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: [email protected] 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