Updates to AMSR-E GPROF over Land Rain Algorithm & Applications to AMSR-2

Ralph Ferraro1,2, Patrick Meyers2,Nai-Yu Wang2, Dave Randel3, Chris Kummerow3

1NOAA/NESDIS2Cooperative Institute for Climate and Satellites (CICS), University of Maryland

College Park, MD

3Cooperative Institute for Research in the Atmosphere (CIRA)Colorado State University

Fort Collins, CO

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Outline• GPROF2010V2

– AMSR-E– TMI– SSMIS– Independent evaluations

• AMSR-2 examples

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Review of GPROF2010 Over Land Changes (N.Y. Wang)

• Driven by TRMM V7 reprocessing• Main focus of effort - make improvements over V6 and develop

synergy with PR V7‒ Previous TRMM versions were always out of sync between TMI and PR‒ TRMM 2A12 V7 = GPROF2010

• What was changed– Improve Convective-Stratiform separation– TB85V-RR relationships to remove warm season bias– More versatile/precise land/ocean/coast (Dave Randel)– Use of GPM X-Cal L1 TB’s

• What was NOT changed– Regional biases– IF-THEN-ELSE screening logic (“Grody” and “GSCAT” heritage)

• End result – closer agreement between TRMM V7 2A12 and 2A25

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A TMI Example

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Note the better gradient in the rain rates in V7 compared to V6 and PR; V6 tended to “smear” rain areas due to poorer CSI

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GPROF2010 V2 (Meyers, Randel, Wang)• Goal was to improve AMSR-E from TMI version

– Beyond TRMM domain, snow becomes a major issue– Can we improve desert sand as well?– Can we start to eliminate IF-THEN-ELSE? (Goal of GPROF2014)

• Also, port to other sensors e.g., AMSR-2, SSMI/S– Time series continuity for climate/”blended” products (e.g.,

NASA/IMERG)• Unbiased retrievals between AMSR-E, SSMI, SSMIS, TMI, AMSR-2, …

– Real-time user continuity (NOAA JPSS/GCOM effort)– “Benchmark” for GPM (GPROF2014) to improve upon

• What we wound up doing:– Use L1C from X-cal, but still some empirical adjustments to TB’s

• TMI vs. AMSR-E – frequencies, etc. Same for AMSR-2, but not for SSM/I and SSMIS– Use of ancillary data sets - Monthly snow climatology, Static desert mask– “Re-engineering” of code– Some changes for coast – definition, databases, etc.

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Example - Screening ProblemConvective core screened out – thinks it’s snow on the ground

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• Interactive Multisensor Snow and Ice Mapping System (IMS)– Northern Hemisphere– Multisensor (AVHRR, SSM/I, GOES, AMSR-E)– Human in the loop– Daily 24 km snow product 1997-present

•AMSR-E Monthly Snow Product– Southern Hemisphere– L3 25km Snow Water Equivalent

• Global 1/4° map of likelihood of snow coverage– If (p > 75%), snow likely, pixel flagged

Climatological Snow Screening

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Climatological Snow Screening

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Climatological Desert Screening

International Geosphere/Biosphere Programme 1/12° scene types

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GPROF2010V2 Land Screening

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New

Old

Now used less often

Putting it all together…

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Example - Screening ProblemConvective core screened out

Convective core intact

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Rain Rate – January 2010mm·day-1

mm·day-1

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AMSR-E GPROF2010

mm·day-1

AMSR-E GPROF2010V2

NEW - OLD

More convective cores included

Less due to snow climatology

AMSR-E Monthly Rain – July 2010

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Comparison to GPCP

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Comparison to GPCP

75% Snow Climo.

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Comparison to GPCP

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Comparison to GPCP

GPCC Gauge Comparisons: Jan – Jun 2013

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GPCC Gauge (mm/mon) GPCC Gauge (mm/mon)

AM

SR

-2 (m

m/m

on)

TMI (

mm

/mon

)

GPCC Gauge (mm/mon)

SS

MIS

F18

I (m

m/m

on)

GPCC Gauge (mm/mon)

ALL

Sat

ellit

eI (m

m/m

on)

MeanGPCC 83.7TMI 60.2SSMIS 78.1AMSR-2 80.9All 73.1

AMSR-2• Teaming with CSU on recently selected NASA

proposal for continuity of AMSR-E time series with AMSR-2

• NOAA is also using AMSR-2 as part of JPSS program through separate arrangement with JAXA

• Show some examples of AMSR-2 usage (P. Meyers)– Super storm Sandy– Moore, OK Tornado– Mid-Atlantic Tornado with rapid imagery and lightning– Matchups with TMI (R. Joyce)

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NEXRAD Reflectivity

Super Storm Sandy – October 2012

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Moore, OK Tornado

May 20, 2013

• Agreement in locations of heaviest precipitation

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NMQ-Q2 Validation

Smoothed Reflectivities

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Cross-Track TB

• Strong scattering in core of storm

• Negative polarization at 89 GHz due to large hail?

• East/West gradient in low frequency polarization suggests changes in soil moisture due to rain

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Mid-Atlantic Severe Weather - 6/13/13

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• 1 minute GOES visible and DCLMA• Simulate GOES-R ABI and GLM• Lightning “jumps” related to tornadic storms

• GPROF AMSR-2 Rain Rates• 1 second lighting from DCLMA• Lightning related to 89 GHz scattering

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GCOM-W1 AMSR2 (top) derived precipitation (mm/hr) TRMM TMI (bottom) 02:30-03:00 UTC 1 March 2013

Courtesy of Bob Joyce, NOAA/NWS/NCEP/CPC

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GCOM-W1 AMSR2 (top) derived precipitation (mm/hr) TRMM TMI (bottom) 04:00-04:30 UTC 1 March 2013

Courtesy of Bob Joyce, NOAA/NWS/NCEP/CPC

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Summary and Future Plans• GPROF2010V2 over land

– Essentially produces unbiased estimates for• TMI, AMSR-E, AMSR-2, SSM/I, SSMIS

– Will allow for continuity from AMSR-E to AMSR-2• Being adopted at NOAA as part of JPSS/GCOM EDR’s

– Ready for AMSR-E reprocessing (if not done already)• Improved features include

– Uses snow and desert climatologies– Better CSI for improved rain rates

• GPROF 2014 – in development (GPM/CSU team)– Eliminate empirical RR calculation over land– Identifies emissivity classes for Bayesian retrieval

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Backup Slides

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GPROF VS Radar

Along-Track Tbs

• Scattering signature down to 18GHz

• Low polarization differences at higher frequencies, as expected

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