大气科学学报2025，Vol.48Issue(3)：476-485,10.DOI:10.13878/j.cnki.dqkxxb.20230905003
两种机器学习模型在甘肃小时气温预报中的应用
Application of two machine learning models for hourly temperature pre-diction in Gansu Province
摘要
Abstract
Numerical model predictions often contain biases when compared to local observations.Correcting these biases is essential for improving forecast accuracy.This study uses CMA-MESO model data from 2020 to 2021(including hourly 2 m temperature,10 m wind components,sea level pressure,etc.)and observations from 340 assessment stations in Gansu Province to develop two time-lag correction models for 2 m air temperature.The correction models are based on machine learning methods,specifically LightGBM and XGBoost.The evaluation and performance analysis revealed the following:1)The accuracy of the LightGBM and XGBoost models in pre-dicting hourly 2 m air temperature was 74.57％and 74.33％,respectively.Both models improved prediction accu-racy by 27.6％and 27.2％compared to SCMOC and by 53.5％and 53.0％compared to the CMA-MESO mod-el.The LightGBM model slightly outperformed XGBoost,particularly in regions where CMA-MESO model per-formed poorly,with improvement rates exceeding 45％.2)Both models significantly reduced the forecast bias in diurnal variation for hourly 2 m air temperature compared to CMA-MESO,though performance at 07:00 BST and 16:00 BST was less accurate than at other times.3)The CMA-MESO model's forecasted 2 m temperatures showed divergent and asymmetric distributions,while the LightGBM and XGBoost models reduced systematic bi-ases,achieving more symmetric and convergent distributions.Both correction models decreased the number of sta-tions with large forecast errors,and their root mean square error distribution approached an unbiased state.The LightGBM,in particular,excelled in correcting areas with significant forecast errors and in predicting temperature peaks.These results demonstrate that machine learning methods offer great potential for improving 2 m temperature predictions in numerical forecast products.关键词
机器学习/小时气温/LightGBM/XGBoost/预报性能Key words
machine learning/hourly air temperature/LightGBM/XGBoost/forecast performance引用本文复制引用
杨秀梅,黄武斌,李天江,王基鑫,王一丞..两种机器学习模型在甘肃小时气温预报中的应用[J].大气科学学报,2025,48(3):476-485,10.基金项目
甘肃省青年科技基金计划项目(22JR5RA751) （22JR5RA751）
甘肃省自然科学基金项目(23JRRA1572) （23JRRA1572）
"飞云"青年拔尖人才项目(2425-rczx) （2425-rczx）
甘肃省气象局气象科研重点项目(Zd20284-B-2) （Zd20284-B-2）
中国气象局青年创新团队项目(CMA2024QN04) （CMA2024QN04）
甘肃省气象局创新团队项目(GSQXCXTD-2024-01) （GSQXCXTD-2024-01）
