Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields

Abstract

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.