Search results for "Forecast verification"

showing 3 items of 3 documents

SAL—A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts

2008

Abstract A novel object-based quality measure, which contains three distinct components that consider aspects of the structure (S), amplitude (A), and location (L) of the precipitation field in a prespecified domain (e.g., a river catchment) is introduced for the verification of quantitative precipitation forecasts (QPF). This quality measure is referred to as SAL. The amplitude component A measures the relative deviation of the domain-averaged QPF from observations. Positive values of A indicate an overestimation of total precipitation; negative values indicate an underestimation. For the components S and L, coherent precipitation objects are separately identified in the forecast and obser…

Atmospheric ScienceMatching (statistics)MeteorologyforecastDiagramprecipitationForecast verificationMeasure (mathematics)Displacement (vector)AmplitudeQuantitative precipitation forecastPrecipitationverificationWolkenphysik und VerkehrsmeteorologieradarMathematicsMonthly Weather Review
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Spatial Forecast Verification Methods Intercomparison Project: Application of the SAL Technique

2009

Abstract In this study, a recently introduced feature-based quality measure called SAL, which provides information about the structure, amplitude, and location of a quantitative precipitation forecast (QPF) in a prespecified domain, is applied to different sets of synthetic and realistic QPFs in the United States. The focus is on a detailed discussion of selected cases and on the comparison of the verification results obtained with SAL and some classical gridpoint-based error measures. For simple geometric precipitation objects it is shown that SAL adequately captures errors in the size and location of the objects, however, not in their orientation. The artificially modified (so-called fake…

Atmospheric ScienceMeasure (data warehouse)MeteorologyComputer scienceOrientation (computer vision)computer.software_genreForecast verificationDomain (software engineering)Feature (computer vision)Quantitative precipitation forecastPrecipitationData miningFocus (optics)computerWeather and Forecasting
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Identification and Handling of Critical Irradiance Forecast Errors Using a Random Forest Scheme – A Case Study for Southern Brazil

2015

Abstract Large forecast errors of solar power prediction cause challenges for the management of electric grids. Here, the classification technique Random Forests is applied to analyze the possible linkage of hourly or daily forecast errors to the actual situation given by a set of meteorological variables. This form a prediction of the forecast error and is thus usable to update the forecast. The performance of this scheme is assessed for the example of irradiance forecasts in Brazil. While limited to none improvements are obtained for next-hour forecasts, significant improvements are obtained for the next-day forecasts.

Meteorologybusiness.industryComputer sciencepost-processingIrradianceLinkage (mechanical)Forecast verificationRandom forestlaw.inventionSet (abstract data type)Identification (information)Energy(all)lawsolar irradiance forecastsbusinessConsensus forecastRandom Forest classificationSolar powerEnergy Procedia
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