Skip to main content

The Global Precipitation Measurement (GPM) Mission

  • Chapter
  • First Online:
Satellite Precipitation Measurement

Abstract

Water is a fundamental component of the Earth’s water and energy cycles and is essential to our economic and social wellbeing. Since precipitation is the primary input into these cycles and affects the availability of water resources over land areas, the measurement of precipitation across the globe is of critical importance. The Global Precipitation Measurement (GPM) Core Observatory (CO), a joint US and Japan mission launched in 2014, extends and enhances the legacy of the Tropical Rainfall Measuring Mission (TRMM). The GPM-CO carries high-quality passive and active microwave instruments designed to observe the structure and intensity of falling rainfall and snowfall. The high standard of accuracy of these sensors also provides a reference standard for other precipitation sensors in the GPM constellation which comprises of a suite of satellites from international organisations, enabling global sampling from passive microwave (PMW) sensors at a 3-hourly interval better than 90% of the time. Together with geostationary (GEO) infrared (IR) observations, these data enable global 30-min, 0.1° × 0.1° precipitation products to be computed and posted in near real-time. Precipitation products are made available to, and utilized by, user communities ranging from numerical weather prediction (NWP) organisations to water resources services.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 

  • Berg, W., Bilanow, S., Chen, R. Y., Datta, S., Draper, D., Ebrahimi, H., Farrar, S., Jones, W. L., Kroodsma, R., McKague, D., Payne, V., Wang, J., Wilheit, T., & Yang, J. X. (2016). Intercalibration of the GPM microwave radiometer constellation. Journal of Atmospheric and Oceanic Technology, 33, 2639–2654. https://doi.org/10.1175/JTECH-D-16-0100.1.

    Article  Google Scholar 

  • Blackwell, W. J., Braun, S., Bennartz, R., Velden, C., DeMaria, M., Atlas, R., Dunion, J., Marks, F., Rogers, R., Annane, B., & Leslie, R. V. (2018). An overview of the TROPICS NASA Earth venture mission. Quarterly Journal of the Royal Meteorological Society, 114(S1), 16–26. https://doi.org/10.1002/qj.3290.

    Article  Google Scholar 

  • Bringi, V. N., Thurai, M., Tolstoy, L., & Petersen, W. A. (2015). Estimation of spatial correlation of rain drop size distribution parameters and rain rate using NASA's S-band polarimetric radar and 2D-video disdrometer network: Two case studies from MC3E. Journal of Hydrometeorology, 16, 1207–1221. https://doi.org/10.1175/JHM-D-14-0204.1.

    Article  Google Scholar 

  • Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W., Tanelli, S., Sy, O. O., Durden, S. L., & Poellot, M. R. (2018). Evaluation of triple-frequency radar retrieval of snowfall properties using coincident airborne in-situ observations during OLYMPEX. Geophysical Research Letters, 45, 5752–5760. https://doi.org/10.1029/2018GL077997.

    Article  Google Scholar 

  • Colle, B. A., Naeger, A. R., & Molthan, A. (2017). Structure and evolution of a warm frontal precipitation band during the GPM Cold Season Precipitation Experiment (GCPEx). Monthly Weather Review, 145, 473–493. https://doi.org/10.1175/MWR-D-16-0072.1.

    Article  Google Scholar 

  • Draper, D. W., Newell, D. A., Teusch, D. A., & Yoho, P. K. (2013). Global precipitation measurement microwave imager hot load calibration. IEEE Transactions on Geoscience and Remote Sensing, 51, 4731–4742. https://doi.org/10.1109/TGRS.2013.2239300.

    Article  Google Scholar 

  • Draper, D. W., Newell, D. A., McKague, D., & Piepmeier, J. (2015a). Assessing calibration stability using the Global Precipitation Measurement (GPM) Microwave Imager (GMI) noise diodes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 4239–4247. https://doi.org/10.1109/JSTARS.2015.2406661.

    Article  Google Scholar 

  • Draper, D. W., Newell, D. A., Wentz, F. J., Krimchansky, S., & Skofronick-Jackson, G. (2015b). The Global Precipitation Measurement (GPM) Microwave Imager (GMI): Instrument overview and early on-orbit performance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3452–3462. https://doi.org/10.1109/JSTARS.2015.2403303.

