRAP and (f) radiative heating tendency averaged over the domain (68-75 longitude and 16-25 latitude)at 2 km resolution obtained using different cloud microphysics parameterization schemes.

51

(Doswell, 2002; Cotton and Anthes, 1989). The MM5 has been used for different applications at cloud

resolving scale by several investigators. Su et al. (1999) used the MM5 model at 2 km resolution to study the

TOGA-COARE convective systems. Das (2003b) used the MM5 model at cloud resolving scale (1 km) to

study the case of a heavy rainfall episode over northern India surrounding Delhi.

An accurate initial condition with dynamically-balanced atmospheric state is crucial for cloud-system-

resolving simulations. Under-resolved explicit dynamics and the relationship of under-resolved dynamics to

convective parameterizations is an emerging issue in high-resolution prediction models in convective conditions

(Moncrieff et al. 2005). In order to study the macro-dynamics and cloud-microphysical structures of the

convective system, Das et al. (2007a) used the MM5 at 2 km grid-resolution over a domain of 600 km x 600

km to study the cloud cluster properties associated with a heavy rainfall episode during 26-28 June 2002 over

the west coast of India during the Arabian Sea Monsoon Experiment (ARMEX). They simulated vertical

profiles of cloud liquid water, rain water, ice, snow, graupel and radiative heating tendency using four different

cloud-microphysics parameterizations. These fields are shown in Figure 3.15.

Figure 3.16: Time mean vertical profiles of (a) Rain water, RNW (b) cloud liquid water,CLW (c) SNOW, (d) ICE, and(e) latent heating rate averaged over the domain (69-730 E and 19-240 N) obtained from TRMM during 26-28 June 2002

52

The simulated hydrometeor profiles match fairly well with the domain averaged time mean observed

profiles by TRMM (2A12 products) shown in Figure 3.16. The profile of cloud liquid water had a maximum

in the lower troposphere and a secondary maximum in the middle troposphere. The maximum values of ice,

snow and graupel were in the upper and middle troposphere. Their results indicated that the hydrometeor

profiles observed by TRMM are usually higher than the simulated values. The profile of the total condensate

showed maximum value in the upper troposphere implying dominance of ice, snow and graupel, and presence

of deep clouds with anvils during the depression.

3.10 Cloud Seeding Experiments

Understanding of cloud microphysical processes is very essential for rain enhancement programs. The

efficiency with which clouds produce rain at the surface varies greatly. The subject of cloud seeding for rain

enhancement can be considered as a part of the cloud microphysical studies. The potential for increase in

rainfall using cloud seeding is strongly dependent on the cloud microphysics (size and concentration of water

droplets and ice particles inside clouds) and dynamics (forces affecting air motions in and around clouds) of

the clouds that are being seeded. The microphysics in turn dependent on background aerosol levels, because

it is the aerosol particles that attract water vapor to form cloud droplets, and in cold clouds, ice particles.

Furthermore, the types and concentrations of aerosol particles can be influenced by trace gases (i.e., air

pollution). Given these dependencies, the microphysics of clouds can differ significantly from one geographical

region to another, and even between seasons in the same region. In some instances, clouds may not be

suitable for seeding, or the frequency of occurrence of suitable clouds may be too low to warrant the investment

in a cloud seeding program.

Numerical cloud models can be used to conduct sensitivity experiments and test hypotheses for weather

modification (Orville, 1996). In the cloud seeding experiments, the key questions are 1) Identification of

clouds that are amenable to seeding; 2) How, when and where to seed? Some clouds will rain no matter what

you do. Other clouds will not rain, regardless of what you do. Somewhere in between lies a range of clouds

that might be modified. Issues related to these tasks can be answered by conducting sensitivity experiments

using numerical cloud models. For example, studies have shown that the seeding could increase precipitation

from orographic clouds when the 500 mb temperature was warmer than –210 C. Seedability of clouds would

decrease with decreasing cloud temperature. Similarly, the importance of cloud liquid water as an indicator of

seedability has been very well recognized. Cloud models can address issues such as seedability for precipitation

enhancement, optimal ice crystal concentrations, seed drop size, liquid water content, updraft speed and level

for seeding. The cloud scale models can also be used for testing the hypotheses by conducting numerical

experiments. Such models help to sort out the several possibilities of cloud seeding. They offer the only

opportunity to see the effects of cloud seeding on identical cloud situations, one seeded and one not seeded.

Cloud and mesoscale models with realistic seeding routines and ice processes should be applied during all

53

phases of the planning, conduct and evaluation of the field programs. Some of the questions that can be

addressed by the cloud scale/ mesoscale models are for example,

1. Can seeding of individual clouds within a cloud group lead to a more favorable environment through

the redistribution of heat, moisture and momentum to facilitate the development of successive generations of

clouds?

2. Can seeding of individual clouds within a cloud field result in suppression of neighboring clouds or,

in the creation of new or bigger and longer lasting clouds through the process of downdraft interaction or

cloud merging?

