Rainfall Estimation fromSatellite Data

Satellite Applications Workshop, September 2003

Beth EbertBMRC

Outline

• Rain estimation systems• Satellite rain estimation methods – how they work

and when to use or not use them– Geostationary (VIS/IR)– Passive and active microwave– Estimates using combined sensors

• Accuracy of satellite rainfall estimates• Tropical Rain Potential (TRaP)

User requirementsShort-term needs• Nowcasting of severe storms• Weather forecasting• Initialisation of NWP• River management• Flood control

Medium-term needs• Intraseasonal variability• Agriculture

Long-term needs• Climate change• Hydrological planning

Rain measurement systems - rain gauges

Advantages:• “True” measurement of rain

Disadvantages:• No coverage over oceans or remote regions• Point measurement not representative of area• Wind underestimates of rain• Different gauge designs

Rain measurement systems - radar

Advantages:• Excellent space and time resolution• Observations in real time

Disadvantages:• Little coverage over oceans or remote regions• Signal calibration• Corrections required for beam filling, bright band,

anomalous propagation, attenuation, etc.• Z-R relationship• Expensive to operate

Rain estimation from model

Advantages:• Excellent space and time resolution• Estimates in real time• Includes meteorological context from other model

fields

Disadvantages:• Forecast, not observation• Model does not represent processes perfectly

When would you use satellite rainfall estimates in real time?

• When you don't have gauge or radar data• When you don't entirely trust the model• To get supporting evidence for model predictions• Typical situations:

– Tropical convection– Mid-latitude convection– Storms moving on shore from sea (especially

tropical cyclones)

IPWG

www.isac.cnr.it/~ipwg/IPWG-nocss.html

Rain measurement systems - geostationary satellite (VIS/IR)

Advantages:• Good space and time resolution• Observations in near real time• Samples oceans and remote regions• Consistent measurement system

Disadvantages:• Measures cloud-top properties instead of rain

May mistake cirrus for rain clouds Does not capture rain from warm clouds

VIS/IR rainfall estimates

Principle: Rainfall at the surface is related to cloud top properties observed from space:

VIS reflectivity

Brighter (thicker clouds) heavier rainfallDark no rain

IR brightness temperature

Colder (deeper clouds) heavier rainfallWarm no rain

NIR brightness temperature

|TNIR-TIR|~0 (large drops or ice) rain more likely|TNIR-TIR|>0 (small water drops) no rain

GOES Precipitation Index (GPI)

Simple threshold method:

R = 3.0 mm/hr * (fraction of pixels with TB 235K)

Arkin (1979)

• Works better over large areas and long times (i.e., monthly)

• Better suited to convective rainfall

Global annual rainfall from GPI

Bob Joyce, NCEP http://tao.atmos.washington.edu/data_sets/gpi/

IR image Rain rate

Power law technique

R = a (T0-TB)b - R0 ( TB 253K)

Auto-Estimator

Based on Scofield’s NESDIS Operational Convective Precipitation Estimation Technique

R = Rfit* RH correction factor * growth correction factor

http://orbit-net.nesdis.noaa.gov/arad/ht/ff/auto.html

GOES Multispectral Rainfall Algorithm (GMSRA)

Rain indicator:

VIS: 0.40

NIR: re (eff. radius) 15 m

OR

T11-T6.7: Negative for deep convective cores (T11< 230K)

Rain amount:

R = probability of rain(T11) * mean rain rate (T11) * RH correction factor * growth correction factor

http://orbit-net.nesdis.noaa.gov/arad/ht/ff/gmsra.html

Rain measurement systems - passive microwave from polar orbiting satellite

Advantages:

• Samples remote regions• Consistent measurement system• More physically based, more accurate than VIS/IR

estimates

Disadvantages:

• Poorer time and space resolution (~3 hr, ~5-50 km)• Not a direct measurement of rain• Beam filling• Does not capture rain from warm clouds over land

Rain measurement systems - passive microwave from polar orbiting satellite

Principle: Rainfall at the surface is related to microwave emission from rain drops (low frequency channels) and microwave scattering from ice (high frequency channels):

Low frequency (emission) channels - ocean only

Warm many raindrops, heavy rainCool no rain

High frequency (scattering) channels

Cold scattering from large ice particles, heavy rain

Warm no rain

Excellent reference: http://www.nrlmry.navy.mil/~kuciausk/esis/

Special Sensor Microwave Imager (SSM/I)

Spatial resolution

25 km

25 km

25 km

12.5 km

Special Sensor Microwave Imager (SSM/I)

Ferriday and Avery, 1994

OCEAN LAND

Special Sensor Microwave Imager (SSM/I)

PRODUCT: RAIN RATE (mm/hr)DATA FOR JULIAN DATE 2002145 SATELLITE F15 IN ASCENDING NODE

SI = a0 + a1T19V + a2T22V + a3T22V2 - T85V

R = a SIb

NOAA algorithm:{

http://orbit-net.nesdis.noaa.gov/arad2/

Special Sensor Microwave Imager (SSM/I)

Profiling algorithms:

• Iteratively match 7-channel TB observations to theoretical values computed from radiative transfer calculations and mesoscale cloud model (table look-up).

