WMO

SDS-WAS Research and Development activities

6th meeting of the WWRP JSC(WMO Headquarters, Geneva, 18-19 July 2013)

E. Cuevas, SDS-WAS NAMEE (AEMET, Spain)

J.M. Baldasano, SDS-WAS NAMEE (BSC-CNS, Spain)

X. Zhang, SDS-WAS EA (CAMS, China)

W. Sprigg, SDS-WAS As (Chapman University, USA)

E. Terradellas SDS-WAS NAMEE (AEMET, Spain)

S. Nickovic (Ex-WMO SDS-WAS, Serbia)

                                                   WMO; AREP

Improved Weather and Seasonal Climate prediction

Human Health (Asthma, infections, Meningitis in Africa, Valley Fever in the America’s)

Agriculture (negative & positive impacts)

Industry (Semi-conductor, etc.)Energy (Thermal solar energy)

Aviation (air disasters)

Ground Transportation (high speed rail)

Marine productivity (negative & positive impacts)

2

Impacts of Sand and DustImpacts of Sand and Dust

3

SDS-WAS MissionSDS-WAS Mission

To enhance the ability of countries to deliver timely and quality sand and dust storm forecasts, observationsforecasts, observations, information and knowledge to usersusers through an international partnership in research and operationsresearch and operations

4

WMO Research Program ComponentsWMO Research Program Components

5

SDS-WAS research issuesSDS-WAS research issues

Sources

physical, optical and chemical properties

interaction with radiation, clouds and precipitation

Model validation and intercomparison

Ensemble forecasting

Impacts of dust (health, transport, agriculture)

Improve weather, climate and air quality modelling

6

History and Milestones in SDS-WASHistory and Milestones in SDS-WAS

2004: International Symposium on SDS & a WMO Experts Workshop on SDS (CMA, Beijing-China).

2005: a questionnaire on SDS to WMO Members indicated 40 interested countries.

2006: Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) proposed (CMA, Beijing-China).

2007: the 14th WMO Congress endorsed the launching of the SDS-WAS

2008: WMO SDS-WAS Implementation plan drafted (Athens, Greece).

2010: First SDS-WAS hands-on training Workshop (BSC, Barcelona-Spain).

Regional node Northen Africa,

Midle-East and Europe

Regional Center

Partner 1

Partner 2Partner 3 Partner 4

Partner 5

Partner n

….

Regional node Americas

WMO SDS-WASWMO SDS-WAS

Preliminary steps

Regional node

West Asia

Regional node

East Asia and Pacific

Regional Center

Partner 1

Partner 2Partner 3 Partner 4

Partner 5

Partner n

….

RC

NEWNEW

SDS-WASSDS-WASa federated systema federated system

8

Collaboration MechanismsCollaboration Mechanisms

Flow of information between SDS-WAS system components for a regional node consisting of a consortium of partners supported by the Regional Steering Group and Regional Centre

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SDS-WAS Node structureSDS-WAS Node structure

Asian Node _Regional Centre Asian Node _Regional Centre http://www.sds.cma.gov.cnhttp://www.sds.cma.gov.cn

11

Na-ME-E Regional Centre; http://sds-was.aemet.esNa-ME-E Regional Centre; http://sds-was.aemet.es

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A Pan-American CenterA Pan-American Center

for the WMO for the WMO SDS-WAS SDS-WAS

• Information relevant to airborne dust (as it impacts socio‐economic and environmental sectors including health, safety, water supplies, air quality, agriculture, fisheries, commerce and defense) is at the Center’s core.

• The Center facilitates research & applications for North, Central and South America.

• The Pan‐Am Center acts as a node to Exchange information, talent, & infrastructure across the Americas to facilitate progress in predicting, adapting to, and avoiding maladies of airborne dust, crossing scales of weather and climate, región & globe.

