a new inter-comparison of three global monthly ssm/i precipitation datasets
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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 - PowerPoint PPT PresentationTRANSCRIPT
A New Inter-Comparison of Three Global Monthly SSM/I
Precipitation Datasets
Matt Sapiano, Phil Arkin and Tom SmithEarth 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 2006F16 Oct 2003 – presentNote: 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
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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– Don’t 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
Pre
cipi
tati
on, m
m d
ay-1
8
Between satellite comparisons• Same local crossing times • RSS (Wentz) has more consistently higher correlations
and lower bias
GPROF V6 SSM/I F11 – F13
RSS V06 (Wentz) F14 – F15
GPROF r(F11,F13) after spatial 1-2-1 smoothing
→ Small spatial errors cause noisy correlation field
mm day-1
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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 2006F16 Oct 2003 – presentNote: 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
F1
4F
10 v
s F
11
NOAA/NESDIS GPROF V6 SSM/I RSS V06 (Wentz)
11
Different time measurement - biasF
13 v
s F
14
F10
vs
F1
1
NOAA/NESDIS GPROF V6 SSM/I RSS V06 (Wentz)
• 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
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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
Ocean only GPROF SSM/I Wentz Ferraro
20ºN – 60ºN
20ºS – 20ºN
60ºS – 20ºS
13
Wentz comparison• Wentz algorithm is quite different• Good advertisement for the benefits of re-processing
Ocean only GPROF SSM/I Wentz Ferraro
Wentz V05
Wentz V06
14
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?
Land only GPROF SSM/I Ferraro
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
Che
n et
al.
GP
CC
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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
60.0r
65.0r
62.0r
GPROF SSM/I Wentz Ferraro
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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?