    Article  Google Scholar 

  • Funk, C., & Verdin, J. (2010). Real-time decision support systems: The famine early warning system network. In M. Gebremichael & F. Hossain (Eds.), Satellite rainfall applications for surface hydrology (pp. 3–22). Dordrecht: Springer, ISBN:978-90-481-2914-0.

    Google Scholar 

  • Grecu, M., Olson, W. S., Munchak, S. J., Ringerud, S., Liao, L., Haddad, Z. S., Kelley, B. L., & McLaughlin, S. F. (2016). The GPM combined algorithm. Journal of Atmospheric and Oceanic Technology, 33, 2225–2245. https://doi.org/10.1175/JTECH-D-16-0019.1.

    Article  Google Scholar 

  • Hamada, A., & Takayabu, Y. N. (2016). Improvements in detection of light precipitation with the Global Precipitation Measurement dual-frequency precipitation radar (GPM/DPR). Journal of Atmospheric and Oceanic Technology, 33, 653–667. https://doi.org/10.1175/JTECH-D-15-0097.1.

    Article  Google Scholar 

  • Hou, A. Y., Kakar, R. K., Neeck, S. A., Azarbarzin, 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 

  • Houze, R. A., Jr., McMurdie, L. A., Petersen, W. A., Schwaller, M. R., Baccus, W., Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak, D. R., Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P., Zagrodnik, J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., Barnes, H. C., & Chandrasekar, V. (2017). The Olympic Mountains experiment (OLYMPEX). Bulletin of the American Meteorological Society, 98, 2167–2188. https://doi.org/10.1175/BAMS-D-16-0182.1.

    Article  Google Scholar 

  • Huang, G., Bringi, V. N., Moisseev, D., Petersen, W. A., Bliven, L., & Hudak, D. (2015). Use of 2D-video disdrometer to derive mean density-size and Ze-SR relations: Four snow cases from the light precipitation validation experiment. Atmospheric Research, 153, 34–48. https://doi.org/10.1016/j.atmosres.2014.07.013.

    Article  Google Scholar 

  • Huffman, G. J., Ferraro, R. R., Kidd, C., Levizzani, V., & Turk, F. J. (2016, April 11–14). Requirements for a robust precipitation constellation. In Proceedings of MicroRad 2016 (pp. 37–41). Espoo: IEEE. https://doi.org/10.1109/MICRORAD.2016.7530500.

  • Iguchi, T., Matsui, T., Tao, W.-K., Khain, A. P., Phillips, V. T. J., Kidd, C., L’Ecuyer, T., Braun, S. A., & Hou, A. Y. (2014). WRF–SBM simulations of melting-layer structure in mixed-phase: Precipitation events observed during LPVEx. Journal of Applied Meteorology and Climatology, 53, 2710–2731. https://doi.org/10.1175/JAMC-D-13-0334.1.

    Article  Google Scholar 

  • Iguchi, T., Kawamoto, N., & Oki, R. (2018). Detection of intense ice precipitation with GPM/DPR. Journal of Atmospheric and Oceanic Technology, 35, 491–502. https://doi.org/10.1175/JTECH-D-17-0120.1.

    Article  Google Scholar 

  • Ikuta, Y. (2016). Data assimilation using GPM/DPR at JMA. CAS/JSC WGNE. Research Activities in Atmospheric and Oceanic Modeling, 1.11–1.13.

    Google Scholar 

  • Jensen, M. P., Petersen, W. A., Bansemer, A., Bharadwaj, N., Carey, L. D., Cecil, D. J., Collis, S. M., Del Genio, A. D., Dolan, B., Gerlach, J., Giangrande, S. E., Heymsfield, A., Heymsfield, G., Kollias, P., Lang, T. J., Nesbitt, S. W., Neumann, A., Poellot, M., Rutledge, S. A., Schwaller, M., Tokay, A., Williams, C. R., Wolff, D. B., Xie, S., & Zipser, E. J. (2016). The mid-latitude continental convective clouds experiment (MC3E). Bulletin of the American Meteorological Society, 97, 1667–1686. https://doi.org/10.1175/BAMS-D-14-00228.1.

