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Using a mesoscale ensemble to predict forecast error and perform targeted observation

Acta Oceanologica Sinica 2014年第1期33卷 83-91页

作者： DU Jun LI Jun YU Rucong CUI Chunguang National Centers for Environmental Prediction(NCEP) National Oceanic and Atmosphereic Administration (NOAA) Chinese Meteorological Administration (CMA) Wuhan Institute of Heavy Rain CMA

Using NCEP short range ensemble forecast（SREF） system,demonstrated two fundamental on-going evolutions in numerical weather prediction（NWP） are through ensemble *** evolution is the shift from traditional single-value deterministic forecast to flow-dependent（not statistical） probabilistic forecast to address forecast *** is from a one-way observation-prediction system shifting to an interactive two-way observation-prediction system to increase predictability of a weather *** the first part,how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a *** result shows that the current capability of predicting forecast error by the 21-member NCEP SREF has reached to a similar or even higher level than that of current state-of-the-art NWP models in predicting precipitation,e.g.,the spatial correlation between ensemble spread and absolute forecast error has reached 0.5 or higher at 87 h（3.5 d） lead time on average for some meteorological *** demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast error with usable skill,which is a remarkable achievement as of *** the good spread-skill relation,the probability derived from the ensemble was also statistically reliable,which is the most important feature a useful probabilistic forecast should *** second part of this research tested an ensemble-based interactive targeting（E-BIT） *** other mathematically-calculated objective approaches,this method is subjective or human interactive based on information from an ensemble of forecasts.A numerical simulation study was performed to eight real atmospheric cases with a 10-member,bred vector-based mesoscale ensemble using the NCEP regional spectral model（RSM,a sub-component of NCEP SREF） to prove the concept of this E-BIT *** method seems to work most effective for basic atmospheric st

关键词： NCEP SREF ensemble spread-skill relation targeted observation

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