3. What is the mode and mechanism of interaction between a seeded cloud field and the mesoscale

environment?

In India cloud seeding experiments have been carried out since 1957. Since then many state governments

including Tamil Nadu, Andhra Pradesh, Maharashtra and Karnataka have been attempting the cloud seeding

experiments to enhance rainfall in their states. NCMRWF had provided inputs on the cloud liquid water

content and potential areas of rainfall during the cloud seeding experiments conducted by the Maharashtra

government in 2004. The inputs based on mesoscale models were found to be very useful in deciding the

areas for cloud seeding. However, the operational programmes carried out in the last few years by various

state governments were not scientifically planned and, no observations of environment as well as clouds were

taken (Vijay Kumar and Kulkarni, 2007). Hence no conclusions could be drawn regarding the impact of the

seeding. Thus, mesoscale models at the cloud resolving scales can be used to address issues such as seedability

for precipitation enhancement programme in future.

55

CHAPTER 4

MESOSCALE DATA ASSIMILATION

56

57

Introduction

Data assimilation is the process by which observations enter a numerical weather prediction model. It

provides a safeguard against the model error growth. With the advent of new observation sources, such as

satellites, radar and many new remote sensing technology, meteorological observations are now available at

very high resolutions. The high resolution observations are not fully utilized if the resolution of a model is very

coarse such as in global models. Mesoscale data assimilation is required for improving the forecast skill of the

mesoscale weather phenomena. The mesoscale models MM5 and WRF have modules for data assimilation

based on 3 dimensional variational technique (3DVAR). Observations can be integrated in to the model either

by nudging or, by variational assimilation. The two methods are briefly described here.

4.1 Observation Nudging

Four dimensional data assimilation (FDDA) is a technique by which observations are incorporated in

the model running with full moist physics (Stauffer and Seaman, 1994; Davis et al., 2001). Observational

measurements keep the model close to the true state and the model atmosphere tends to dynamical consistency.

This alleviates errors in the initial analysis as well as deficiencies in the model physics (i.e., convection, boundary

layer and micro-physics parameterizations). There are two methods: i) analysis or grid-nudging and ii)

observational or station nudging.

In the analysis nudging, Newtonian relaxation terms are added to the prognostic equations for wind,

temperature, and water vapor using the equation

/)(

obst

where á is a prognostic variable (u, v, t, q), ¢ the domain-averaged quantity, the subscript ‘obs’ indicate

observation and ô is the relaxation timescale. This procedure rapidly relaxes the simulated domain-averaged

variable toward observations when ô is small. The observational nudging is carried out by interpolating the

analyzed variables to the observation locations. Many studies have been carried out in India utilizing the

analysis nudging (Bhaskar Rao and Hari Prasad, 2005; Mukhopadhyay et al., 2005; Routray et al., 2005;

Xavier et al., 2006; Sandeep et al., 2006; Das et al., 2006ab).

58

Figure 4.1: Wind at 850 hPa on 00 UTC 28 June 2002 (a) with FDDA, (b) control without FDDA, and (c) METEOSAT cloud imagery.

59

Figure 4.1 (obtained from Das et al. 2007a) presents some results on the impact of FDDA in correcting

the initial position of vortex that caused heavy rainfall over Gujarat region during 26-28 June 2002. The actual

position of the system is illustrated by the satellite cloud imagery shown in Figure 4.1c. The analyzed position

without FDDA is shown in Figure 4.1b. It shows that the centre of the vortex shifted far away from the actual

position. The rainfall forecast (not shown here) based on this initial condition was much less and displaced

from the observed location. However, when the FDDA was performed, the initial position of the vortex was

corrected and brought close to the observed location (Figure 4.1). Consequently, the rainfall forecasts also

improved substantially. The results of Das et al. (2007a) also demonstrated that the best results were obtained

only when the FDDA was operated for a sufficiently long period, i.e. 10 days in their case. The nudging is

required for a longer period to achieve proper circulations in tropical regions where the large-scale balance is

weaker than in the mid-latitudes. These results emphasize the importance of mesoscale data assimilation for

real-time prediction of intense precipitating events in the tropics.

4.2 Variational Assimilation

The basic goal of three dimensional variational assimilation (3DVAR) system is to produce an “optimal”

estimate of the true atmospheric state at any desired analysis time through iterative solution of a prescribed

cost-function (Ide et al. 1997).

)()()(2

1)()(

2

1)( 110 oTobTbb yyFEyyxxBxxJJxJ

where x is the analysis state , xb , the background

yo is the observation, B, E and F are the background,

observation (instrumental) and representivity error covariance matrices respectively. Representivity error is

an estimate of inaccuracies introduced in the observation operator H used to transform the gridded analysis x

to observation space y=Hx.

