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UV Index Modeling by Autoregressive Distributed Lag (ADL Model)

  • Identification data

    Identifier: imarina:5128888
    Authors:
    Alexandre Boleira LopoMaria Helena Constantino SpyridesPaulo Sérgio LucioJavier Sigró
    Abstract:
    The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases non-melanoma skin cancer in northeast of Brazil. The methodology utilized an Autoregressive Distributed Lag model (ADL) or Dynamic Linear Regression model. The monthly data of UV index were measured in east coast of the Brazilian Northeast (City of Natal-Rio Grande do Norte). The Total Ozone is single explanatory variable to model and was obtained from the TOMS and OMI/AURA instruments. The Predictive Mean Matching (PMM) method was used to complete the missing data of UV Index. The results mean squared error (MSE) between the observed UV index and interpolated data by model was of 0.36 and for extrapolation was of 0.30 with correlations of 0.90 and 0.91 respectively. The forecast/extrapolation performed by model for a climatological period (2012-2042) indicated a trend of increased UV (Seasonal Man-Kendall test scored τ = 0.955 and p-value < 0.001) if the Total Ozone remain on this tendency to reduce. In those circumstances, the model indicated an increase of almost one unit of UV index to year 2042.
  • Others:

    Author, as appears in the article.: Alexandre Boleira Lopo; Maria Helena Constantino Spyrides; Paulo Sérgio Lucio; Javier Sigró
    Department: Geografia
    URV's Author/s: Sigro Rodríguez, Francisco Javier
    Abstract: The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases non-melanoma skin cancer in northeast of Brazil. The methodology utilized an Autoregressive Distributed Lag model (ADL) or Dynamic Linear Regression model. The monthly data of UV index were measured in east coast of the Brazilian Northeast (City of Natal-Rio Grande do Norte). The Total Ozone is single explanatory variable to model and was obtained from the TOMS and OMI/AURA instruments. The Predictive Mean Matching (PMM) method was used to complete the missing data of UV Index. The results mean squared error (MSE) between the observed UV index and interpolated data by model was of 0.36 and for extrapolation was of 0.30 with correlations of 0.90 and 0.91 respectively. The forecast/extrapolation performed by model for a climatological period (2012-2042) indicated a trend of increased UV (Seasonal Man-Kendall test scored τ = 0.955 and p-value < 0.001) if the Total Ozone remain on this tendency to reduce. In those circumstances, the model indicated an increase of almost one unit of UV index to year 2042.
    Thematic Areas: Planejamento urbano e regional / demografia Interdisciplinar Geociências Engenharias i Ciências biológicas i Ciências ambientais Ciências agrárias i Biotecnología Biodiversidade
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: javier.sigro@urv.cat javier.sigro@urv.cat
    Author identifier: 0000-0003-0969-0338 0000-0003-0969-0338
    Record's date: 2024-05-23
    Papper version: info:eu-repo/semantics/publishedVersion
    Papper original source: Atmospheric And Climate Science (Print).
    APA: Alexandre Boleira Lopo; Maria Helena Constantino Spyrides; Paulo Sérgio Lucio; Javier Sigró (2014). UV Index Modeling by Autoregressive Distributed Lag (ADL Model). Atmospheric And Climate Science (Print), (), -. DOI: 10.4236/acs.2014.42033
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2014
    Publication Type: Journal Publications
  • Keywords:

    Planejamento urbano e regional / demografia
    Interdisciplinar
    Geociências
    Engenharias i
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Biotecnología
    Biodiversidade
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