1. Sensitivity of Predictions of the Urban Surface Energy Balance and Heat Island to Variations of Urban Canopy Parameters in Simulations with the WRF Model.
- Author
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NEMUNAITIS-BERRY, KODI L., KLEIN, PETRA M., BASARA, JEFFREY B., and FEDOROVICH, EVGENI
- Subjects
URBAN heat islands ,SURFACE energy ,ATMOSPHERIC models ,SKIN temperature ,CIRCADIAN rhythms ,TEMPERATURE ,HEAT flux - Abstract
As NWP and climate models continue to evolve toward finer grid spacing, efforts have been undertaken to better represent urban effects. For this study, the single-layer urban canopy model (SLUCM) of the High-Resolution Land Data Assimilation System (HRLDAS) and WRF Model was used to investigate the sensitivity of near-surface air temperatures and energy fluxes to SLUCM parameters in uncoupled (land) and coupled (land-atmosphere) predictions. Output from HRLDAS and WRF was compared with observations from the Oklahoma Mesonet and Joint Urban 2003 experiment. Variations in roof albedo (0.04-0.4) produced 40-135 W m
22 changes in net radiation and sensible heat fluxes. Sensible and ground heat fluxes varied by 40-100 W m22 with changes in roof thermal conductivity (0.05-1.4). The urban fraction was found to be the only SLUCM parameter to significantly impact latent heat fluxes. Near-surface air temperatures, particularly during the daytime, did not show significant variations with SLUCM parameters (remaining within the 0.5-K range). Differences in urban air temperatures due to the change in boundary layer scheme were greater than the temperature changes due to SLUCM parameter variations. The sensitivity of near-surface air temperatures to SLUCM parameters depended on the method used to calculate the skin temperature of the impervious surface. For all simulations, predicted 2-m urban air temperatures were consistently higher than observations, with deviations approaching 8K during the day and below 3K at night. These large errors affected the model's skill in reproducing the diurnal cycle of UHI intensity. [ABSTRACT FROM AUTHOR]- Published
- 2017
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