The 3DVAR system consists of four components (Barker et al., 2003, 2004): (1) Background pre-

processing, (2) Observation pre-processing and quality control, (3) Variational analysis, and (4) Updation of

boundary conditions. Details of these components are given in Barker et al. (2004). Before proceeding to the

variational analysis, a background error field of the model is required. The background error fields are usually

calculated based on the NMC method (Parish and Derber 1992).

Four dimensional variational data assimilation (4DVAR) introduced in the mid 80’s has grown from an

initial theoretical formulation based on optimal control theory to highly complex and successful implementations

in operational numerical weather prediction centers around the world. About a decade after the introduction

of 4DVAR, an original suggestion made by Evensen culminated with an attractive data assimilation procedure

now known as the Ensemble Kalman Filter (EnKF). In the last decade, an increased interest in this approach

has led to significant improvement in its theoretical foundations and practical implementations. Although 4DVAR

and EnKF can be written in similar symbolic form, their specific theoretical and implementation limitations are

60

of high interest and work is needed to clarify the methodologies in the face of complex operational

implementations that are currently envisaged. The methodology to generate members of an ensemble is an

important issue. Presently, a number of procedures are followed at different operational centres. In this method,

perturbations are generated through 4DVAR assimilation with different information (observation) content,

either through a variation in the frequency of observations or a variation in the length of assimilation window.

Generally a representative non-linear system, viz. the three variables Lorenz system is adopted (Gouda et al.,

2005). It shows that ensemble forecasts generated through 4DVAR assimilation have less error than those

generated from initial conditions (with similar amplitude of perturbation) adopted arbitrarily. Another advantage

of the procedure is that it provides an (variable-specific) estimate of the maximum allowed amplitude and

spread of perturbations. The 4D-variational assimilation methodology is one of the most advanced assimilation

techniques today, and only a handful of operational centres (C-MMACS, NCMRWF and, IITD) have

succeeded in implementing it. Presently, research is in progress to implement 4D-Var system in the mesoscale

models.

4.3 Assimilation of Satellite observations

The latest generation of satellite sensors provide wide variety of measurements of the atmosphere.

Remotely-sensed data are becoming the major information source of the state of the atmosphere. It is

important to assimilate these data in the assimilation system, so that better initial conditions (analysis) can be

prepared for NWP models. Different types of satellite observations are presently being assimilated in India on

routine basis at operational centres like the NCMRWF and IMD in their global and regional models. As the

resolutions of the latest generation of satellites are increasing, they are boon to the mesoscale modelling

studies. Observations from different satellite sensors (i.e., ATOVS, SSMI, QSCAT, MODIS, AMSU, GPS,

etc) have been utilised for mesoscale modelling studies in India (Das Gupta et al., 2004; Xavier et al., 2006;

Sandeep et al., 2006; Roy Bhowmik et al., 2006b).

More recently, attempts have also been made to assimilate the satellite radiances and GPS observations

in the mesoscale model (George and Barker, 2007). Since satellites provide a huge amount of atmospheric

soundings, assimilation of these data has become an essential component of modern data assimilation systems.

Improvements in the accuracy of the analyzed and forecasted fields from leading operational weather forecasting

centers testify the advantage of using satellite radiance data compared to derived products in the assimilation

system. The success of radiance assimilation largely depends on the observation error tuning, quality control

and bias correction of the observations. Detailed quality control procedures are included in WRF-Var. Currently,

only clear-sky radiances are assimilated. Radiances from precipitating areas and cloudy regions (cloud liquid

water above 0.2 mm) are not included in the assimilation. Other quality control checks include non-use of

radiance data over mixed surfaces and elevated surfaces for (some channels), removal of data from the scan

edge, avoiding channels close to surface and above the model top etc. To study the impact of the radiance

assimilation over Indian region, George and Barker (2007) made a 10 day assimilation forecast experiment

61

from 01 August to 10 August, 2005 using WRF model. The assimilation was done in 6 hourly cycles. All GTS

observations were used in the assimilation in addition to AMSU-A radiance data from channel 5 to 9 of

NOAA-15 and NOAA-16 satellites. These AMSU-A channels represent the temperature structure of the

atmosphere roughly between 4 to 20 km from surface (weighting function of channel 5 to 9 peaks between 4

and 20 km). The observation cut off time was chosen as ± 2 hours. Two sets of analysis and forecasts were

prepared, one with radiance (radiance + GTS observation, hereafter radiance analysis) and another with only

GTS observations (hereafter no_radiance analysis). At every 00 UTC and 12 UTC, 48 hr forecasts were

made. NCEP-FNL data was used for the preparation of initial and boundary conditions. Figure 4.2 depicts

the mean verification of radiance analysis and forecast for 06, 12, 24 and 48 hr forecasts. The figure shows

that the forecast error growth is maximum in the first 6 hr forecast. This indicates the need for tuning of the

WRF model (with different physics/dynamics options) for the Indian region. Proper tuning of the WRF model

over Indian region may improve the first guess and hence the analysis.

