A novel model to estimate sensible heat fluxes in urban areas using satellite-derived data

A novel model for estimating sensible heat flux in urban areas using satellite data is presented here. Sensible heat flux (QH) is a primary component of the urban surface energy budget and is critical to regulating air temperature in cities. The model employs data from the NASA & NOAA GOES-16 geostationary satellite, ground-based observations from NOAA Automated Surface Observation Stations (ASOS), and land cover data from the National Land Cover Database (NLCD) as inputs for an iterative algorithm that is based on surface-layer similarity theory for turbulence parameterization. The application of this model specifically to urban areas is enabled by the spatial extent of the GOES-16 satellite and an element roughness height estimation method based on NLCD land cover data. Model results were independently validated using three flux towers located in different areas of New York City. Statistics generated over a year-long validation period from June 2019 to May 2020 show a root-mean-square error (RMSE) of 47.32 W-m−2, a mean bias error (MBE) of 16.58 W-m−2, and an R2 correlation value of 0.70. Model results were also compared to results from the urbanized Weather Research and Forecasting (uWRF) model relative to flux tower observational data to allow for a comparison between numerical models. The dedicated QH model outperformed the uWRF model relative to observational data, with an RMSE reduction of 63.5 W-m−2, an MBE reduction of 17.5 W-m−2, and an R2 increase of 0.08. Validation results show good agreement between model and observed values and performance comparison results show an improvement over a current numerical method for estimation of QH, suggesting the use of satellite data as a cost-effective and accessible option for estimating QH in urban areas.