深圳单点气温的时间滞后集合预报研究

A study on the time-lagged ensemble prediction of the air temperature at Shenzhen

利用欧洲中心TL799 L91模式在深圳的单点气温预报数据和深圳本地的气温观测数据,对比分析了时间滞后集合预报方法与传统确定性预报的预报能力。结果表明:(1)TL799 L91模式提供的确定性预报对于深圳单点气温有一定预报能力,它的预报误差总体上有随预报提前量增加而增加的趋势,但最近时次的确定性预报并不一定是最佳的预报;(2)时间滞后集合预报总体上优于确定性预报,而且参与集合的成员数量越多则预报准确率大体上越高。所以,时间滞后集合预报可在一定程度上改进单点气温的预报质量,是充分利用更早起报时刻数值预报价值的一种有效途径。

Based on the air temperature prediction data from the TL799L91 model of the European Centre for Medium-Range Weather Forecasts（ECMWF）,the prediction capabilities of time-lagged ensemble prediction and the traditional deterministic prediction for a single point were studied.The comparison between the time series of the observed data at Shenzhen and those from the deterministic prediction/time-lagged ensemble prediction is performed,which shows that：（1） The prediction capability of TL799L91 model can reach as long as 240 h for the air temperature of Shenzhen.Generally speaking,for deterministic prediction,the prediction error increases with the increase of forecast lead time,however,the latest prediction is not necessarily the best one.（2） The time-lagged ensemble prediction does help improve the prediction quality.The accuracy of the time-lagged ensemble prediction is directly related to the number of ensemble members.Generally speaking,the increase of ensemble members will lead to more accurate prediction results.The study of this paper shows that the time-lagged ensemble prediction is an effective way to make use of the numerical prediction of earlier initiation.