Improvement of Rainfall Intensity from COMS using GPM GPROF products Ki-Hong PARK, Geun-Hyeok RYU, Yun-Bok LEE, and Jae-Dong JANG

National Meteorological Satellite Center, KMA, Republic of Korea E-mail: parkkihong@korea.kr

Background Results

8th IPWG and 5th IWSSM Joint Workshop, Bologna, Italy, 3-7 October, 2016

Fig. 1. The GPM Mission concept

Data and Method

GPM Core satellite and constellation satellites data

2A-GPROF-GMI & 2A-GPROF-constellation (V04A, V03X)

Surface Precipitation (mm/hr)

Period : 2015. 7. 9. ~ 10. (Typhoon)

2016. 7. 1. ~ 15. (Jangma)

Area : 0~60°N, 100~155°E (E-Asia)

Satellite Sensor Organization

GPM GMI NASA/JAXA

F16 SSMIS

U.S. DMSP F17 SSMIS

F18 SSMIS

F19 SSMIS

GCOM-W1 AMSR2 JAXA

MetOp-A MHS EUMETSAT

MetOp-B MHS

Megha-Tropiques SAPHIR CNES/ISRO

NOAA 18 MHS NOAA

NOAA 19 MHS

Suomi NPP ATMS NASA/NOAA

GPM GPROF data (NASA)

COMS IR10.8 Brightness Temperature (TB)

Resolution : 4 km / 15 min.

COMS Cloud mask (CLD)

Resolution : 4 km / 15 min.

Table 1. List of GPM constellation satellites

COMS data (KMA)

The look-up table is obtained relationship

between COMS IR TB and GPROF rainrate for

each satellite data.

There is the difference between imager and

sounding sensors. Especially, sounding sensors

show relatively lower rainrates at same

temperature.

It represents a different result by the each of

the satellites of the sensor characteristics and

the observed precipitation system.

Look-up Table

This study was supported by “The Development of Meteorological Data Utilization and

Operation Supportive Technology" of NMSC/KMA.

KMA/NMSC, 2016: COMS Meteorological Data Processing System Rainfall Intensity ATBD.

Kummerow C., Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, R. Ferraro, G. Petty, D-B. Shin,

and T. T. Wilheit, 2001 : The evolution of the Goddard profiling algorithm (GPROF) for rainfall

estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801–1820.

NASA, 2016: Precipitation Processing System Global Precipitation Measurement File Specification for

GPM Products. 817-923p.

Acknowledgement and References

P1.30

The core satellite of Global Precipitation Measurement (GPM) project, the successor to

the Tropical Rainfall Measuring Mission (TRMM), had successfully launched on February

28th 2014, and it is released with the newly produced GPM data together with the

international constellation of research and operational satellites.

In order to improve rainfall intensity (RI) of Communication, Ocean and Meteorological

Satellite (COMS), the reference Passive Microwave (PMW) Precipitation products are

changed from SSMIS rainfall itself to GPM Goddard Profiling Algorithm (GPROF) rainfall.

And this algorithm uses simple relationship between precipitation from PMW and COMS IR

brightness temperature (TB). In order to make a Look-up table between COMS IR and

GPROF rainrates, the probability matching method is applied.

COMS RI are varied with GPROF product, for example, when GMI precipitation is used,

COMS RI seems strong than other sensors, and COMS IR from sounding sensors such as

MetOp, NOAA shows relative low rainrate than imager sensors.

Fig. 2. Structure of COMS and Sample images of 5 channels of the COMS meteorological imager

Fig. 3. The flowchart of retrieval algorithm

KMA operational Radar-AWS Rainrate data (QPE)

250 km radius composite rainrate (mm/hr)

1.5 km CAPPI

Resolution : 1 km / 10 min.

Using 4 km remapping data (00, 30 min.)

Validation data

Fig. 4. Radar-AWS Rainrate

COMS IR TB & GPROF footprint matching method

Data Collocation

≥ 0.1 mm/hr

4 km (COMS)

CLD 100%

15 km (GPROF)

7.5 km

Rainrates retrieved from each GPROF data

Rainrates from sounding sensors shows relative low rainrate than imager sensors.

Fig. 6. Look-up table between COMS IR TB and GPROF rainrate for each satellite data at 00:00 UTC July 9 2015

Validation

Fig. 7. Rainrates retrieved over East Asia region on July 9 2015 (00:00 UTC)

The instantaneous rainrates of COMS IR TB and

COMS Cloud mask with small spatial resolution are

averaged for area in 7.5 km radius centered on each

GPROF footprint.

Inverse Distance Weighted (IDW) interpolation is

applied.

(A) This study (B) Oper. COMS RI

Oper. COMS RI

(D) GSMaP_NOW

Fig. 8. Comparisons of the retrieved rainrate (A), Oper. COMS RI (B), IMERG_early (c), and GSMaP_NOW (D) for July 2 2016 (03:30 UTC)

Fig. 9. Time series of validation results of the COMS RI and retrieved rainrate for July 12 2016

This study (A) shows wide rain area, compared with operational COMS RI (B). Also, Rain area

of (A) is similar to IMERG (C) and GSMaP (D).

However, rainrate of (A) is lower than that of (C) and (D). and underestimate in mid-latitude.

Fig. 10. Scatter plots of the COMS RI and retrieved rainrate for July 12 2016

This study

The statistic results of retrieved rainrate is better than that of operational COMS RI.

The Retrieved rainrate tends to estimate rain rates higher than operational COMS RI.

In order to adjust underestimate in mid-latitude, the algorithm utilizes an additional

adjustment procedure for weighting function of latitude.

(C) IMERG_early

Fig. 5. The diagram for the collocated COMS TB and GPROF pixels

Oper. COMS F16 F17 F18

F19 GCOM-W1 GPM MetOp-A

MetOp-B MT1 NOAA 18 NOAA 19