William A. Sprigg

Chapman University (USA)

A support from the West Asian UNEP officeA support from the West Asian UNEP office

A short-term project to perform an assessment report on skills and needs for establishing a new WMO SDS-WAS node

A technical UNEP/WMO SDS conference was held in Abu Dhabi (May 2013) to discuss further steps

Future West Asia (Gulf Countries + Iran + Turkey) Future West Asia (Gulf Countries + Iran + Turkey)

for the WMO for the WMO SDS-WAS SDS-WAS

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Current research topics in SDS-WAS Regional Centers Current research topics in SDS-WAS Regional Centers

1. Model validation/evaluation

2. High resolution modelling

3. Data assimilation

4. Long term forecasting

5. Improvement/characterization of dust observations

6. Dust impacts

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1. Model validation/evaluation

MODEL INSTITUTION RUN TIME

DOMAIN DATA ASSIMILATION

BSC-DREAM8b BSC-CNS 12 Regional No

CHIMERE LMD 00 Regional No

LMDzT-INCA LSCE 00 Global No

MACC ECMWF 00 Global MODIS AOD

DREAM-NMME-MACC

SEEVCCC 12 Regional MACC analysis

NMMB/BSC-Dust

BSC-CNS 12 Regional No

MetUM U. K. Met Office 00 Global No

GEOS-5 NASA 00 Global MODIS reflectances

NGAC NCEP 00 Global No

• VARIABLES: Dust surface concentration – Dust Optical Depth at 550 nm• LEAD TIME: 0 – 72 hours, every 3 hours• GEOGRAPHICAL DOMAIN: 25ºW – 60ºE, 0 – 65ºN

• VARIABLES: Dust surface concentration – Dust Optical Depth at 550 nm• LEAD TIME: 0 – 72 hours, every 3 hours• GEOGRAPHICAL DOMAIN: 25ºW – 60ºE, 0 – 65ºN

The modelsThe models

Joint visualization. Dust AOD at 550 nmJoint visualization. Dust AOD at 550 nm

Joint visualization. Surface concentrationJoint visualization. Surface concentration

Model outputs are bi-linearly interpolated to a common 0.5ºlon x 0.5ºlat grid mesh. Then, different multi-model products are generated:•CENTRALITY: median - mean•SPREAD: standard deviation – range of variation

Model outputs are bi-linearly interpolated to a common 0.5ºlon x 0.5ºlat grid mesh. Then, different multi-model products are generated:•CENTRALITY: median - mean•SPREAD: standard deviation – range of variation

Generation of multimodel productsGeneration of multimodel products

Homogenization forecasts Homogenization forecasts in the “Asian Node” in the “Asian Node”

CUACE/Dust +CUACE/Dust +

MASINGAR +MASINGAR +

ADAM +ADAM +

..

..

..

N modelN model

Homogenization forecasts Homogenization forecasts in the “Asian Node” in the “Asian Node”

CUACE/Dust +CUACE/Dust +

MASINGAR +MASINGAR +

ADAM +ADAM +

..

..

..

N modelN model

Asian Node _Regional Centre Asian Node _Regional Centre http://www.sds.cma.gov.cnhttp://www.sds.cma.gov.cn

TAMANRASSET_INMSANTA_CRUZ_TENERIFE

NRT evaluation using AERONET dataNRT evaluation using AERONET data

• Bias Error• Root Mean Square Error• Correlation Coefficient• Fractional Gross Error

• Bias Error• Root Mean Square Error• Correlation Coefficient• Fractional Gross Error

• Near Real Time Observations• Common data processing and

calibration procedures

• Near Real Time Observations• Common data processing and

calibration procedures

Model validation with AERONETModel validation with AERONET

http://sds-was.aemet.es/forecast-products/forecast-evaluation/model-evaluation-metrics

Evaluations• Monthly• Seasonal• Annual

Evaluations• Monthly• Seasonal• Annual

Evaluation of Saharan dust transport over the Atlantic

Source: WHOI

NRT evaluation of dust forecasts with MODIS deepblue: NMMB-BSC/Dust Since June 2013

BIAS

CORRELATION FGE

RMSE

Validation of MACC-fszd reanalysis with PM10 from AMMA

Daily mean values.

