Spatiotemporal post-calibration in a Numerical Weather Prediction model for quantifying building energy consumption

Abstract

Characterizing localized climate conditions is becoming important in many aspects of modern society. The Weather Research and Forecasting (WRF) models have been used to predict localized environmental variations. Further, the recently developed Urban Canopy Model (UCM), derived from energy balance equations, represents more detailed urban characteristics, when it is coupled with the WRF model. However, such physics-based numerical models can exhibit a spatially and temporarily heterogeneous discrepancy pattern compared to actual climate conditions possibly due to inappropriate model specifications and/or incorrect choices of model parameters. This study devises a new method that post-calibrates geographically and temporally-varying discrepancy in an integrative framework. Tested on urban temperature data collected in the central Texas region during heat wave events, our case study demonstrates that the proposed method substantially reduces prediction errors over the original WRF/UCM projection and other alternative approaches. Based on the results, we quantify the building energy consumption at spatially dispersed locations.