Passive Microwave Rain Passive Microwave Rain Rate Remote Sensing Rate Remote Sensing

Christopher D. Elvidge, Ph.D. Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 NOAA-NESDIS National Geophysical Data Center E/GC2

325 Broadway, Boulder, Colorado 80305 USA 325 Broadway, Boulder, Colorado 80305 USA Tel. 1-303-497-6121 Tel. 1-303-497-6121 Fax. 1-303-497-6513 Fax. 1-303-497-6513

Email: Email: chris.elvidge@noaa.govchris.elvidge@noaa.gov http://http://dmsp.ngdc.noaa.govdmsp.ngdc.noaa.gov// http://http://sabr.ngdc.noaa.govsabr.ngdc.noaa.gov//

OutlineOutline

• Why do we need microwave sensors?Why do we need microwave sensors?• Evolution of passive microwave sensorEvolution of passive microwave sensor

– HistoryHistory– FutureFuture

• Rain rate retrieval Rain rate retrieval – Physical bases Physical bases – Algorithm performance Algorithm performance

• ExamplesExamples• Application to disaster warningApplication to disaster warning

Microwave Remote Sensing from Microwave Remote Sensing from SpaceSpace

• Penetration through non-Penetration through non-precipitating clouds.precipitating clouds.

• Highly stable instrument Highly stable instrument calibration.calibration.

• Radiance is linearly Radiance is linearly related to temperature related to temperature (i.e. the retrieval is nearly (i.e. the retrieval is nearly linear).linear).

• OO22 is uniformly mixed gas is uniformly mixed gas throughout the throughout the atmosphere.atmosphere.

• Larger field of views Larger field of views (10-50 km) compared (10-50 km) compared to vis/IR.to vis/IR.

• Variable emissivity Variable emissivity over land.over land.

• Polar orbiting Polar orbiting satellites provide satellites provide discontinuous discontinuous temporal coverage.temporal coverage.

Advantages Disadvantages

Why do We Need Observations in Lower Why do We Need Observations in Lower Troposphere?Troposphere?

Convective events in well mixed layer during

daytime heating

The planetary boundary layer contains the

Fog and low clouds under nocturnal

inversion

Layer of air containing the roots of

summertime convection

Depth of cold air in winter to tops of stratocumulus

Low-level jet

for weather

History and Future of Passive History and Future of Passive Microwave Earth ObservationMicrowave Earth Observation

• NASA 1970-1987 Nimbus satellite ESMR-1, ESMR-2, NASA 1970-1987 Nimbus satellite ESMR-1, ESMR-2, SMMRSMMR

• NOAA 1978-1999 POES MSU, 1999-present POES NOAA 1978-1999 POES MSU, 1999-present POES AMSUAMSU

• DMSP 1982-present SSM/T 1987-present SSM/I, DMSP 1982-present SSM/T 1987-present SSM/I, 1991-present SSM/T2, 2004-2015? SSMIS1991-present SSM/T2, 2004-2015? SSMIS

• NASDA 1997-present TRMM TMI & PR, 2002-present NASDA 1997-present TRMM TMI & PR, 2002-present Aqua AMSR, 2003-present ADEOS-2 AMSRAqua AMSR, 2003-present ADEOS-2 AMSR

• NPOESS 2007-2030? CMISNPOESS 2007-2030? CMIS

DMSP SSM/I SensorDMSP SSM/I Sensor

• Flown on DMSP F8 - F15• It is a conical scan sensor.

AbbreviationFrequency (GHz)

Resolution(km)

19V 19.35 70x45

19H 19.35 70x45

22V 22.235 60x40

37V 37.0 38x30

37H 37.0 38x30

85V 85.5 16x14

85H 85.5 16x14

NOAA AMSU SensorNOAA AMSU Sensor•Flown on NOAA-15 (May 1998) and NOAA-16 (Sept. 2000) satellites•Contains 20 channels:

•AMSU-A•15 channels•23 – 89 GHz

•AMSU-B•5 channels•89 – 183 GHz

•6-hour temporal sampling:•130, 730, 1330, 1930 LST

AMSU-A and –B Scan Pattern

•Cross-track scan geometry

•AMSU-A (30 FOV/scan; 48 km @ nadir)

•AMSU-B (90 FOV/scan; 16 km @ nadir)

•2200 km swath width

Precipitation MonitoringPrecipitation MonitoringTornadic Storm on Sept 24 2001 Tornadic Storm on Sept 24 2001

AMSU NEXTRAD

AMSR image of Typhoon 6June 17, 2003

Conclusion Conclusion • Passive microwave remote sensing has a Passive microwave remote sensing has a

unique capability to detect rain and estimate unique capability to detect rain and estimate rain rates from orbit.rain rates from orbit.

• This complements ground based weather This complements ground based weather radar and precipitation measurements.radar and precipitation measurements.

• Algorithms work over water, may extend to Algorithms work over water, may extend to land with more advanced sensors.land with more advanced sensors.

• Satellite data from multiple systems can be Satellite data from multiple systems can be used to get greater integration time, provide used to get greater integration time, provide a more complete depiction of rain rate a more complete depiction of rain rate through the day and prediction of severe through the day and prediction of severe rain events.rain events.