Remote Sensing of Precipitation

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Remote Sensing Remote Sensing of Precipitationof Precipitation

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World’s Disaster Statistics

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There are three major ways to measure precipitation:

rain gauges, ground radars and satellites

(www.roc.noaa.gov)(cpc.ncep.noaa.gov) (cpc.ncep.noaa.gov)

Other possible ways:• Cell phone network signals (Messer, 2007)

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• Weather radars and rain gauges (primary source of rainfall) are typically restricted to populated areas on the Earth and can only extend out over water bodies 150 km or so.

Satellite-based methodologies serve to fill in these huge data voids, especially over

unpopulated regions and oceans.

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Satellite-based rainfall estimation methods• Satellite rainfall retrievals are generally categorized into LEO and GEO. • Retrieval algorithms are typically classified on their observing spectrum (VIS, IR, PMW, AMW) or “multi-spectral” (i.e., use of one or more of these individual spectrums). • If the methodology uses multiple satellites or other information such as radar or gauges is classified as a “blended” technique.

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Ten widely used high-resolution rain datasets

CPC-UNINOAA CPC Unified Daily Gauge

Analysis 0.25○ 24 h gauges Chen et al. 2008

CPCNOAA CPC near-real-time daily

precipitation analysis 0.25○ 24 h gaugesHiggins et al.,

2000

STIV NOAA NCEP Stage IV data 4-km 1 h NEXRAD and gaugesLin and Mitchell,

2005

PRISMParameter-elevation Regression

on Idependent Slopes Model4-km monthly gauges

Daly et al. 1994; Daly et al. 2002

GSMaPGlobal Satellite Mapping of

Precipitation (GSMaP MVK+ Version 4.8.4)

0.1○ 1hIR, SSM/I, TRMM, AMSU-B, AMSR-E

Okamoto et al., 2005; Kubota et

al., 2007

3B42TRMM Multi-satellite Precipitation Analysis research product 3B42

Version 60.25○ 3 h

IR, SSM/I, TRMM, AMSU-B, AMSR-E,

gauges

Huffman et al., 2007

3B42RTTRMM Multi-satellite Precipitation Analysis Real-time experimental

product 3B42RT 0.25○ 3 h

IR, SSM/I, TRMM, AMSU-B, AMSR-E

Huffman et al., 2007

CMORPHNOAA Climate Prediction Center

(CPC) MORPHing technique 0.25○ 3 hIR, SSM/I, TRMM, AMSU-B, AMSR-E

Joyce et al., 2004

PERSIANNPrecipitation Estimation from Remotely Sensed Information

using Artificial Neural Networks0.25○ 3 h IR, TRMM

Hsu et al, 1997; Sorooshian et

al., 2000

NRLNaval Research Laboratory's

blended technique 0.25○ 3 hIR, VIS, SSM/I,

TRMM, AMSU-B, AMSR-E

Turk and Miller, 2005

ReferencesFull nameDatasetSpatial

resolutionTemporal resolution

Sensor platforms

4 ground-based

datasets

6 satellite-based

datasets

7Total biases (mm) for DJF 2006 (2005-06 winter)

Winter -- Gauge and radar-based estimates are similar and have small biases.

Satellite-based data underestimated in the West.

8Total biases (mm) JJA 2006 (2006 summer)

Summer -- Gauge and radar-based estimates are similar and have small biases.

Satellite-based data overestimated in the central U.S.

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VIS/IR Methods

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Part I: re profile & LWP estimation

Previous Studies of LWP estimation

Problem : Assume vertically constant re. re is retrieved from single NIR channel and weighted toward cloud top.

re(h) re

• Overestimate LWP when re increased with height (IreP)

• Underestimate LWP when re decreased with height (DreP)

• Chang and Li’s linear Re profile (re1-top, re2-base) retrieval using 1.6µm, 2.1µm, and 3.7µm, and LWP estimation with re profile

re(h)re

erLWP 3

2

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Warm Rain Estimation A quick look of A-Train observations

• 20:55~23:35 UTC at 01/06/08 over eastern pacific

• AMSR-E misses the shallow warm rain, MODIS cloud observation shows correlation with warm rain

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Passive Microwave Methods

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Passive Microwave (PMW) Techniques• Microwave energy

can penetrate clouds, in particular, cirrus clouds• Frequencies from 6 GHz to 190 GHz on most PWM sensors.• Below 20 GHz, emission by precipitation-size drops dominates and ice particles above the rain layer are nearly transparent. • Above 60 GHz, ice scattering dominates and the radiometers cannot sense the rain drops below the freezing layer.

