{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:31Z","timestamp":1760146351007,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Meteorological Administration","award":["KMA2018-00125"],"award-info":[{"award-number":["KMA2018-00125"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This research presents a novel fuzzy-logic-based algorithm aimed at detecting and removing interference lines from Micro Rain Radar (MRR-2) data. Interference lines, which are non-meteorological echoes with unknown origins, can severely obscure meteorological signals. Leveraging an understanding of interference line characteristics, such as temporal continuity, we identified and utilized eight key variables to distinguish interference lines from meteorological signals. These variables include radar moments, Doppler spectrum peaks, and the spatial\/temporal continuity of Doppler velocity. The algorithm was developed and validated using data from MRR installations at three sites (Seoul, Suwon, and Incheon) in South Korea, from June to September 2021\u20132023. While there is a slight tendency to eliminate some weak precipitation, results indicate that the algorithm effectively removes interference lines while preserving the majority of genuine precipitation signals, even in complex scenarios where both interference and precipitation signals are present. The developed software, written in Python 3 and available as open-source, outputs in NetCDF4 format, with customizable parameters for user flexibility. This tool offers a significant contribution to the field, facilitating the accurate interpretation of MRR-2 data contaminated by interference.<\/jats:p>","DOI":"10.3390\/rs16213965","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T03:46:04Z","timestamp":1729827964000},"page":"3965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fuzzy-Logic-Based Approach for Eliminating Interference Lines in Micro Rain Radar (MRR-2)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-7277","authenticated-orcid":false,"given":"Kwonil","family":"Kim","sequence":"first","affiliation":[{"name":"School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794, USA"}]},{"given":"GyuWon","family":"Lee","sequence":"additional","affiliation":[{"name":"BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu 41566, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4422","DOI":"10.1175\/MWR-D-15-0117.1","article-title":"The Evolution of Lake-Effect Convection during Landfall and Orographic Uplift as Observed by Profiling Radars","volume":"143","author":"Minder","year":"2015","journal-title":"Mon. Weather Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/JTECH-D-19-0085.1","article-title":"Rainfall and DSD Parameters Comparison between Micro Rain Radar, Two-Dimensional Video and Parsivel2 Disdrometers, and S-Band Dual-Polarization Radar","volume":"37","author":"Adirosi","year":"2020","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chang, W.-Y., Lee, G., Jou, B.J.-D., Lee, W.-C., Lin, P.-L., and Yu, C.-K. (2020). Uncertainty in Measured Raindrop Size Distributions from Four Types of Collocated Instruments. Remote Sens., 12.","DOI":"10.3390\/rs12071167"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11955","DOI":"10.5194\/acp-21-11955-2021","article-title":"Impact of Wind Pattern and Complex Topography on Snow Microphysics during International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic Winter","volume":"21","author":"Kim","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chang, W.-Y., Yang, Y.-C., Hung, C.-Y., Kim, K., and Lee, G. (2024). Estimating the Snow Density Using Collocated Parsivel and MRR Measurements: A Preliminary Study from ICE-POP 2017\/2018. EGUsphere.","DOI":"10.5194\/egusphere-2023-3147"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.5194\/amt-5-2661-2012","article-title":"Improved Micro Rain Radar Snow Measurements Using Doppler Spectra Post-Processing","volume":"5","author":"Maahn","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garcia-Benadi, A., Bech, J., Gonzalez, S., Udina, M., Codina, B., and Georgis, J.-F. (2020). Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology. Remote Sens., 12.","DOI":"10.3390\/rs12244113"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3569","DOI":"10.5194\/amt-15-3569-2022","article-title":"ERUO: A Spectral Processing Routine for the Micro Rain Radar PRO (MRR-PRO)","volume":"15","author":"Ferrone","year":"2022","journal-title":"Atmos. Meas. Tech."},{"key":"ref_9","unstructured":"METEK (2017). MRR Physical Basics Version 5.2.0.1, Metek GmbH. Available online: https:\/\/www.inscc.utah.edu\/~hoch\/CFOG\/4DHIRAJ\/mrr-physicalbasics.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.5194\/essd-13-1233-2021","article-title":"Meteorological Observations Collected during the Storms and Precipitation Across the Continental Divide Experiment (SPADE), April\u2013June 2019","volume":"13","author":"Pomeroy","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"E367","DOI":"10.1175\/BAMS-D-21-0184.1","article-title":"ICE GENESIS: Synergetic Aircraft and Ground-Based Remote Sensing and In Situ Measurements of Snowfall Microphysical Properties","volume":"104","author":"Grazioli","year":"2023","journal-title":"Bull. Amer. Meteor. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thurai, M., Bringi, V., Wolff, D., Pabla, C., Lee, G., and Bang, W. (2023). 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Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"E1243","DOI":"10.1175\/BAMS-D-20-0246.1","article-title":"Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign","volume":"103","author":"McMurdie","year":"2022","journal-title":"Bull. Amer. Meteor. Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3965\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:20:10Z","timestamp":1760113210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":15,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16213965"],"URL":"https:\/\/doi.org\/10.3390\/rs16213965","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,10,25]]}}}