MIT REMOTE SENSING AND ESTIMATION GROUP 1 Geosynchronous Microwave Sounding of Precipitation Parameters at Convective Scales David H. Staelin and Chinnawat Surussavadee Presented at the Third Workshop of the International Precipitation Working Group Melbourne, Australia, October, 2006 OUTLINE MM5, radiative transfer, and retrievals GeoMicrowave instrument concepts Precipitation retrieval method Instrument options Movies: MM5 vs. GEM Summary and conclusions Staelin and Surussavadee October 2006

MIT REMOTE SENSING AND ESTIMATION GROUP 2 MM5 vs. AMSU T B ’s (K) Surussavadee Staelin AMSU-B GODDARD REISNER SCHULTZ 150 GHz AMSU-B GODDARD 183  7 GHz (K)

MIT REMOTE SENSING AND ESTIMATION GROUP 3 The NCEP/MM5/DDSCAT/F( ) model is initialized with NCEP 1-degree data and forecast with MM5 (5-km) for 4-6 hours using the Goddard cloud- resolving model. TBSCAT with the F( ) Mie-scattering model for ice is used, where the ice density F( ) is determined using DDSCAT for snow (hexagonal plates) and graupel (6- point rosettes) to match total ice scattering cross-sections.  F   snow > 15%  graupel > 25%,  Backscattering > 1%,  P snow/graupel > 25mb MM5 vs. AMSU-A/B Model Sensitivity Studies Unambiguous discrepancies with AMSU brightness-temperature histograms appeared when: Surussavadee Staelin MM5 vs. AMSU T B Histograms; F( ) Model 183  7  3  GHz Pixels/ o K 50.3 GHz  7  3  1 Assumed DDSCAT F( ) K 230K

MIT REMOTE SENSING AND ESTIMATION GROUP 4 F( ) Model: Sphere ice density = F( ) Question: Match F( ) to DDSCAT  s or to back- scatter? Answer: Total  s works better because coldest pixels scatter many times, losing sense of direction Fluffy ice, Mie F( )  f(L<5mm) Snow Graupel Best-fit F( ) using AMSU, not DDSCAT Snow Graupel 240K >80% multi- scattered 183  1  3  7 GHz F( ) Ice Scattering Model Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP 5 AMSU Retrievals vs. AMSR-E over Ocean AMSR-E (Goddard) Surface precipitation rate (mm/h) 04:03 UTC 03:27 UTC AMSU (NOAA-16) AMSR-E (Wentz) AMSU neural-net retrievals use 10, 5, and 1 neuron. Inputs are: 4 PC’s from nadir-corrected A1-8 and B1-5,  T B (Ch 4-8), sec . AMSR-E (Goddard) Surussavadee Staelin Chadarong McLaughlin

MIT REMOTE SENSING AND ESTIMATION GROUP :17 UTC 00:07 UTC AMSR-E (Goddard) AMSU (N16) Africa AMSU Retrievals vs. AMSR-E over Land 7:26 UTC 8:44 UTC AMSR-E (Goddard) AMSU (N16) Canada Great Lakes Surussavadee Staelin Chadarong McLaughlin

MIT REMOTE SENSING AND ESTIMATION GROUP 7 AMSU Retrievals over U.S. Midwest mm/h June 17, UTC August 18, UTC June 9, UTC Surussavadee Staelin Chadarong McLaughlin

MIT REMOTE SENSING AND ESTIMATION GROUP 8 Instrument Precipitation-Rate Comparisons Correlation coefficients: NOWRAD vs. AMSU/MM5 for: Estimated snow (0.73) Snow+rain+graupel (0.61) Rain rate (0.57) Graupel (0.55) (Based on 23M 5-km pixels, U.S. Midwest, summer ’04) Note high sensitivity of NOWRAD to snow aloft RR weighted dist = RR x #Pixels in log bin Stratiform Convective AMSU/MM5 bias correction (106 global storms) Applied only to movies Pixels per log bin All comparisons were over the U.S. Midwest, summer, An “event” is a 15-minute period where either NOWRAD or the overlapping instrument saw >0.01 mm/h in 0.05 o squares. A “pixel” is a 0.05 o square > 0.01 mm/h for either NOWRAD or the listed instrument. All pixel counts are normalized to coincident NOWRAD data. 668 events, 15M pixels 672 events, 23M pixels 425 events, 1.6M pixels 900 events, 5.1M pixels 384 events, 2.3M pixels (Avg)

