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dc.contributor.authorMittermaier, MP
dc.contributor.authorStephenson, David B.
dc.date.accessioned2015-12-14T14:42:56Z
dc.date.issued2015-10
dc.description.abstractSynoptic observations are often treated as error-free representations of the true state of the real world. For example, when observations are used to verify numerical weather prediction (NWP) forecasts, forecast-observation differences (the total error) are often entirely attributed to forecast inaccuracy. Such simplification is no longer justifiable for short-lead forecasts made with increasingly accurate higher-resolution models. For example, at least 25% of t + 6 h individual Met Office site-specific (postprocessed) temperature forecasts now typically have total errors of less than 0.2 K, which are comparable to typical instrument measurement errors of around 0.1 K. In addition to instrument errors, uncertainty is introduced by measurements not being taken concurrently with the forecasts. For example, synoptic temperature observations in the United Kingdom are typically taken 10 min before the hour, whereas forecasts are generally extracted as instantaneous values on the hour. This study develops a simple yet robust statistical modeling procedure for assessing how serially correlated subhourly variations limit the forecast accuracy that can be achieved. The methodology is demonstrated by application to synoptic temperature observations sampled every minute at several locations around the United Kingdom. Results show that subhourly variations lead to sizeable forecast errors of 0.16-0.44 K for observations taken 10 min before the forecast issue time. The magnitude of this error depends on spatial location and the annual cycle, with the greater errors occurring in the warmer seasons and at inland sites. This important source of uncertainty consists of a bias due to the diurnal cycle, plus irreducible uncertainty due to unpredictable subhourly variations that fundamentally limit forecast accuracy.en_GB
dc.description.sponsorshipNCAR-DTCen_GB
dc.identifier.citationVol. 143, pp. 4236 - 4243en_GB
dc.identifier.doi10.1175/MWR-D-15-0173.1
dc.identifier.urihttp://hdl.handle.net/10871/18975
dc.language.isoenen_GB
dc.publisherAmerican Meteorological Societyen_GB
dc.rights.embargoreasonPublisher Policyen_GB
dc.rightsPermission 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.en_GB
dc.subjectForecast verification/skillen_GB
dc.subjectForecastingen_GB
dc.subjectMathematical and statistical techniquesen_GB
dc.subjectNumerical analysis/modelingen_GB
dc.subjectObservational techniques and algorithmsen_GB
dc.subjectSamplingen_GB
dc.subjectStatistical techniquesen_GB
dc.subjectSurface observationsen_GB
dc.titleInherent bounds on forecast accuracy due to observation uncertainty caused by temporal samplingen_GB
dc.typeArticleen_GB
dc.identifier.issn0027-0644
dc.description© Copyright 2015 American Meteorological Society (AMS).en_GB
dc.descriptionAuthor Affiliations: MARION P. MITTERMAIER (Numerical Modelling, Weather Science, Met Office, Exeter, United Kingdom). DAVID B. STEPHENSON (Exeter Climate Systems, Department of Mathematics and Computer Science, Exeter University, Exeter, United Kingdom)en_GB
dc.identifier.journalMonthly Weather Reviewen_GB


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