    Article  Google Scholar 

  • Kidd, C., Tan, J., Kirstetter, P., & Petersen, W. (2018). Validation of the version 05 precipitation products from the GPM core observatory and constellation satellite sensors. Quarterly Journal of the Royal Meteorological Society, 144(S1), 313–328. https://doi.org/10.1002/qj.3175.

    Article  Google Scholar 

  • Kim, M. J., Jin, J., McCarty, W., Todling, R., Gelaro, R., & Gu, W. (2016). All-sky microwave radiance data assimilation in NASA GEOS-5 system: developments, impacts, and future plans. In 20th Conference on integrated observing assimilation systems for the atmosphere, oceans, and landsurface, AMS 2016, New Orleans, LA. Available at https://ams.confex.com/ams/. Last accessed 16 Dec 2018.

  • Kirschbaum, D., & Stanley, T. (2018). Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness. Earth’s Future, 6, 505–523. https://doi.org/10.1002/2017EF000715.

    Article  Google Scholar 

  • Kirschbaum, D. B., Huffman, G. J., Adler, R. F., Braun, S., Garrett, K., Jones, E., McNally, A., Skofronick-Jackson, G., Stocker, E. F., Wu, H., & Zaitchik, B. F. (2017). NASA’s remotely sensed precipitation: A reservoir for applications users. Bulletin of the American Meteorological Society, 98, 1169–1184. https://doi.org/10.1175/BAMS-D-15-00296.1.

    Article  Google Scholar 

  • Kroodsma, R. A., McKague, D. S., & Ruf, C. S. (2017). Vicarious cold calibration for conical scanning microwave imagers. IEEE Transactions on Geoscience and Remote Sensing, 55, 816–827. https://doi.org/10.1109/TGRS.2016.2615552.

    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 

  • Kucera, P. A., Ebert, E. E., Turk, F. J., Levizzani, V., Kirschbaum, D., Tapiador, F. J., Loew, A., & Borsche, M. (2013). Precipitation from space: Advancing Earth system science. Bulletin of the American Meteorological Society, 94, 365–375. https://doi.org/10.1175/BAMS-D-11-00171.1.

    Article  Google Scholar 

  • Kummerow, C. D., 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 

  • Kummerow, C. D., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker, E., Adler, R. F., Hou, A., Kakar, R., Wentz, F., Ashcroft, P., Kozu, T., Hong, Y., Okamoto, K., Iguchi, T., Kuroiwa, H., Im, E., Haddad, Z., Huffman, G., Ferrier, B., Olson, W. S., Zipser, E., Smith, E. A., Wilheit, T. T., North, G., Krishnamurti, T., & Nakamura, K. (2000). The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. Journal of Applied Meteorology, 39, 1965–1982. https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    Article  Google Scholar 

  • Kummerow, C. D., Hong, Y., Olson, W. S., Yang, S., Adler, R. F., McCollum, J., Ferraro, R., Petty, G., Shin, D.-B., & Wilheit, T. T. (2001). The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. Journal of Applied Meteorology, 40, 1801–1820. https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    Article  Google Scholar 

  • Kummerow, C., Randel, D. L., Kulie, M., Wang, N.-Y., Ferraro, R., Munchak, S. J., & Petkovic, V. (2015). The evolution of the Goddard profiling algorithm to a fully parametric scheme. Journal of Atmospheric and Oceanic Technology, 32, 2265–2280. https://doi.org/10.1175/JTECH-D-15-0039.1.

    Article  Google Scholar 

  • Levizzani, V., Kidd, C., Aonashi, K., Bennartz, R., Ferraro, R., Huffman, G., Roca, R., Joseph, T. F., & Wang, N.-Y. (2018). The activities of the international precipitation working group. Quarterly Journal of the Royal Meteorological Society, 144, 3–15. https://doi.org/10.1002/qj.3214.

    Article  Google Scholar 

  • Liao, L., Meneghini, R., & Tokay, A. (2014). Uncertainties of GPM DPR rain estimates caused by DSD parameterizations. Journal of Applied Meteorology and Climatology, 53, 2524–2537. https://doi.org/10.1175/JAMC-D-14-0003.1.