Nimbostratus

Cumulonimbus

0 C

0 C

0 C

0 C

Emission – freq’s <40 GHzScattering – freq’s >60 GHz

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Vertical View of Atmospheric Profiles in Microwave Frequency

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Basic Relationship Between PMW Frequency and Rainrates

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NOAA / AMSU-B

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The Advanced Microwave Sounding Unit (AMSU A and B)

AMSU-A:

Pixel IFOV = 3.3

IFOV Size (Nadir) = 48

km

AMSU-B - MHS:

Pixel IFOV = 1.1

IFOV Size (Nadir) =

16 km

AMSU-BAMSU-B

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AMSU-A:

Pixel IFOV = 3.3

IFOV Size (Nadir) = 48

km

AMSU-B - MHS:

Pixel IFOV = 1.1

IFOV Size (Nadir) =

16 km

AMSU-BAMSU-B

1-5

1,2 3 4,5 6,7 SSMI

6-7

1-5

1,2 3 4,5 6,7 SSMI

6-7

The Advanced Microwave Sounding Unit (AMSU A and B)

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Rainfall retrieval using AMSU

B A

Ice Cloud Scattering Parameter

Physical retrieval of ice water path (IWP) and particle size (De) using AMSU-B 89 and 150 GHz:

• De ~ (89)/(150)

• IWP ~ De*(/(89,150))

IWP to rain rate based on limited cloud model data and comparisons with in situ data:

RR = A0 + A1*IWP + A2*IWP2

(from Zhao and Weng, 2002)

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AMSU Rain-Rate Scattering Approach

• Advantages: • Availability of three NOAA POES satellites spaced approximately 4 h apart with a spatial resolution of 16 km at nadir (Metop-A is also incorporated with the same capabilities). • Wider swath than SSM/I sensors.• Moisture channels (not available in SSMI)

• Weaknesses: • Lack of low frequency channels with appropriate spatial resolution.• Inability to retrieve rain that has little or no ice (only scattering is available).• Cross-scan characteristics of the instrument (different footprints for different local zenithal angles). • Mixed polarization (SSMI V,H polarization)

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Frequency ratio (rr>0/rr≥0) for April 2005.

Upper panel: AMSU retrieval

Bottom panel: SSMI GPROF 6.0

AMSU

SSMI – GPROF 6.0

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DOD SSM/I

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Comparison of SSMI and SSMI/S Sensors characteristics for those specific channels used in the hydrological product generation

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SSM/I Precipitation SSM/I Precipitation Product Product

• Develop empirical fits between SSM/I F15 and SSMI/S F16 during period of close overpass times (3/06 – 2/07)– All channels– Stratify via

land/ocean; rain/no-rain

– RADCAL correction applied to F15 8/06 and forward

SSSMIrainsfctypechanrainsfctypechanISSM TATA /,,,,/ *

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USE THE HERITAGE OF THE EXISTING ALGORITHMS

HOW TO CONTINUE WITH THE MONTHLY DATA SET?

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NASA TRMM

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TRMM Satellite

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TMITMIPRPR

reflectivityreflectivityprofilesprofiles

TMITMIfootprintsfootprints

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Precipitation radar (PR):

13.8 GHz

4.3 km footprint

0.25 km vertical res.

