{"id":"https://openalex.org/W2170198132","doi":"https://doi.org/10.1109/igarss.2007.4423293","title":"Processing disdrometer raindrop spectra time series from various climatological regions using estimation and autoregressive methods","display_name":"Processing disdrometer raindrop spectra time series from various climatological regions using estimation and autoregressive methods","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2170198132","doi":"https://doi.org/10.1109/igarss.2007.4423293","mag":"2170198132"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2007.4423293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071120785","display_name":"Mario Montopoli","orcid":"https://orcid.org/0000-0003-0099-0393"},"institutions":[{"id":"https://openalex.org/I26415053","display_name":"University of L'Aquila","ror":"https://ror.org/01j9p1r26","country_code":"IT","type":"education","lineage":["https://openalex.org/I26415053"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"M. Montopoli","raw_affiliation_strings":["Department of Electrical Engineering and Information and CETEMPS, University of L''Aquila, L'Aquila, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Information and CETEMPS, University of L''Aquila, L'Aquila, Italy","institution_ids":["https://openalex.org/I26415053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064157096","display_name":"Gianfranco Vulpiani","orcid":"https://orcid.org/0000-0002-8008-8799"},"institutions":[{"id":"https://openalex.org/I26415053","display_name":"University of L'Aquila","ror":"https://ror.org/01j9p1r26","country_code":"IT","type":"education","lineage":["https://openalex.org/I26415053"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"G. Vulpiani","raw_affiliation_strings":["Department of Electrical Engineering and Information and CETEMPS, University of L''Aquila, L'Aquila, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Information and CETEMPS, University of L''Aquila, L'Aquila, Italy","institution_ids":["https://openalex.org/I26415053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001400039","display_name":"Marios N. Anagnostou","orcid":"https://orcid.org/0000-0002-6828-085X"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. N. Anagnostou","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103070698","display_name":"Emmanouil N. Anagnostou","orcid":"https://orcid.org/0000-0002-1468-9422"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"E. N. Anagnostou","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043070577","display_name":"Frank S. Marzano","orcid":"https://orcid.org/0000-0002-5873-204X"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Frank Silvio Marzano","raw_affiliation_strings":["Department of Electronic Engineering and CETEMPS, University of Roma La Sapienza, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and CETEMPS, University of Roma La Sapienza, Italy","institution_ids":["https://openalex.org/I861853513"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":null,"first_page":"2268","last_page":"2271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/disdrometer","display_name":"Disdrometer","score":0.8509360551834106},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.741719126701355},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4804743826389313},{"id":"https://openalex.org/keywords/intermittency","display_name":"Intermittency","score":0.45686978101730347},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44012919068336487},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.43543633818626404},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4154145121574402},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3989068865776062},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.39743882417678833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28559625148773193},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28095149993896484},{"id":"https://openalex.org/keywords/precipitation","display_name":"Precipitation","score":0.23702135682106018},{"id":"https://openalex.org/keywords/rain-gauge","display_name":"Rain gauge","score":0.22800558805465698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12455713748931885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11671996116638184},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07238787412643433}],"concepts":[{"id":"https://openalex.org/C116810829","wikidata":"https://www.wikidata.org/wiki/Q1230193","display_name":"Disdrometer","level":4,"score":0.8509360551834106},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.741719126701355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4804743826389313},{"id":"https://openalex.org/C2780388094","wikidata":"https://www.wikidata.org/wiki/Q1666248","display_name":"Intermittency","level":3,"score":0.45686978101730347},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44012919068336487},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.43543633818626404},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4154145121574402},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3989068865776062},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.39743882417678833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28559625148773193},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28095149993896484},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.23702135682106018},{"id":"https://openalex.org/C120961793","wikidata":"https://www.wikidata.org/wiki/Q190052","display_name":"Rain gauge","level":3,"score":0.22800558805465698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12455713748931885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11671996116638184},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07238787412643433},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C196558001","wikidata":"https://www.wikidata.org/wiki/Q190132","display_name":"Turbulence","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2007.4423293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W223995386","https://openalex.org/W1977070284","https://openalex.org/W1990491458","https://openalex.org/W1994162645","https://openalex.org/W2024089567","https://openalex.org/W2052926657","https://openalex.org/W2060169694","https://openalex.org/W2102295079","https://openalex.org/W2122808347","https://openalex.org/W2125838338","https://openalex.org/W2153839935","https://openalex.org/W2177746725","https://openalex.org/W4253573210"],"related_works":["https://openalex.org/W174413164","https://openalex.org/W2176370030","https://openalex.org/W2145334709","https://openalex.org/W3093223428","https://openalex.org/W2954280036","https://openalex.org/W2564644595","https://openalex.org/W2750916290","https://openalex.org/W2084752502","https://openalex.org/W2169083421","https://openalex.org/W2349019353"],"abstract_inverted_index":{"A":[0],"large":[1],"data":[2,50],"set":[3,51],"of":[4,30,39,55,65,78,85,100,104,115,141],"rain":[5,106,124],"drop":[6],"size":[7],"distribution":[8,114],"(RSD)":[9],"measurements":[10],"collected":[11],"with":[12],"Joss-Waldvogel":[13],"(JWD)":[14],"and":[15,24,60,123,177],"2D":[16],"video":[17],"disdrometers":[18],"(2DVD)":[19],"in":[20,91,175],"UK,":[21],"Athens,":[22],"Japan":[23],"USA":[25],"are":[26,33],"analyzed.":[27],"The":[28,144],"objective":[29],"this":[31,48],"work":[32],"manifold:":[34],"i)":[35],"show":[36],"the":[37,52,56,75,79,113,116,138],"differences":[38],"a":[40,62,86,132],"wide":[41],"climatological":[42],"DSD-derived":[43],"moments;":[44],"ii)":[45],"retrieve":[46],"from":[47],"disdrometer":[49],"driving":[53],"parameters":[54,82],"normalized":[57],"gamma":[58],"RSD":[59,81,153,167],"perform":[61],"sensitivity":[63],"analysis":[64],"these":[66],"results":[67],"by":[68],"using":[69],"different":[70],"best-fitting":[71],"techniques;":[72],"iii)":[73],"exploit":[74],"correlation":[76],"structure":[77],"estimated":[80],"as":[83],"input":[84],"vector":[87],"autoregressive":[88],"stationary":[89],"model":[90],"order":[92],"to":[93,130,136,148],"simulate":[94],"time":[95,154],"series":[96,155],"(or":[97,119,126,151],"horizontal":[98],"profiles)":[99],"RSDs":[101],"and,":[102],"consequently,":[103],"either":[105],"rate":[107],"or":[108],"path":[109],"attenuation;":[110],"iv)":[111],"characterize":[112],"inter-rain":[117],"duration":[118,125],"dry":[120],"periods:":[121,128],"DP)":[122],"wet":[127],"WP)":[129],"design":[131],"simple":[133],"semi-Markov":[134],"chain":[135],"represent":[137],"intermittency":[139],"feature":[140],"rainfall":[142],"process.":[143],"overall":[145],"stochastic":[146,166],"procedure":[147],"randomly":[149],"synthetize":[150],"generate)":[152],"is":[156],"named":[157],"Vector":[158],"Autoregressive":[159],"Raindrop":[160],"Markov":[161],"Synthesizer":[162],"(VARMS)":[163],"model.":[164],"This":[165],"generation":[168],"tool":[169],"may":[170],"find":[171],"useful":[172],"applications":[173],"both":[174],"hydro-meteorology":[176],"radio-propagation.":[178]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
