25-28 Octo ber 2004 2nd IPWG Monterey, CA The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm Ralph Ferraro NOAA/NESDIS College Park, MD USA Fuzhong Weng, Norman Grody, Limin Zhao, Paul Pellegrino, Cezar Kongoli, Huan Meng, Mark Liu
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25-28 October 2004 2nd IPWG Monterey, CA The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm Ralph Ferraro NOAA/NESDIS College Park,
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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
25-28 October 2004 2nd IPWG
Monterey, CA
25-28 October 2004 2nd IPWG
Monterey, CA
25-28 October 2004 2nd IPWG
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
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Monterey, CA
25-28 October 2004 2nd IPWG
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
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Web Sitehttp://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/main.
• 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
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Ground Truth
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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
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Monterey, CA
1200 UTC 25 January 2004 1300 UTC 25 January 2004
25-28 October 2004 2nd IPWG
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
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January 2004 – Snowfall Frequency
ALG245
ALG240
x
x
x
x
xx
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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
25-28 October 2004 2nd IPWG
Monterey, CA
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
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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