Temperature Seasonal Predictability of the WRF Model
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It is a common sense that there is no usable forecast skill at seasonal lead times due to the rapid drop-off of the predictability after a few days from the initialization of a simulation. However, there is some skill in predicting anomalies in the seasonal average of the weather such as anomalies of the persistent atmospheric patterns or even deviation from the climatology. In this study, the forecast skill on a seasonal scale of the mean monthly temperature at 850 hPa is statistically assessed against gridded GFS analyses. The simulations are based on the global version of the Weather Research and Forecasting model (GWRF) modified appropriately in order to simulate long-term atmospheric circulation. Model initializations are based on a customized version of the Lagged Average Forecast (LAF) formulation. GWRF seasonal scale simulations are initialized from the daily global analyses, assuming each analysis as a perturbation of the previous one due to the long forecast window of 12 months ahead. Evaluation results indicate that the forecast skill is independent of the forecast horizon and reveal a key role of the model initialization on the seasonal predictability.
KeywordsMonthly Temperature Forecast Skill Seasonal Forecast Forecast Horizon Seasonal Predictability
Greek Free/Open Source Software Society (GFOSS) is gratefully acknowledged for the funding of the development of the software which was used for the statistical evaluation. Also, the Greek Research and Technology Network (GRNET) is gratefully acknowledged for the provision of the High Performance Computer (HPC) ARIS, where the entire simulations of this study have been performed.
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