AMMA PM10 where filtered by wind direction to assure dust conditions

MACC 3h data were averaged to obtain daily mean surface concentration

Cuevas et al. 2013

Comparative AOD climatology at selected AERONET sites

2007-2008

Cuevas et al. 2013

Comparative spatial climatology of AOD

WINTER Cuevas et al. 20132007-2008

28

Validation MACC-fszd extinction vertical profiles with lidars at M’Bour (Senegal) and Tenerife (The Canary Islands, Spain)

Cuevas et al. 2013

M’Bour-Senegal lidar station from LOA (CNRS-Univ. Lille)

WINTER2007-2008

LIDAR (LIRIC algorithm) – MODELS comparison

60 – 80 dust events within the period Jan 2011 – Jun 2013

BSC-DREAM8B_v2

NMMB-BSC/Dust

DREAM8-NMME-MACC

test footer 30

2. High resolution modelling

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Model nesting strategy

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7:45 PM Phoenix as the dust storm neared.

W.A.Sprigg, S. Nickovic, G. Pejanovic, J. Galgiani, A. Vukovic

Phoenix (Arizona) Haboob, 5 July 2005

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DUST SIMULATION

– 6-km model 10m WIND MAGNITUDE

W.A.Sprigg, S. Nickovic, G. Pejanovic, A. Vukovic

NASA Applied Science support led

to this high-resolution forecast &

simulation capability

Successful simulation of the Phoenix haboob

(Chapman University dust modelling group)

Phoenix

Phoenix

10m WIND MAGNITUDE

NASA Applied Science support led to this high-resolution forecast & simulation capability

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3.3. Data assimilationData assimilation

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Operational Dust assimilation at CMA

36WWRP JSC 21-24 February 2011

Assimilation MODIS AOD in ECMWF MACC

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4.4. Long-term forecastingLong-term forecasting

• (average=15)

SDS Seasonal Prediction : DGAE/SSDS

Yellow is anomaly from average number

of SDS

Yellow is anomaly from average number

of SDS

Light blue and dark blue represents the first and second level (13-14cases) respectively for less than average

Light blue and dark blue represents the first and second level (13-14cases) respectively for less than average

Light red, peach blow and red wine represents the first,

second and third level (16 cases)

respectively for more than average.

Light red, peach blow and red wine represents the first,

second and third level (16 cases)

respectively for more than average.

2007200720072007

2009200920092009

39

5.5. Improvement/Characterization of dust Improvement/Characterization of dust observationsobservations

AOD distribution of CARSNET stations

Station name

Ake

da

laB

eiji

ng

Ch

an

gd

eC

he

ng

du

Da

lian

Da

ton

gD

on

gsh

en

gD

on

gta

nD

un

hu

an

gE

jina

Gu

che

ng

Ha

mi

Ho

tan

Hu

imin

Jiu

qu

an

La

nzh

ou

Lh

asa

Lin

'an

Min

qin

Mt.G

ao

lan

Mt.L

on

gfe

ng

Mt.T

ai

Mt.W

alig

ua

nN

an

nin

gS

ha

ng

dia

nzi

Sh

an

gri-

La

Ta

zho

ng

Tia

njin

Uru

mq

iW

uzh

on

gq

iX

i'an

Xili

nh

ot

Ya

na

nY

inch

ua

nY

ulin

Yu

she

Zh

an

gb

ei

Zh

en

gzh

ou

Zh

urih

e

AO

D a

t 44

0n

m

0.0

.2

.4

.6

.8

1.0

1.2

1.4

Quality Assured AOD results

CARSNET Mean AOD from 40 stations since 2002

Zhang XY et al., ACP, 2012

Aerosol/dust climatologies

Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations

S.Basart, C. Pérez, E. Cuevas, J.M. Baldasano, and G.P. Gobbi

(Atmos. Chem. Phys. October-2009)

Tamanrasset- Argelia

Cairo (Egypt)

Thala (Tunisia)

Ouazarzate (Morocco)

Sand and Dust Storm Sand and Dust Storm Warning System Regional Warning System Regional Center for Norther Africa, Center for Norther Africa, Middle East and EuropeMiddle East and Europe

Increasing the observation capacity in North Africa

44

6.6. Dust impactsDust impacts

Improve Weather Forecast via Dust Radiative Forcing (RF) Feedback by CUACE/Dust Operational Forecasting System in

