【作者】 杨仁勇；

【导师】 沈桐立； 闵锦忠；

【作者基本信息】 南京气象学院 ， 气象学， 2004， 硕士

【摘要】 强降水预报是中尺度数值预报的难点之一。资料同化是提高数值模式预报能力的有效方法。为了提高数值预报模式的强降水预报能力，本文在前人研究成果的基础上，主要从事应用性试验研究。将现有的静止气象卫星红外云图资料定量加入到模式中。首先，云图灰度的定量应用采用了传统的统计回归反演方法反演出中低层的温度和相对湿度要素，反演后的要素经变分同化后应用于模式中。其次，云图T_BB资料的定量应用采用了T_BB资料温度订正和T_BB资料相对湿度增强订正的方法，经订正后的温度和相对湿度直接应用于模式中。对2003年7月9日至10日发生在我国长江流域的一次特大暴雨天气过程用MM5模式进行了数值模拟。同化模拟试验结果表明，卫星云图资料同化后模拟预报的降水场结果有了相当程度的改善，预报的强降水与实况比较接近。本文中采用的T_BB资料温度订正方法和T_BB资料相对湿度增强订正方法可将与云参数相关的T_BB资料直接同化应用于模式，与统计反演后再应用的方法不同，相比之下物理意义更明确。而且模拟试验结果也表明，用T_BB资料温度订正法和在较强对流云区T_BB资料相对湿度增强订正方法可取得比传统的统计反演方法更好的效果。更多还原

【Abstract】 It is one of the difficulties of mesoscale-numerical forecasting in rainfall. Data assimilation is an effective approach to improve the numerical forecasts skill. Based on earlier research results, this paper aims at improving the forecast skill for rainfall by experiments, which add satellite infrared cloud image into a forecast model MM5 quantificationally. Statistical regressive method are employed to retrieve temperature and humidity fields in low-level atmosphere, and then assimilated into MM5 by variational technique. Secondly a method is applied to add cloud TBB data to numerical model, which corrects the temperature and amplify the humidity simultaneously. Experiments for the case of the rainfall occurred in the Changjiang River during 9-10,July,2003, are carried out. Numerical experiments results on modeling the rainfall, show that: l)The rainfall field modeled is improved and much similar to real rain field when TBB data is assimiliated into initial fields. The correction methed mentioned above,employed in this paper can assimilate TBB data to numerical model directly, and it has phisical significance clearly other than regressive method also. 2) The experiments verify that correction method gives better results than conventional regressive method in intensively convective zone.更多还原