Figure 4.2. Radiance analysis & forecast mean RMSE and Bias profilesfor u-wind, v-wind, temperature and water vapor mixing ratio.

Similarly, the constellation of Low Earth Orbit satellites (LEO) launched in 2006 under the Constellation

Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission, provides large number of

radio occultation (RO) soundings around the globe daily. The phase and the amplitude measurements during

the time of occultation (LEO satellites with respect to GPS satellites) can be used to derive the vertical profiles

of the bending angle. The refractivity profiles are then computed from bending angle through Abel inversion.

Different COSMIC data products are available near real time in the COSMIC web site. Several NCAR

62

studies indicate the positive impact of GPS refractivity data on the assimilation and forecast (i.e., Cucurull et

al, 2006). Attempts have been made by George and Barker (2007) to assimilate the GPS refractivity

observations in the WRF-Var system. The period from 28 June to 02 July, 2006 was chosen for the assimilation

experiment with GPS COSMIC data (a deep depression was present in the Bay of Bengal from 02 to 05

July, 2006). Cyclic assimilation (6 hr cycle) were carried out with and without GPS data, to study the sensitivity

of the GPS RO data over Indian region. Twenty four hour forecasts were made from 00 UTC analysis of both

experiments. Since very few occultation observations (only 2 occultation profiles) were available during this

period over the domain, they found no significant differences between the forecast experiments. However,

Table 4.1 shows the RMSE of the refractivity data for OI (observation minus first guess) and AO (analysis

minus observation) during the assimilation period. The table indicates that the GPS refractivity data does

make significant difference to the analysis fields. More assimilation-forecast experiments are required to assess

the impact of GPS data over Indian region.

Table 4.1: RMSE of OI and AO of GPS COSMIC refractivity assimilation

Time Observation RMSE RMSE

Number OI (Refractivity) AO (Refractivity)

06 UTC 28 Jun, 2006 23 1.7911 0.9788

18 UTC 01 Jul, 2006 20 2.2890 0.416

4.4 Assimilation of Radar observations

Doppler Weather Radar (DWR) observations are becoming very important for assimilation in the high

resolution Numerical Weather Prediction models. The conventional observations at surface from Automatic

Weather Stations (AWS) and upper air measurements such as Radio-sonde, Pilot balloons, etc are usually

not available at high resolution. But, as the resolution of the model increases, we need observations at finer

resolution to initialize the model. Thus, the DWR and the satellite observations play an important role in

mesoscale data assimilation. Sun and Crook (1994, 1997) used adjoint technique and a variational Doppler

radar analysis system (VDRAS) to retrieve three-dimensional wind, temperature, pressure and microphysical

fields in a gust front based on observations from a single Doppler radar and a convective scale model. With

the availability of multiple Doppler radars researchers have employed adjoint technique and cloud model to

derive the thermodynamic and microphysical fields. The DWR provides observations of radial velocity and

reflectivity with a spatial resolution of a few hundred meters every 3 to 10 minutes and, is practically the only

equipment capable of sampling the four-dimensional structure of storm-scale flows.

The last one decade has witnessed growing applications of DWR in NWP as they have capability of

measuring radial winds and hydrometeor profiles inside clouds, besides providing digital reflectivity fields. The

India Meteorological Department (IMD) has installed four DWRs at Chennai, Kolkata, Machilipatnam and

Visakhapatnam.

63

Three more DWRs are operated one by ISRO at SHAR and two by IAF at Patiala and Gujarat region.

Das et al. (2006b), Abhilash et al. (2007) used wind fields retrieved from the Kolkata & Chennai DWR in a

mesoscale data assimilation system. They studied three strong convective events each over Kolkata and

Chennai. The model (MM5) was run on double nested domains at 30 and 10 km resolutions. Keeping all

model physics same, three experiments were conducted for each case. In the control experiment (CTRL) the

model was initialized using global analysis of NCMRWF T80 model. In the second experiment

(3DVAR_NoDWR) conventional and non-conventional data were used in the six-hour assimilation cycle

with cold–start at 00 UTC of the previous day. The third experiment (3DVAR_DWR) was same as second,

but the DWR wind data was also included in the assimilation cycle. Results show many encouraging features.

Figure 4.3 illustrates the reflectivity MAX(Z) observed by DWR at Kolkata on 6th May 2005. Severe

thunderstorm lashed Kolkata region on the day. The simulated rainfalls obtained from the three experiments

are shown in Figure 4.4 along with observed rainfall from TRMM. The diagrams show that the rainfall

distributions were close to observations in the third experiment when the DWR observations were assimilated

in the model. The intensity and organization of convective systems were also better predicted in the second

and third experiments. Figure 4.5 presents the root mean square error (RMSE) of rainfall with respect to the

domain averaged TRMM observations for the three experiments. The RMSE were computed based on the

composite rainfall simulations made for the six cases over Kolkata and Chennai. The diagram shows very

encouraging results. It shows that the RMSE score of rainfall is better through out the forecast length when

3DVAR is used. The forecasts are further improved up to 30 hours when the data assimilation is carried out

with DWR observations.