• Use cloud model to estimate rain rate

• Basis for TRMM algorithm

Kummerow et al., 1994

Advanced Microwave Sounding Unit (AMSU)

AMSU-A (~50 km spatial resolution):1 23.8 GHz2 31.4 GHz3-14 50.3-57.29 GHz15 89 GHz

AMSU-B (~17 km spatial resolution):1 89 GHz2 150 GHz3-5 ~183.3 GHz (water vapour line)

Advanced Microwave Sounding Unit (AMSU)

Rain rate is based on estimated ice water path (IWP) and rain rate relation derived from the MM5 cloud model data.

RR = a0 + a1 IWP + a2 IWP2

http://amsu.cira.colostate.edu/ (browse images-rain)

McIDAS – USPOL/AMSURR

Updated hourly, written at 6, 18 UTC

Tropical Rain Measuring Mission (TRMM)

TRMM Microwave Imager (TMI), 780 km swath: Band Frequency Polarization Horiz. Resol.

(GHz) (km)1 10.7 V, H 38.3 2 19.4 V, H 18.43 21.3 H 16.54 37.0 V, H 9.75 85.5 V, H 4.4

Precipitation Radar, 220 km swath: Horizontal resolution of 4 km Profile of rain and snow from surface to ~20 km altitude

» Use precipitation radar to tune TMI rain

Tropical Rain Measuring Mission (TRMM)

“Instantaneous” rain rate

http://trmm.gsfc.nasa.gov/trmmreal/

McIDAS – USPOL/TRMMRAIN

Updated hourly, written at 6, 18 UTC

V

D

TRaP = (Ravg X D) V-1

Hurricane GeorgesDMSP SSMI Rain Rates

1436 utc Sept 27 1998

Tropical Rainfall Potential (TRaP)

http://www.ssd.noaa.gov/PS/TROP/trap-img.html

TRaP – TC Sam, December 2000

SSM/I rain rate 24 hr rain estimate

Satellites used to perform TRaP

DMSP SSMI NOAA AMSU NASA TRMM NESDIS AE Resolution 15km 16km 5km 4km

Frequency 1-2 per 12hrs 1 per 6-12hrs 1 per 24hrs 1 per 30min

# Satellites 3 2 1 2

Max RR 35mm/hr 20mm/hr 60mm/hr 50mm/hr

Priority 1 2 3 4

Slides courtesy of Sheldon Kusselson, NOAA/NESDIS Satellite Services Division

Future TRaP Initiatives Continued validation Automated operational TRaP products for:

ALL STORMS... ALL THE TIME... WORLDWIDE

AMSU-b Rain Rates

Chantal

AMSU-b TRaP

SSM/I Rain Rates

SSM/I TRaP

Barry

TRMM Rain Rates

TRMM TRaP

Humberto

AE Rain Rates

AE TRaP

Helene

TRaP Training: http://www.cira.colostate.edu/ramm/visit/trap.html

Combined geostationary / passive microwave rainfall estimates

Combines the best features of both approaches:• Good space/time resolution of geostationary

estimates• Better accuracy of microwave estimates

How to do the combination? 1. Blend rain estimates using weighted averages2. Use matched VIS/IR and microwave image set to:

(a) get a field of multiplicative correction factors (b) recalibrate VIS/IR algorithm coefficients(c) map IR TB onto microwave rainrates(d) adaptive neural network(e) morph microwave rainfall in space/time

Global Precipitation Climatology Project (GPCP)

Weighted average of rain estimates from:IR (GPI), SSM/I, TOVS, rain gauges

Products available from GPCP @ 2.5° monthly resolution:monthly average rain rate 4-, 8-hour lag correlations of rain rate standard deviation of instantaneous rain rate frequency of rainsampling error for the monthly rain rate estimate fractional rainy areaalgorithm error for the monthly rain rate estimate number of available samples

http://orbit-net.nesdis.noaa.gov/arad/gpcp/

Global Precipitation Climatology Project (GPCP)

