We develop probabilistic models for meso-scale near-surface urban air temperature.
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We calibrate/validate the models on simulated data for New York City and Pittsburgh.
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A Kalman filter/smoother updates the proposed models for adaptive forecast.
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The proposed models use 3–8% the computing resources used by a comparable model.
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24-h ahead forecasts show 0.97–1.13 °C prediction error.
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 [email protected]). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost.
