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

2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 20

Putting it all together….

2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 21

Another Example

2-3 June 2010 AMSR-E Science Team Meeting Huntsville, AL 22

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