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Title: Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias

Abstract

The Weather and Research Forecast (WRF) model version 3.0.1 is used to explore California wintertime model wet bias. In this study, two wintertime storms are selected from each of four major types of large-scale conditions; Pineapple Express, El Nino, La Nina, and synoptic cyclones. We test the impacts of several model configurations on precipitation bias through comparison with three sets of gridded surface observations; one from the National Oceanographic and Atmospheric Administration, and two variations from the University of Washington (without and with long-term trend adjustment; UW1 and UW2, respectively). To simplify validation, California is divided into 4 regions (Coast, Central Valley, Mountains, and Southern California). Simulations are driven by North American Regional Reanalysis data to minimize large-scale forcing error. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to evaluate the robustness of microphysics and cumulus parameterizations to resolution changes. We find that the choice of validation dataset has a significant impact on the model wet bias, and the forecast skill of model precipitation depends strongly on geographic location and storm type. Simulations with right physics options agree better with UW1 observations. In 12-km resolution simulations, the Lin microphysicsmore » and the Kain-Fritsch cumulus scheme have better forecast skill in the coastal region while Goddard, Thompson, and Morrison microphysics, and the Grell-Devenyi cumulus scheme perform better in the rest of California. The effect of planetary boundary layer, soil-layer, and radiation physics on model precipitation is weaker than that of microphysics and cumulus processes for short- to medium-range low-resolution simulations. Comparison of 2-km and 12-km resolution runs suggests a need for improvement of cumulus schemes, and supports the use of microphysics schemes in coarser-grid applications.« less

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
989337
Report Number(s):
LLNL-JRNL-415533
Journal ID: ISSN 0027-0644; MWREAB; TRN: US201019%%445
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Journal Article
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Volume: 138; Journal Issue: 9; Journal ID: ISSN 0027-0644
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; BOUNDARY LAYERS; CALIFORNIA; COASTAL REGIONS; MOUNTAINS; PHYSICS; PRECIPITATION; RADIATIONS; RESOLUTION; SOUTHERN OSCILLATION; STORMS; VALIDATION; WEATHER

Citation Formats

Chin, H S, Caldwell, P M, and Bader, D C. Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias. United States: N. p., 2009. Web.
Chin, H S, Caldwell, P M, & Bader, D C. Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias. United States.
Chin, H S, Caldwell, P M, and Bader, D C. 2009. "Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias". United States. https://www.osti.gov/servlets/purl/989337.
@article{osti_989337,
title = {Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias},
author = {Chin, H S and Caldwell, P M and Bader, D C},
abstractNote = {The Weather and Research Forecast (WRF) model version 3.0.1 is used to explore California wintertime model wet bias. In this study, two wintertime storms are selected from each of four major types of large-scale conditions; Pineapple Express, El Nino, La Nina, and synoptic cyclones. We test the impacts of several model configurations on precipitation bias through comparison with three sets of gridded surface observations; one from the National Oceanographic and Atmospheric Administration, and two variations from the University of Washington (without and with long-term trend adjustment; UW1 and UW2, respectively). To simplify validation, California is divided into 4 regions (Coast, Central Valley, Mountains, and Southern California). Simulations are driven by North American Regional Reanalysis data to minimize large-scale forcing error. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to evaluate the robustness of microphysics and cumulus parameterizations to resolution changes. We find that the choice of validation dataset has a significant impact on the model wet bias, and the forecast skill of model precipitation depends strongly on geographic location and storm type. Simulations with right physics options agree better with UW1 observations. In 12-km resolution simulations, the Lin microphysics and the Kain-Fritsch cumulus scheme have better forecast skill in the coastal region while Goddard, Thompson, and Morrison microphysics, and the Grell-Devenyi cumulus scheme perform better in the rest of California. The effect of planetary boundary layer, soil-layer, and radiation physics on model precipitation is weaker than that of microphysics and cumulus processes for short- to medium-range low-resolution simulations. Comparison of 2-km and 12-km resolution runs suggests a need for improvement of cumulus schemes, and supports the use of microphysics schemes in coarser-grid applications.},
doi = {},
url = {https://www.osti.gov/biblio/989337}, journal = {Monthly Weather Review},
issn = {0027-0644},
number = 9,
volume = 138,
place = {United States},
year = {Wed Jul 22 00:00:00 EDT 2009},
month = {Wed Jul 22 00:00:00 EDT 2009}
}