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Irving I. Gringorten

742 JOURNAL OF APPLIED METEOROLOGY VO~.UME6Verification to Determine and Measure Forecasting Skill IRVrSG I. GR~GORTENAir Force Cambridge Rese~rt:h Laboratories, Bedford, Mass.(Revised manuscript received 23 February 1967)ABSTRACT Three distinct purposes for the verification and scoring of forecasts have been generally recognized: determination of the accuracy of the forecasts, their operationat

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Gregor Skok and Veronika Hladnik

all altitudes in global models at synoptic scales with large forecast times (e.g., 9 days) since the synoptic-scale features, such as cyclones and frontal systems, might be significantly displaced. Thus, the spatial displacement error of wind is a common occurrence, and a double-penalty problem will arise, much like in the case of precipitation. The fractions skill score (FSS; Roberts and Lean 2008 ; Roberts 2008 ) is a popular spatial verification metric used for verifying precipitation. It

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Hong Guan and Yuejian Zhu

climatology. To test the sensitivity of the ANF and EFI skill to their reference, we make verification comparisons with two different references (30-yr CFSRR and 40-yr reanalysis) in Figs. 10 and 11 . The ANF and EFI calculated relative to the CFSRR climatology have slightly better HR, FBI, and ETS than those of the reanalysis climatology ( Fig. 10 ). The relative forecasting performance with the two references can be also identified from the performance diagram ( Fig. 11 ). The plotted positions for

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Marion P. Mittermaier

to create probability of precipitation (PoP) forecasts, and compared these to gauge totals using standard probabilistic verification metrics such as the Brier score ( Brier 1950 ). To test the benefit of using a neighborhood, they computed a Brier skill score (BSS) relative to what is termed direct model output (DMO), which is the nearest model grid point or bilinearly interpolated value. The aim of this paper is to explore the use of the single-observation neighborhood-forecast (SO NF) concept

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Jingzhuo Wang, Jing Chen, Jun Du, Yutao Zhang, Yu Xia, and Guo Deng

believed to be a common problem. Therefore, it is important to explicitly address and emphasize this pitfall in verifying an EPS. A true assessment of an EPS is important for EPS developers to focus on real problems related to ensembling techniques but not to the model itself. The shift of the mean position of an ensemble distribution will not only affect the ensemble mean but also the spread–skill relationship and probabilistic forecasts. Model bias cannot only negatively impact verification metrics

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Chung-Chieh Wang, Fang-Ching Chien, Sahana Paul, Dong-In Lee, and Pi-Yu Chuang

given rainfall threshold and accumulation period, they are defined as where H , M , and FA are the counts of hits (observed and predicted), misses (observed but not predicted), and false alarms (predicted but not observed), respectively, among all verification points N . Computed using Eq. ( 2 ), R is the number of random hits, given by the total observed ( O = H + M ) and forecast points ( F = H + FA), and is excluded in Eq. ( 1 ) since it reflects no model skill. As a result, ETS

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Marion Mittermaier and Nigel Roberts

1. Introduction What is a good precipitation forecast? Murphy (1993) describes the general characteristics of a good forecast in terms of consistency, quality (or goodness), and value. In addition, the forecast skill assessed by some measure should be consistent with forecaster judgment and a mismatch may be an indication that the verification score is not performing as it should. We know that higher spatial resolution precipitation forecasts look more realistic ( Mass et al. 2002 ; Done et

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Nathan M. Hitchens, Harold E. Brooks, and Michael P. Kay

verification of rare-event forecasts: development of appropriate baselines for skill given that forecast difficulty varies from situation to situation. Efforts to identify “no skill” baselines date back to Gilbert (1884) and have focused primarily on the use of climatological (either sample or long term) data. Such efforts attempt to limit the credit given to forecasters for making easy, correct forecasts, guessing, or forecasting the same thing all the time, particularly when the observations are

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Grace Zalenski, Witold F. Krajewski, Felipe Quintero, Pedro Restrepo, and Steve Buan

of the models used by the NWS. Notable exceptions are the works by Welles and Sorooshian (2009) and Welles et al. (2007) . Welles et al. (2007) published a verification study using 10 years’ worth of data from four forecast sites in Oklahoma, and 20 years of data from 11 sites along the Missouri River. They found that below flood stage, NWS forecasts demonstrate some skill for 1-, 2-, and 3-day forecasts, but above flood stage, only the 1-day forecasts show skill. After these studies, there

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M. P. Mittermaier and G. Csima

, and cascaded from short- to longer wavelengths. Lorenz showed this too, though this aspect has generally been less publicized. Irrespective of the error sources, the loss of predictability and the impact on forecast skill remain among the main reasons why an ensemble approach is necessary at the kilometer scale, with a new verification approach to match this modeling requirement. For many national weather services, the value added by running a kilometer-scale ensemble over a deterministic

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