Journal
JOURNAL OF HYDROMETEOROLOGY
Volume 21, Issue 1, Pages 93-108Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-19-0109.1
Keywords
In situ atmospheric observations; Reanalysis data
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Funding
- NASA Science Utilization of the Soil Moisture Active-Passive Mission Program [NNX16AQ47G]
- NASA High Mountain Asia Program [NNX16AQ89G]
- NASA [896546, 896259, NNX16AQ89G, NNX16AQ47G] Funding Source: Federal RePORTER
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This study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been implemented over Oklahoma, where high-resolution, high-quality observations are available for validation purposes. Hourly North America Land Data Assimilation System version 2 (NLDAS-2) meteorological data (i.e., near-surface air temperature, pressure, humidity, wind speed, and incident longwave and shortwave radiation) have been spatially downscaled from their original 1/8 degrees resolution to a 500-m grid over the study area during 2015. Results show that correlation coefficients between the downscaled products and ground observations are consistently higher than the ones between the native resolution NLDAS-2 data and ground observations. Furthermore, the downscaled variables present smaller biases than the original ones with respect to ground observations. Results are therefore encouraging toward the use of the 500-m dataset for land surface and hydrological modeling. This would be especially useful in regions where ground-based observations are sparse or not available altogether, and where downscaled global reanalysis products may be the only option for model inputs at scales that are useful for decision-making.
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