Deep Learning Grid-point Temperature Forecasting in Changde City with Multi-scale Feature Integration 

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  • 1. Hunan Key Laboratory of Meteorological Disaster Prevention and ReductionChangsha 410118HunanChina
    2. Changde Meteorological Bureau of Hunan ProvinceChangde 415000HunanChina
    3. Center for Applied Mathematics of Jiangsu ProvinceNanjing University of Information Science and TechnologyNanjing 210044JiangsuChina
    4. Hunan Meteorological ObservatoryChangsha 410118HunanChina
    5. Hunan Weather Modification CenterChangsha 410118HunanChina

Online published: 2025-05-08

Abstract

This study aims to enhance the accuracy of urban grid temperature forecasts by integrating multi-scale features from both upper-air and surface levels. Utilizing forecast data from the European Centre for MediumRange Weather ForecastsECMWFmodel and hourly temperature grid observation data from the China Meteorological Administration's High-Resolution Land Data Assimilation SystemHRCLDASfor the Changde region from April to September 2021-2024a high-resolution Multi-Scale U-NetMU-NETmodel was employed. Three sets of experiments were designed to develop a deep learning model capable of predicting hourly temperatures in Changde City for the next 24 hours. The experimental results demonstrate that the MU-NET modelwhich integrates surface and upper-air featuresexhibits the best correction performance within the study area. The mean absolute errorMAEand root mean square errorRMSEof the MU-NET model were reduced by 22% and 25%respectivelycompared to the ECMWF model. Additionallythe MU-NET model achieved the lowest MAE values in diurnal variationsparticularly excelling in the prediction of daily maximum temperatureswith an average reduction of over 0. 4 ℃. On a spatial scalethe MU-NET model showed a 60%~80% improvement in forecast skill scores over the ECMWF model in high-altitude regionswith the most significant improvements observed in plain and Dongting Lake areas. During two critical weather events in 2024the MUNET model demonstrated stable forecast performance due to its integration of surface and upper-air featuresparticularly in handling complex weather phenomena. The findings of this study provide new insights for improving the accuracy of temperature forecasts and offer valuable references for practical meteorological forecasting.

Cite this article

LI Lu, YOU Xiaoxiong, CHEN Jingjing, HU Zhenju, LU Shu, LI Qiong . Deep Learning Grid-point Temperature Forecasting in Changde City with Multi-scale Feature Integration [J]. Plateau Meteorology, 0 : 1 . DOI: 10.7522/j.issn.1000-0534.2025.00040

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