数值天气预报业务模式现状与展望

动力统计结合是提高天气、气候预报水平的重要途径之一，关键问题是如何将数值模式与历史资料进行有效结合；相似预报这一传统方法与动力统计的结合是未来提高天气、气候预报水平的一个重要方向，尽管其原理目前仍停留在相似假设基础上且缺乏坚实的物理基础。文中从准确模式的初值问题出发，提出准确模式初值扰动概念，进而发展了动力统计相似集合预报（Dynamical Statistical Analog Ensemble Forecast，DSAEF）理论。DSAEF理论不仅回答了为什么可以进行相似预报，同时还指出了如何进行相似预报，即其原理是利用准确模式来做预报，并采用集合预报的方式实现预报。基于 DSAEF 理论，建立了登陆台风降水动力统计相似集合预报DSAEF_LTP （Landfalling Typhoon Precipitation，LTP）模型，该模型包括4个步骤：台风路径预报、广义初值构建、初值相似性判别和台风降水集合，其中广义初值由影响台风降水的物理因子构成。
DSAEF_LTP模型具有可持续发展特性—可通过引入新因子或改善模型参数来改进模型的性能；目前该模型发布了广义初值包含台风路径、登陆季节和台风强度3个物理因子的1.0版和在此基础上改进了“相似区域”和“集合方案”的1.1版。该模型的性能提升很快，已完成的最新版本（1.1版）3次大样本预报试验均显示，与ECMWF、CMA-GFS、NCEP-GFS和SMS-WARMS （上海区域模式）对比，对≥100 mm和≥250 mm台风过程降水预报的TS评分，DSAEF_LTP模型（V1.1）排名第1。今后，围绕广义初值不断改善，研究引入更多影响登陆台风降水的物理因子，DSAEF_LTP 模型的发展前景广阔。

An overview on recent progresses of the operational numerical weather prediction models

Combining dynamical and statistical methods is one of the important ways to improve weather and climate prediction. A key issue is how to effectively combine numerical model results with historical data. Combining the above two methods with an analog method is an important direction for future improvement of weather and climate prediction, although the analog method is still limited to similarity assumptions and lacks solid physical basis. Based on the initial condition problem of a perfect model, this work proposes the concept of initial condition perturbation of the perfect model and develops a Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) theory. The DSAEF theory shows not only why the analogue-based forecast can be conducted, but also how it can be conducted. That is, the perfect model is used to produce forecasts and an ensemble prediction scheme is used to achieve the forecast accuracy. Based on the DSAEF theory, the DSAEF_LTP (Landfalling Typhoon Precipitation, LTP) model has been developed. This model includes the following four steps: (Ⅰ) forecast typhoon track, (Ⅱ) Construct generalized Initial Value (GIV), (Ⅲ) identify analogs from historical observations, and (Ⅳ) produce an ensemble forecast of typhoon precipitation. The GIV is constructed by physical variables that affect LTP.
The DSAEF_ LTP model has the characteristic of sustainable development, which can be improved by introducing new variables or refining the existing parameters of the model. At present, the model versions 1.0 and 1.1 have been released. In version 1.0, GIVs include three physical variables, i.e., typhoon track, landfall season and typhoon intensity. In the version 1.1, two extra improved parameters of