Preprints
https://doi.org/10.5194/egusphere-2022-465
https://doi.org/10.5194/egusphere-2022-465
 
05 Jul 2022
05 Jul 2022
Status: this preprint is open for discussion.

UVBoost (v0.5): a hybrid radiative transfer and machine learning model for estimating ultraviolet radiation

Marcelo de Paula Corrêa Marcelo de Paula Corrêa
  • Instituto de Recursos Naturais, Universidade Federal de Itajubá, Itajubá/MG, 37500-903, Brazil

Abstract. This article presents UVBoost, a hybrid radiative transfer estimator based on a Supervised Machine Learning (SML) regression model powered by high precision ultraviolet radiation (UVR) calculations provided by a conventional Radiative Transference Model (RTM). The proposed regression model takes UVR as a dependent variable, and the Solar Zenith Angle (SZA), Total Ozone Content (TOC), and Aerosol Optical Depth (AOD), as the independent predictive variables. UVBoost was developed to increase computational speed for conducting calculations with large databases, without sacrificing result accuracy. Furthermore, this method employs a user-friendly code, which can be used by laymen or researchers in other areas. UVBoost can be used to disseminate UVR data online anywhere in different spatiotemporal scales, or for climatological projection studies on a global scale. The model was developed by comparing seven regression SML tools via cross validation. These results were validated using non-parametric statistical tests. Of all the tested tools, the Categorical Boosting (CatBoost) method showed the best accuracy at the lowest computational cost. Two additional studies were carried out, one at the global scale, and another at the local scale, to compare the traditional RTM vs. the UVBoost results. The first study simulated a global UVR field (1°x1°), with 64800 grid points, with input data from CMIP6, available at https://pcmdi.llnl.gov/CMIP6/. The differences between the RTM and the UVBoost were less than ±5 % for approximately 95 % of all points, except for points with high SZA. The computational speed of UVBoost surpassed that of the RTM by more than three orders of magnitude. The second study simulated the daily UVR at eight different locations on Earth. The results showed that the UVBoost was very efficient in simulating accumulated UVR doses during the day, with negligible differences (< ±3 %), which means it can be used in studies on UVR and human health. In the future, UVBoost will include other geophysical parameters and be extended to other bands in the electromagnetic spectrum.

Marcelo de Paula Corrêa

Status: open (until 01 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Marcelo de Paula Corrêa

Marcelo de Paula Corrêa

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Short summary
UVBoost is an UV radiative transfer estimator based on a machine learning regression tool powered by high precision database. The model have increased computational speed in three orders of magnitude, without sacrificing result accuracy. It is a user-friendly code, which can be used by laymen or researchers in other areas. UVBoost can be used to disseminate UV index data online anywhere in different spatiotemporal scales, or for climatological projection studies on a global scale.