An XGBoost-Based Method for Temperature Forecasting Correction
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摘要: 以日内最高、最低温度以及逐小时整点温度为预报对象,并以ECMWF模式不同起报时间对同一时次的预报项、ECMWF模式前期的预报误差项、前期24小时的实况温度演变项以及用于区分站点(格点)的空间位置信息等共同作为模型的预报因子,采用时空堆叠结合eXtreme Gradient Boosting(XGBoost)方法,与每个预报对象分别构建预报模型,实现了降时间尺度到逐小时的预报以及站点建模到格点预报转化的温度订正预报模型。一年的独立样本试验结果表明:(1) 订正后的日内最高和最低温度预报准确率较中央气象台 (National Meteorological Center,NMC)温度指导预报产品(NMC)有明显提高,其中08时和20时起报的预报产品分别提高了28.6%、23.2%和25.5%、16.9%。(2) 逐小时整点温度订正预报中,两种预报产品在每日最高气温出现时段的16时附近均是预报误差比较大的时段;但在绝大多数预报时效的预报中,XGBoost模型显示了比 NMC预报产品更好的预报精度。此外,预报个例分析表明,新方案对于较明显的转折天气存在预报滞后性问题。目前,该订正预报方法已实现业务化运行。Abstract: This article focuses on the forecasting of daily maximum and minimum temperatures, as well as hourly temperatures. It utilizes different forecast start times of the ECMWF model for the same time period, early-stage forecast errors of the ECMWF model, the evolution of actual temperatures in the previous 24 hours, and spatial information used to distinguish between stations (grid points) as forecast factors. The approach combines spatiotemporal stacking with the XGBoost method to develop separate forecast models for each temperature variable, achieving a temperature correction forecast model that reduces the time scale to hourly forecasts and transforms from station modeling to grid point forecasting. The results of the independent sample experiment for one year show that (1) the accuracy of the corrected daily maximum and minimum temperature forecast has significantly improved compared to the temperature guidance forecast product (NMC) of the China Meteorological Administration. The forecast products for 08: 00 and 20: 00 have increased by 28.6%, 23.2%, 25.5%, and 16.9%, respectively. (2) In the hourly temperature correction forecast, both forecast products have larger forecast errors around 16:00 when the daily temperature peaks. However, in most forecast periods, the XGBoost model shows better forecast accuracy than the NMC forecast product. Moreover, case analysis of the forecast shows that the new method has a lag problem for more pronounced weather changes. Currently, the temperature forecasting correction method has been implemented for operational use.
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