| A winter season minimum temperature formula for Bakersfield, California, using multiple regression / Michael J. Oard - :6429 | National Weather Service (NWS) | Office of Oceanic and Atmospheric Research (OAR)
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A winter season minimum temperature formula for Bakersfield, California, using multiple regression / Michael J. Oard
  • Published Date:
    1977
Filetype[PDF - 1.24 MB]


Details:
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  • Corporate Authors:
    United States, National Weather Service., Western Region,
  • Description:
    "Multiple regression Is a statistical technique where independent variables most correlated with some dependent variable (like minimum temperature) are selected in order of importance. This is done in a stepwise manner in which, at each step, the best variable is added from a large pool of remaining variables forming a new equation with all the previously selected variables. This method has been known for many years and has been used during the past 10 years by the National Meteorological Center (NMC) with increasing effectiveness. Operational prediction equations for numerous weather parameters using output from numerical models is now an important part of the daily NMC routine. DetaiIs of this so-called Model Output Statistics (MQS) program are given in numerous Technical Procedures Bulletins and papers in Monthly Weather Review, Journal of Applied Meteorology, and the Bulletin of the American Meteorological Society, e.g., Technical Procedures Bulletin No. 94 (1973); Klein and Hammons (1975); KIein and Glahn (1974)"--Introduction.

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