Using a mesoscale ensemble to predict forecast error and perform targeted observation
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Using a mesoscale ensemble to predict forecast error and perform targeted observation DU Jun1∗ , YU Rucong2 , CUI Chunguang3 , LI Jun3 1
National Centers for Environmental Prediction (NCEP), National Oceanic and Atmosphereic Administration (NOAA), Washington DC 20740, USA 2 Chinese Meteorological Administration (CMA), Beijing 100081, China 3 Wuhan Institute of Heavy Rain, CMA, Wuhan 430074, China Received 7 September 2010; accepted 10 June 2012 ©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2014
Abstract Using NCEP short range ensemble forecast (SREF) system, demonstrated two fundamental on-going evolutions in numerical weather prediction (NWP) are through ensemble methodology. One evolution is the shift from traditional single-value deterministic forecast to flow-dependent (not statistical) probabilistic forecast to address forecast uncertainty. Another is from a one-way observation-prediction system shifting to an interactive two-way observation-prediction system to increase predictability of a weather system. In the first part, how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a month. The result shows that the current capability of predicting forecast error by the 21member 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 variables. This 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 today. Given 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 have. The second part of this research tested an ensemble-based interactive targeting (E-BIT) method. Unlike 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. The method seems to work most effective for basic atmospheric state variables, moderately effective for convective instabilities and least effective for precipitations. Precipitation is a complex result of many factors and, therefore, a more challenging field to be improved by targeted observation. Key words: NCEP SREF ensemble, spread-skill relation, targeted observation Citation: Du Jun, Yu Rucong, Cui Chunguang, Li Jun. 2014. Using a mesoscale ensemble to predict forecast error and perform targeted observation. Acta Oceanologica Sinica, 33(1): 83–91, doi: 10.100
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