Figure 4.3: Reflectivity MAX(Z) observed by DWR at 16-17 UTC,6 May 2005 over Kolkata

64

Figure 4.4: Simulated rainfall obtained from the three experiments (CTRL_GSFC), 3DVAR_NODWR) and3DVAR_DWR. Observed rainfall from TRMM are also shown.

Figure 4.5: RMSE of rainfall obtained from the three experiments CTRL_GSFC, 3DVAR_NoDWR(denoted by TDVAR) and 3DVAR_DWR.

Wind and reflectivity observations from the DWR are very valuable information that can be included in

the analysis. During the past several years, capabilities have been developed to assimilate Doppler radial

velocity and reflectivity data in the WRF-Var assimilation system (Xiao et al. 2005). The observations operator

used in WRF-Var for Doppler radar radial velocity is

RM SE

0.0

0.1

0.2

0.3

0.4

6 12 18 24 30 36 42 48

HOUR S

Ra

infa

ll (

cm

)

GSFC

TDVAR

DWR

65

where (u, v, w) are the components of the wind, (x, y, z) and (xi, y

i, z

i) are the radar location and observation

location respectively. The terminal velocity vT is defined as

where, and qr is the rainwater mixing ratio. The observation operator for the reflectivity (Z)

used in the WRF-Var is,

Partitioning of moisture (qr from q) during the iteration is done through warm rain process included in

the WRF-Var.

George and Barker (2007) assimilated the Doppler radar data from SHAR (13.6 N 80.2 E) using

WRF-Var (This radar is India’s first indigenously made Doppler radar). Reflectivity and radial velocity data

from 00 UTC of 29 October to 00 UTC of 30 October, 2006 were processed. Two 6 hourly assimilation

cycle run from 06 UTC of 28 October to 00 UTC of 01 November 2006; one with radar data (radar +

GTS) and another with no radar data (only GTS) were prepared. Twenty four hour forecasts were made at

00 UTC of 29, 30 and 31 October 2006. Figure 4.6 depicts the Doppler radar data (all data) at 0.5 elevation

angle from a scan of 00 UTC of 29 October 2006. Figure 4.7 depicts the mean 00 UTC analysis and 24 hr

forecast RMSE and BIAS against the observations. Results show that the differences in the RMSE and Bias

values of radar and no_radar values are reduced in the forecast compared to analysis. However, these are

preliminary results and many more experiments need to be carried out.

Figure 4.6: SHAR (13.6 N, 80.2 E) Doppler radar observation for reflectivity and radialvelocity for 00 UTC 29 October (at elevation angle 0.5 deg).

66

Figure 4.7: RMSE and Bias for u-wind, v-wind, temperature and mixing ratio profiles forRadar and No_Radar analysis and 24 hr forecast against observations.

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CHAPTER 6

RECOMMENDATIONS

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In India, the use of high resolution mesoscale models for the studies of atmospheric processes signifi-

cantly increased during the last decade. This is because of the availability of these models on the web domain

and with very good documentation and user support. MM5 and WRF models available from NCAR fall into

this category, whereas RAMS, ARPS and other models had limited application. A review of the application

of these models by various research groups in India demonstrate considerable skill of these mesoscale models

in predicting regional/ local circulations driven by topography and land surface variations. Such forecasts

were often not possible by operational models due to then coarse resolution (Davis et al 1999; Das 2003).

A basic query is “Does the high resolution mesoscale models produce better forecasts that are statisti-

cal significant”? Though the available studies over Indian region have their limitation with respect to the large

number of numerical experiments required for statistical evaluation, some of these studies (Das et al 2007)

indicate that the present mesoscale models have improved mesoscale prediction and need to be further

improved.

A critical examination of the materials presented in this report indicates that the mesoscale atmosphere

phenomena such as tropical cyclones, severe local storm, mountain weather, fog and western disturbances

etc. are to be studied with specific strategy keeping in view of the associated problems of their understanding

and prediction. There is also a general consensus that human resources development to work in these spe-

cific, special applications is to be improved. In the following, recommendations are suggested for each of the

mesoscale atmospheric phenomena followed by the general requirements.

At the national level the problems regarding the understanding and prediction of the various atmo-

spheric phenomena such as tropical cyclones, heavy rainfall events and severe local storms are to be identified

and financial support may be provided to the research groups who are willing to undertake one of the specific

identified problems. The outcome from each of these groups could be reviewed periodically (i.e.) every six

months. DST/MoES should also overview the utilization of these results to be absorbed into operational

prediction actively.

