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.
Highlights
The modeling of Ultraviolet Radiation (UV) and its index (UV Index or UVI) is important, among other reasons; How to cite this paper: Lopo, A.B., et al (2014) UV Index Modeling by Autoregressive Distributed Lag (ADL Model)
The input parameters are the coordinates of UVSIM time, geographic, cloud cover and Ozone concentration estimated by instrument OMI (Ozone Monitoring Instrument) of satellite AURA/NASA
The results suggest that climate change will alter the balance of tropospheric ozone and UV Index, which would have consequences for radiative forcing in the troposphere, air quality and human health and ecosystems, the amount of ozone in the atmosphere is recuperating as the result of the Montreal Protocol, in force since 1989 [21]-[23]
Summary
The modeling of Ultraviolet Radiation (UV) and its index (UV Index or UVI) is important, among other reasons; How to cite this paper: Lopo, A.B., et al (2014) UV Index Modeling by Autoregressive Distributed Lag (ADL Model). This fact leads researchers to invest in development of computational, physical, statistical and stochastic models to estimate or forecast/projections of UV/UVI [4] [5]. The results showed a high correlation model (0.8) with observed values. In this model, the input parameters are the coordinates of UVSIM time (hour, day), geographic (latitude, longitude), cloud cover and Ozone concentration estimated by instrument OMI (Ozone Monitoring Instrument) of satellite AURA/NASA. The model UVGAME (UV Global Atmospheric Model) was used in research on the UV Index variations and regional and seasonal distribution of the number of cases of skin cancer in the Brazilian’s skin color [8]
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