Go to  Advanced Search

Updating Short-Term Probabilistic Weather Forecasts of Continuous Variables Using Recent Observations.

Show full item record

Files in this item

Files Size Format Description   View
Stull_AMS_2011_WAF-D-11-00022.pdf 772.7Kb Adobe Portable Document Format   View/Open
 
Title: Updating Short-Term Probabilistic Weather Forecasts of Continuous Variables Using Recent Observations.
Author: Nipen, Thomas; West, Greg; Stull, Roland B.
Issue Date: 2011
Publicly Available in cIRcle 2011-11-22
Publisher American Meteorological Society
Citation: Nipen, Thomas N.; West, Greg; Stull, Roland B. 2011. Updating Short-Term Probabilistic Weather Forecasts of Continuous Variables Using Recent Observations. Weather and Forecasting, 26 (4) 564-571, http://dx.doi.org/10.1175/WAF-D-11-00022.1
Abstract: A statistical postprocessing method for improving probabilistic forecasts of continuous weather variables, given recent observations, is presented. The method updates an existing probabilistic forecast by incorporating observations reported in the intermediary time since model initialization. As such, this method provides updated short-range probabilistic forecasts at an extremely low computational cost. The method models the time sequence of cumulative distribution function (CDF) values corresponding to the observation as a first-order Markov process. Verifying CDF values are highly correlated in time, and their changes in time are modeled probabilistically by a transition function. The effect of the method is that the spread of the probabilistic forecasts for the first few hours after an observation has been made is considerably narrower than the original forecast. The updated probability distributions widen back toward the original forecast for forecast times far in the future as the effect of the recent observation diminishes. The method is tested on probabilistic forecasts produced by an operational ensemble forecasting system. The method improves the ignorance score and the continuous ranked probability score of the probabilistic forecasts significantly for the first few hours after an observation has been made. The mean absolute error of the median of the probability distribution is also shown to be improved. Copyright 2011 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 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyright@ametsoc.org.
Affiliation: Earth and Ocean Sciences, Dept. of (EOS), Dept of
URI: http://hdl.handle.net/2429/39213
Peer Review Status: Reviewed
Scholarly Level: Faculty

This item appears in the following Collection(s)

Show full item record

All items in cIRcle are protected by copyright, with all rights reserved.

UBC Library
1961 East Mall
Vancouver, B.C.
Canada V6T 1Z1
Tel: 604-822-6375
Fax: 604-822-3893