1. Tropical cyclones

The prediction of the intensification and track of the tropical cyclones is an old age problem and con-

tinuous efforts are being made for their improvements. Since the evolution and movement of the tropical

cyclones involve interaction of different scales of motion i.e. from the cloud scale to synoptic scale circulation,

the researchers have a strong opinion that it is desirable to use models with a high domain resolution of ~ 1-

10 km. The available mesoscale models include non-hydrostatic dynamics and so can be used with resolution

of less than 10 km. The problems concerning the tropical cyclones are;

i. What should be the domain size and domain resolution? Can they be optimized?

ii. What are the advantages and disadvantages of the single and nested domain approaches?

iii. What is the sensitivity of model prediction to different hypotheses that form the basis of the param-

eterization of cumulus convection, PBL and cloud microphysics? Some of the earlier studies show

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significant sensitivity to the combination of these schemes. This has to be further explored to have

results which are statistically significant.

iv. Can the model predict the sudden intensification? The role of latent heat/ sensible heat energy

transfer from the ocean to the tropical cyclones and the dynamical interaction of the radius of

deformation and convection are to be critically examined.

v. Why are the present models not able to predict cyclogenesis? Is it possible to identify the require-

ments in terms of model and data?

vi. What is the predictability of the developing and non-developing disturbances? It is generally no-

ticed that the models tend to overestimate the intensification of weaker cyclones and underesti-

mate the stronger cyclones.

vii. What is the role of bogus vortex in the prediction of the intensification and the movement? Can the

models do away with bogus vortex? If bogus vortex is necessary, how to design it? All the

available methods of bogus vortex design are to be examined and critically evaluated. In this

context, some of the recently available JRA-25 Reanalysis are to be studied as this analysis has a

special feature of the inclusion of tropical cyclone circulation.

viii. Observations associated with tropical cyclones need considerable improvement as these forms

and intensify over oceans. It is recommended that the Government of India should make efforts to

collect data in tropical cyclones through reconnaissance flights and drop-sondes, ocean buoys.

2. Heavy rainfall events, Off-shore vortices and Mid- Tropospheric cyclones

In recent times, the occurrences of heavy and extreme precipitation events are noted to increase which

is attributed to global warming (Goswami 2006). The Mumbai rainfall event with the occurrence of 94 cm

within the duration of 12 hours and subsequent heavy rainfall events recorded at Chennai, Bangalore, Hyderabad

etc. have drawn the attention of scientific community as well as administration. Since these heavy precipitation

events are regionalized (localized), the model strategy is to be reviewed and evaluated. Some of the important

points for consideration are:

i. What should be the domain resolution and domain size? For example, the model resolution is to

be between 1-5 km. Since the models cannot be run at the desired high resolution in operational

mode, the possibilities of the impending extreme precipitation events from the mesoscale models

are to be evaluated. Once the possibilities are identified, special model runs can be made at the

desired high resolution.

ii. What are the parameterization schemes of convection and cloud microphysics and their combina-

tion that may provide the best prediction? The sensitivity of the prediction with respect to real time

land use and high resolution terrain is to be established.

iii. The choice of the parameterization schemes of convection, PBL and cloud microphysics are to be

examined and evaluated. Is it possible to arrive at the best choice of these parameterized schemes?

Since the prediction of heavy rainfall events over the metropolitan cities are to be given primary

99

importance due to the socio-economic impact, mesoscale data observations over the identified

metropolitan cities are to be established. Experiments with adaptive observations are to be carried

out to optimize the number and locations of the observation stations. AWS network can be estab-

lished based on these experiments.

iv. The methods of data assimilation for AWS and other remote sensing platform are to be critically

examined and evaluated. For example, the methods of 3DVAR, 4DVAR and FDDA available

with MM5 and WRF models are to be thoroughly studied.

3. Severe local storms

Severe local storms are short lived, violent disturbances associated with strong gusty winds and with or

without rain. Since their spatial scale is few kilometers and time scale is few hours, separate model strategy is

to be evolved to understand their life cycle and predictability. Apart from MM5 and WRF models, RAMS

and ARPS may also provide good prediction.

i. What should be the domain size and resolution? Is it necessary that the resolution should be few

hundred meters to 1 kilometer?

ii. What is the lead time with which models can predict the intensification of the system?

iii. What is the critical input data that will be crucial for triggering the convection? What are the data

assimilation methods that can assimilate the thermodynamic structure of the environment?

iv. How best are the models able to simulate their structure and characteristics?

v. The parameterization of cloud microphysics is very important and all the available cloud micro-

physics schemes are to be examined and evaluated.

4. Western disturbances, Mountain weather, Avalanches and Cold Waves

At the present time western disturbances are well predicted due to their extra-tropical nature. Though

their characteristics and movement could be predicted, there is a need for improvement in the prediction of

these WD which cause heavy snowfall in the complex terrain regions of Himalayas and neighborhood. These

are also known to cause cold waves and the prediction of cold waves will improve with the prediction of WDs.

i. The sensitivity of model prediction to the complex terrain region is to be studied. The model

resolution may have to be ~1 km for proper mesoscale prediction. Sensitivity experiments to

model resolution are to be performed and evaluated.

ii. Data network over the Himalayan region is to be expanded. Since this is a sensitive region, SASE

of DRDO, NCMRWF and IMD can participate in data collection and model experimentation.