Asian Regional Centre

With dust RFfeedback

Forecastedsurface T

ForecastedVertical T

With dust RFfeedback

Wang et al., JGR, 2010

75%

78%

S/Ca ratio

Normal: 0.73 ； Fog: 6.11

Li et al., 2009 无定形的覆盖物无法使用SAED确定其晶体类型

Individual particle analysis show that 70% of aerosol particles in North China Plain are internally mixed with other two or three sourced aerosols, in which mineral dust play a key role.

test footer 47

Impact of Saharan dust clouds over North Atlantic on trop-O3

Andrey et al., 2013

48

Cuevas et al; 2011: Meningitis linked to mineral dust transport in the Sahel (MACC Report)

Pérez et al; 2013: Soil dust aerosols as predictors of seasonal meningitis incidence in Niger (Environmental Health Perspectives (EPH))

49

WMO

Capacity buildingCapacity building• 8-12 Nov 2010: Training Week on Satellite Meteorology. Barcelona-Spain

• 13 Nov 2010: Lectures on Atmospheric Mineral Dust and its Impact on Human Health, Environment and Economy. Barcelona-Spain

• 15-19 Nov 2010 Training Week on WMO SDS-WAS products. Barcelona-Spain

• 22-26 Feb 2011: Training on Meteorological Services, SDS Forecast and Early Warning System. Istanbul-Turkey

• 21-25 Nov 2011: 2nd Training Course on WMO SDS-WAS (satellite and ground observation and modelling of atmospheric dust). Antalya-Turkey

• 5-9 Nov 2012: II Lectures on Atmospheric Mineral Dust. Barcelona-Spain

• 26-28 Nov 2012: Workshop on Meteorology, Sand and Dust Storm (SDS), Combating Desertification and Erosion. Ankara-Turkey

• 8-12 Nov 2010: Training Week on Satellite Meteorology. Barcelona-Spain

• 13 Nov 2010: Lectures on Atmospheric Mineral Dust and its Impact on Human Health, Environment and Economy. Barcelona-Spain

• 15-19 Nov 2010 Training Week on WMO SDS-WAS products. Barcelona-Spain

• 22-26 Feb 2011: Training on Meteorological Services, SDS Forecast and Early Warning System. Istanbul-Turkey

• 21-25 Nov 2011: 2nd Training Course on WMO SDS-WAS (satellite and ground observation and modelling of atmospheric dust). Antalya-Turkey

• 5-9 Nov 2012: II Lectures on Atmospheric Mineral Dust. Barcelona-Spain

• 26-28 Nov 2012: Workshop on Meteorology, Sand and Dust Storm (SDS), Combating Desertification and Erosion. Ankara-Turkey

WMO

Capacity buildingCapacity building

CAS CBS

Research

SDS-WAS

Op

eration

al fo

recasts

SDS-WAS

Exp

erimen

tal fo

recasts

Current

Future

Research

From Research to OperationsFrom Research to Operations

WMO

Successful transition from Research to OperationsSuccessful transition from Research to Operations

The mandatory functions of the Regional Specialized Meteorological Centre (s) with activity specialization in Atmospheric Sand and Dust storm Forecasts (RSMC-ASDF) have been defined and accepted

The consortium AEMET / BSC-CNS designated to host the first RSMC with specialization on atmospheric sand and dust forecast (Dec 2013)

EC-65 Geneva, May 2013EC-65 accepts Spain’s bid and designates Barcelona to host the first RSMC-ASDF

RSMC Barcelona (Northern Africa, Middle East and Europe)

SDS-WAS Research Implementation Plan SDS-WAS Research Implementation Plan

Postponed deadline for final draft (early Spring 2013 ?)

To be reviewed by the Regional Nodes partners

To be submitted to WWRP/JSC (July 2013) for approval• Updated regulations for the RSG and Steering Committee

membership• Emphasizing establishing the SDS-WAS Trust Fund

www.wmo.int

Thank you for your attentionWMO SDS-WAS (TNakazawa@wmo.int)

SDS-WAS NAMEE (eterradellasj@aemet.es)

SDS-WAS EA (xiaoye@cams.cma.gov.cn)

SDS-WAS As (wsprigg@email.arizona.edu)