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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology

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Proc. IAHS, 369, 7-11, 2015
https://doi.org/10.5194/piahs-369-7-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Jun 2015
Downscaling medium-range ensemble forecasts using a neural network approach
M. Ohba, S. Kadokura, Y. Yoshida, D. Nohara, and Y. Toyoda Central Research Institute of Electric Power Industry, Abiko, Japan
Abstract. In this study, we present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation in Japan. SOM is simultaneously employed on four elemental variables derived from the JRA55 reanalysis over area of study (Southwestern Japan), whereby a two-dimensional lattice of weather patterns (WPs) dominated during the 1958–2008 period is obtained. Downscaling weekly ensemble forecasts to local precipitation are conducted by using the obtained SOM lattice based on the WPs of the global model ensemble forecast. A probabilistic local precipitation is easily and quickly obtained from the ensemble forecast. The predictability skill of the ensemble forecasts for the precipitation is significantly improved under the downscaling technique.

Citation: Ohba, M., Kadokura, S., Yoshida, Y., Nohara, D., and Toyoda, Y.: Downscaling medium-range ensemble forecasts using a neural network approach, Proc. IAHS, 369, 7-11, https://doi.org/10.5194/piahs-369-7-2015, 2015.
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We present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation. Downscaling weekly ensemble forecasts to local precipitation are conducted by using the obtained SOM lattice based on the WPs of the global model ensemble forecast. A probabilistic local precipitation is easily and quickly obtained from the ensemble forecast.
We present an application of self-organizing maps (SOMs) to downscaling weekly ensemble...
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