dor_id: 4128792
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Atmósfera", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, Web Of Science (WoS); SCImago Journal Rank (SJR)
561.#.#.u: https://www.atmosfera.unam.mx/
650.#.4.x: Físico Matemáticas y Ciencias de la Tierra
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: https://www.revistascca.unam.mx/atm/index.php/atm/index
351.#.#.b: Atmósfera
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53155/46891
100.1.#.a: Guerreiro Miranda, Bruno; Galante Negri, Rogério; Albertani Pampuch, Luana
524.#.#.a: Guerreiro Miranda, Bruno, et al. (2023). Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil. Atmósfera; Vol. 37, 2023; 365-381. Recuperado de https://repositorio.unam.mx/contenidos/4128792
245.1.0.a: Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
264.#.0.c: 2023
264.#.1.c: 2023-03-29
653.#.#.a: clustering algorithms; precipitation; southeastern Brazil; GPM
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx
884.#.#.k: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53155
001.#.#.#: 022.oai:ojs.pkp.sfu.ca:article/53155
041.#.7.h: eng
520.3.#.a: Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.
773.1.#.t: Atmósfera; Vol. 37 (2023); 365-381
773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index
022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236
310.#.#.a: Trimestral
300.#.#.a: Páginas: 365-381
264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
doi: https://doi.org/10.20937/ATM.53155
handle: 76f530a110935a9e
harvesting_date: 2023-06-20 16:00:00.0
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file_creation_date: 2022-08-01 18:16:11.0
file_modification_date: 2022-08-01 18:16:11.0
file_creator: Graciela B Raga
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last_modified: 2023-06-20 16:00:00
license_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode.es
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