Show simple item record

contributor authorStamus, Peter A.
contributor authorCarr, Frederick H.
contributor authorBaumhefner, David P.
date accessioned2017-06-09T16:08:37Z
date available2017-06-09T16:08:37Z
date copyright1992/01/01
date issued1992
identifier issn0027-0644
identifier otherams-61902.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4202735
description abstractA scale-separation technique based on two-dimensional Fourier decomposition is applied to the comparison and verification of analyses and forecasts produced by regional numerical weather prediction systems. A major emphasis of this study is the verification of secondary or derived parameters in addition to the evaluation of primary model variables. Two prediction models are used to illustrate the technique for a variety of forecast fields separated into three separate wavenumber bands. Three different sets of analyses, one from each model system and an independent set, are used for both analysis intercomparison and model verification. The comparison of the analyses is essential to establishing the level of uncertainty for each variable as a function of scale. The synoptic-scale database used to produce the analyses for this study does not allow the verification of scales 800 km or less, no matter how fine the resolution of the model. Examining the spectra of difference fields with time allows one to study the evolution of model error (or differences between two models) as a function of wavenumber. In some instances where traditional statistical measures of skill indicated good agreement between two forecasts, spectral scale selection of the difference fields shows that the spatial distribution of the errors was quite different, pointing to different error-growth characteristics of the models. The technique allows one to partially separate phase and amplitude errors and, hence, barotropic-versus baroclinic-type error structure. It was found, as expected, that forecast skill decreases more rapidly with time for smaller scales, but this is not true for all parameters examined. The presence of lateral boundary conditions strongly influences the evaluation of skill in a regional model for the primary variables, but not as much for some secondary variables. Verification of secondary variables nearly always indicates significant errors in the forecast before serious problems in the primary variables are detected.
publisherAmerican Meteorological Society
titleApplication of a Scale-Separation Verification Technique to Regional Forecast Models
typeJournal Paper
journal volume120
journal issue1
journal titleMonthly Weather Review
identifier doi10.1175/1520-0493(1992)120<0149:AOASSV>2.0.CO;2
journal fristpage149
journal lastpage163
treeMonthly Weather Review:;1992:;volume( 120 ):;issue: 001
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record