Global Satellite Map** of Precipitation (GSMaP) Products in the GPM Era

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Satellite Precipitation Measurement

Abstract

As the Japanese Global Precipitation Measurement (GPM) product, the Global Satellite Map** of Precipitation (GSMaP) has been provided by the Japan Aerospace Exploration Agency (JAXA) to distribute hourly global precipitation map with 0.1° × 0.1° lat/lon grid. Since JAXA started near-real-time processing of the GSMaP on November 2007, there have been various significant improvements to the GSMaP. This paper summarizes GSMaP products and related algorithms in the GPM era and shows validation results in Japan and the United States.

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References

  • Aonashi, K., & Liu, G. (2000). Passive microwave precipitation retrievals using TMI during the Baiu period of 1998. Part I: Algorithm description and validation. Journal of Applied Meteorology, 39, 2024–2037. https://doi.org/10.1175/1520-0450(2000)039<2024:PMPRUT>2.0.CO;2.

    Article  Google Scholar 

  • Aonashi, K., Awaka, J., Hirose, M., Kozu, T., Kubota, T., Liu, G., Shige, S., Kida, S., Seto, S., Takahashi, N., & Takayabu, Y. N. (2009). GSMaP passive, microwave precipitation retrieval algorithm: Algorithm description and validation. Journal of the Meteorological Society of Japan, 87A, 119–136. https://doi.org/10.2151/jmsj.87A.119.

    Article  Google Scholar 

  • Aonashi, K., Ohwada, H., Okamoto, K., Ishimoto, H., & Yamaguchi, M. (2016). Development of the next-generation microwave imager precipitation retrieval algorithm (No. 4). MSJ spring meeting 2016, C154, Tokyo, Japan, May 2016 (in Japanese).

    Google Scholar 

  • Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., & Wood, E. F. (2017). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21, 6201–6217. https://doi.org/10.5194/hess-2017-508.

    Article  Google Scholar 

  • Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., & Yoshida, R. (2016). An introduction to Himawari-8/9— Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan, 94, 151–183. https://doi.org/10.2151/jmsj.2016-009.

    Article  Google Scholar 

  • Chen, M., Shi, W., **e, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., & Janowiak, J. E. (2008). Assessing objective techniques for gauge-based analyses of global daily precipitation. Journal of Geophysical Research, 113, D04110. https://doi.org/10.1029/2007JD009132.

    Article  Google Scholar 

  • Ebert, E. E., Manton, M. J., Arkin, P. A., Allam, R. J., Holpin, G. E., & Gruber, A. J. (1996). Results from the GPCP algorithm Intercomparison programme. Bulletin of the American Meteorological Society, 77, 2875–2887. https://doi.org/10.1175/1520-0477(1996)077<2875:RFTGAI>2.0.CO;2.

    Article  Google Scholar 

  • Furuzawa, F. A., Masunaga, H., & Nakamura, K. (2012) Development of a land surface emissivity algorithm for use by microwave rain retrieval algorithms. In Proceedings of SPIE, Remote Sensing of the Atmosphere, Clouds, and Precipitation IV, 8523, W1-12, Kyoto. https://doi.org/10.1117/12.977237.

  • Harada, Y., Kamahori, H., Kobayashi, C., Endo, H., Kobayashi, S., Ota, Y., Onoda, H., Onogi, K., Miyaoka, K., & Takahashi, K. (2016). The JRA-55 reanalysis: Representation of atmospheric circulation and climate variability. Journal of the Meteorological Society of Japan, 94, 269–302. https://doi.org/10.2151/jmsj.2016-015.

    Article  Google Scholar 

  • Hashizume, H., Kubota, T., Aonashi, K., Shige, S., & Okamoto, K. (2006). Development of over-ocean SSM/I rain retrieval algorithm in the GSMaP project. In Proceedings of IGARSS 2006, 2588-2591, Denver, CO. https://doi.org/10.1109/IGARSS.2006.669.

  • Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., & Iguchi, T. (2014). The Global Precipitation Measurement mission. Bulletin of the American Meteorological Society, 95, 701–722. https://doi.org/10.1175/BAMS-D-13-00164.1.

    Article  Google Scholar 

  • Kachi, M., Kubota, T., Ushio, T., Shige, S., Kida, S., Aonashi, K., & Okamoto, K. (2011). Development and utilization of “JAXA global rainfall watch” system. IEEJ Transactions Fundamentals and Materials, 131, 729–737. (In Japanese with English abstract).

    Article  Google Scholar 

  • Kida, S., Shige, S., Kubota, T., Aonashi, K., & Okamoto, K. (2009). Improvement of rain/no-rain classification methods for microwave radiometer observations over ocean using the 37-GHz emission signature. Journal of the Meteorological Society of Japan, 87A, 165–181. https://doi.org/10.2151/jmsj.87A.165.

