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

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 fromtraditional single-value deterministic forecast to flow-dependent (not statistical) probabilistic forecast to address forecast uncertainty. Another is froma 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 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 variables. This demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast errorwith 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 math ematically-calculated objective approaches, thismethod is subjective or human interactive based on information froman 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 spectralmodel (RSM, a sub-component of NCEP SREF) to prove the concept of this E-BIT method. The method seems to workmost 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.