Probabilistic weather prediction with an analog ensemble

AMS Citation:
Delle Monache, L., F. A. Eckel, D. L. Rife, B. Nagarajan, and K. R. Searight, 2013: Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141, 3498-3516, doi:10.1175/MWR-D-12-00281.1.
Resource Type:article
Title:Probabilistic weather prediction with an analog ensemble
Abstract: This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.
Subject(s):Ensembles, Short-range prediction, Forecasting techniques, Numerical weather prediction/forecasting
Peer Review:Refereed
Copyright Information:Copyright 2013 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Law (17 USC, as revised by P.L. 94-553) does not require the Society's permission. Republication, systematic reproduction, posting in electronic form on servers, or other uses of this material, except as exempted by the above statements, requires written permission or license from the AMS. Additional details are provided in the AMS Copyright Policies, available from the AMS at 617-227-2425 or Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work.
OpenSky citable URL: ark:/85065/d73f4qjx
Publisher's Version: 10.1175/MWR-D-12-00281.1
  • Luca Delle Monache - NCAR/UCAR
  • F. Eckel
  • Daran Rife
  • Badrinath Nagarajan - NCAR/UCAR
  • Keith Searight - NCAR/UCAR
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