| 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 |
| Using a mesoscale ensemble to predict forecast error and perform targeted observation |
| Using a mesoscale ensemble to predict forecast error and perform targeted observation |
| Received:September 07, 2010 Revised:June 10, 2012 |
| DOI：10.1007/s13131-014-0426-5 |
| Key words:NCEP SREF ensemble spread-skill relation targeted observation |
| 中文关键词: NCEP SREF ensemble spread-skill relation targeted observation |
| 基金项目:the National Natural Science Foundation of China under contract No. 41275107. |
| Author Name | Affiliation | E-mail | | DU Jun | National Centers for Environmental Prediction (NCEP), National Oceanic and Atmosphereic Administration (NOAA), Washington DC 20740, USA | Jun.Du@noaa.gov | | YU Rucong | Chinese Meteorological Administration (CMA), Beijing 100081, China | | | CUI Chunguang | Wuhan Institute of Heavy Rain, CMA, Wuhan 430074, China | | | LI Jun | Wuhan Institute of Heavy Rain, CMA, Wuhan 430074, China | |
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| 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 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. |
| 中文摘要: |
| 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. |
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