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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. Outline. Review of AMSU and NOAA POES Current AMSU Algorithm

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The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm


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    1. 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 2nd IPWG Monterey, CA

    2. 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 2nd IPWG Monterey, CA

    3. 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 2nd IPWG Monterey, CA

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    5. 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 2nd IPWG Monterey, CA

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    8. Ancillary AMSU-A Tb’s AMSU-B Tb’s Land or Water? Water Land Land or Water? Water Land Emissivity Sea-Ice Conc. Snow Ice? Yes Ice? No Surface Temperature Snow? Yes IWP & De No TPW & CLW No IWP & De Rain Rate Precipitation Rate AMSU-A Swath AMSU-B Swath 2nd IPWG Monterey, CA

    9. 2nd IPWG Monterey, CA

    10. 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 2nd IPWG Monterey, CA

    11. Example of Real Time Data 2nd IPWG Monterey, CA

    12. Example of Monthly Data 2nd IPWG Monterey, CA

    13. Web Sitehttp://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/main.html 2nd IPWG Monterey, CA

    14. 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. 2nd IPWG Monterey, CA

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    19. Performance vs. GPCC (8/03 – 7/04) 2nd IPWG Monterey, CA

    20. 2nd IPWG Monterey, CA

    21. AMSR-E and SSM/I Comparisons 2nd IPWG Monterey, CA

    22. N-16 vs. AMSR-E Land Ocean 2nd IPWG Monterey, CA

    23. 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 2nd IPWG Monterey, CA

    24. Applications at NOAA • Weather forecasting & analysis • Tropical Cyclones • Climate Monitoring • Development of merged precipitation analysis 2nd IPWG Monterey, CA

    25. Hurricane Ivan – 15 Sept 04 2nd IPWG Monterey, CA

    26. NOAA/NESDIS TRaPHurricane Ivan – 15 Sept 04 2nd IPWG Monterey, CA

    27. Ground Truth 2nd IPWG Monterey, CA

    28. 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 2nd IPWG Monterey, CA

    29. 1300 UTC 25 January 2004 1200 UTC 25 January 2004 2nd IPWG Monterey, CA

    30. Snowfall Detection Algorithm No No No Yes Yes TB176 < 255 and TB180 < 253 and TB182<250? TB176> 255 and TB180 < 253 and TB23<262? Snow on ground or Tsfc < 269K? TB54L< Cold Snow*? MSPPS Land Rain Rate TB89-TB150>4 ? Yes Yes No Yes No Yes TB150-TB176> -16 and TB176-TB180> -3 and TB89-TB150<10 ? Precipitation= MSPPS Rain Rate Value No Precipitation Precipitation Is Indeterminate Snow Is Falling No *Note: Cold Snow is 240 or 245 K 2nd IPWG Monterey, CA

    31. January 2004 – Snowfall Frequency ALG245 x x x x x x ALG240 2nd IPWG Monterey, CA

    32. CONUS Validation Statistics 2nd IPWG Monterey, CA

    33. 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 2nd IPWG Monterey, CA

    34. 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? 2nd IPWG Monterey, CA