MIT REMOTE SENSING AND ESTIMATION GROUP 9 Nodding Subreflector Even a 2-m dish Can be integrated on GOES  T rms  0.5K (400 GHz,  = 0.04s) Weight ~50 kg, 130 watts 10 km 400 km 10 km/sec Geo-Microwave Sensor Concepts GeoSTAR’ GEM 2 meters 2 m 1.2 m GOES GEM GeoSTAR 2 m GEM’ Sketch by Ball Sketch by JPL Sketch by MIT Lincoln Laboratory Staelin and Surussavadee October 2006

MIT REMOTE SENSING AND ESTIMATION GROUP 10 Sharpened 30-km res. Sharpened pattern 260 o K km Blurred 30-km resolution with noise 22.1 km Original pattern Requires Nyquist sampling G’(  ) = FFT {W(f  )} To minimize MSE: Noise increases with sharpening Original 5-km image Image Sharpening Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP 11 Clear-air Incremental weighting functions (IWF) for temperature and humidity vs. offset (MHz) from line center (from Klein and Gasiewski, 2000). Peaks of GEM/GeoSTAR weighting functions analyzed here. Note that high humidity can preclude penetration below ~2 km at 118, 166 GHz. GEM Channel Selection Issues 118 GHz (O 2 ) 425 GHz (O 2 ) 183 GHz (H 2 O)380/340 GHz (H 2 O) Staelin and Surussavadee October 2006

MIT REMOTE SENSING AND ESTIMATION GROUP 12 Geo-Microwave Precipitation Retrievals Staelin and Surussavadee October 2006 Rain rate estimate R (mm/h) 118 GHz (4 O 2 Channels) GHz (4 H 2 O Channels) 380 GHz (4 H 2 O Channels) 425 GHz (4 O 2 Channels) Land/sea, elevation R > 8? R (mm/h) Neural Net 2 Neural Net 3 Yes No Training (NCEP/MM5, 122 global storms) 1 Nets 2 and 3 were trained with different R values (NN  1.3 better matches MM5, retrieval colors) 8 Neural Net 1 (for categorization) 34 Rain-rate estimates: input = two T B spectra 15 minutes apart at 40° zenith angle. Water-path estimates are based on one spectrum (18 numbers). All networks have 10, 5 and 1 neurons in their input, hidden, and output layers, respectively. Same general estimator architecture was used for analyzing GeoSTAR options 2 Simulations or observations

MIT REMOTE SENSING AND ESTIMATION GROUP m GEM antenna; 118/183/380/425-GHz bands Rain Rate and Water Path Retrievals Surussavadee Staelin Chinese Front, June 22, 2003 Snow water path (mm) GEM retrieval MM5 Truth 35N 115E 120E Surface precipitation rate (mm/h) Rain water path (mm) Graupel water path (mm)

MIT REMOTE SENSING AND ESTIMATION GROUP 14 Spatial Resolution (km) at Nadir Antenna Diameter,  B Frequency Band (GHz) m dish 0.95 /D m dish (S) 1.3 /D m dish 1.3 /D m dish (S) 0.95 /D rcvrs/band 0.5 /D rcvrs/band 0.5 /D 25 Instrument Options Evaluated Staelin and Surussavadee October 2006 Frequency A B C D E F* G H # I* J K Band (GHz)1.21.2S22S *D max = 2.8 m for U array # D max = 5.6 m for U array Aperture synthesis systems must average longer (x10 for bandwidth, x4 for 4-bands, ~x10 for area coverage,  10 for receiver noise)

MIT REMOTE SENSING AND ESTIMATION GROUP km Representative Instrument Options Surface precipitation rate images for four instrument options (mm/h) Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP km Effects of Beam Sharpening Warm rain Front Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP 17 MM5 vs. 1.2-m GEM Rain Rate (mm/h) Surussavadee Staelin MM5 with 15-km resolution

MIT REMOTE SENSING AND ESTIMATION GROUP m vs. 1.2-m Precipitation Rate (mm/h) Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP 19 MM5 vs. 1.2-m GEM Rain Rate (mm/h) Surussavadee Staelin MM5 with 15-km resolution

MIT REMOTE SENSING AND ESTIMATION GROUP m vs. 1.2-m Precipitation Rate (mm/h) Surussavadee Staelin

MIT REMOTE SENSING AND ESTIMATION GROUP 21 Summary and Conclusions Staelin and Surussavadee October 2006 F( ) radiative transfer matches MM5 to AMSU observations well GEM and AMSU retrievals are feasible for: -rain and snow (mm/h) over land and sea (not yet over ice, snow) -water paths for rain water, snow, and graupel (mm) Both 1.2- and 2-m micro-scanned antennas can be integrated on GOES Image sharpening (optional processing) yields antenna resolution ~  1.3 The simplest aperture synthesis systems comparable to a 1.2-meter GEM antenna use at least 900 receivers in two bands, are  5.6m across, and cannot repeat so frequently as GEM GEM should be part of PMM -- unique for convective-scale evolution