    Article  Google Scholar 

  • Kida, S., Shige, S., Manabe, T., L’Ecuyer, T. S., & Liu, G. (2010a). Cloud liquid water path for the rain/no-rain classification method over ocean in the GSMaP algorithm. Transaction JSASS Aerospace Tech. Japan, 8, No. ists27, Pn_19-Pn_23.

    Google Scholar 

  • Kida, S., Shige, S., & Manabe, T. (2010b). Comparison of rain fractions over tropical and sub-tropical ocean obtained from precipitation retrieval algorithms for microwave sounders. Journal of Geophysical Research, 115, D24101. https://doi.org/10.1029/2010JD014279.

    Article  Google Scholar 

  • Kida, S., Kubota, T., Shige, S., & Mega, T. (2017). Development of a rain/no-rain classification method over land for the microwave sounder algorithm. In T. Islam, Y. Hu, A. Kokhanovsky, & J. Wang (Eds.), Remote sensing of aerosols, clouds, and precipitation (pp. 249–265). Amsterdam: Elsevier, ISBN:9780128104378.

    Google Scholar 

  • Kirstetter, P.-E., Hong, Y., Gourley, J. J., Chen, S., Flamig, Z., Zhang, J., Schwaller, M., Petersen, W., & Amitai, E. (2012). Toward a framework for systematic error modeling of spaceborne precipitation radar with NOAA/NSSL ground radar–based National Mosaic QPE. Journal of Hydrometeorology, 13, 1285–1300. https://doi.org/10.1175/JHM-D-11-0139.1.

    Article  Google Scholar 

  • Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., & Takahashi, K. (2015). The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan, 93, 5–48. https://doi.org/10.2151/jmsj.2015-001.

    Article  Google Scholar 

  • Kozu, T., Kawanishi, T., Kuroiwa, H., Kojima, M., Oikawa, K., Kumagai, H., Okamoto, K., Okumura, M., Nakatsuka, H., & Nishikawa, K. (2001). Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. IEEE Transactions on Geoscience and Remote Sensing, 39, 102–116. https://doi.org/10.1109/36.898669.

    Article  Google Scholar 

  • Kozu, T., Iguchi, T., Kubota, T., Yoshida, N., Seto, S., Kwiatkowski, J., & Takayabu, Y. N. (2009). Feasibility of raindrop size distribution parameter estimation with TRMM Precipitation Radar. Journal of the Meteorological Society of Japan, 87A, 53–66. https://doi.org/10.2151/jmsj.87A.53.

    Article  Google Scholar 

  • Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S., Hirose, M., Takayabu, Y. N., Ushio, T., Nakagawa, K., Iwanami, K., Kachi, M., & Okamoto, K. (2007). Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Transactions on Geoscience and Remote Sensing, 45, 2259–2275. https://doi.org/10.1109/TGRS.2007.895337.

    Article  Google Scholar 

  • Kubota, T., Shige, S., Aonashi, K., & Okamoto, K. (2009a). Development of nonuniform beamfilling correction method in rainfall retrievals for passive microwave radiometers over ocean using TRMM observations. Journal of the Meteorological Society of Japan, 87A, 153–164. https://doi.org/10.2151/jmsj.87A.153.

    Article  Google Scholar 

  • Kubota, T., Ushio, T., Shige, S., Kida, S., Kachi, M., & Okamoto, K. (2009b). Verification of high resolution satellite-based rainfall estimates around Japan using gauge-calibrated ground radar dataset. Journal of the Meteorological Society of Japan, 87A, 203–222. https://doi.org/10.2151/jmsj.87A.203.

    Article  Google Scholar 

  • Kubota, T., Shige, S., Kachi, M., & Aonashi, K.. (2011). Development of SSMIS rain retrieval algorithm in the GSMaP project. In Proceedings of 28th ISTS, 2011-n-46.

    Google Scholar 

  • Kubota, T., Liu, G., Tashima, T., & Oki, R. (2018). Development of snowfall estimation method in Global Satellite Map** of Precipitation (GSMaP) product. JpGU meeting 2018, Chiba, Japan, May 2018.

    Google Scholar 

  • Kummerow, C., Barnes, W., Kozu, T., Shiue, J., & Simpson, J. (1998). The tropical rainfall measuring Mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15, 809–817. https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.

    Article  Google Scholar 

  • Liu, G. (1998). A fast and accurate model for microwave radiance calculation. Journal of the Meteorological Society of Japan, 76, 335–343. https://doi.org/10.2151/jmsj1965.76.2_335.

    Article  Google Scholar 

  • Liu, G., & Seo, E.-K. (2013). Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach. Journal of Geophysical Research, 118, 1376–1387. https://doi.org/10.1002/jgrd.50172.

