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Title: Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling

Abstract

The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures the main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.

Authors:
 [1];  [1]
  1. Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
U.S. Department of Defense (DOD)
OSTI Identifier:
1396297
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Geophysical Research: Atmospheres; Journal Volume: 119; Journal Issue: 14
Country of Publication:
United States
Language:
English
Subject:
Dynamical Downscaling; High Resolution; Regional Climate; Regional Climate Model

Citation Formats

Wang, Jiali, and Kotamarthi, Veerabhadra R. Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling. United States: N. p., 2014. Web. doi:10.1002/2014JD021696.
Wang, Jiali, & Kotamarthi, Veerabhadra R. Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling. United States. doi:10.1002/2014JD021696.
Wang, Jiali, and Kotamarthi, Veerabhadra R. Sun . "Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling". United States. doi:10.1002/2014JD021696.
@article{osti_1396297,
title = {Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling},
author = {Wang, Jiali and Kotamarthi, Veerabhadra R.},
abstractNote = {The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures the main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.},
doi = {10.1002/2014JD021696},
journal = {Journal of Geophysical Research: Atmospheres},
number = 14,
volume = 119,
place = {United States},
year = {Sun Jul 27 00:00:00 EDT 2014},
month = {Sun Jul 27 00:00:00 EDT 2014}
}
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