Toshiro Inoue 1) 、 Tomoo Ushio 2), Daisuke Katagami 3)

1) Meteorological Research Institute

2) Osaka University

3) Osaka Prefecture University

Applications of Split Window Data for Rainfall Estimation

1. Cloud motion vector　　 To construct high temporal rainfall map, we have to fill the observation gap from MW onboard LEO. 2. Convective/Stratiform rainfall type Dominant rainfall type depends on life stage of deep convection. 　

Cloud Motion Vector

• Filling the observation gap from the passive microwave onboard LEO becomes important issue to construct a higher temporal rainfall map.

• A method to estimate half-hourly rainfall map using cloud motion vector( Joyce et al., 2004) .

• Use of cloud motion vector is better than simple interpolation (Ushio et al., 2005) to fill the gap.

Cloud motion vector by Split Window

Better rainfall area delineation by split window

Optically thicker cloud defined by the split window corresponds well to the rainfall area compared to the cloud area defined by single IR (Inoue and Aonashi, 2000; Inoue,1987)

Computing the cloud motion vector from split window is expected to be better than single IR.

Data and MethodGOES-9 (over Japan)September, 2003Split Window (m,12m)Vector: 2D cross-correlation Template 6° lat/lon Every 3° lat/lon (Interpolated to 0.1 lat/lon)

Radar AMeDAS

GSMaP (Global Satellite Mapping of Precipitation) is constructing hourly rainfall map over the globe with 0.1° lat/lon.

Considering current MW observation,we studied the time interpolation up to 5 hours.

Radar AMeDAS image at 7 JST on 8th September, 2003 overlaid the cloud area defined as 273K or colder.

Radar AMeDAS image at 7 JST on 8th September, 2003 superimposed on the cloud area defined as 273K or colder with brightness temperature difference between the split window less than 2K.

Same as former slides except for the 17 JST on 5th September, 2003.

Cloud Motion Vector and Rainfall Motion Vector 18UTC 5th September, 2003

u

v

IR Split Window Radar AMeDAS

1chBTD　2.5

BTD　2.0

BTD　1.5

BTD　1.0

BTD　0.5

1Hour0.594

0.600

0.710

0.678

0.617

0.544

2Hour0.430

0.460

0.521

0.477

0.515

0.351

3Hour0.378

0.418

0.438

0.341

0.417

0.225

4Hour0.399

0.355

0.373

0.263

0.289

0.151

5Hour0.207

0.187

0.236

0.164

0.117

0.108

Correlation coefficients between cloud tracked rainfall rate and Radar AMeDAS

on 5th September, 2003

BTD　2.5

BTD　2.0

BTD　1.5

BTD　1.0

BTD　0.5

1Hour 0.98 1.08 1.29 1.20 1.16

2Hour 1.90 1.66 2.12 2.30 1.85

3Hour 1.83 1.65 1.72 2.05 1.56

4Hour 1.24 1.62 1.69 1.46 1.04

5Hour 1.42 1.08 1.34 1.24 1.62

Relative correlation coefficients (Split Window/ Single IR) We studied 30 cases during September, 2003.11 cases indicate better correlation coefficients.

The use of Split Windowfor computing Cloud Motion Vector

• We studied 30 rainfall cases during September 2003, and split window data indicated better score in 11 cases out of 30.

• The correlation coefficients are improved by more than 50% for the 11 cases.

Convective/Stratiform rainfall type

Split Window can classify Ci and Cb

The % of Ci within the deep convection is a good indicator to tell the life stage.

Depending on the life stage, dominance of rainfall type (conv/stra) is different..

Data: Hourly data of GOES-W

Period: Jan., 2001-Dec., 2001 (except Sep., 2001)

Deep Convection: Cloud area colder than 253K

Life Cycle: Birth: First time of DC detected 　 End: Last time of DC detected

Case: No merge No split

% of Ci defined by Split Window within DC

Life Cycle of Deep Convection Defined by Split Window

BTD Image by MSG(Ci is whiter, Cb/Cu black)

IR Image by MSG

Cloud Type Map Classified by the Split Window

GOES-W 2 hourly from 12UTC May 09, 2001

1°

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11

Time from DC Initiation

% of C

i with

in 253

K (D

C) 3HR

4HR 5HR 6HR 7HR 8HR 9HR10HR11HR

% of Ci within DC for each life time category

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11

Time from DC Initiation

Ci % w

ithin

253K Mean+s

MeanMean-s

Ci %

wit

hin

DC

developing

mature

decaying

Life Stage of Deep Convection

Comparison between % of Ci within 253K cloud area and PR/TRMM rainfall data (3G68)

% of convective rain and % of Ci within DC

Rainfall Rate and % of Ci within DC

% of Ci within DC

Good indicator the life stage of deep convection

Good indicator for dominance of Convective /Stratiform rainfall type classification as a system

Possible to apply the passive microwave estimation which has a tendency of overestimation at decaying stage comparing to PR estimation

Thank you for your patience.