Updates to AMSR-E GPROF over Land R ain A lgorithm & Applications to AMSR-2
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Transcript of Updates to AMSR-E GPROF over Land R ain A lgorithm & Applications to AMSR-2
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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