Li Zhang1,2 and Kenneth J. Davis1,3
1Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA; 2Planning, Rule Development, and Implementation Division, South Coast Air Quality Management District, Diamond Bar, CA, USA; 3Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA
Land-surface energy dynamics and boundary layer mixing are pivotal to the fidelity of meteorology prediction and air quality modeling. Yet their accurate representation in the current generation of regional weather models remains a challenge due to the complexity of the land surface and limited observational constraints. Here, we integrate comprehensive satellite-based land-surface measurements into a state-of-the-art regional weather forecast model—NASA Unified Weather Research and Forecast model coupled with the NASA Land Information System (Nu-WRF/LIS). Leveraging real-time assimilation of high-resolution satellite measurements of land cover, greenness, leaf area index, and soil moisture datasets, we notably improve model performance in surface energy fluxes (i.e., sensible and latent heat fluxes) and atmospheric boundary layer mixing across four seasons over various land covers in the western U.S. (e.g., arid areas, forests, grassland, and croplands). The improvements are most prominent over irrigated croplands and grasslands, where the assimilation of land-surface remote sensing in Nu-WRF/LIS reduces the model biases in annual surface energy fluxes by 50% to 200%. Model biases in atmospheric boundary layer height are reduced by ~60 m on an annual average basis. These improvements to numerical weather reanalyses strengthen the scientific foundation for the formulation of state-level air quality implementation strategies and offer a precise basis for shaping emission regulatory policies.
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