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Impacts of Tiled Land Cover Characterization on Global Meteorological Predictions Using the MPAS-A
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2020
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Source: ournal of Geophysical Research: Atmospheres, 125, e2019JD032093.
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Journal Title:Journal of Geophysical Research: Atmospheres
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Description:Parameterization of subgrid‐scale variability of land cover characterization (LCC) is an active area of research and can improve model performance compared to the dominant (i.e., most abundant tile) approach. The “Noah” land surface model implementation in the global Model for Prediction Across Scales‐Atmosphere (MPAS‐A), however, only uses the dominant LCC approach that leads to oversimplification in regions of highly heterogeneous LCC (e.g., urban/suburban settings). Thus, in this work we implement a subgrid tiled approach as an option in MPAS‐A, version 6.0, and assess the impacts of tiled LCC on meteorological predictions for two gradually refining meshes (92–25 and 46–12 km) focused on the conterminous United States for January and July 2016. Compared to the dominant approach, results show that using the tiled LCC leads to pronounced global changes in 2‐m temperature (July global average change approximately −0.4 K), 2‐m moisture, and 10‐m wind speed for the 92–25 km mesh. The tiled LCC reduces mean biases in 2‐m temperature (July U.S. average bias reduction approximately factor of 4) and specific humidity in the central and western United States for the 92–25 km mesh and improves the agreement of vertical profiles (e.g., temperature, humidity, and wind speed) with observed radiosondes; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic temperature biases found in numerical weather prediction models, particularly those that employ a dominant LCC approach.
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Source:ournal of Geophysical Research: Atmospheres, 125, e2019JD032093.
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