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Sensitivity of WRF Regional Climate Simulations to Choice of Land Use Dataset
Citation:
Mallard, M., T. Spero, AND S. Taylor. Sensitivity of WRF Regional Climate Simulations to Choice of Land Use Dataset. 2016 CMAS Conference, Chapel Hill, NC, October 24 - 26, 2016.
Impact/Purpose:
The purpose is to communicate the results of a sensitivity study done in which different land use datasets are used to drive regional climate runs performed with the Weather Research and Forecasting (WRF) model.
Description:
The goal of this study is to assess the sensitivity of regional climate simulations run with the Weather Research and Forecasting (WRF) model to the choice of datasets representing land use and land cover (LULC). Within a regional climate modeling application, an accurate representation of air-surface interactions is critical in order to properly simulate key variables, such as near-surface temperatures, humidity, and precipitation. It can be anticipated that these fields would be sensitive to the source of LULC data as several variables that play an important role in the surface energy and radiative budgets are set based on the LULC type in WRF. These include but are not limited to surface albedo, emissivity, and green vegetation fraction. In the present study, WRF is run with the Noah land surface model (LSM) for a set of 3-year (1988-1990) historical downscaling simulations performed with 108- and 36-km grid spacing in a nested domain set-up. WRF is driven with two LULC datasets: the 24-category U.S. Geological Survey (USGS) dataset and the 40-category 2006 National Land Cover Dataset (NLCD). A small modification to the source code of WRF version 3.6.1 within the Noah LSM is needed in order to prevent NLCD shrub and grasslands from being incorrectly treated as urban areas, and results from this modified simulation will be compared with results from the released code. However, the analysis will focus on the contrast between the USGS- and NLCD-driven runs by comparing both monthly mean temperature and precipitation as well as daily extremes.
