Short-term probabilistic forecasting of meso-scale near-surface urban temperature fields. (November 2021)

• Record Type: Journal Article
• Authors: Choi, Byeongseong; Bergés, Mario; Bou-Zeid, Elie; Pozzi, Matteo
• Is Part Of: Environmental modelling & software. Volume 145(2021)
• Publication Date: 2021-11
• Subjects: Urban heat -- Probabilistic model -- Spatio-temporal model -- Latent space -- State-space model -- Kalman filter; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105189
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: We develop probabilistic models for meso-scale near-surface urban air temperature. We calibrate/validate the models on simulated data for New York City and Pittsburgh. A Kalman filter/smoother updates the proposed models for adaptive forecast. The proposed models use 3–8 % the computing resources used by a comparable model. 24-h ahead forecasts show 0.97–1.13 °C prediction error.