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A neural network nonlinear multi-model ensemble for predicting precipitation over ConUS



Details

  • Personal Author:
    Krasnopolʹsky, V. M. ; Lin, Ying
  • Corporate Authors:
    National Centers for Environmental Prediction (U.S.)
  • NOAA Program & Office:
    NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)
  • Description:
    A novel multi-model ensemble approach based on the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and 'optimal' forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the regular multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upon regular multi-model and multiple linear regression ensembles: (1) it significantly reduces high bias at low precipitation level; (2) it significantly reduces low bias at high precipitation level, and (3) it sharpens features making them closer to the observed ones. The NN multi-model ensemble performs at least as well as human forecasters supplied with the same information. The developed NN approach is a generic approach that can be applied to other multi-model ensemble fields as well as to single model ensembles.
  • Content Notes:
    V.M. Krasnopolsky and Y. Lin. ; "MMAB Contribution No. 296." ; "January 2012." ; System requirements: Adobe Acrobat Reader. ; Includes bibliographical references (pages 25-27).
  • Series:
    Office note (National Centers for Environmental Prediction (U.S.)) ; 470
  • Document Type:
    Office Note
  • Place as Subject:
    United States
  • Rights Information:
    Public Domain
  • Compliance:
    Library
  • Download URL:
    https://repository.library.noaa.gov/view/noaa/6943/noaa_6943_DS1.pdf
  • File Type:
    Filetype[PDF - 1.09 MB]
  • Collection(s):
    National Weather Service (NWS)
  • Main Document Checksum:
    urn:sha-512:e69ce1500936b4d8264a6f673e5d458b4c0bc6039b384756acd9b6bcdb85d8f9e8ca841d1d637acf5da2bc2d214ce6687038fa443317590214ecaedb714f2cc0
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