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基于XGBoost的温度订正预报方法研究
赵华生, 黄小燕
广西壮族自治区气象科学研究所,广西 南宁 530022
摘要:
以日最高、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,对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) 订正后的日最高和最低温度预报准确率较中央气象台 (NationalMeteorological Center,NMC)温度指导预报产品有明显提高,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。
关键词:  XGBoost  温度订正预报  数值模式释用  格点化预报  时空堆叠
DOI:10.16032/j.issn.1004-4965.2024.066
分类号:
基金项目:
An XGBoost-Based Method for Temperature Forecasting Correction
ZHAO Huasheng, HUANG Xiaoyan
Guangxi Zhuang Autonomous Region Institute of Meteorological Sciences, Nanning 530006, China
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.
Key words:  XGBoost  temperature forecasting correction  numerical model post-processing  gridded forecast  space-time stacking
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