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MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 1
Geosynchronous Microwave Sounding of Precipitation
Parameters at Convective ScalesDavid H. Staelin and Chinnawat Surussavadee
Presented at the Third Workshop of theInternational Precipitation Working Group
Melbourne, Australia, 23-27 October, 2006
OUTLINEMM5, radiative transfer, and retrievals GeoMicrowave instrument conceptsPrecipitation retrieval methodInstrument optionsMovies: MM5 vs. GEMSummary and conclusions
Staelin andSurussavadee October 2006
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 2
MM5 vs. AMSU TB’s (K)
SurussavadeeStaelin
AMSU-B GODDARD
REISNER SCHULTZ
150 GHz
AMSU-B
AMSU-B
GODDARD
GODDARD
1837 GHz
1837 GHz
(K)
(K)
(K)
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 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.
Fsnow > 15% graupel > 25%, Backscattering > 1%, Psnow/graupel > 25mb
MM5 vs. AMSU-A/B
Model Sensitivity StudiesUnambiguous discrepancies with AMSU brightness-temperature histograms appeared when:
SurussavadeeStaelin
MM5 vs. AMSU TB Histograms; F() Model
1837 3 1
50.3 GHz 89 150
Pixels/oK
50.3 GHz 89 150
1837 3 1
Assumed DDSCAT F() + 0.05
130K 230K
130K 230K
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 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
1831 3 7 GHz
F() Ice Scattering Model
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 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, TB(Ch 4-8),
sec .
AMSR-E (Goddard)
SurussavadeeStaelin Chadarong McLaughlin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 6
00:17 UTC00:07 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 Great Lakes
SurussavadeeStaelin Chadarong McLaughlin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 7
AMSU Retrievals over U.S. Midwest
mm/h
June 17, 2004 0100-0115 UTC
August 18, 2004 2015-2030 UTC
June 9, 2004 1300-1315 UTC
SurussavadeeStaelin Chadarong McLaughlin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 8
Instrument Precipitation-Rate ComparisonsCorrelation 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
StratiformConvective
AMSU/MM5 bias correction (106 global storms)
Applied only to movies
Pixels per log bin
All comparisons were over the U.S. Midwest, summer, 2004. An “event” is a 15-minute period where either NOWRAD or the overlapping instrument saw >0.01 mm/h in 0.05o squares. A “pixel” is a 0.05o 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 GROUPhttp://rseg.mit.edu 9
NoddingSubreflector
Even a 2-m dishCan be integrated on GOES
Trms 0.5K (400 GHz, = 0.04s) Weight ~50 kg, 130 watts
10 km400 km
10 km/sec
Geo-Microwave Sensor Concepts
GeoSTAR’
GEM
2 meters
2 m
1.2 mGOES
GEM
GeoSTAR
2 mGEM’
Sketch by Ball Sketch by JPLSketch by MIT Lincoln Laboratory
Staelin andSurussavadee October 2006
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 10
Sharpened 30-km res.
Sharpened pattern260 oK
220
180
30 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
12
2A
N (f )W(f ) 1
T (f )
Original 5-km image
Image Sharpening
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 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 Issues118 GHz (O2) 425 GHz (O2)
183 GHz (H2O) 380/340 GHz (H2O)
Staelin andSurussavadee October 2006
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 12
Geo-Microwave Precipitation Retrievals
Staelin andSurussavadee October 2006
Rain rate estimate R (mm/h)
118 GHz(4 O2 Channels)
166-183 GHz(4 H2O Channels)
380 GHz (4 H2O Channels)
425 GHz (4 O2 Channels)
Land/sea, elevation
R > 8? R
(mm/h)
Neural Net 2
Neural Net 3
Yes
No
8
8
8
Training (NCEP/MM5, 122 global storms)1
Nets 2 and 3 were trained with different R values (NN1.3 better matches MM5, retrieval
colors)
8Neural Net 1
(for categorization)
34
Rain-rate estimates: input = two TB 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 GROUPhttp://rseg.mit.edu 13
1.2-m GEM antenna; 118/183/380/425-GHz bands
Rain Rate and Water Path Retrievals
SurussavadeeStaelin
Chinese Front, June 22, 2003
Snow water path (mm)
GE
M r
etri
eval
MM
5 T
ruth
35N
115E 120E
Surface precipitation rate (mm/h)
Rain water path (mm)
Graupel water path (mm)
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 14
Spatial Resolution (km) at Nadir
Antenna Diameter, B
Frequency Band (GHz)
53 118 166 183 380 425
1.2-m dish 0.95/D 97 69 63 30 27
1.2-m dish (S) 1.3/D 62 44 42 22 20
2-m dish 1.3/D 58 42 38 18 17
2-m dish (S) 0.95/D 38 28 26 14 12
300 rcvrs/band 0.5/D 50 50 50 50
600 rcvrs/band 0.5/D 25 25 25 25
Instrument Options Evaluated
Staelin andSurussavadee October 2006
Frequency A B C D E F* G H# I* J K Band (GHz) 1.2 1.2S 2 2S 1.2 1200 600 900 600 300 300
52.8 25 50 50 25 50118.75 97 62 58 38 97 25 50 50
166 69 44 42 28 69 25183.31 63 42 38 26 63 25380.2 30 22 18 14424.76 27 20 17 12
*Dmax = 2.8 m for U array#Dmax = 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 GROUPhttp://rseg.mit.edu 15
25km
Representative Instrument Options
Surface precipitation rate images for four instrument options (mm/h)
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 16
25km
Effects of Beam Sharpening
Warm rain
Front
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 17
MM5 vs. 1.2-m GEM Rain Rate (mm/h)
SurussavadeeStaelin
MM5 with 15-km resolution
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 18
2-m vs. 1.2-m Precipitation Rate (mm/h)
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 19
MM5 vs. 1.2-m GEM Rain Rate (mm/h)
SurussavadeeStaelin
MM5 with 15-km resolution
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 20
2-m vs. 1.2-m Precipitation Rate (mm/h)
SurussavadeeStaelin
MIT REMOTE SENSING AND ESTIMATION GROUPhttp://rseg.mit.edu 21
Summary and Conclusions
Staelin andSurussavadee 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