25-28 october 2004 2nd ipwg monterey, ca the status of the noaa/nesdis operational amsu...

25-28 October 2004 2nd IPWG

Monterey, CA

The Status of the NOAA/NESDIS Operational

AMSU Precipitation AlgorithmRalph FerraroNOAA/NESDIS

College Park, MD USA

Fuzhong Weng, Norman Grody, Limin Zhao, Paul Pellegrino, Cezar Kongoli, Huan Meng, Mark Liu


Monterey, CA

Outline

• Review of AMSU and NOAA POES• Current AMSU Algorithm

– 89/150 GHz Scattering Technique• Performance and limitations

– Applications • Example: Tropical Rainfall Potential

– Solid Precipitation over land• Performance and limitations

• Future:– Algorithm improvements– NOAA-N, N’ and METOP


Monterey, CA

NOAA AMSU Sensor•Flown on NOAA-15 (5/98), NOAA-16 (9/00) & NOAA-17 (5/02) satellites•Contains 20 channels:

•AMSU-A (45 km nadir FOV)•15 channels•23 – 89 GHz

•AMSU-B (15 km nadir FOV)•5 channels•89 – 183 GHz

•Operational “imaging” products:•TPW, CLW, rain rate, snow cover, sea-ice, etc.

•~4-hour temporal sampling:•130, 730, 1030, 1330, 1930, 2230 LST


Monterey, CA


Monterey, CA

AMSU Rain Rate Algorithm• Basis for algorithm – work of Weng, Grody and Zhao

– Physical retrieval of IWP and De – 89 & 150 GHz

• Algorithm adopted for use with AMSU-B– Use of other window and sounding channels

• Derive needed parameters• Filters for false signatures

– Use of ancillary data– Use of other AMSU derived products

• IWP to rain rate based on limited MM5 model data and RTE calculations:– RR = A0 + A1*IWP + A2*IWP2


Monterey, CA


Monterey, CA

AMSU-ATb’s

AMSU-BTb’s

Ancillary

Land or Water? Land or

Water?

Land Water

Sea-IceConc.

TPW &CLW

Ice?No

Yes

Emissivity

SurfaceTemperature

AMSU-ASwath

LandWater

AMSU-BSwath

IWP & De

SnowIce?

No

Snow?Yes

No

Rain Rate IWP & De

PrecipitationRate


Monterey, CA


Monterey, CA

AMSU Rain Rate Algorithm - Chronology

• Original algorithm suffered from several problems– Unrealistic PDF’s in IWP and rain rate– Too low rain in convection, too high in stratiform– Large discontinuities between land and ocean– Over sensitivity to small IWP

• Improvements developed and implemented 8/03:– Two stream corrections for TB89 & TB150 as function of Θ– Two sets of coefficients based on size of De – Utilization of 183 GHz bands to determine depth of

precipitation• Developed a “Convective Index” (CI) based on differences

and magnitudes of TB183+1, TB183+3, TB183 +7

• Developed two IWP to RR relationships based on CI


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Example of Real Time Data


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Example of Monthly Data


Monterey, CA

Web Sitehttp://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/main.

html


Monterey, CA

Validation/Evaluation• Land:

– Instantaneous – NCEP Stage IV (Janowiak)– Monthly – GPCC Gauges (Rudolph) & SSM/I – Monthly – Australia (Ebert)– Monthly - AMSR-E

• Ocean:– Monthly – SSM/I– Monthly – AMSR-E

• User Feedback– Joyce, Huffman, Turk, etc.


Monterey, CA


Monterey, CA

N15N16N17 AMSU vs. GPCC - 8/03 to 7/0460 S - 60 N

y = 0.7922x + 8.2134R2 = 0.4975

0

100

200

300

400

500

600

700

800

0 100 200 300 400 500 600 700 800

GPCC (mm/month)

AM

SU

(m

m/m

on

th)

F13F15 SSMI vs. GPCC - 8/03 to 7/0460 S - 60 N

y = 0.6778x + 7.8219R2 = 0.4326

0

100

200

300

400

500

600

700

800

0 100 200 300 400 500 600 700 800

GPCC (mm/month)

SS

MI

(mm

/mo

nt


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Mean Mean Mean Bias Bias

SSMI AMSU GPCC SSMI AMSU

60S-60N 58.54 67.50 74.84 0.78 0.90

40S-40N 71.43 80.76 80.47 0.89 1.00

20S-20N 98.25 117.47 128.87 0.76 0.91

R R

SSMI AMSU

60S-60N 0.66 0.71

40S-40N 0.71 0.76

20S-20N 0.76 0.79

Performance vs. GPCC (8/03 – 7/04)