215 km swath

Microwave radiometer (TMI):

10.7, 19.3, 21.3, 37.0

85.5 GHz (dual polarized

except for 21.3 V-only)

10x7 km FOV at 37 GHz

760 km swath

Visible/infrared radiometer (VIRS):

0.63, 1.61, 3.75, 10.8, and 12mm

at 2.2 km resolution

Additional EOS instruments:

CERES (Cloud & Earth Radiant Energy System) 720 km swath

LIS (Lightning Imaging Sensor)Launch Date: 11/22/1997Already achieved 10 yr mission

TRMM Sensors

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Orbit Circlar (Non-Sunsynchronous)

Altitude 350 km

Inclination 35 degree

Precipitation Radar (PR)

Instruments TRMM µ wave Imager (TMI)

Visible and Infrared Scanner (VIRS)

Clouds and the Earth Radiant Energy System (CERES)

Lightning Imaging Sensor (LIS)

Tracking & S/C Operation by NASA Tracking and Data Relay Satellite System (TDRSS)

Major Characteristics of TRMM

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Major Characteristics of TRMM

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1998-2005 Mean Monthly Rainfall (5°x5°)

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Nine-year TRMM Zonal Average (Ocean [1998-2006])

From 2A12 (TMI[passive microwave]), 2A25 (PR[radar]), and 2B31 (TMI&PR)

0

20

40

60

80

100

120

140

160

180

200

220

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

Latitude

Pre

cip

itati

on

(m

m/m

on

th)

TRMM MeanTRMM MaximimTRMM Minimum

Adler and Wang

Original TRMM Climate Question: How much is it raining in the Tropics (especially over the ocean)?

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TRMM Data Used for Hurricane/Typhoon MonitoringTRMM TMI data used by U.S. and international weather agencies for tropical cyclone detection,

location and intensity estimation--600 TRMM-based tropical cyclone “fixes”per year

TRMM orbit advantageous for tropical cyclone monitoring--it is always in tropics, sampling best in

10-35º latitude storm band. TMI resolution twice as good as operational sensors, about same as AMSR.

Precessing orbit provides off-time observations relative to sun-synchronous microwave observations.

TRMM image from U.S. Navy Tropical Cyclone web site

Hurricane Katrina

TRMM radar (PR) cross-sections of hurricanes available in real-time for operational analysis from TRMM web site

from TRMM web site

Hurricane Katrina-2005

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TRMM Precipitation Radar Views Typhoon Etau

13-km tall hot towers

Intense convective rains in deep eyewall towers power intensification of Etau, through latent heat release.

Only Space-Based Instrument that Provides Vertical Structure in Tropical Rain Systems

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GPM Reference ConceptGPM Reference Concept

Core Satellite•TRMM-like spacecraft (NASA)•H2-A rocket launch (NASDA)•Non-sun-synchronous orbit ~ 65° inclination ~400 km altitude•Dual frequency radar (NASDA) K-Ka Bands (13.6-35 GHz) ~ 4 km horizontal resolution ~250 m vertical resolution•Multifrequency radiometer (NASA) 10.7, 19, 22, 37, 85, (150/183 ?)

GHz V&H

Constellation Satellites•Pre-existing operational-

experimental & dedicated satellites with PMW radiometers

•Revisit time 3-hour goal at ~90% of time•Sun-synch & non-sun- synch

orbits 600-900 km altitudes

OBJECTIVES•Understand horizontal &

vertical structure of rainfall, its macro- & micro-physical nature, & its associated latent heating

•Train & calibrate retrieval algorithms for constellation radiometers

OBJECTIVES•Provide sufficient global

sampling to significantly reduce uncertainties in short-term rainfall accumulations

•Extend scientific and societal applications

Precipitation Processing Center•Produces global precipitation

products•Products defined by GPM partners

Precipitation Validation Sites for Error Characterization

•Select/globally distributed ground validation “Supersites” (research quality radar, up looking radiometer-radar-profiler system, raingage-disdrometer network, & T-q soundings)

• Dense & frequently reporting regional raingage networks

Core Constellation

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Ground-based Precipitation Radar

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Rain and SnowObservable Characteristics

Precipitation rate - R (intensity)is the volume flux of precipitation through a horizontal area. In cgs units, R is expressed as cm3 cm-2 sec-

1. However, R is usually expressed in mm/h. R is sometimes called the rainfall rate or equivalent rainfall rate. R varies from trace amounts up to several hundred mm/h. R for snow tends to be about 0.1 Rrain.

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Rainfall Rate and Drop-Size Distribution Function

Where N(D)dD - the number of drops per unit

volume with diameters between D and D + dD,

V - the fall velocity of drops of size D.

