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The Florida State University (FSU) multimodel superensemble forecast is evaluated against several other operational weather models for the Southeast Asia region. The superensemble technique has demonstrated its exceptional skills in forecasting precipitation, motion and mass fields compared to either individual global operational or ensemble mean forecasts. The motion field investigation for the season of 2001 reveals that the superensemble forecasts are closer to the observed data compared to the other global member operational models through its low systematic errors at the 850 hPa level. The FSU multimodel superensemble forecasts exhibit the lowest root mean square errors (RSMEs), the highest correlation against the best observed data and the lowest systematic errors compared to the other operational model members. These forecasts have the potential to provide better daily weather predictions over the Southeast Asia region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asia region. 相似文献
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基于TIGGE资料, 采用均方根误差分别对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和英国气象局4个中心集合预报的地面气温场集合平均结果进行检验评估, 比较各中心地面气温的预报效果。并利用超级集合、多模式集合平均和消除偏差集合平均3种方法对4个中心的地面气温预报进行集成, 同时对预报结果进行分析。结果表明: 2007年夏季日本气象厅与欧洲中期天气预报中心在北半球大部分地区预报效果最好, 各中心在不同地区预报效果不同。超级集合与消除偏差集合平均降低了预报误差, 预报效果优于最好的单个中心预报和多模式集合平均。对于较长的预报时效, 消除偏差集合平均表现出了更好的预报性能。 相似文献
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利用CMIP5的15个全球气候系统模式对东亚及周边地区(70~150°E,0°~60°N)地面气温的回报结果进行超级集合(简称SUP)试验,以欧洲中期天气预报中心ERA逐月气温资料作为观测值,并采用均方根误差(RMSE)、距平相关系数(ACC)、绝对误差(MAE)对多模式集合平均(EMN)以及超级集合(SUP)的回报结果进行检验和评估。结果表明,超级集合回报结果一定程度上取决于训练期的长度。随训练期长度的增加,距平相关系数呈增大的趋势,均方根误差呈减小的趋势,但训练期达到一定长度后,误差不再有明显的减小,甚至出现误差增长。15个全球气候系统模式对东亚及周边地区的地面气温具有一定的回报能力,可以较好地回报出地面气温的年际变化和空间分布,海洋上回报的均方根误差小于陆地。但不同模式回报的结果不尽相同,在单模式中CCSM4对地面气温的回报效果最好。多模式集成的回报效果优于单模式的回报效果,SUP的回报效果优于EMN,其区域平均的均方根误差比多模式集合平均小0.43℃,超级集合极大地改善了地面气温的回报效果。 相似文献
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Improving multimodel weather forecast of monsoon rain over China using FSU superensemble 总被引：3，自引：0，他引：3
In this paper we present the current capabilities for numerical weather prediction of precipitation
over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes
the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are: ECMWF, UKMO, JMA,
NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts
phase, for these the periods chosen for this study include the months February through September for the years 2007
and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to
Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of
an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that
include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of
precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days
1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite.
Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of
precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product
was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle
of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel
superensemble make it a very useful product for such real time events. 相似文献
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《Comptes Rendus Geoscience》2015,347(2):53-63
Skilful prediction of the monthly and seasonal summer monsoon rainfall over India at a smaller spatial scale is a major challenge for the scientific community. The present study is aimed at achieving this objective by hybridising two mathematical techniques, namely synthetic superensemble (SSE) and supervised principal component regression (SPCR) on six state-of-the art Global Climate Models (GCMs). The performance of the mathematical model is evaluated using correlation analysis, the root mean square error, and the Nash–Sutcliffe efficiency index. Results feature reasonable improvement over central India, which is a zone of maximum rainfall activity in the summer monsoon season. The study also highlights improvement in the monthly prediction of rainfall over raw GCMs (15–20% improvement) with exceptional improvement in July. The developed model is also examined for anomalous years of monsoon and it is found that the model is able to capture the signs of anomalies over different gridpoints of the Indian domain. 相似文献
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A Comparison of Three Kinds of Multimodel Ensemble Forecast Techniques Based on the TIGGE Data 总被引：13，自引：0，他引：13 
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下载免费PDF全文 Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts),JMA (Japan Meteorological Agency),NCEP (National Centers for Environment... 相似文献
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In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional
spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day
of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields
(analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization
(a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall
forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution
physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian
summer monsoon region are achieved using this version of the regional model with physical initialization.
A relatively new concept, called the ‘multimodel/multianalysis superensemble’ is described in this paper and is applied for
the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa
level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble
approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological
Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon
region. 相似文献
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