MIT REMOTE SENSING AND ESTIMATION GROUP 1 Geosynchronous Microwave Sounding of Precipitation Parameters at Convective Scales David

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


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)


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


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


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


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



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



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)


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



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


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)



Geo-Microwave Precipitation Retrievals


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


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)


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


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)


25km

Representative Instrument Options

Surface precipitation rate images for four instrument options (mm/h)

SurussavadeeStaelin


25km

Effects of Beam Sharpening

Warm rain

Front

SurussavadeeStaelin


MM5 vs. 1.2-m GEM Rain Rate (mm/h)

SurussavadeeStaelin

MM5 with 15-km resolution


2-m vs. 1.2-m Precipitation Rate (mm/h)

SurussavadeeStaelin


MM5 vs. 1.2-m GEM Rain Rate (mm/h)

SurussavadeeStaelin

MM5 with 15-km resolution


2-m vs. 1.2-m Precipitation Rate (mm/h)

SurussavadeeStaelin


Summary and Conclusions


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

Documents

MIT REMOTE SENSING AND ESTIMATION GROUP 1 Geosynchronous Microwave Sounding of Precipitation Parameters at Convective Scales David