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Title: Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

Abstract

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. Themore » new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.« less

Authors:
 [1];  [1];  [2]; ORCiD logo [3]
  1. Leibniz Inst. for Tropospheric Research (ITR), Leipzig (Germany); Universität Leipzig (Germany)
  2. McGill Univ., Montreal, QC (Canada)
  3. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1561245
Report Number(s):
BNL-212054-2019-JAAM
Journal ID: ISSN 1867-8548
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Measurement Techniques (Online)
Additional Journal Information:
Journal Name: Atmospheric Measurement Techniques (Online); Journal Volume: 12; 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

Kalesse, Heike, Vogl, Teresa, Paduraru, Cosmin, and Luke, Edward. Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm. United States: N. p., 2019. Web. doi:10.5194/amt-12-4591-2019.
Kalesse, Heike, Vogl, Teresa, Paduraru, Cosmin, & Luke, Edward. Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm. United States. doi:10.5194/amt-12-4591-2019.
Kalesse, Heike, Vogl, Teresa, Paduraru, Cosmin, and Luke, Edward. Fri . "Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm". United States. doi:10.5194/amt-12-4591-2019. https://www.osti.gov/servlets/purl/1561245.
@article{osti_1561245,
title = {Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm},
author = {Kalesse, Heike and Vogl, Teresa and Paduraru, Cosmin and Luke, Edward},
abstractNote = {In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.},
doi = {10.5194/amt-12-4591-2019},
journal = {Atmospheric Measurement Techniques (Online)},
number = 8,
volume = 12,
place = {United States},
year = {2019},
month = {8}
}

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