基于多模式集成输出天气变量的参考作物腾发量预报

参考作物腾发量（reference evapotranspiration, ET0）是农业生产中一项重要的参数，对评估未来的干旱程度和实现农业精细化管理具有重要意义。为进一步提高ET0的预报精度，该研究将多模式集成方法应用于ET0的预报，运用遗传算法-回归型支持向量机对欧洲中期天气预报中心、美国国家环境预报中心、日本气象厅和韩国气象厅4个中心全球集合预报模式输出的天气变量进行多模式集成处理，基于最优的模式和方案使用Penman-Monteith公式对山西运城站未来1～7 d的ET0进行预报，并对其在站点附近农业试验田的适用性进行验证。结果表明，多模式集成能够调和单一模式在气象预报中的优劣，从而提高ET0预报的精度和长预见期下的稳定性；在ET0预报中，多模式方案的性能明显优于原始单一模式，由最优模式和方案组成的重组方案预报性能最好，具有最小的均方根误差、平均绝对百分比误差，分别为0.65～0.81 mm/d和19.43%～23.78%，以及最高的决定系数（0.83～0.89）。在对试验田未来1～7 d的ET0预报中，重组方案仍表现出良好的预报性能，均方根误差、平均绝对百分比误差不超过0.83 mm/d和34.57%。该研究能有效提升数值天气预报在运城站下属乡镇地区的适应性，为当地农业实际生产提供准确的ET0预报信息，对于农业需水预测以及水资源优化管理具有重要意义。

Reference crop evapotranspiration forecast using multi-model integrated output weather variables

Reference evapotranspiration (ET0) is one of the most important parameters to predict the drought degree in agricultural production and precision management. In this research, the multi-model ensemble method was applied to improve the forecasting precision for ET0. Genetic algorithm-support vector regression was utilized to forecast the weather variables output by European Centre for Medium-Range Weather Forecasts, National Centers for Environmental Prediction, Japan Meteorological Agency, and Korea Meteorological Administration models. The prediction accuracy and reliability of the model were evaluated by three indicators: root mean square error (RMSE), mean absolute percentage error (MAPE), and determination coefficient (R2). Penman-Monteith equation was used to forecast the ET0 from 1 to 7 days in the future, according to the optimal models and schemes. Finally, the applicability of the improved model was verified in agricultural test field, including the Yuncheng Station in Shanxi Province of China. The results show that the multi-mode integration improved the prediction performance of air temperature, actual water vapor pressure, and wind speed under a single mode, with the largest improvement in the daily maximum temperature, followed by the actual water vapor pressure, wind speed, and daily minimum temperature. The prediction performance showed a downward trend with the increase in the prediction period. There was no trend of multi-mode integration on the prediction performance of solar radiation. The forecast accuracy of a single model in the ET0 forecast decreased rapidly with the growth of the forecast period. By contrast, the multi-mode integration scheme improved the accuracy of ET0 forecasting and the stability in a long forecast period, and then balanced the tradeoff of weather forecasting accuracy and duration. In ET0 forecasts, the performance of the multi-model schemes was significantly better than that of the original single models. The best prediction performance was achieved in the forecasting precision of the recombination scheme with the optimal models. The smallest RMSE and MAPE were 0.65-0.81 mm/d and 19.43%~23.78%, respectively, and the highest R2 was 0.83-0.89. There was excellent forecast performance of the reorganization scheme in the seasonal ET0 forecast throughout the year, except for the long forecast period in summer. The forecast performance in spring and winter was the best of all the schemes, whereas, the forecast accuracy in autumn was also maintained at a high level. The reorganization scheme still showed better prediction performance in the ET0 prediction of the test field in the next 1-7 days, with the RMSE and the average absolute percentage error not exceeding 0.83 mm/d and 34.57%, respectively. The adaptability of numerical weather forecasts can be verified in the township areas under Yuncheng Station, providing accurate ET0 forecast information for local agricultural production. It was of great significance for agricultural water demand prediction and optimal management of water resources. However, the ET0 prediction performance and applicability of the multi-mode integration scheme need to be verified for the other agricultural production areas and seasons.