    Article  Google Scholar 

  • Maggioni, V., & Massari, C. (2018). On the performance of satellite precipitation products in riverine flood modeling: A review. Journal of Hydrology, 558, 214–224, ISSN 0022-1694. https://doi.org/10.1016/j.jhydrol.2018.01.039.

    Article  Google Scholar 

  • Maidment, R. I., Allan, R. P., & Black, E. (2015). Recent observed and simulated changes in precipitation over Africa. Geophysical Research Letters, 42, 8155–8164. https://doi.org/10.1002/2015GL065765.

    Article  Google Scholar 

  • Masaki, T., Kubota, T., Oki, R., Furukawa, K., Kojima, M., Miura, T., Iguchi, T., Hanado, H., Kai, H., Yoshida, N., & Higashiuwatoko, T. (2015). Current status of GPM/DPR level 1 algorithm development and DPR calibration. In Proceedings IEEE International Conference Geoscience Remote Sensing Symposium 2015, Milan, pp. 2615–2618. https://doi.org/10.1109/IGARSS.2015.7326348.

  • Moore, S. M., Azman, A., Zaitchik, B. F., Mintz, E. D., Brunkard, J., Legros, D., Hill, A., Mckay, H., Luquero, F. J., Olson, D., & Lessler, J. (2017). El Niño and the shifting geography of cholera in Africa. PNAS, 114(17), 4436–4441. https://doi.org/10.1073/pnas.1617218114.

    Article  Google Scholar 

  • NAS. (2018). Thriving on our changing planet: A decadal strategy for Earth observation from space. Washington, DC: The National Academies Press. https://doi.org/10.17226/24938.

    Book  Google Scholar 

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

  • Okamoto, K., Aonashi, K., Kubota, T., & Tashima, T. (2016). Experimental assimilation of the GPM-Core DPR reflectivity profiles for Typhoon Halong. Monthly Weather Review, 144, 2307–2326. https://doi.org/10.1175/MWR-D-15-0399.1.

    Article  Google Scholar 

  • Panegrossi, G., Rysman, J.-F., Casella, D., Marra, A. C., Sanò, P., & Kulie, M. S. (2015). CloudSat-based assessment of GPM microwave imager snowfall observation capabilities. Remote Sensing, 9, 1263. https://doi.org/10.3390/rs9121263.

    Article  Google Scholar 

  • Petersen, W. A., Houze, R. A., McMurdie, L., Zagrodnik, J., Tanelli, S., Lundquist, J., & Wurman, J. (2016). The Olympic Mountains Experiment (OLYMPEX): From ocean to summit. Meteorological Technology International, 2016, 22–26.

    Google Scholar 

  • Seto, S., Iguchi, T., Shimozuma, T., & Hayashi, S. (2015). NUBF correction methods for the GPM/DPR level-2 algorithms. International geoscience and remote sensing symposium, IEEE, Milan, pp. 2612–2614. https://doi.org/10.1109/IGARSS.2015.7326347.

  • Shepherd, J. M., Burian, S., Lui, C., & Bernardes, S. (2016). Satellite precipitation metrics to study the Energy-Water-Food Nexus within the backdrop of an urbanized globe. Earthzine. Available at https://earthzine.org/satellite-precipitation-metrics-to-study-the-energy-water-food-nexus-within-the-backdrop-of-an-urbanized-globe/. Last accessed 7 Dec 2018.

  • Shige, S., Takayabu, Y. N., Tao, W.-K., & Johnson, D. E. (2004). Spectral retrieval of latent heating profiles from TRMM PR data. Part I: Development of a model-based algorithm. Journal of Applied Meteorology, 43, 1095–1113. https://doi.org/10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2.

    Article  Google Scholar 

  • Shige, S., Takayabu, Y. N., Tao, W.-K., & Shie, C.-L. (2007). Spectral retrieval of latent heating profiles from TRMM PR data. Part II: Algorithm improvement and heating estimates over tropical ocean regions. Journal of Applied Meteorology and Climatology, 46, 1098–1124. https://doi.org/10.1175/JAM2510.1.