Already scientists at SASE have started modeling experiments and further experiments may be

encouraged.

iii. The model prediction capability of the rate and amount of snowfall prediction is to be evaluated.

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5. Fog

Fog is localized phenomena but known to be influenced by certain synoptic situation such as western

disturbances. The occurrence (i.e.) onset and duration of fog over metropolitan cities assume lot of impor-

tance due to their affect on visibility affecting the flight operations and train services. Since the prediction of fog

over metropolitan cities is of primary importance, their prediction strategy could be combined with those of

heavy rainfall events. The same mesonetwork that is proposed for heavy rainfall events could be used for the

prediction of fog.

i. What should be the optimized model resolution for the prediction of fog?

ii. Since boundary layer and surface processes play an important role, the sensitivity to different

schemes of soil processes and PBL schemes is to be examined and evaluated.

iii. The problem of data assimilation is as of the prediction of heavy precipitation events.

iv. The presently available empirical models of fog prediction are to be critically examined using the

model derived variables.

6. Data and Assimilation

For improvements in mesoscale predictions, the data availability over the regions of interest should

improve. AWS network has to be established over the entire country in phases, starting with metropolitan

cities. The radio-sonde observation stations are also to be increased with at least one station in a 200 km

radius. Accurate high resolution surface data of soil moisture, vegetation fraction, land use and topography for

the Indian region are to be made available to the atmospheric modelling scientists.

The available data assimilation methods with MM5, WRF and other models have to be extensively

studied for each of the different mesoscale atmospheric phenomena. The impact of the assimilation of wind,

temperature and wind are to be studied in detail. Emphasis has to be given for the assimilation of ever

increasing satellite and radar observations into the models. The methods of 3DVAR, 4DVAR and FDDA are

to be reviewed and evaluated. Each of these methods involves specification of parameters derived from

historical data and experiments. Experiments are to be performed to improve these parameters as suitable for

the Indian region. The problem of data assimilation is to be viewed as a huge task due to the increasing

availability of data from remote sensing platforms. Specific groups in the country are to be identified and

entrusted with specific tasks of the validation and improvement of the existing methods. NCMRWF may take

a lead role in this regard.

7. Human Resources

For the successful implementation of the above mentioned mesoscale modeling program, highly skilled

human resources are being made available. DST/MoES have to implement specific programs to attract promising

young scientists at the post graduation level and induce them into atmospheric modeling research. This may be

done through allocation of separate fellowships and monitoring the awardees. Special programme may also

101

be envisaged through which senior scientists, professors may visit different institutions to deliver lectures on

the challenges in atmospheric and oceanic sciences and provide counseling to the young student community.

Regular training/workshops should be organized annually at identified institutions to provide theoretical and

practical training on the current status of the mesoscale modeling and data assimilation problems. It is also

recommended that computer software professionals be trained to work on weather models so that they may

be able to develop and improve the computer codes of different models as suitable for computer architec-

tures. Similarly, experts in numerical techniques and mathematical analysis should be inducted in atmospheric

modeling, so that they may be able to make fundamental contributions to the development of dynamics and

numeric parts of the model.

8. Computational Resources

The application of mesoscale atmospheric models requires high performance computing resources at

as many as possible institutions in the country where the related experiments are to be carried out. The

computing power required to run the mesoscale models with data assimilation, experimentation and validation

may be of the order of 1-100 Teraflops. The disc storage space necessary to store the inputs and outputs may

be of the order of 10-100 GB/day. Most of the models such as MM5 and WRF are available with coding

structure suitable for parallel processing. The present day technology is able to provide near super computing

power through the design of a cluster. TIFR and CDAC have already produced super computing power

through cluster design and CSIR-NAL produced a low and 8-nodes parallel cluster suitable for non-real time

research application.

As per this report about 12 groups are actively involved in the use of mesoscale models and so the high

performance computer (HPC) facility at these centers may be augmented. It is also desirable to encourage

new groups of other institutions to enter into this specific program. A centralized supercomputing facility may

also be established at one of the institutions (such as NCMRWF), which may provide remote log-in access to

all the universities and scientific communities in the country to facilitate research on mesoscale atmospheric

modeling. This computer may have several thousand processors with large mass storage device, and may be

exclusively used for sharing within the research communities in the country.

9. Field Experiments

Regular field experiments may be conducted similar to the STORM program of India to collect inten-

sive observations of specific mesoscale phenomena. The special observations thus collected may be utilized

for understanding the structure, mechanisms and modeling of the phenomena. The country must procure

dedicated aircraft fitted with meteorological instrumentations, mobile Doppler radars, and other equipments

on mobile platforms for utilization in the field experiments.

