K 近邻非参数回归概率预报技术及其应用

针对参数回归技术制作概率预报存在拟合好、但预报结果不稳定的现象, 提出了用K近邻非参数回归技术制作概率预报的新途径。K 近邻非参数回归技术包括历史样本数据库、近邻子集生成和优化以及预报量估计4 个主要部分。利用该技术进行了单要素概率预报(主要包括云量和降水)和多维联合概率预报(降水、总云量、风速和气温)试验, 并对试验结果进行了检验。实例研究结果表明:该文所给出的计算方案预报稳定性好, 准确率较高,具有良好的业务应用价值。

K-nearest Neighbor Nonparametric Regression for Probability Forecasting with Its Applications

Although probability forecasts based on a parametric regression scheme have good fitting rates the results are not so stable. For this reason, a new approach is proposed to such forecasts by means of a K-nearest neighbor nonparametric regression technique, and the technique includes 4 main components such as a database of historical samples, production of nearest neighbor subsets, their optimization and estimate of predictands. Case experiments are conducted on univariate (cloudiness or precipitation) and multivariate joint (e.g., rainfall, total cloudiness, wind speed and temperature) probability forecasting, with the results tested. Results show that forecasts from the nonparametric regression scheme are high-stability, with good prospects in operational weather forecast.