    Article  Google Scholar 

  • Mega, T., & Shige, S. (2016). Improvements of rain/no-rain classification methods for microwave radiometer over coasts by dynamic surface-type classification. Journal of Atmospheric and Oceanic Technology, 33, 1257–1270. https://doi.org/10.1175/JTECH-D-15-0127.1.

    Article  Google Scholar 

  • Mega, T., Ushio, T., Matsuda, T., Kubota, T., Kachi, M., & Oki, R. (2019). Gauge-adjusted Global Satellite Map** of Precipitation (GSMaP_Gauge). IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2870199.

  • Okamoto, K., Iguchi, T., Takahashi, N., Iwanami, K., & Ushio, T. (2005). The global satellite map** of precipitation (GSMaP) project. In Proceedings of IGARSS 2005, Seoul, 3414–3416. https://doi.org/10.1109/IGARSS.2005.1526575.

  • Otsuka, S., Kotsuki, S., & Miyoshi, T. (2016). Nowcasting with data assimilation: A case of global satellite map** of precipitation. Weather and Forecasting, 31, 1409–1416. https://doi.org/10.1175/WAF-D-16-0039.1.

    Article  Google Scholar 

  • Petty, G. W. (1994). Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part I: Theoretical characteristics of normalized polarization and scattering indices. Meteorology and Atmospheric Physics, 54, 79–99. https://doi.org/10.1007/BF01030053.

    Article  Google Scholar 

  • Scofield, R. A., & Kuligowski, R. J. (2003). Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather and Forecasting, 18, 1037–1051. https://doi.org/10.1175/1520-0434(2003)018<1037:SAOOOS>2.0.CO;2.

    Article  Google Scholar 

  • Seto, S., Takahashi, N., & Iguchi, T. (2005). Rain/no-rain classification methods for microwave radiometer observations over land using statistical information for brightness temperatures under no-rain conditions. Journal of Applied Meteorology, 44, 1243–1259. https://doi.org/10.1175/JAM2263.1.

    Article  Google Scholar 

  • Seto, S., Kubota, T., Takahashi, N., Iguchi, T., & Oki, T. (2008). Advanced rain/no-rain classification methods for microwave radiometer observations over land. Journal of Applied Meteorology and Climatology, 47, 3016–3029. https://doi.org/10.1175/2008JAMC1895.1.

    Article  Google Scholar 

  • Seto, S., Kubota, T., & Shige, S. (2016). Production of the rain/no-rain classification database for GPM microwave radiometer observations over land. MSJ spring meeting 2016, D405, Tokyo, Japan, May 2016 (in Japanese).

    Google Scholar 

  • Shige, S., Yamamoto, T., Tsukiyama, T., Kida, S., Ashiwake, H., Kubota, T., Seto, S., Aonashi, K., & Okamoto, K. (2009). The GSMaP precipitation retrieval algorithm for microwave sounders. Part I: Over-ocean algorithm. IEEE Transactions on Geoscience and Remote Sensing, 47, 3084–3097. https://doi.org/10.1109/TGRS.2009.2019954.

    Article  Google Scholar 

  • Shige, S., Kida, S., Ashiwake, H., Kubota, T., & Aonashi, K. (2013). Improvement of TMI rain retrievals in mountainous areas. Journal of Applied Meteorology and Climatology, 52, 242–254. https://doi.org/10.1175/JAMC-D-12-074.1.

    Article  Google Scholar 

  • Shige, S., Yamamoto, M. K., & Taniguchi, A. (2014). Improvement of TMI rain retrieval over the Indian subcontinent. In V. Lakshmi (Ed.), Remote sensing of the terrestrial water cycle (Geophysical Monograph) (Vol. 206, pp. 27–42). AGU. ISBN:9781118872031.

    Google Scholar 

  • Sims, E. M., & Liu, G. (2015). A parameterization of the probability of snow–rain transition. Journal of Hydrometeorology, 16, 1466–1477. https://doi.org/10.1175/JHM-D-14-0211.1.

    Article  Google Scholar 

  • Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., & Wilheit, T. (2017). The Global Precipitation Measurement (GPM) mission for science and society. Bulletin of the American Meteorological Society, 98, 1679–1695. https://doi.org/10.1175/BAMS-D-15-00306.1.

    Article  Google Scholar 

  • Smith, E. A., Lamm, J. E., Adler, R. F., Alishouse, J., Aonashi, K., Barrett, E., Bauer, P., Berg, W., Chang, A., Ferraro, R., Ferriday, J., Goodman, S., Grody, N., Kidd, C., Kniveton, D., Kummerow, C., Liu, G., Marzano, F., Mugnai, A., Olson, W., Petty, G., Shibata, A., Spencer, R., Wentz, F., Wilheit, T., & Zipser, E. (1998). Results of the WetNet PIP-2 project. Journal of the Atmospheric Sciences, 55, 1483–1536. https://doi.org/10.1175/1520-0469(1998)055<1483:ROWPP>2.0.CO;2.

