基于监测指数的贵州省4月气温预测模型及检验

基于1981~2019年贵州省4月气温与同期130项监测指数的相关分析建立了固定监测指数、最优监测指数、逐步回归的统计预测模型，并对1981~2020年预测模型回报结果进行检验。结果表明:在短期气候趋势预测业务中，按异常级进行预测可提高气候预测技巧。三种统计预测模型中逐步回归的预测效果最好，其次是最优监测指数，而固定监测指数效果最差。与省级和国家级预测产品相比，统计预测模型在近9年的回报效果表现出一定的优势。以7个最优指数建立的回归预测模型对2020年4月气温的回报Ps评分最高，较省级预报提高14.1分，相对国家指导预报提高66.7分。 

Prediction Model and Test of Temperature in April in Guizhou Based on Monitoring Index

Based on the correlation analysis between temperature in April and 130 monitoring indexes in Guizhou Province from 1981 to 2019, a fixed monitoring index, optimal monitoring index, and stepwise regression statistical prediction model were established, and the results of the prediction model from 1981 to 2020 were tested. The results show that: in the work of forecasting climate trends, forecasting at anomalous level can improve climate forecasting skills. Among the three types of statistical prediction models, the stepwise regression has the best prediction effect, followed by the best monitoring index, and the fixed monitoring index has the worst effect. Compared with the provincial and national forecast products, the statistical forecast model has shown certain advantages in the return effect of the past 9 years. The regression prediction model established with the 7 optimal indexes has the highest Ps score for the temperature in April 2020, which is 14.1 points higher than that of the provincial forecast Ps score and 66.7 points higher than that of the national guidance forecast.