Downscaling of ERA-Interim Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherAMER METEOROLOGICAL SOC
CitationTang, G., A. Behrangi, Z. Ma, D. Long, and Y. Hong, 2018: Downscaling of ERA-Interim Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning. J. Hydrometeor., 19, 1215–1233, https://doi.org/10.1175/JHM-D-18-0041.1
JournalJOURNAL OF HYDROMETEOROLOGY
Rights© 2018 American Meteorological Society.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractPrecipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain-snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature T-a downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim T-a is downscaled from the original 0.75 degrees to 0.1 degrees. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the freeair temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of T-a. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature T-w for rain-snow classification, using T-a and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate T-w, compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain-snow partitioning is conducted using a critical threshold of T-w, with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based T-w, using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality T-a and T-w, with high resolution and improving rain-snow partitioning, particularly in mountainous regions.
Note6 month embargo; published online: 31 July 2018
VersionFinal published version
SponsorsNational Key Research and Development Program of China [2016YFE0102400]; National Natural Science Foundation of China [71461010701, 91437214, 91547210]; NASA Energy and Water Cycle Study program [NNH13ZDA001N-NEWS]; NASA weather program [NNH13ZDA001N-Weather]; China Scholarship Council (CSC)