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A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets

A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets. Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary Center, University of Maryland. Motivation. Currently working on a new reanalysis of precipitation

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A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets

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  1. A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary Center, University of Maryland

  2. Motivation • Currently working on a new reanalysis of precipitation • Aim to use Optimal Interpolation to combine data sources • Special Sensor Microwave/Imager (SSM/I) • One definite constituent of the reanalysis • Longest MW precipitation dataset (starts 1987) • Several algorithms exist for estimation of precipitation • Goddard Profiling algorithm • NOAA/NESDIS algorithm (Ferraro) • Remote Sensing Systems algorithm (Wentz) • Last comparison of these data was several years ago • So: compare them to inform precipitation analysis → Monthly averages, 2.5º resolution

  3. Some SSM/I facts… • Defense Meteorological Satellite Program Special Sensor Microwave/Imager • 7 channels: 19.35 (H+V), 21.235 (V), 37.0 (H+V), 85.5 (H+V) • Data from 1987 - present F08 Jul 1987 – Dec 1991 F10 Dec 1990 – Nov 1997 F11 Dec 1991 – May 2000 F13 May 1995 – present F14 May 1997 – present F15 Dec 1999 – Aug 2006 F16 Oct 2003 – present Note: Not all channels were available during the record; notably, the 85GHz channel onboard the F08 satellite was unavailable from June 1990.

  4. NOAA/NESDIS (Ferraro) • Scattering technique over land • Grody Scattering Index (SI) from 19, 22 & 85 GHz channels • Precip occurrence determined by SI>10 • Screening for snow and ice • Precip empirically estimated from SI • Scattering and emission over ocean • Precip occurrence from SI or emission (Q) • Precip empirically estimated from SI or Q • Used 37GHz channel when 85GHz unavailable in 1990-91 • No overlapping periods for satellites that have similar local equator crossing times

  5. RSS (Wentz) • Physically based retrieval of rain, wind, water vapor • Estimate transmittance of liquid water from brightness temperature, apply beam filling correction and derive atmospheric attenuation • Mie scattering theory used to estimate columnar rain rate • Columnar rain rate converted to surface rain rate using assumed column height from SST • New version of algorithm released September 2006 (Version 06) • Improved beam filling • Improved relationship between column height and SST

  6. GPROF SSM/I Version 6 • Goddard Profiling algorithm • Inversion scheme to retrieve vertical structure • Instantaneous rainfall rates calculated from weighted average of existing hydrometeor profiles created using numerical cloud model • Goddard Cumulus Ensemble Model • Land: Scattering technique • Ocean: Emission technique • Most recent version (V7) not applied to full SSM/I dataset, so V6 is used here • Don’t be confused by naming conventions!!!

  7. GPROF V6 Sea Ice Issue • Problem of sea ice contamination in GPROF SSM/I Version 6 • First NH (20-60º) EOF shows unphysical anomalies • Clearly an artifact (larger over Sea of Okhotsk) • Correction applied here to remove anomalously large values • Gridpoint mean plus five times the zonal mean standard deviation Precipitation, mm day-1

  8. GPROF r(F11,F13) after spatial 1-2-1 smoothing → Small spatial errors cause noisy correlation field Between satellite comparisons • Same local crossing times • RSS (Wentz) has more consistently higher correlations and lower bias RSS V06 (Wentz) F14 – F15 mm day-1 GPROF V6 SSM/I F11 – F13

  9. Some SSM/I facts… • Defense Meteorological Satellite Program Special Sensor Microwave/Imager • 7 channels: 19.35 (H+V), 21.235 (V), 37.0 (H+V), 85.5 (H+V) • Data from 1987 - present F08 Jul 1987 – Dec 1991 F10 Dec 1990 – Nov 1997 F11 Dec 1991 – May 2000 F13 May 1995 – present F14 May 1997 – present F15 Dec 1999 – Aug 2006 F16 Oct 2003 – present Note: Not all channels were available during the record; notably, the 85GHz channel onboard the F08 satellite was unavailable from June 1990.

  10. Different time measurement - correlations • Correlations from different overpass times for overlapping periods • Differences reflect diurnal cycle F13 vs F14 F10 vs F11 NOAA/NESDIS GPROF V6 SSM/I RSS V06 (Wentz)

  11. Different time measurement - bias • Bias from different overpass times • Wentz has good agreement between satellites • Different biases over land and ocean • High tropical land diurnal variability is of consistent sign • Problem with biases at high latitudes in GPROF due to sea ice F13 vs F14 F10 vs F11 NOAA/NESDIS GPROF V6 SSM/I RSS V06 (Wentz)

  12. Ocean only GPROF SSM/I Wentz Ferraro Algorithm comparison - ocean • Zonal mean precipitation from all three algos • Multiple lines represent the different satellites – diurnal cycle is evident • Good agreement between Ferraro and Wentz • Annual cycle dominates extra-tropics 20ºN – 60ºN 20ºS – 20ºN 60ºS – 20ºS

  13. Ocean only GPROF SSM/I Wentz Ferraro Wentz comparison • Wentz algorithm is quite different • Good advertisement for the benefits of re-processing Wentz V05 Wentz V06

  14. Land only GPROF SSM/I Ferraro Algorithm comparison - Land • Only NOA/NESDIS and GPROF V6 as RSS is ocean only • Good agreement in annual cycle at higher latitudes, but magnitudes disagree – GPROF V6 gives higher winter precipitation • Is this a problem with snow contamination? 20ºN – 60ºN 20ºS – 20ºN 60ºS – 20ºS

  15. Gauge validation • Correlation with Chen et al. (2002) [GHCN+CAMS] and GPCC gauge analyses (monitoring product) • NOAA/NESDIS data better correlated with gauges at higher latitudes • Lack of profiles at high latitudes for GPROF V6? • Snow contamination problem again? NOAA/NESDIS GPROF V6 SSM/I Chen et al. GPCC

  16. GPROF SSM/I Wentz Ferraro TAO buoy validation • Correlations with TAO/TRITON buoy rain gauge data • Data from ATLAS 2 self siphoning gauges • Data has been quality controlled and an empirical wind correction was applied • All three algorithms have high correlations with oceanic precipitation • RSS (Wentz) V06 data has the highest correlations (not statistically significant though!) NOAA/NESDIS GPROF V6 RSS V06

  17. Conclusions and Further Work • SSM/I data continues to increase in value as a climate data record • RSS V6 algorithm performs well over oceans • RSS also most homogeneous over the changing satellite record • RSS V06 bias appears to be superior to V05 bias • Over land, NOAA/NESDIS appears to have better properties than GPROF SSM/I V6 at higher latitudes • GPROF SSM/I V6 is more homogeneous over the tropics • Lower correlations at mid/high latitudes is a problem • Results from GPROF V6 SSM/I not applicable to most recent TMI product • Need for reprocessing of SSM/I using most recent GPROF algorithm [This would make a nice recommendation for this workshop!] • Single satellite available before 1992 • Is data homogeneous? Effect of 85GHz failure?

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