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Title: Assimilation of Z DR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments

Abstract

Achieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System’s cloud analysis is modified from its original reflectivity-based formulation to provide moisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivitymore » structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Finally, based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted.« less

Authors:
 [1];  [2];  [3];  [3]
  1. School of Meteorology, and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
  2. NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
  3. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
Publication Date:
Research Org.:
Univ. of Oklahoma, Norman, OK (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1413571
Alternate Identifier(s):
OSTI ID: 1541864
Grant/Contract Number:  
SC0014295
Resource Type:
Published Article
Journal Name:
Monthly Weather Review
Additional Journal Information:
Journal Name: Monthly Weather Review Journal Volume: 145 Journal Issue: 12; Journal ID: ISSN 0027-0644
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; meteorology & atmospheric sciences; radars/radar obsrevations; numerical weather prediction/forecasting; cloud resolving models; data assimilation

Citation Formats

Carlin, Jacob T., Gao, Jidong, Snyder, Jeffrey C., and Ryzhkov, Alexander V. Assimilation of Z DR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments. United States: N. p., 2017. Web. doi:10.1175/MWR-D-17-0103.1.
Carlin, Jacob T., Gao, Jidong, Snyder, Jeffrey C., & Ryzhkov, Alexander V. Assimilation of Z DR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments. United States. doi:10.1175/MWR-D-17-0103.1.
Carlin, Jacob T., Gao, Jidong, Snyder, Jeffrey C., and Ryzhkov, Alexander V. Fri . "Assimilation of Z DR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments". United States. doi:10.1175/MWR-D-17-0103.1.
@article{osti_1413571,
title = {Assimilation of Z DR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments},
author = {Carlin, Jacob T. and Gao, Jidong and Snyder, Jeffrey C. and Ryzhkov, Alexander V.},
abstractNote = {Achieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System’s cloud analysis is modified from its original reflectivity-based formulation to provide moisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivity structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Finally, based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted.},
doi = {10.1175/MWR-D-17-0103.1},
journal = {Monthly Weather Review},
number = 12,
volume = 145,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1175/MWR-D-17-0103.1

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Cited by: 7 works
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Works referencing / citing this record:

Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction
journal, April 2019

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