    Article  Google Scholar 

  • Shige, S., Takayabu, Y. N., & Tao, W.-K. (2008). Spectral retrieval of latent heating profiles from TRMM PR data. Part III: Estimating apparent moisture sink profiles over tropical oceans. Journal of Applied Meteorology and Climatology, 47, 620–640. https://doi.org/10.1175/2007JAMC1738.1.

    Article  Google Scholar 

  • Shige, S., Takayabu, Y. N., Kida, S., Tao, W.-K., Zeng, X., Yokoyama, C., & L'Ecuyer, T. (2009). Spectral retrieval of latent heating profiles from TRMM PR data. Part IV: Comparisons of lookup tables from two- and three-dimensional cloud-resolving model simulations. Journal of Climate, 22, 5577–5594. https://doi.org/10.1175/2009JCLI2919.1.

    Article  Google Scholar 

  • Simpson, J., Adler, R. F., & North, G. R. (1988). A proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bulletin of the American Meteorological Society, 69, 278–295. https://doi.org/10.1175/1520-0477(1988)069<0278:APTRMM>2.0.CO;2.

    Article  Google Scholar 

  • Skofronick-Jackson, G., Hudak, D., Petersen, W. A., Nesbitt, S. W., Chandrasekar, V., Durden, S., Gleicher, K. J., Huang, G.-J., Joe, P., Kollias, P., Reed, K. A., Schwaller, M., Stewart, R., Tanelli, S., Tokay, A., Wang, J. R., & Wolde, M. (2015). Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For measurement sake let it snow. Bulletin of the American Meteorological Society, 96, 1719–1741. https://doi.org/10.1175/BAMS-D-13-00262.1.

    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 

  • Skofronick-Jackson, G., Kirschbaum, D., Petersen, W. A., Huffman, G. J., Kidd, C., Stocker, E. F., & Kakar, R. (2018). The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: Reviewing four years of advanced rain and snow observations. Quarterly Journal of the Royal Meteorological Society, 144(S1), 27–48. https://doi.org/10.1002/qj.3313.

    Article  Google Scholar 

  • Tan, B.-Z., Petersen, W. A., Kirstetter, P., & Tian, Y. (2017a). Performance of IMERG as a function of spatiotemporal scale. Journal of Hydrometeorology, 18, 307–319. https://doi.org/10.1175/JHM-D-16-0174.1.

    Article  Google Scholar 

  • Tan, B.-Z., Petersen, W. A., Kirchengast, G., Goodrich, D. C., & Wolff, D. B. (2017b). Evaluation of global precipitation measurement rainfall estimates against three dense gauge networks. Journal of Hydrometeorology, 19, 517–532. https://doi.org/10.1175/JHM-D-17-0174.1.

    Article  Google Scholar 

  • Tanner, A. B., Wilson, W. J., Lambrigsten, B. H., Dinardo, S. J., Brown, S. T., Kangaslahti, P. P., Gaier, T. C., Ruf, C. S., Gross, S. M., Lim, B. H., Musko, S. B., Rogacki, S., & Piepmeier, J. R. (2007). Initial results of the geostationary synthetic thinned array radiometer. IEEE Transactions on Geoscience and Remote Sensing, 45. https://doi.org/10.1109/TGRS.2007.894060.

  • Tao, W.-K., Lang, S., Olson, W. S., Yang, S., Meneghini, R., Simpson, J., Kummerow, C., Smith, E. A., & Halverson, J. (2001). Retrieved vertical profiles of latent heating release using TRMM rainfall products for February 1998. Journal of Applied Meteorology, 40, 957–982. https://doi.org/10.1175/1520-0450(2001)040<0957:RVPOLH>2.0.CO;2.

    Article  Google Scholar 

  • Tao, W.-K., Smith, E. A., Adler, R. F., Haddad, Z. S., Hou, A. Y., Iguchi, T., Kakar, R., Krishnamurti, T. N., Kummerow, C. D., Lang, S., Meneghini, R., Nakamura, K., Nakazawa, T., Okamoto, K., Olson, W. S., Satoh, S., Shige, S., Simpson, J., Takayabu, Y., Tripoli, G. J., & Yang, S. (2006). Retrieval of latent heating from TRMM measurements. Bulletin of the American Meteorological Society, 87, 1555–1572. https://doi.org/10.1175/BAMS-87-11-1555.

