Download - Updates on Rain over Land Algorithm
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 1
Updates on Rain over Land Algorithm
Ralph Ferraro, Nai-Yu Wang, Kaushik Gopalan and Arief Sudradjat
NOAA/NESDIS, College Park, MD Cooperative Institute for Climate Studies, College Park, MD
Significant contributions from Chuntao Liu and Dan Cecil
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 2
Outline
• TMI V7 GPROF/land algorithm update– Reduction in warm season bias– Surface screening not addressed
• Prototype, unified land surface identification– Elimination of surface screening with ancillary data
• Future plans– What we can do in next year– Beyond
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 3
Algorithm Development
• Used TMI-PR matchups 2002-2008 from the Utah Precipitation Feature database
• Filter out co-locations:– Warm rain (no TB85 depressions)– Anomalous scattering due to surface
• Improve TB-RR relationships
• Improve Convective-Stratiform separationCaveat – used V6 data; V7 PR is now coming in and is
vastly different – will repeat process using subset to see what differences might exist.
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 4
Convective
120 140 160 180 200 220 240 260 28010
15
20
25
30
35
40
45
50
TMI 85V Tb (K)
PR
RR
(m
m/h
r)
Convective Rain
Fal Tr
Fal SubTrWin Tr
Win SubTr
Spr Tr
Spr SubTrSum Tr
Sum SubTr
Regional/Seasonal differencesnot significant enough to warrantpartitioning for TRMM V7
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 5
Stratiform
210 220 230 240 250 260 2701
1.5
2
2.5
3
3.5
4
4.5
5
TMI 85V Tb (K)
PR
RR
(m
m/h
r)Stratiform Rain
Fal Tr
Fal SubTrWin Tr
Win SubTr
Spr Tr
Spr SubTrSum Tr
Sum SubTr
Regional/Seasonal differencesnot significant enough to warrantpartitioning for TRMM V7
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 6
Final regressions
120 140 160 180 200 220 240 260 2800
5
10
15
20
25
30
35
40
45
TMI 85V Tb (K)
PR
RR
(m
m/h
r)
Stratiform
Convective
Cubic fit
Linear fit
As in TRMM V6, single curves used
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 7
P(Conv) - Convective ratio
• Weighted average of several Conv. Storm indicators – PIWD
• Based on 85V Tb gradients in along-scan and along track directions
– Std. Dev. of 85V Tb in meso-scale• Npol
– Normalized 85 GHz polarization
RR = RR_conv*P(Conv) + RR_strat*(1-P(Conv))
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 8
P(Conv)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
P(Conv)
Pd
f
TMI P(Conv)
PR Convective ratio
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
P(Conv)
Pd
f
TMI P(Conv)
PR Convective ratio
TMI V6 vs. PR V6 TMI V7 vs. PR V6(PR V7 likely different)
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 9
0 10 20 30 40 50 60 70 80 900
5
10
15
20
25
30
35
40
45
50
Months from Jan 2002
% R
R b
ias
(TM
I -
PR
)
Regression fit
2A12 v6
Mean conditional monthly biases (Jan 2002 – Dec 2008)
Avg. global bias for TMI – PR rain collocations
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 10
0 10 20 30 40 50 60 70 80 90-20
-10
0
10
20
30
40
50
60
Months from Jan 2002
TM
I -
PR
RR
bia
s (%
)
Regression fit
2A12 v6
Mean unconditional monthly biases (Jan 2002 – Dec 2008)
Includes non-raining pixels. Effect of screening errors is seen here.
RR Algorithm
Mean RR (mm/d)
PR 2A25 2.20
TMI 2A12 v6 2.83
TMI 2A12 v7 2.41
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 11
Seasonal TMI – PR bias map : DJF
Longitude (deg)
Lat
(deg
)
TMI v6 - PR bias (mm/month)
-150 -100 -50 0 50 100 150-50
0
50
-150
-100
-50
0
50
100
150
Longitude (deg)
Lat
(deg
)
TMI regression - PR bias (mm/month)
-150 -100 -50 0 50 100 150-50
0
50
-150
-100
-50
0
50
100
150
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 12
Seasonal TMI – PR bias map : JJA
Longitude (deg)
Lat
(deg
)
TMI v6 - PR bias (mm/month)
-150 -100 -50 0 50 100 150-50
0
50
-150
-100
-50
0
50
100
150
Longitude (deg)
Lat
(deg
)
TMI regression - PR bias (mm/month)
-150 -100 -50 0 50 100 150-50
0
50
-150
-100
-50
0
50
100
150
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 13
Global RR Prob. density function
10 20 30 40 50 60
10-6
10-5
10-4
10-3
10-2
10-1
RR (mm/hr)
Pd
f
Regression
v6PR
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 14
Global RR Cumulative distribution
5 10 15 20 25
0.988
0.99
0.992
0.994
0.996
0.998
1
RR (mm/hr)
Cd
f
Regression
v6PR
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 15
TRMM V7 Check Out - Preliminary
Having PR > TMI should be adesirable result assuming PR seeswarm rain and TMI does not
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 16
Prototype, Generic Land Surface Classification
FOV
Static surface type(s) identification
Climatological parameters
Climatological parameters
Climatological parameters
Dynamic info: snow cover, soil mositure,
NDVI, emissivity, etc.
Surface type(s) screen
Desert Semi arid
Vegetated land
Replace This ………………………………………………With This
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 17
Moveable vs. Fixed Grid
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 18
Features of a “Generic” Scheme
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 19
Impact on 85 GHz
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Putting it all together….
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Another Example
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Yet another one
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 23
JJA (2008) vs. PR
Values are (2A12-2A25)/2A25
V6
Prototype
TMI too highTMI too low
ElevationMaskFixed Arid
too rigid
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 24
DJF (2007-8) vs. PR
Values are (2A12-2A25)/2A25
V6
Prototype
TMI too highTMI too low
DynamicSnow Cover
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 25
Comparisons vs. GPCPDJF JJA
Values are (2A12-GPCP)/GPCP
Note – GPCP – SSMI, IR, Gauges
TMI too high
TMI too low
V6
Prototype
V6
Prototype
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 26
Relevant Publications
• Wang, N.-Y., C. Liu, R. Ferraro, D. Wolff, E. Zipser, and C. Kummerow, 2009: TRMM 2A12 Land Precipitation Product - Status and Future Plans. Journal of the Meteorological Society of Japan, 87, 237–253.
• Gopalan, K., N-Y. Wang, R. Ferraro, C. Liu, 2010: Status of the TRMM 2A12 Land Precipitation Algorithm. In Press, J. Appl. Meteor. Climo.
• Sudradjat, A., N-Y. Wang, K. Gopalan, R. Ferraro, 2010: Prototyping a Generic, Unified Land Surface Classification and Screening Methodology for GPM-era Microwave Land Precipitation Retrieval Algorithms, submitted, J. Appl. Meteor. Climo.
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 27
Next Steps – Short Term
• Work with CSU team to insure GPROF2008 is implemented properly for AMSR-E– Depending on timing/needs for AMSR-E reprocessing, can
incorporate some improvements for land surface screening
• Elevation?• Other surfaces?
• Through PMM Science Team, AMSR-E team/ this effort, begin to investigate rainfall regimes– AMSR-E channel co-variances– Use of ancillary information
• Land surface temp, emissivity, etc.– Aqua products preferred
2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 28
Next Steps – Long Term
• If new proposal successful…– Fully develop generic land surface
characterization/screening scheme for use in GPROF20xx
– Restructure GPROF databases for rainfall regimes
– Focus would be using Aqua products for the AMSR-E version
– Continue to provide user support for product