Research on Daily Maximum and Minimum Temperature Forecasts by Integrating Terrain Features and Neural Networks

Accurate forecasting of daily maximum （T max） and minimum （T min） temperatures is essential for meteorological operational， as enhanced precision safeguards socioeconomic stability in agriculture， transportation and public health.To address pronounced systematic biases in numerical weather prediction models over complex terrain-particularly regions with heterogeneous underlying surfaces-this study develops an advanced deep learning framework integrating geographic feature clustering.The study focuses on China’s Hunan Province， characterized by a distinctive concave-shaped， three-tiered topography encompassing mountains， hills， basins， and plains.A baseline convolutional neural network （CNN） was constructed using ECMWF forecast fields， high-resolution CLDAS （China Land Data Assimilation System） reanalysis data， and multi-dimensional geographic variables （elevation， slope， aspect， terrain roughness index）.Three comparative experiments rigorously evaluated terrain-processing efficacy： Method 1 （K-means clustered geographic variables delineating topographic regimes）， Method 2 （Conventionally standardized non-clustered geographic variables）， and Method 3 （Geographic variable exclusion as terrain-agnostic control）.Validation confirmed Method 1's superiority， achieving 24-hour mean absolute error （MAE） reductions of 4.7% for T max and 9.4% for T min relative to Method 3， while improving forecast skill by 2.5% （T max） and 1.4% （T min） compared to Method 2.These statistically significant gains validate the benefits of explicit terrain feature clustering.Building on this approach， the CNN-Terrain Correction （CNN-TC） model was developed for 72-hour Tmax/T min predictions.CNN-TC framework delivers transformative improvements.Versus ECMWF outputs， T max MAE decreased by 23.5%~37.3% and T min MAE by 20.8%~26.9% across 24~72 hour lead times.Compared to operational SCMOC products， errors reduced 18.7%~27.6% （T max） and 26.8%~32.3% （T min）.Critically， the model compresses spatial error dispersion， narrowing 24-hour MAE ranges from 1.2~5.8 ℃/0.8~5.9 ℃ （ECMWF） to a stable 0.9~1.7 ℃/0.8~1.7 ℃ for T max/T min-demonstrating breakthrough operational stability.Monthly verification confirmed persistent superiority， with T max MAE reductions of 5.6%~59.1% and T min improvements of 6.3% to 47.8% relative to ECMWF.During the November 2022 cold-air outbreak， the model captured intricate spatiotemporal cooling patterns， outperforming ECMWF and SCMOC with over 30% error reduction， underscoring its capability in extreme weather.This study verifies that deep learning combined with terrain clustering effectively mitigates systematic biases over complex terrain.The CNN-TC framework establishes a robust solution for refined meteorological services in mountainous regions.Cross-regional implementation requires localized hyperparameter optimization and cluster retraining to address spatial heterogeneity in climate regimes and surface properties.