    Article  Google Scholar 

  • Tao, W.-K., Lang, S., Zeng, X., Shige, S., & Takayabu, Y. (2010). Relating convective and stratiform rain to latent heating. Journal of Climate, 23, 1874–1893. https://doi.org/10.1175/2009JCLI3278.1.

    Article  Google Scholar 

  • Tao, W.-K., Wu, D., Matsui, T., Peters-Lidard, C., Lang, S., Hou, A., Reinecker, M., & Petersen, W. A. (2013). The diurnal variation of precipitation during MC3E: A modeling study. Journal of Geophysical Research, 118, 7199–7218. https://doi.org/10.1002/jgrd.50410/asset/jgrd50410.

    Article  Google Scholar 

  • Tao, W.-K., Takayabu, Y. N, Lang, S., Olson, W., Shige, S., Hou, A, Jiang, X., Lau, W., Krishnamurti, T., Waliser, D., Zhang, C., Johnson, R., Houze, R., Ciesielski, P., Grecu, M., Hagos, S., Kakar, R., Nakamura, N., Braun, S., & Bhardwaj, A. (2016). TRMM latent heating retrieval and comparison with field campaigns and large-scale analyses, American meteorological society meteorological monographs – Multi-scale convection-coupled systems in the tropics, Chapter 2. https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0013.1.

  • Thurai, M., Gatlin, P. N., Bringi, V. N., Petersen, W., Kennedy, P., Notaros, B., & Carey, L. D. (2017). Towards completing the rain drop size spectrum: Case studies involving 2D-video disdrometer, droplet spectrometer, and polarimetric radar measurements. Journal of Applied Meteorology and Climatology, 56, 877–896. https://doi.org/10.1175/JAMC-D-16-0304.1.

    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 Mapping 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 

  • von Lerber, A., Moisseev, D., Marks, D. A., Petersen, W. A., Harri, A., & Chandrasekar, V. (2018). Validation of GMI snowfall observations by using a combination of weather radar and surface measurements. Journal of Applied Meteorology and Climatology, 57, 797–820. https://doi.org/10.1175/JAMC-D-17-0176.1.

    Article  Google Scholar 

  • WCRP. (2019). WCRP grand challenges. Available at https://www.wcrp-climate.org/grand-challenges/grand-challenges-overview. Last accessed 3 Dec 2018.

  • Wentz, F. J., & Draper, D. (2016). On-orbit absolute calibration of the global precipitation measurement microwave imager. Journal of Atmospheric and Oceanic Technology, 33, 1393–1412. https://doi.org/10.1175/JTECH-D-15-0212.1.

    Article  Google Scholar 

  • Williams, C. R., Bringi, V. N., Carey, L. D., Chandrasekar, V., Gatlin, P. N., Haddad, Z. S., Meneghini, R., Munchak, S. J., Nesbitt, S. W., Petersen, W. A., Tanelli, S., Tokay, A., Wilson, A., & Wolff, D. B. (2014). Describing the shape of raindrop size distributions using uncorrelated raindrop mass spectrum parameters. Journal of Applied Meteorology and Climatology, 53, 1282–1296. https://doi.org/10.1175/JAMC-D-13-076.1.

    Article  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 

  • You, Y., Wang, N.-Y., Ferraro, R., & Rudlosky, S. (2016). Quantifying the snowfall detection performance of the global precipitation measurement (GPM) microwave imager channels over land. Journal of Hydrometeorology, 17, 1101–1117. https://doi.org/10.1175/JHM-D-16-0190.1.

    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. J., 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. C., Tang, L., Grams, H., Wang, Y. D., 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 

Download references

Acknowledgments

The authors are grateful to Prof. Kenji Nakamura and the late Dr. Arthur Hou for their dedications as the GPM project scientists who oversaw the GPM mission development. Dr. Ramesh Kakar, and Dr. Riko Oki are acknowledged as the program scientists who have led the mission. In addition, dozens of scientists in the US, Japan and other countries have taken part in the key activities summarised in this article that make GPM the success that it is today.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher Kidd .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kidd, C. et al. (2020). The Global Precipitation Measurement (GPM) Mission. 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_1

Download citation

Publish with us

Policies and ethics