Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Shigenori Otsuka RIKEN Advanced Institute for Computational Science, Kobe, Japan

Search for other papers by Shigenori Otsuka in
Current site
Google Scholar
PubMed
Close
,
Shunji Kotsuki RIKEN Advanced Institute for Computational Science, Kobe, Japan

Search for other papers by Shunji Kotsuki in
Current site
Google Scholar
PubMed
Close
, and
Takemasa Miyoshi RIKEN Advanced Institute for Computational Science, Kobe, Japan, and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland, and Japan Agency for Marine–Earth Science and Technology, Yokohama, Japan

Search for other papers by Takemasa Miyoshi in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

Denotes Open Access content.

Corresponding author address: Shigenori Otsuka, RIKEN Advanced Institute for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan. E-mail: shigenori.otsuka@riken.jp

Abstract

Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

Denotes Open Access content.

Corresponding author address: Shigenori Otsuka, RIKEN Advanced Institute for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan. E-mail: shigenori.otsuka@riken.jp
Save
  • Bowler, N. E. H., Pierce C. E. , and Seed A. , 2004: Development of a precipitation nowcasting algorithm based upon optical flow techniques. J. Hydrol., 288, 7491, doi:10.1016/j.jhydrol.2003.11.011.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E. H., Pierce C. E. , and Seed A. , 2006: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quart. J. Roy. Meteor. Soc., 132, 21272155, doi:10.1256/qj.04.100.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., Milleer M. , and Palmer T. N. , 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 28872908, doi:10.1002/qj.49712556006.

    • Search Google Scholar
    • Export Citation
  • Dixon, M., and Wiener G. , 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Hunt, B. R., Kostelich E. J. , and Szunyogh I. , 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112126, doi:10.1016/j.physd.2006.11.008.

    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kubota, T., and Coauthors, 2007: Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans. Geosci. Remote Sens., 45, 22592275, doi:10.1109/TGRS.2007.895337.

    • Search Google Scholar
    • Export Citation
  • Laroche, S., and Zawadzki I. , 1994: A variational analysis method for retrieval of three-dimensional wind field from single-Doppler radar data. J. Atmos. Sci., 51, 26642682, doi:10.1175/1520-0469(1994)051<2664:AVAMFR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, L., Schmid W. , and Joss J. , 1995: Nowcasting of motion and growth of precipitation with radar over a complex orography. J. Appl. Meteor., 34, 12861300, doi:10.1175/1520-0450(1995)034<1286:NOMAGO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Liu, X.-D., Osher S. , and Chan T. , 1994: Weighted essentially non-oscillatory schemes. J. Comput. Phys., 115, 200212, doi:10.1006/jcph.1994.1187.

    • Search Google Scholar
    • Export Citation
  • Miyoshi, T., 2005: Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, MD, 197 pp. [Available online at http://drum.lib.umd.edu/handle/1903/3046.]

  • Miyoshi, T., and Yamane S. , 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 38413861, doi:10.1175/2007MWR1873.1.

    • Search Google Scholar
    • Export Citation
  • Miyoshi, T., Yamane S. , and Enomoto T. , 2007: Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF). SOLA, 3, 8992, doi:10.2151/sola.2007-023.

    • Search Google Scholar
    • Export Citation
  • Otsuka, S., and Coauthors, 2016: Precipitation nowcasting with three-dimensional space–time extrapolation of dense and frequent phased-array weather radar observations. Wea. Forecasting, 31, 329340, doi:10.1175/WAF-D-15-0063.1.

    • Search Google Scholar
    • Export Citation
  • Rinehart, R. E., and Garvey E. T. , 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287289, doi:10.1038/273287a0.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Klemp J. B. , 1992: The stability of time-split numerical methods for the hydrostatic and the nonhydrostatic equations. Mon. Wea. Rev., 120, 21092127, doi:10.1175/1520-0493(1992)120<2109:TSOTSN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ushio, T., and Kachi M. , 2009: Kalman filtering application for the Global Satellite Mapping of Precipitation (GSMaP). Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 105–123, doi:10.1007/978-90-481-2915-7_7.

  • Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137151, doi:10.2151/jmsj.87A.137.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and Hamill T. M. , 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, doi:10.1175/MWR-D-11-00276.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 924 310 15
PDF Downloads 662 131 13