Passive Microwave Rain Rate Remote Sensing Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 325 Broadway, Boulder, Colorado USA Tel Fax

Outline Why do we need microwave sensors? Why do we need microwave sensors? Evolution of passive microwave sensor Evolution of passive microwave sensor –History –Future Rain rate retrieval Rain rate retrieval –Physical bases –Algorithm performance Examples Examples Application to disaster warning Application to disaster warning

Microwave Remote Sensing from Space Penetration through non- precipitating clouds. Penetration through non- precipitating clouds. Highly stable instrument calibration. Highly stable instrument calibration. Radiance is linearly related to temperature (i.e. the retrieval is nearly linear). Radiance is linearly related to temperature (i.e. the retrieval is nearly linear). O 2 is uniformly mixed gas throughout the atmosphere. O 2 is uniformly mixed gas throughout the atmosphere. Larger field of views (10-50 km) compared to vis/IR. Larger field of views (10-50 km) compared to vis/IR. Variable emissivity over land. Variable emissivity over land. Polar orbiting satellites provide discontinuous temporal coverage. Polar orbiting satellites provide discontinuous temporal coverage. AdvantagesDisadvantages

Why do We Need Observations in Lower 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 Microwave Earth Observation NASA Nimbus satellite ESMR-1, ESMR-2, SMMR NASA Nimbus satellite ESMR-1, ESMR-2, SMMR NOAA POES MSU, 1999-present POES AMSU NOAA POES MSU, 1999-present POES AMSU DMSP 1982-present SSM/T 1987-present SSM/I, present SSM/T2, ? SSMIS DMSP 1982-present SSM/T 1987-present SSM/I, present SSM/T2, ? SSMIS NASDA 1997-present TRMM TMI & PR, 2002-present Aqua AMSR, 2003-present ADEOS-2 AMSR NASDA 1997-present TRMM TMI & PR, 2002-present Aqua AMSR, 2003-present ADEOS-2 AMSR NPOESS ? CMIS NPOESS ? CMIS

DMSP SSM/I Sensor Flown on DMSP F8 - F15 It is a conical scan sensor. Abbreviation Frequency (GHz) Resolution (km) 19V x45 19H x45 22V x40 37V37.038x30 37H37.038x30 85V85.516x14 85H85.516x14

NOAA 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 nadir) AMSU-B (90 FOV/scan; 16 nadir) 2200 km swath width

Precipitation Monitoring Tornadic Storm on Sept AMSU NEXTRAD

AMSR image of Typhoon 6 June 17, 2003

Conclusion Passive microwave remote sensing has a unique capability to detect rain and estimate rain rates from orbit. Passive microwave remote sensing has a unique capability to detect rain and estimate rain rates from orbit. This complements ground based weather radar and precipitation measurements. This complements ground based weather radar and precipitation measurements. Algorithms work over water, may extend to land with more advanced sensors. Algorithms work over water, may extend to land with more advanced sensors. Satellite data from multiple systems can be used to get greater integration time, provide a more complete depiction of rain rate through the day and prediction of severe rain events. Satellite data from multiple systems can be used to get greater integration time, provide a more complete depiction of rain rate through the day and prediction of severe rain events.