摘要
将人工神经网络应用于南京夏季梅雨期短期降水分级预报。根据梅雨期天气特点,用统计和动力学方法从HLAFS(高分辨率有限区域预报系统)资料中寻找预报因子;然后分别用两种方法选取输入因子对人工神经网络进行训练,并分别利用抽取的五天做降水分级预报检验。通过对人工神经网络方法预报降水的结果与HLAFS降水预报以及逐步回归预报的结果对比发现:与HLAFS降水预报相比,降水预报准确率由原来的66.7%提高到88.2%,漏报、错报明显减少;与逐步回归预报相比,大到暴雨的预报准确率得到了明显提高。
Artificial neural network （ANN） is applied to make categorical forecasting of short-term precipitation in Meiyu period of Nanjing. At first, based on the weather characteristics of Meiyu period, in terms of the statistical and dynamic method, the predictors are searched in data of HLAFS. Secondly, utilizing import predictors that are selected by two methods trains the ANN,and then five days precipitation data are taken out in order to test the level forecast of precipitation. The results show that the simulating effect is very well and the ability of short-term precipitation forecast is obviously improved.
出处
《气象》
CSCD
北大核心
2005年第8期31-36,共6页
Meteorological Monthly
