We use various temperature profilers located in and around New York City to observe the structure and evolution of the thermal boundary layer. The primary focus is to highlight the spatial variability of potential-temperature profiles due to heterogeneous surface forcing in an urban environment during different flow conditions. Overall, the observations during the summer period reveal the presence of thermal internal boundary layers due to the interaction between the marine atmospheric boundary layer and the convective urban environment. The summer daytime potential-temperature profiles within the city indicate a superadiabatic layer is present near the surface beneath a mildly stable layer. Large spatial variability in the near-surface (0-300 m) potential temperature is detected, with the thermal profile in the lower atmosphere uniquely determined by the underlying surface forcing and the distance from the coast. The summer and winter average night-time potential-temperature profiles show that the atmosphere is still convective near the surface. The seasonal averages of mixing ratio show large variability in the vertical direction.Keywords Coastal boundary layer · Microwave radiometer · Moisture boundary layer · Thermal boundary layer · Urban boundary layer
Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance.
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