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Zonal Mean Land Rainfall (8/03 - 7/04)

0

20

40

60

80

100

120

140

160

180

-45 -35 -25 -15 -5 5 15 25 35 45 55

Latitude

Rai

nfa

ll (

mm

)

SSMI

AMSU

GPCC


Monterey, CA

AMSR-E and SSM/I Comparisons


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N-16 vs. AMSR-E

Land Ocean


Monterey, CA

Summary/Limitations• Land

– In general, performs well• Too high in convective situations?• Regional biases (of course!), esp. too high in drier regimes

– 3-satellite estimates outperform (dual SSM/I)– Better sensitivity to lighter rain rates

• Ocean– Restricted to convective precipitation

• Overall, too low due to missing precipitation without ice (generally lighter rain intensities)• Rain coverage less than other sensors

– Conditional rain rates too high?• Cloud base temperature estimate incorrect?

• Coastlines– Not adequately handled

• View angle dependencies– Larger FOV on scan edges results in varying rain rate distributions

• Larger errors due to beam filling likely• Lower rain rates expected over larger area, but makes more difficult for users• May miss detecting some rain at scan edge


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Applications at NOAA

•Weather forecasting & analysis•Tropical Cyclones•Climate Monitoring•Development of merged

precipitation analysis


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Hurricane Ivan – 15 Sept 04


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NOAA/NESDIS TRaPHurricane Ivan – 15 Sept 04


Monterey, CA

Ground Truth


Monterey, CA

Falling Snow over Land from AMSU• Use of AMSU-B 183 GHz bands along with

AMSU-A 53.6 GHz allows for expansion of current algorithm to over cold and snow covered surfaces:– AMSU-B channels allow for detection of scattering

associated with precipitation, but surface blind when “sufficient moisture” exists

– AMSU-A channel 5 allows for discrimination between “rain” and “snow”

• Feature added in 11/03, snowfall detection only (assigned arbitrary rate of 0.1 mm/hr)

• Validation over CONUS winter 2003-04


Monterey, CA

1200 UTC 25 January 2004 1300 UTC 25 January 2004


Monterey, CA

Snowfall Detection Algorithm

Snow on groundor

Tsfc < 269K?

MSPPSLand

Rain Rate

No

YesTB54L <

Cold Snow*?

*Note:Cold Snow is240 or 245 K

Yes

NoTB89-TB150>4 ?

No

NoPrecipitati

on

Yes TB176 < 255and

TB180 < 253and

TB182<250?

Yes

TB176 > 255and

TB180 < 253and

TB23<262?

No

TB150-TB176> -16and

TB176-TB180 > -3and

TB89-TB150<10 ?

Yes

No

Precipitation=

MSPPS Rain Rate

Value

PrecipitationIs

Indeterminate

Snow Is

Falling

No

Yes


Monterey, CA

January 2004 – Snowfall Frequency

ALG245

ALG240

x

x

x

x

xx


Monterey, CA

CONUS Validation Statistics

ALG245 ALG240 ALG245 ALG240 ALG245 ALG240

CXY CXY ROA ROA MIT MIT

POD 0.48 0.52 0.69 0.64 0.17 0.07

FAR 0.17 0.20 0.00 0.07 0.00 0.25

TS 0.44 0.46 0.69 0.61 0.17 0.07

ETS 0.41 0.43 0.68 0.59 0.16 0.05

HSS 0.94 0.94 0.98 0.97 0.95 0.87


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Summary/Limitations• Algorithm Performance

– Can detect snowfall associated with synoptic scale systems– Low false alarms– Can increase region of application by lowering TB54L

threshold up to 5 K• Some increase in false alarms

• Limitations– Relative moist atmospheres - -5 to 0 C– Southern extent of snow pack/temperate latitudes– Precip layer needs to extend to ~4-5 km or higher– No signal in extreme cold climate regimes and shallow

snow


Monterey, CA

Future• Near term algorithm improvements

– FOV issues – L3 nadir equivalent product– Coastlines– Incorporation of CLW into ocean (1DVar)– Snowfall rates; land & ocean

• Longer term– 1DVar, including land surface emissivity (with JCSDA)– Climate regime classification– Snowfall rates

• Upcoming launches– NOAA-N (Feb 05)

• MHS replaces AMSU-B– METOP-1 (Jan 06?)

• Pipeline processing• Continued interactions with NASA and international partners on GPM

– NOAA funds FY08?

25-28 october 2004 2nd ipwg monterey, ca the status of the noaa/nesdis operational amsu...

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