Impact of remotely sensed albedo and vegetation fraction on simulation of urban climate in WRF‐urban canopy model: A case study of the urban heat island in Los Angeles

Abstract

Modeling the climate of urban areas is of interest for studying urban heat islands (UHIs). Reliable assessment of the primary causes of UHIs and the efficacy of various heat mitigation strategies requires accurate prediction of urban temperatures and realistic representation of land surface physical characteristics in models. In this study, we expand the capabilities of the Weather Research and Forecasting (WRF) model by implementing high‐resolution, real‐time satellite observations of green vegetation fraction (GVF) and albedo. Satellite‐based GVF and albedo replace constant values that are assumed for urban pixels in the default version of WRF. Simulations of urban meteorology in Los Angeles using the improved model show marked improvements relative to the default model. The largest improvements are for nocturnal air temperatures, with a reduction in root‐mean‐square deviation between simulations and observations from 3.8 to 1.9°C. Utilizing the improved model, we quantify relationships between surface and 2 m air temperatures versus urban fraction, GVF, albedo, distance from the ocean, and elevation. Distance from the ocean is found to be the main contributor to variations in temperatures around Los Angeles. After conditionally sampling pixels to minimize the influence of distance from the ocean and elevation, we find that variations in GVF and urban fraction are responsible for up to 58 and 27% of the variance in temperatures. The satellite‐supported meteorological modeling framework reported here can be used for studying UHIs in other cities and can serve as a foundation for testing the efficacy of various heat mitigation strategies.

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

“…Beside the immediate local effects of the UHI, changes in land surface albedo with urban development may alter the regional surface energy balance and climate more broadly (Barnes & Roy, 2010), and could be an important component of the total climate forcing effect of current and future urban land cover (Bounoua et al, 2015;Reinmann et al, 2016). Conversely, the potential to counteract global and UHI temperature effects by albedo manipulation, for instance through using more reflective materials on roofs, has been the subject of widespread discussion and numeric modeling studies (e.g., Akbari et al, 2012;Jacobsen & Ten Hoeve, 2012;Li et al, 2014;Vahmani & Ban-Weiss, 2016).…”

Urban meteorological modeling using WRF: a sensitivity study

“…Some examples are the proximity to ocean, sea, or lake (Chen et al , ; Salamanca et al , ; Sharma et al , ), type of vegetation and land‐cover (Georgescu et al , ; Sharma et al , , ), presence of streets, buildings, parking lots, residential versus commercial development, city morphology (e.g. horizontal vs vertical structure of buildings), surface albedo (Vahmani and Ban‐Weiss, ), types of construction materials (Santamouris et al , ), and sources of AH [e.g. waste heat from air‐conditioning (A/C) (Salamanca et al , ), power plants (Zevenhoven and Beyene, ), transportation and manufacturing (Sailor, ), etc.…”

Interacting implications of climate change, population dynamics, and urban heat mitigation for future exposure to heat extremes

“…A recent study [44] reported that WRF-UCM simulations can benefit from replacing the default representation of land surface physical characteristics based on climatological information and tabulated values with satellite-based real-time information. To improve the model performance and capture the current climate with high accuracy, we incorporated MODIS-based real-time domain-specific monthly maps of green vegetation fraction (GVF), leaf area index (LAI), and albedo in our WRF-UCM modeling framework.…”