DOI: 10.5281/zenodo.3364143
title: Data for exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models

description: Topography exerts major control on land surface processes. To improve representation of topographic impacts on land surface processes, a new topography-based subgrid structure has been introduced to the Energy Exascale Earth System Model representing the subgrid heterogeneity of surface elevation. Four topography-based methods of downscaling grid precipitation to the subgrids have been explored. The data utilized for the study include precipitation, surface elevation, and height rise data derived from wind speed and Brunt Vaisala parameter and outputs of downscaled precipitation and statistical metrics calculated in this study. Results show that utilizing hypsometric elevation of the subgrid landscape within the model grid cell improves downscaling of precipitation in mountainous areas. Furthermore, accounting for blocking of airflow further improves precipitation downscaling slightly in mountainous regions consistently across multiple grid sizes. The data files include: daily_prism_precip.zip: high resolution precipitation data (4 km) obtained from PRISM [Daly et al. 1994, Daly et al. 2008]. dem_4km4.nc: 4 km surface elevation data derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013] fr_number.zip: Height rise of airflow calculated from wind speed and Brunt Vaisala parameter derived from the North American Regional Reanalysis data. output_from_dwnscaling_methods_at_128km.zip: Output data of the downscaling methods at 128 km spatial resolution. output_from_dwnscaling_methods_at_96km.zip: Output data of the downscaling methods at 96 km spatial resolution. output_from_dwnscaling_methods_at_64km.zip: Output data of the downscaling methods at 64 km spatial resolution. output_from_dwnscaling_methods_at_32km.zip: Output data of the downscaling methods at 32 km spatial resolution. ppt_spatial_downscaling_daily_data_flatten_withFr_test_filt0_v3rev_64.py: Python code used to calculate downscaled precipitation data from aggregated grid precipitation data. stns_precip_2015.csv: Precipitation data at rain gauge stations in the Conterminous US extracted from the Daymet station-level input datasets are used for evaluation of the downscaled results Other datasets used to calculate wind speed and Brunt Vaisala parameter were extracted from the North American Regional Reanalysis (NARR) including wind speed, temperature, surface pressure, specific humidity and relative humidity [Mesinger et al. 2006]. <strong>References:</strong> Daly, C., et al. (1994). "A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied Meteorology <strong>33</strong>(2): 140-158. Daly, C., et al. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology <strong>28</strong>(15): 2031-2064. Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94. Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186. Mesinger, F., et al. (2006). "NORTH AMERICAN REGIONAL REANALYSIS." Bulletin of the American Meteorological Society <strong>87</strong>(3): 343-360.