What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting

Murphy, Allan H.

doi:10.1175/1520-0434(1993)008<0281:wiagfa>2.0.co;2

Cited by 665 publications

(612 citation statements)

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“…Systematic biases, linear association of interannual variability between forecasts and observations, the accuracy of the forecasts, and the forecast skill are some basic aspects that may contribute to the quality of a seasonal forecast from the deterministic viewpoint (Murphy, 1993). Therefore, the purpose of this paper was to assess the performance of individual models and the MME in terms of forecast quality of the seasonal mean ISMR on the basis of the attributes as mentioned above and the datasets derived from the DEMETER project.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

Rodó

Doblas‐Reyes

2011

Intl Journal of Climatology

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On the basis of the observed all-India rainfall (AIR), Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), and the multimodel rainfall simulated in the Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER) project, the model performance in terms of forecast quality of the seasonal mean Indian summer monsoon rainfall (ISMR) was evaluated. The approach used included the individual model (single-model ensemble) comparison as well as its comparison with the multimodel ensemble (MME). The results obtained reveal that systematic biases are the main source of the low skill in predicting the ISMR, while improved reproduction of interannual variability may increase the overall forecast quality for the ISMR. The MME shows superior performance in reproducing AIR features than the single-model ensembles. This improved forecast quality achieved by the MME with respect to the ISMR variations primarily depends on model performance and secondly on ensemble size. The MME has improved capability of simulating the ISMR variations during 1972-1990 decades, whereas it exhibits a lower performance in the 1990s. The reason of this decadal varying forecast quality clearly deserves more investigation. In summary, these results suggest that the MME's increase in skill absolutely depends on enhancing the individual model quality.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

Rodó

Doblas‐Reyes

2011

Intl Journal of Climatology

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“…As much as users and forecasters desire the simplicity of deterministic forecasts, such forecasts are clearly inferior in terms of quality or value to end users. In fact, neglecting uncertainty in the forecast process generally results in extreme reductions in both quality and value (Murphy 1993).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Short-Range Multimodel Ensemble System

et al. 2001

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Forecasts from the National Centers for Environmental Prediction's experimental short-range ensemble system are examined and compared with a single run from a higher-resolution model using similar computational resources. The ensemble consists of five members from the Regional Spectral Model and 10 members from the 80-km Eta Model, with both in-house analyses and bred perturbations used as initial conditions. This configuration allows for a comparison of the two models and the two perturbation strategies, as well as a preliminary investigation of the relative merits of mixed-model, mixed-perturbation ensemble systems. The ensemble is also used to estimate the short-range predictability limits of forecasts of precipitation and fields relevant to the forecast of precipitation.Whereas error growth curves for the ensemble and its subgroups are in relative agreement with previous work for large-scale fields such as 500-mb heights, little or no error growth is found for fields of mesoscale interest, such as convective indices and precipitation. The difference in growth rates among the ensemble subgroups illustrates the role of both initial perturbation strategy and model formulation in creating ensemble dispersion. However, increase spread per se is not necessarily beneficial, as is indicated by the fact that the ensemble subgroup with the greatest spread is less skillful than the subgroup with the least spread.Further examination into the skill of the ensemble system for forecasts of precipitation shows the advantage gained from a mixed-model strategy, such that even the inclusion of the less skillful Regional Spectral Model members improves ensemble performance. For some aspects of forecast performance, even ensemble configurations with as few as five members are shown to significantly outperform the 29-km Meso-Eta Model.

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“…Resolution is an attribute of forecast quality (Murphy 1993) that measures the success of a forecast system in distinguishing one type of event, i.e. one SIT mode from another.…”

Section: Apd Csd Catmentioning

confidence: 99%

Dynamical prediction of Arctic sea ice modes of variability

et al. 2018

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This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

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What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting

Cited by 665 publications

References 0 publications

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

Evaluation of a Short-Range Multimodel Ensemble System

Dynamical prediction of Arctic sea ice modes of variability

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