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Title: A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

Abstract

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events.more » Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.« less

Authors:
 [1];  [2]; ORCiD logo [3]
  1. Univ. of Utah, Salt Lake City, UT (United States). Dept. of Atmospheric Sciences
  2. Univ. of Wisconsin, Madison, WI (United States). Cooperative Inst. for Meteorological Satellite Studies
  3. Univ. of Wisconsin, Madison, WI (United States). Dept. of Atmospheric and Oceanic Sciences
Publication Date:
Research Org.:
Univ. of Wisconsin, Madison, WI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Science Foundation (NSF)
OSTI Identifier:
1392997
Grant/Contract Number:  
SC0016045; 1531930; 1303965
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Measurement Techniques (Online)
Additional Journal Information:
Journal Name: Atmospheric Measurement Techniques (Online); Journal Volume: 10; Journal Issue: 7; Journal ID: ISSN 1867-8548
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Cooper, Steven J., Wood, Norman B., and L'Ecuyer, Tristan S. A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations. United States: N. p., 2017. Web. doi:10.5194/amt-10-2557-2017.
Cooper, Steven J., Wood, Norman B., & L'Ecuyer, Tristan S. A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations. United States. doi:10.5194/amt-10-2557-2017.
Cooper, Steven J., Wood, Norman B., and L'Ecuyer, Tristan S. Thu . "A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations". United States. doi:10.5194/amt-10-2557-2017. https://www.osti.gov/servlets/purl/1392997.
@article{osti_1392997,
title = {A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations},
author = {Cooper, Steven J. and Wood, Norman B. and L'Ecuyer, Tristan S.},
abstractNote = {Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.},
doi = {10.5194/amt-10-2557-2017},
journal = {Atmospheric Measurement Techniques (Online)},
number = 7,
volume = 10,
place = {United States},
year = {2017},
month = {7}
}

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