This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature in regions of dimension about 150km∼200km, with 1km grid spacing and 30-minutes interval. Such probabilistic models can accurately forecast short-term temperature fields and serve as a computationally less expensive alternative to physics-based models that necessitate high-performance computing. The probabilistic models here are calibrated from simulations of a physics-based model, the Princeton Urban Canopy Model, coupled to the Weather Research and Forecasting Model (WRF-PUCM). We assess the performance of the calibrated models to forecast short-term near-surface temperature in various cases. In the numerical campaign, our models achieve 0.97-1.13°C root mean squared error (RMSE) for 24 hours ahead forecast; generating three days of forecast takes between 20 and 170 seconds on a single processor computer. Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.

本文介绍了一种在150km~200km维度、1km网格间距和30分钟间隔的区域内近地表温度时空高分辨率中尺度建模的概率方法。这种概率模型可以准确预测短期温度场，并作为需要高性能计算的基于物理的模型的计算成本较低的替代方案。这里的概率模型是根据基于物理的模型、普林斯顿城市冠层模型与天气研究和预测模型 (WRF-PUCM) 的模拟校准的。我们评估校准模型在各种情况下预测短期近地表温度的性能。在数值活动中，我们的模型实现了 0.97-1.13°C 的均方根误差 (RMSE)，可提前 24 小时进行预测；在单处理器计算机上生成三天的预测需要 20 到 170 秒。因此，所提出的方法以相对较高的准确度和较低的计算成本提供预测。