Prediction of UV Index (UVI) using TUV model over Iran

Document Type : Research Article

Authors

1 Corresponding Author, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran. E-mail: m-rahnama@irimo.ir

2 Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran. E-mail: savizsehat@yahoo.com

3 Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran. E-mail: mohamadi.atefeh@yahoo.com

4 Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran. E-mail: pahlavan1977@yahoo.com

Abstract

Ultraviolet radiation is defined as electromagnetic radiation with wavelengths in the range of 200-400 nm and is divided into three different bands. UVC is related to the wavelength from 200 to 280 nm, while UVB is related to the wavelength ranging from 280 to 315 nm and UVA is related to the wavelength from 315 nm to the visible level (400 nm). Ultraviolet radiation has beneficial effects such as making vitamin D and disinfecting effects. On the other hand, it causes harm such as burns and skin cancer, and damage to the eyes and immune system. Predicting the amount of UV radiation based on the UV index can be of great help to people's health. In this study, the tropospheric ultraviolet-visible (TUV) model was used to predict the UVI index. This model requires ozone, whiteness, and Aerosol Optical Depth (AOD) to forecast UVI. WACCM model data was used for ozone and whiteness column values from the ozone data of the GFS and AOD global forecasting systems. 612 case studies in the whole year of 2020 were selected from each of the 12 months of the year from different parts of the country. GFS, WACCM, and OMI data were extracted for the mentioned dates and interpolated at the desired points. Because OMI data is available locally at noon everywhere, case studies have been selected for noon. Then the interpolated values along with the length, width, and height of the points were given as input to the TUV model, and the UVI value was predicted. Due to the lack of access to the actual value of UVI in the country, OMI data was assumed as observational data and used to compare with the predicted value. Conventional statistical measures ME, MAE, RMSE, and Pearson correlation coefficient were used to validate the prediction value with observational data. The results showed that in January, February, April, November, and December, which are the coldest months of the year and the day length is shorter and the sun is less intense, so the error rate is lower than in other months (warm months of the year). However, in general, the forecast is very accurate. So that in all selected study cases, the values of ME, MAE, RMSE, and R are 0.16, 0.85, 1.13, and 0.93, respectively, which indicates the high accuracy of the forecast. The results also showed that the forecast error has a linear relationship with the AOD value. Thus, the higher the AOD value, the more negative the forecast error and underestimated forecast value.
In the warmer months of the year, the length of the day is longer and the intensity of the sun's radiation is higher, resulting in more errors. The amount of error is also related to the amount of light depth of the particles; the greater the AOD, the greater the error. The correlation coefficient diagram also showed that there is a high correlation between the forecast and observation values. This research is the first research in the field of forecasting the UV index in the country and has had satisfactory results.

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References

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Journal of the Earth and Space Physics
Volume 49, Issue 1
online publication date: 14 June 2023
June 2023
Pages 213-227

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