A New InterComparison of Three Global Monthly SSMI Precipitation Datasets

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

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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
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Some SSM/I facts

• Defense Meteorological Satellite Program Special
  Sensor Microwave/Imager
• 7 channels 19.35 (HV), 21.235 (V), 37.0 (HV),
  85.5 (HV)
• 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.
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NOAA/NESDIS (Ferraro)

• Scattering technique over land
• Grody Scattering Index (SI) from 19, 22 85 GHz
  channels
• Precip occurrence determined by SIgt10
• 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

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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

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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
• Dont be confused by naming conventions!!!

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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
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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
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Some SSM/I facts

• Defense Meteorological Satellite Program Special
  Sensor Microwave/Imager
• 7 channels 19.35 (HV), 21.235 (V), 37.0 (HV),
  85.5 (HV)
• 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.
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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)
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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)
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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
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Wentz comparison

• Wentz algorithm is quite different
• Good advertisement for the benefits of
  re-processing

Wentz V05
Wentz V06
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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
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Gauge validation

• Correlation with Chen et al. (2002) GHCNCAMS
  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
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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
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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?