Combined sensors

Monthly mean rainfall

Weighted average of TRMM, SSM/I, IR, rain gauges

http://trmm.gsfc.nasa.gov/images_dir/avg_rainrate.html

Near real time SSM/I+TRMM3-hourly rainfall

http://trmm.gsfc.nasa.gov/publications_dir/precipitation_msg.html

Maps IR TB onto microwave rainrates,uses microwave in preference to IR where available

http://www.nrlmry.navy.mil:80/sat-bin/rain.cgi?GEO=aus

NRL hourly rainfall - "Blend"

Maps IR TB onto microwave rainrates

NRL hourly rainfall - "Merge"

http://www.nrlmry.navy.mil:80/sat-bin/rain.cgi?GEO=aus

Weighted average of several microwave rain estimates

PERSIANN

http://www2.hwr.arizona.edu/persiann/

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks

Uses an adaptive neural network to update the coefficients of an IR algorithm using surface or passive microwave rainfall observations.

Actual Microwave Observations

t+0 t+2 hrs

t+ 1/2 hr t+1 hr t+1.5 hr

IR

t+1/2 hr t+1 hr t+1.5 hr

CMORPH

http://www.cpc.ncep.noaa.gov/products/janowiak/MW-precip_index.html

Interpolated “observations”

How accurate are satellite rain estimates?

http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/sat_val_aus.html

Daily verification

Summer 2002-03 Winter 2003

wave IR wave+IR wave IR wave+IR

IR+microwave blending techniquesAustralian summer 2002-03

RMS (%) CorrelationEq. threat

score

Recalibrate IR coefficients (GPCP 1DD) 73% 0.517 0.358

Map IR TB to wave RR (NRL "blend") 75% 0.466 0.296

Adaptive neural network (U. Ariz. PERSIANN) 69% 0.522 0.308

Morphing (NOAA CPC CMORPH) --- --- ---

NWP (mesoLAPS) 63% 0.531 0.369

IR+microwave blending techniquesAustralian winter 2003

RMS (%) CorrelationEq. threat

score

Recalibrate IR coefficients (GPCP 1DD) 55% 0.140 0.065

Map IR TB to wave RR (NRL "blend") 45% 0.235 0.096

Adaptive neural network (U. Ariz. PERSIANN) 38% 0.249 0.152

Morphing (NOAA CPC CMORPH) 34% 0.435 0.258

NWP (mesoLAPS) 29% 0.677 0.430

GPCP Algorithm IntercomparisonProject results (1990's)

• Skill greater over tropics than over higher latitudes

• Passive microwave algorithms give most accurate instantaneous rain rate, esp. outside tropics

• Geostationary algorithms give best monthly estimates due to better sampling

• More recent IPWG results suggest that combining lots of microwave estimates overcomes microwave sampling limitations

Case study – 24 January 2003

http://trmm.gsfc.nasa.gov/publications_dir/australia_rain.html

TRMMmosaic

(go to training/case0.html)

24 hours totals

24 January 2003

Forecast situation: Convection in remote region with potential

flash flooding

Which satellite guidance would you choose?

Want rapid time sampling geostationary imagery

Blended IR+microwave better than IR-only

If not available then choose IR power-law algorithm, do a "reality check" against microwave estimates when possible

Forecast situation: Tropical storm moving onshore

Which satellite guidance would you choose?

Rapid time sampling perhaps less critical

Over the ocean microwave-only may be better than blended IR+microwave

TRMM most accurate but worst sampling; AMSU and SSM/I have better sampling

If named tropical cyclone, then use TRaP

Forecast situation: Mid-latitude cold front moving onshore

Which satellite guidance would you choose?

Want good time sampling geostationary imagery

Blended IR+microwave better than IR-only

The models generally handle this situation quite well – you may not need the satellite estimates

Forecast situation: Shallow rain showers moving onshore

Which satellite guidance would you choose?

NONE!!

Reasons:

• IR algorithms only expect rain in deep systems

• Although microwave instruments can measure warm rain over the see, the microwave footprint is to big to "see" small-sized showers

Forecast situation: Orographic rainfall in the mountains

Which satellite guidance would you choose?

None, unless convection also developed

Reasons:

• IR algorithms only expect rain in deep systems

• Over land microwave instruments cannot measure rain from warm-topped clouds.

Satellite rainfall estimates over Australia available within a few hours of real time

McIDAS• AMSU• TRMM• (Don't currently have an IR estimate…)

Web-based• NRL "blended" and "merged" IR+microwave

http://www.nrlmry.navy.mil:80/sat-bin/rain.cgi?GEO=aus

• TRMM-based hourly IR and 3-hourly microwave and IR+microwavehttp://trmm.gsfc.nasa.gov/publications_dir/precipitation_msg.html

Global Precipitation Measurement Mission (GPM)

In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the passive microwave rain retrieval algorithms used with the "constellation" radiometers.

Radar + passive

microwave radiometer

Passive microwave radiometers