Document Type

Article

Publication Date

7-1-2010

Abstract

The number of surface observations from nonstandardized networks across the United States has appreciably increased the last several years. Automated Weather Services, Inc. (AWS), maintains one example of this type of network offering nonstandardized observations for similar to 8000 sites. The present study assesses the utility of such a network to improve short-term (i.e., lead times < 12 h) National Digital Forecast Database (NDFD) forecasts for three parameters most relevant to the energy industry-temperature, dewpoint, and wind speed. A 1-yr sample of 13 AWS sites is chosen to evaluate the magnitude of forecast improvement (skill) and influence of physical location (siting) on such improvements. Hourly predictions are generated using generalized additive modeling (GAM)-a nonlinear statistical equation incorporating a predetermined set of the most significant AWS and NDFD predictors. Two references are used for comparison: (i) persistence climatology (PC) forecasts and (ii) NDFD forecasts calibrated to the AWS sites (CNDFD). The skill, measured via the percent improvement (reduction) in the mean absolute error (MAE), of forecasts generated by the study's technique (CNDFD+) is comparable (< 5%) to PC for lead times of 1-3 h for dewpoint and wind speed. Skill relative to PC slowly increases with lead time, with temperature exhibiting the greatest relative-to-PC skill (similar to 30% at 12 h). When compared to baseline CNDFD forecasts, the MAE of the generated CNDFD+ forecasts is reduced 65% for temperature and dewpoint at the 1-h lead time. An exponential drop in improvement occurs for longer lead times. Wind speed improvements are notably less, with little skill (< 5%) demonstrated for forecasts beyond 4 h. Overall, CNDFD+ forecasts have the greatest accuracy relative to CNDFD and PC for the middle (3-7 h) lead times tested in the study. Variations in CNDFD+ skill exist with respect to AWS location. Tested stations located in complex terrain generally exhibit greater skill relative to CNDFD than the 13-station average for temperature (and, to a lesser degree, dewpoint). Relative to PC, however, the same subset of stations exhibits skill below the 13-station average. No conclusive relationship can be made between CNDFD+ skill and the sample stations located near water.

Publication Title

Journal of Applied Meteorology and Climatology

ISSN

1558-8424

Publisher

American Meteorological Society

Volume

49

Issue

7

First Page

1397

Last Page

1411

DOI

10.1175/2010JAMC2137.1

Comments

© Copyright 2010 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 September 2010 Page 2 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 copyrights@ametsoc.org.

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