For snow, D is the melted diameter of a drop, and

R is the equivalent rainfall rate.

R 6

N(D)D3V(D)dD

0

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Precipitation Water Content

The precipitation water content L is independent of the fall speed and is measured in terms of mass/volume

L 6

w N(D)D3 dD

0

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Weather Radar

Radar - acronym for RADio Detection and Ranging

Main components are:• Transmitter which generates short pulses of

electromagnetic energy• Antenna which focuses the energy into a

narrow beam• Receiver which detects that portion of the

transmitted energy that has been reflected (scattered) by objects with refractive characteristics different from air

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Weather Radar:Important Parameters

• Peak Power - Pt - (instantaneous power in a pulse)10 < Pt < 5000 kW

• Radio frequency - Radio wavelength - - (c/3 < GHz (1 GHz = 109 sec-1)(wavelengths from 1 to 30 cm)

• Pulse repetition frequency (fr) (PRF)

200 < fr < 2000 sec-1

• Pulse duration - 0.1 < < 5 µsec

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Rayleigh Scattering

Define the scattering size parameter for a sphere as

2r

the ratio of the circumference of the sphere to the wavelength

For << 1, scattering is in the Rayleigh region, and for a sphere or radius ro is given as

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5

4 2ro6

(m2 1)

(m2 2) and m n ikwhere

n is the refractive index and k the absorption coefficient.

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Weather Radar Equation - cont.

Assuming Rayleigh scattering spheres of diameter D

P r Pt

G25

4 3r42 2 D6

Introduce the radar reflectivity factor Z, where

Z D6

v N(D)

0

D6dD

where the summation extends over a unit volume,and N(D)dD is the number of drops per unit volumeof a given diameter.

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After accounting for the scattering volume and the beam pattern, the weather radar range equation is

P r

3c1024ln 2

PtG22

2

2 Z

r2

where is the pulse duration and is the beam width.

radar target

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• Power in decibels is related to the reflectivity factor as measured on the decibel scale

• Pr - measured in milliwatts, 10 log Pr is the power in dBm (decibels relative to a milliwatt

• Z is measured in mm6/m3 and 10 log Z is the reflectivity factor in dBz.

10logP r 10logZ 20logr C

where C is a constant determined by radar parametersand dielectric characteristics of the target

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Radar Displays

PPI - Plan position IndicatorMaps the received signals on polar coordinates in plan view. The antenna scans 360° at fixed elevation angle. At every azimuth the voltage output of the receiver as a function of range is used to intensity-modulate a tube with polar coordinates (Rogers and Yau, 1989). This produces a plan view of the distribution of precipitation.

Without careful calibration, PPI records are only useful to show the location and time occurrence of precipitation.

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230 km range PPI

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Radar Displays - cont.

RHI - Range Height IndicatorThis display is generated when the antenna scans in elevation with fixed azimuth, thereby showing the details of the vertical structure of precipitation.

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Homework, Due April 201. Read the following articles and write a review

of earth radiation budget (ERB) retrieval and advancement of our knowledge in ERB:

• Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to Planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76, 2125–2153.

• Li, Z., L. Moreau, A. Arking, 1997: On solar energy disposition, A perspective from observation and modeling, Bull. Amer. Meteor. Soc., 78, 53-70

• Li, Z., 2004, On the solar radiation budget and cloud absorption anomaly debate, In "Observation, Theory, and Modeling of the Atmospheric Variability", (ed. Zhu), World Scientific Pub. Co., p437-456.

2. Read the following articles and write a summary of passive microwave (PMW) remote sensing of precipitation (5 pages)

• Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model output. Bull. Amer. Meteor. Soc., 78, 2539–2558.

• Olson, W.S., C.D. Kummerow, S. Yang, G.W. Petty, W.-K. Tao, T.L. Bell, S.A. Braun, Y. Wang, S.E. Lang, D.E. Johnson, and C. Chiu, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: Method and uncertainties. J. Appl. Meteor., 45, 702-720.

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Acknowledgements

Some slides used in this lecture were provided by Ralph Ferraro,

Daniel VileYudong Tian, Song Yang,

Remote Sensing of Precipitation

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