A combined microwave and infrared radiometer approach for a high resolution global precipitation map in the GSMaP Japan

Tomoo Ushio, K. Okamoto, K. Aonashi, T. Inoue, T. Kubota, H. Hashizume, T. Simomura, T. Iguchi, N. Takahashi, R. Oki, M. Kachi

Outline Background

On the GSMaP project Microwave radiometer based precipitation map Needs for the Infrared data (IR)

Methodology Cloud motion and Kalman filter approach from the Geo-IR data

Results Demonstration of our product Initial evaluation of our product PEHRPP activities in Japan

Summary and future directions

Goals of the project

Production of high precision and high resolution global precipitation map by using satelliteborne microwave radiometer data

-e.g. Spatial resolution: 0.1 ﾟ ✕ 0.1 ﾟ , Temporal resolution: 1 day -Microwave radiometers (TMI, SSM/I ×3, AMSR-E, AMSR) -Precipitation radar, IR data

Development of reliable microwave radiometer algorithm -Based on the common physical precipitation model which precipitation

radar also uses. Even in version 6 TRMM algorithms, about 10-15% discrepancy can be seen in monthly average rainfall rates retrieved by TMI and PR.

Establishment of precipitation map production technique by using multi-satellite data for the coming GPM era

Obs. Data

Precip. Retrieval

Flow of the GSMaP Project

Routine Obs.Campaign Obs.

Data base

ParameterSensitivity Exp.

Match-upData Anal.

Look-upTable

Verify

Precip. MapProducts

Obs. Data

High TemporalResolution Map

Obs.Data

RadarAlgorithm

Meteor. Satellites

Global Precip. Map TRMM/PR

Ground Obs.

Precip. MapData base

Precip. Physical Model

Algorithm Ｇ .

Precip. Physical Model G.

Ground Radar Obs. G.

Global Precip. Map Ｇ .

Algorithm

Microwave Radiometer

Interpolation Algo.

How do we get a global precipitation map?

The accurate estimation of surface rainfall on a global scale with high resolution has been one of the major goals in global water cycle 　 and its related area.

Ground based approach Fairly good estimation Generally suffer from spatial coverage problems.

Satellite based approach Fairly good coverage and reasonably good estimation There is not a single space-born sensor to detect surface rainfall

in near real time on a global basis. We need to combine the data from multiple satellites.

Approach of the GSMaP project

We use the Aonashi Algorithm to retrieve rainfall rate.

The sensors for the analysis are TMI, AMSR-E, AMSR, SSMI (F13, 14, 15).

Name Data availableTRMM (TMI) Jan. 1998 to Dec. 2005

Aqua (AMSR-E) Jan. 2003 to Oct. 2005

ADEOS-II (AMSR) Apr. 2003 to Oct. 2003

DMSP (SSMI:F13, F14, F15) Sep. 2003, July. 2005 and several

Monthly precipitation accumulation from DMSP/SSMI (F13, 14, 15) for Sep. 2003

F13

F15

F14

6 hourly MWR combined map

Combined6 hourly

TMI AMSR & AMSR-E

SSM/I （ F13, F14, F15 ）

How can we get a global precipitation map with temporal resolution of 3 hours or less? Infrared radiometers (IR)

can provide information on cloud top layers (not precipitation) Can ensure a global coverage with high temporal resolution (> 30 min)

due to the geo-synchronous orbit (GEO)

Microwave radiometers (MW) Can detect cloud structure and precipitation with high spatial resolution The major draw back is temporal sampling due low earth orbit satellite

(LEO)

The LEO-MW and GEO-IR radiometry are quite complementary for monitoring the highly variable parameters like precipitation.

How do we combine the MWR and IR data?

Combination of the moving vector and Kalman filtering method

The moving vector method was introduced by Joyce et al. [2004]. Joyce R., J. Janowiak, P. Arkin, and P. Xie, CMORPH: A method that produces global precipitation estimates from passive microw

ave and ifrared data at high spatial and temporal resolution, J. Hydrometeorology, 487-503, 2004

Advantage MWR based approach (not Tb but cloud motion) Fast processing time

Disadvantage Not include the developing and decaying process of precipitation

Kalman filter approach Refine precipitation rate on Kalman gain after propagating the rain pixel The Kalman gain is determined from the database on the relationship be

tween the IR Tb and surface rain rate.

New!!