    Article  Google Scholar 

  • Takahashi, N., & Awaka, J. (2005). Introduction of a melting layer model to a rain retrieval algorithm for microwave radiometers. In Proceedings of 25th IGARSS, (pp. 3404–3409). https://doi.org/10.1109/IGARSS.2005.1526573.

  • Takayabu, Y. N. (2008). Observing rainfall regimes using TRMM PR and LIS data. GEWEX Newsletter, 18(2), 9–10. Available at https://www.gewex.org/gewex-content/files_mf/1432208504May2008.pdf, last accessed 16 Oct 2018.

  • Tan, J., Petersen, W. A., & Tokay, A. (2016). A novel approach to identify sources of errors in IMERG for GPM ground validation. Journal of Hydrometeorology, 17, 2477–2491. https://doi.org/10.1175/JHM-D-16-0079.1.

    Article  Google Scholar 

  • Taniguchi, A., Shige, S., Yamamoto, M. K., Mega, T., Kida, S., Kubota, T., Kachi, M., Ushio, T., & Aonashi, K. (2013). Improvement of high-resolution satellite rainfall product for Typhoon Morakot (2009) over Taiwan. Journal of Hydrometeorology, 14, 1859–1871. https://doi.org/10.1175/JHM-D-13-047.1.

    Article  Google Scholar 

  • Ushio, T., Kubota, T., Shige, S., Okamoto, K., Aonashi, K., Inoue, T., Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T., & Kawasaki, Z. (2009). A Kalman filter approach to the Global Satellite Map** of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan, 87A, 137–151. https://doi.org/10.2151/jmsj.87A.137.

    Article  Google Scholar 

  • Vicente, G. A., Scofield, R. A., & Menzel, W. P. (1998). The operational GOES infrared rainfall estimation technique. Bulletin of the American Meteorological Society, 79, 1883–1898. https://doi.org/10.1175/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2.

    Article  Google Scholar 

  • Vicente, J., Davenport, C., & Scofield, R. A. (2002). The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution. International Journal of Remote Sensing, 23, 221–230. https://doi.org/10.1080/01431160010006935.

    Article  Google Scholar 

  • Yamaji, M., Kubota, T., Hamada, A., Takayabu, Y. N., Kachi, M., & Aonashi, K. (2017) Drop size distribution observed by Dual-frequency precipitation radar onboard Global Precipitation Measurement core satellite. In Proceedings of ISTS 2017, 2017-n-17.

    Google Scholar 

  • Yamamoto, M. K., & Shige, S. (2015). Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers. Atmospheric Research, 163, 36–47. https://doi.org/10.1016/j.atmosres.2014.07.024.

    Article  Google Scholar 

  • Yamamoto, M. K., Shige, S., Yu, C.-K., & Cheng, L.-W. (2017). Further improvement of the heavy orographic rainfall retrievals in the GSMaP algorithm for microwave radiometers. Journal of Applied Meteorology and Climatology, 56, 2607–2619. https://doi.org/10.1175/JAMC-D-16-0332.1.

    Article  Google Scholar 

  • Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A., Van Cooten, S., Kelleher, K., Kitzmiller, D., Ding, F., Seo, D., Wells, E., & Dempsey, C. (2011). National mosaic and multi-sensor QPE (NMQ) system: Description, results, and future plans. Bulletin of the American Meteorological Society, 92, 1321–1338. https://doi.org/10.1175/2011BAMS-D-11-00047.1.

    Article  Google Scholar 

  • Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., & Kitzmiller, D. (2016). Multi-radar multi-sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bulletin of the American Meteorological Society, 97, 621–638. https://doi.org/10.1175/BAMS-D-14-00174.1.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank members of the JAXA PMM Science Team for their valuable contributions. The authors would like to thank Mrs. T. Higashiuwatoko and S. Ohwada of RESTEC for helpful computing assistance. The dataset of the DBNet was provided by the JMA. The dataset of the Level-3 MRMS was provided by the NASA Ground Validation team. The dataset of the Hydro-Estimator was provided by the NOAA/NESDIS.

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Correspondence to Takuji Kubota .

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Kubota, T. et al. (2020). Global Satellite Map** of Precipitation (GSMaP) Products in the GPM Era. In: Levizzani, V., Kidd, C., Kirschbaum, D.B., Kummerow, C.D., Nakamura, K., Turk, F.J. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-24568-9_20

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