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WRF模式海南地区地表要素最优初始化时间研究
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Abstract:
数值天气预报的准确性极大地依赖于模式初始化场的质量及其平衡收敛过程，而这一过程在地形复杂、海陆交互显著的热带岛屿区域显得尤为关键。本研究基于WRF模式针对海南岛区域开展了不同分辨率初始场对模式平衡收敛特征的系统研究。采用ERA5 (0.25？)和ERA-Interim (0.75？)再分析资料作为初始场，通过设计短期和长期平行对比试验，分析了2米温度(T2)、2米比湿(Q2)及10米风场(U10、V10)等近地面要素的平衡收敛特征。研究发现，高分辨率初始场显著提升了模式的平衡收敛效率，ERA5驱动的模拟在长期积分中温度场平均收敛时间较ERA-Interim缩短2.7小时(17.4 vs 20.1小时)，比湿场缩短3.3小时(18.1 vs 21.4小时)，风场缩短3.0-3.5小时(U10：20.2 vs 23.2小时，V10：21.1 vs 24.6小时)。短期模拟结果表明，不同物理量具有显著的时间依赖特征：温度场的平均收敛时间为2.8小时，比湿场为3.3小时，风场则需要3.7~4.0小时。特别是在18时起报的预报中，ERA5温度场的动态时间规整(Dynamic Time Warping, DTW)相关系数达到最高值0.93，而ERA-Interim降至0.87，表明ERA5在处理日落前后的温度变化方面具有独特优势。基于研究结果，ERA5在各物理量的预报中均表现出更快的收敛速度和更高的预报准确性，这对提升热带海岛地区数值预报水平具有重要的参考价值。
The accuracy of numerical weather prediction heavily depends on the quality of model initialization fields and their spin-up process, which is particularly crucial in tropical island regions characterized by complex terrain and significant land-sea interactions. This study systematically investigated the impact of initial fields with different resolutions on model spin-up characteristics over Hainan Island using the WRF model. Using ERA5 (0.25？) and ERA-Interim (0.75？) reanalysis data as initial fields, we analyzed the spin-up characteristics of near-surface variables including 2-meter temperature (T2), 2-meter specific humidity (Q2), and 10-meter wind fields (U10, V10) through both short-term and long-term parallel experiments. Results demonstrated that high-resolution initial fields significantly enhanced model spin-up efficiency. In long-term simulations, ERA5-driven experiments showed shorter convergence times compared to ERA-Interim: temperature field convergence time decreased by 2.7 hours (17.4 vs 20.1 hours), specific humidity field by 3.3 hours (18.1 vs 21.4 hours), and wind fields by 3.0~3.5 hours (U10: 20.2 vs 23.2 hours, V10: 21.1 vs 24.6 hours). Short-term simulation results revealed distinct temporal dependencies among different physical variables: temperature field averaged 2.8 hours for convergence, specific humidity field required 3.3 hours, while wind fields needed 3.7~4.0 hours. Notably, in forecasts initialized at 18, ERA5 temperature field achieved the highest DTW correlation coefficient of 0.93, while ERA-Interim dropped to 0.87, indicating ERA5’s superior performance in capturing temperature variations during sunset transitions. Based on these findings, ERA5 demonstrated superior performance in both convergence speed and forecast accuracy across
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