Downscaling of ERA-interim temperature in the contiguous United States and its implications for rain-snow partitioning

Guoqiang Tang; Ali Behrangi; Ziqiang Ma; Di Long; Yang Hong

doi:10.1175/JHM-D-18-0041.1

Downscaling of ERA-interim temperature in the contiguous United States and its implications for rain-snow partitioning

Guoqiang Tang, Ali Behrangi, Ziqiang Ma, Di Long, Yang Hong

Hydrology and Atmospheric Science

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Precipitation 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° to 0.1°. 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 free-air 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.

Original language	English (US)
Pages (from-to)	1215-1233
Number of pages	19
Journal	Journal of Hydrometeorology
Volume	19
Issue number	7
DOIs	https://doi.org/10.1175/JHM-D-18-0041.1
State	Published - Jul 1 2018

Keywords

Classification
Rainfall
Reanalysis data
Satellite observations
Snowfall

ASJC Scopus subject areas

Atmospheric Science

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10.1175/JHM-D-18-0041.1

Cite this

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title = "Downscaling of ERA-interim temperature in the contiguous United States and its implications for rain-snow partitioning",

abstract = "Precipitation 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 Ta 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 Ta is downscaled from the original 0.75° to 0.1°. 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 free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain-snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw 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 Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw 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 Ta and Tw with high resolution and improving rain-snow partitioning, particularly in mountainous regions.",

keywords = "Classification, Rainfall, Reanalysis data, Satellite observations, Snowfall",

author = "Guoqiang Tang and Ali Behrangi and Ziqiang Ma and Di Long and Yang Hong",

note = "Funding Information: Acknowledgments. This study was financially supported by the National Key Research and Development Program of China (2016YFE0102400), and the National Natural Science Foundation of China (Grants 71461010701, 91437214, and 91547210). A. Behrangi is supported by NASA Energy and Water Cycle Study(NNH13ZDA001N-NEWS) and NASA weather (NNH13ZDA001N–Weather) programs. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Additional support came from a scholarship from the China Scholarship Council (CSC). Editors{\textquoteright} and reviewers{\textquoteright} comments for improving this manuscript are highly appreciated. Publisher Copyright: {\textcopyright} 2018 American Meteorological Society.",

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doi = "10.1175/JHM-D-18-0041.1",

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AU - Tang, Guoqiang

AU - Behrangi, Ali

AU - Ma, Ziqiang

AU - Long, Di

AU - Hong, Yang

N1 - Funding Information: Acknowledgments. This study was financially supported by the National Key Research and Development Program of China (2016YFE0102400), and the National Natural Science Foundation of China (Grants 71461010701, 91437214, and 91547210). A. Behrangi is supported by NASA Energy and Water Cycle Study(NNH13ZDA001N-NEWS) and NASA weather (NNH13ZDA001N–Weather) programs. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Additional support came from a scholarship from the China Scholarship Council (CSC). Editors’ and reviewers’ comments for improving this manuscript are highly appreciated. Publisher Copyright: © 2018 American Meteorological Society.

PY - 2018/7/1

Y1 - 2018/7/1

N2 - Precipitation 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 Ta 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 Ta is downscaled from the original 0.75° to 0.1°. 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 free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain-snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw 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 Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw 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 Ta and Tw with high resolution and improving rain-snow partitioning, particularly in mountainous regions.

AB - Precipitation 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 Ta 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 Ta is downscaled from the original 0.75° to 0.1°. 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 free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain-snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw 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 Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw 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 Ta and Tw with high resolution and improving rain-snow partitioning, particularly in mountainous regions.

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KW - Rainfall

KW - Reanalysis data

KW - Satellite observations

KW - Snowfall

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Downscaling of ERA-interim temperature in the contiguous United States and its implications for rain-snow partitioning

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Keywords

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