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Title: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

Abstract

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s -1, respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s -1. The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. Lastly, themore » results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.« less

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [4];  [5]; ORCiD logo [6]
  1. McGill Univ., Montreal, QC (Canada). Dept of Atmospheric and Oceanic Sciences
  2. Stony Brook Univ., NY (United States). School of Marine and Atmospheric Sciences
  3. Stony Brook Univ., NY (United States). School of Marine and Atmospheric Sciences; Brookhaven National Lab. (BNL), Upton, NY (United States). Dept. of Environmental and Climate Sciences
  4. Brookhaven National Lab. (BNL), Upton, NY (United States). Dept. of Environmental and Climate Sciences
  5. Argonne National Lab. (ANL), Argonne, IL (United States). Environmental Science Division
  6. Univ. of Oklahoma, Norman, OK (United States). Cooperative Inst. for Mesoscale Meteorological Studies, and School of Meteorology, NOAA/OAR/National Severe Storms Lab.
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1376172
Alternate Identifier(s):
OSTI ID: 1395009
Report Number(s):
BNL-114147-2017-JA
Journal ID: ISSN 1867-8548; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:
SC0012704; AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Atmospheric Measurement Techniques (Online)
Additional Journal Information:
Journal Name: Atmospheric Measurement Techniques (Online); Journal Volume: 10; Journal Issue: 8; Journal ID: ISSN 1867-8548
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

North, Kirk W., Oue, Mariko, Kollias, Pavlos, Giangrande, Scott E., Collis, Scott M., and Potvin, Corey K. Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E. United States: N. p., 2017. Web. doi:10.5194/amt-10-2785-2017.
North, Kirk W., Oue, Mariko, Kollias, Pavlos, Giangrande, Scott E., Collis, Scott M., & Potvin, Corey K. Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E. United States. doi:10.5194/amt-10-2785-2017.
North, Kirk W., Oue, Mariko, Kollias, Pavlos, Giangrande, Scott E., Collis, Scott M., and Potvin, Corey K. Fri . "Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E". United States. doi:10.5194/amt-10-2785-2017. https://www.osti.gov/servlets/purl/1376172.
@article{osti_1376172,
title = {Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E},
author = {North, Kirk W. and Oue, Mariko and Kollias, Pavlos and Giangrande, Scott E. and Collis, Scott M. and Potvin, Corey K.},
abstractNote = {The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s-1, respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s-1. The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. Lastly, the results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.},
doi = {10.5194/amt-10-2785-2017},
journal = {Atmospheric Measurement Techniques (Online)},
number = 8,
volume = 10,
place = {United States},
year = {Fri Aug 04 00:00:00 EDT 2017},
month = {Fri Aug 04 00:00:00 EDT 2017}
}

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