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10. Targeted numerical experimentation and Ensemble forecasting

The research community may collectively conduct numerical experiments on specific mesoscale phe-

nomena by utilizing fixed set of initial & boundary conditions. The outputs of the experiments may be dis-

cussed at regular seminars and, the results may be used to improve the models. This will avoid duplicity and

focus the scientific community. Ensemble forecasting experiments may be conducted to provide probabilistic

forecasting of severe weather events.

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List of Acronyms

AFCNWP Air Force Centre for Numerical Weather Prediction

AMSU Advance Microwave Sounding Unit

ARPS Advanced Regional Prediction System

ARMEX Arabian Sea Monsoon Experiment

ATOVS Advance TIROS Observation Vertical Sounder

AU Andhra University

BARC Bhabha Atomic Research Centre

BOBMEX Bay of Bengal Monsoon Experiment

CEO Chief Executive Officer

C-MMACS Centre for Mathematical Modelling & Computer Simulations

COSMIC Constellation Observing System for Meteorology, Ionosphere and Climate

CRM Cloud Resolving Model

CSRM Cloud System Resolving Model

DRDO Defence Research & Development Organization

DST Department of Science & Technology

DWR Doppler Weather Radar

ECMWF European Centre for Medium Range Weather Forecasts

ETA ETA coordinate mesoscale model

FDDA Four dimensional data assimilation

GAME GEWEX Asian Monsoon Experiment

GCM General Circulation Model

GEWEX Global Energy & Water Cycle Experiment

GFS Global Forecasting System

GPS Global Positioning System

GTS Global telecommunication System

HRM High Resolution Model

IAF Indian Air Force

IGCAR Indira Gandhi Centre for Atomic Research

IIT Indian Institute of Technology

IITM Indian Institute of Tropical Meteorology

IMD Indian Meteorological Department

ISRO Indian Space Research Organization

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LAM Limited Area Model

LASPEX Land-Surface Process Experiment

LEO Low Earth Orbit

LMD Laboratoire de Météorologie Dynamique

MODIS Moderate Resolution Imaging Spectro-Radiometer

MoES Ministry of Earth Sciences

MONEX Monsoon Experiment

MM5 PSU/ NCAR 5th generation Mesoscale Model

NASA National Aeronautics & Space Administration

NCAR National Centre for Atmospheric Research

NCEP National Centre for Environmental Prediction

NCMRWF National Centre for Medium Range Weather Forecasting

NDVI Normalized Difference Vegetation Index

NMC National Meteorological Centre

NOAA National Oceanic & Atmospheric Administration

NWP Numerical Weather Prediction

PBL Planetary Boundary Layer

PRWONAM Prediction of Regional Weather using Observational Meso-Network andAtmospheric Modelling

PSU Pennsylvania State University

QSCAT Quick bird-satellite Scatterometer

RAMS Regional Atmospheric Modelling System

RCM Regional Climate Model

RMC Regional Meteorological Centre

RMSE Root Mean Square Error

RO Radio Occultation

RSM Regional Spectral Model

SASE Snow & Avalanche Study Establishment

SHAR Sriharikota High Altitude range

SPL Space Physics Laboratory

SPOT Systeme Pour l’Observation de la Terre

SSMI Special Sensor Microwave Imager

TIROS Television Infrared Observation Satellite

TKE Turbulent Kinetic Energy

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TOGA-COARE Tropical Ocean Global Atmosphere – CoupledOcean-Atmosphere Response Experiment

TRMM Tropical Rainfall Measuring Mission

UKMO UK Meteorological Office

USGS United States Geological Survey

UTC Universal Time Convention

VDRAS Variational Doppler Radar Analysis System

VSSC Vikram Sarabhai Space Centre

WRF Weather Research & Forecasting

3DVAR 3 Dimensional VARiational assimilation system

4DVAR 4 Dimensional VARiational assimilation system

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1 Dr. Someshwar Das National Centre for Medium Range Weather Forecasting, Noida

2 Prof. D.V Bhaskar Rao Andhra University, Visakhapatnam

3 Dr. H. R. Hatwar

Dr. Y. V. Rama Rao

India Meteorological Department

4 Sri Shivhare

Sri P. K. Arora

Indian Air Force

5 Dr. P. Mukhopadhyay

Sri J. Sanjay

Indian Institute of Tropical Meteorology, Pune

6 Prof. U.C. Mohanty Indian Institute of Technology, Delhi

7 Prof. A. Chandrasekhar Indian Institute of Technology, Kharagpur

8 Dr. K. Srinivasan Snow & Avalanche Study Establishment, Chandigarh

9 Dr. D. Bala Subramanyam Space Physics Laboratory, Trivandrum

10 Dr. Prasant Goswami Centre for Mathematical Modelling & Computer Simulations, Bangalore

11 Dr. T.N. Venkatesh Indira Gandhi Centre for Atomic Research, Kalapakkam

12 Dr. Lohar Jadavapur University, Kolkata

13 Dr. P. Sanjeeva Rao Department of Science & Technology (DST), Govt. of India, New Delhi

List of Contributors and Institutions