11.4 μm Geo IR1 hour before

11.4 μm Geo IRPresent

Infrared (IR) Data

Microwave Radiometer (MWR) Data

1 hr Moving Vector

GSMaP Data

GSMaPPresent

GSMaP1 hour before

1 hr MWRPresent

Algorithm flow

Predicted GSMaP

Kalman Filter

）（　　 EquationnObservatioxy vkk

State and observation equation used in Kalman filter

）　 （ EquationStatexx wkk 1

： Rain rate at time k 　　　　： Infrared Tb 　　　　　　　 ： Rain rate at time k+1： System noise： Observation noise

1kx

kx

ky

vw

180

230

280

0 5 10 15 20 25 30Rain Rate[mm/ h]

Brig

htne

ss T

empe

ratu

re[T

]

Predicted rain rate

IR Tb 　　

Predicted rain rate

Refinement

Prediction

xx ˆ

xyKxx ˆ

GSMaP 　　　x̂

x

x

Kalman Filter

2

42 K

2

2

v

w

Obs. Noise 　　

System Noise 　　

v

w

y

Correlation between radar and the GSMaP product as Correlation between radar and the GSMaP product as a function of the past microwave satellite overpass a function of the past microwave satellite overpass

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6

Time (hour)

Cor

rela

tion

mv(2000_8)mvk(2000_8)

With Kalman filter

Without Kalman filterMoving vector only

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Effect of Kalman Filter （ Aug. 2000 ）‐ TRMM/TMI only‐

Corre

latio

n

Time （ｈｒ）

GSMaP 　ＶＳ　 Radar rain gauge network in Japan

■ ： with Kalman filter

▲ ： Moving vector only

On the PEHRPP web in Japan

We started to make the evaluation web site using the radar-rain gauge network data around Japan in 2005.

The IDL codes to make the web are all from Dr. Beth Ebert.

A comparison of the GSMaP with CMORPH from the PEHRPP web in Japan

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

7/5/

2005

7/6/

2005

7/7/

2005

7/8/

2005

7/9/

2005

7/10

/200

5

7/11

/200

5

7/12

/200

5

7/13

/200

5

7/14

/200

5

7/15

/200

5

7/16

/200

5

7/17

/200

5

7/18

/200

5

7/19

/200

5

7/20

/200

5

7/21

/200

5

7/22

/200

5

7/23

/200

5

7/24

/200

5

7/25

/200

5

date

Cor

rela

tion

CMORPHGSMaP_MWRGSMaP_MVK

PEHRPP web site in Japan

http://www.radar.aero.osakafu-u.ac.jp/~gsmap/IPWG/dailyval.html

Or you can access this site by clicking the address on the DVD.

Summary Initial results of the global precipitation

map from the MWR and from IR and MWR combined algorithm were introduced and demonstrated.

The details of the GSMaP project are in the DVD I brought.

Acknowledgements

Thanks to Dr. Bob Adler and Kris Kummerow, we could kick off this project.

Thanks to Dr. Beth Ebert and Dr. Phil Arkin, we could make the web site.

Thank you!! 謝謝 !! Danke!! Merci!! ありがとう！！

Global Satellite Mapping of Precipitation projectOrganization of Research Team in FY 2005

Principal Investigator K. Okamoto

Administrative Assistant K. Matsukawa

Ground Radar Observation Group K. Iwanami ( Leader )　 K. Nakagawa ， H. Hanado ， K. KitamuraPrecipitation Physical Model Production Group

　 N. Takahashi ( Leader ), J.Awaka,

　 T. Kozu ， S. Satoh ， Y.

Takayabu ， M.HiroseAlgorithm Developing Group

　 T. Iguchi ( Leader ) ， M. Fujita ， T.

Inoue,

　 K. Aonashi ， S. Shimizu, S.Seto, H.Eito,

　 K.TakahashiSatellite Data Processing and Global Map Production Group

　 T. Ushio ( Leader ) ， S. Shige,

　 H.Hashizume, R. Oki ， M. Kachi,

　 T. Kubota, Y. Iida, H.Sasaki

What, When, Where, and How do we analyze for?

Purpose: To map the global precipitation map with 0.1 degree/1 hour resolution

What: IR: 1hour global IR data from Goddard/DAAC MWR: TMI, AMSR-E, AMSR, and SSM/I×3 When:July 2005 Where: 60 degree in latitude around globe How: By interpolating precipitation between

MWR overpasses using the cloud motion and Kalman filtering inferred from 1 hour IR images.