Developing short-term probabilistic forecasts of meso-scale near-surface urban temperature fields
Developing short-term probabilistic forecasts of meso-scale near-surface urban temperature fields
复制标题
发展中尺度近地表城市温度场的短期概率预测
DOI：
10.1016/j.envsoft.2021.105189
复制
发表时间：
2021
影响因子：
4.9
通讯作者：
Pozzi, Matteo
中科院分区：
文献类型：
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作者：
Choi, Byeongseong;Berges, Mario;Bou-Zeid, Elie;Pozzi, Matteo
本文介绍了一种时空高分辨率中尺度近地面温度模式的概率方法，并将其应用于150 ~ 200 km的区域，网格间距为1 km，间隔为30 min。我们的概率方法基于线性高斯模型和降维，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。这里的概率模型是从基于物理的模型，普林斯顿城市冠层模型，耦合到天气研究和预报模型（WRF-PUCM）的模拟校准。我们评估的性能校准模型预测短期近地表温度在各种情况下。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;在单个处理器（Intel Xeon E5-2690 v4@2.60GHz）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高的精度和低的计算成本提供预测。
This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature and applies it to regions of dimension about 150∼200 km, with 1 km grid spacing and 30-min interval. Our probabilistic approach, based on linear Gaussian models and dimensionality reduction, 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 sec on a single processor (Intel Xeon E5-2690 v4@2.60GHz). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.