Identification

Title

A practical approach to sequential estimation of systematic error on near-surface mesoscale grids

Abstract

Statistical analysis arguments are used to construct an estimation algorithm for systematic error of near-surface temperatures on a mesoscale grid. The systematic error is defined as the observed running-mean error, and an averaging length of 7 days is shown to be acceptable. Those errors are spread over a numerical weather prediction model grid via the statistical analysis equation. Two covariance models are examined: 1) a stationary, isotropic function tuned with the observed running-mean errors and 2) dynamic estimates derived from a recent history of running-mean forecasts. Prediction of error is possible with a diurnal persistence model, where the error at one time of day can be estimated from data with lags of 24-h multiples. The approach is tested on 6 months of 6-h forecasts with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5) over New Mexico. Results show that for a quantity such as 2-m temperature, the systematic component of error can be effectively predicted on the grid. The gridded estimates fit the observed running-mean errors well. Cross validation shows that predictions of systematic error result in a substantial error reduction where observations are not available. The error estimates show a diurnal evolution, and are not strictly functions of terrain elevation. Observation error covariances, localization operators, and covariance functions in the isotropic case must be tuned for a specific forecast system and observing network, but the process is straightforward. Taken together, the results suggest an effective method for systematic error estimation on near-surface mesoscale grids in the absence of a useful ensemble. Correction for those errors may provide benefits to forecast users.

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document

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http://n2t.net/ark:/85065/d78g8n1s

codeSpace

Dataset language

eng

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geoscientificInformation

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title

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reference date

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publication

effective date

2016-01-01T00:00:00Z

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publication

effective date

2007-12-01T00:00:00Z

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Copyright 2007 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 amspubs@ametsoc.org. 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.

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None

Responsible organisations

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contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

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pointOfContact

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