Forecasting the UV-Index and Analyzing its Dependence on Influencing Factors

Solar ultraviolet (UV) radiation at Earth’s surface poses a well documented risk for human health [1]. The World Health Organization has defined the UV-
Index to quantify the amount of UV radiation as integer numbers in a range of typically 1 to 10 [2].

The UV-Index is typically forecasted on the scale of days to warn the public in the case of high UV-Index values. In Germany this is done by Deutscher Wetterdienst (DWD) who use their weather model ICON (ICOsahedral Nonhydrostatic model) [3] in combination with external datasets for Ozone forecasts and UV radiation calculations [4].

In order to make the UV-Index forecast more self-consistent, we present a setup that provides atmospheric ozone via the LINearized OZone (LINOZ) scheme [5] that is used for UV radiation calculations via the Cloud-J scheme [6] from within ICON and the coupled Aerosols and Reactive Trace gases (ART) module [7].

The result is a setup that can forecast ozone and UV-Index fields for a time frame of January to April 2025 with a precision of ±1 for 94.9% of the data points in comparison to ground measurement stations. Ozone columns stay within 5% agreement for a time frame of four months in the northern hemisphere in comparison to Ozonewatch satellite data.

We use this setup for an analysis of the influencing factors on UV radiation that finds that the solar zenith angle is the quantity that introduces most variability on the UV-Index. Aerosol optical depth, cloud cover and overhead ozone introduce smaller variabilites while the effect of surface albedo and altitude is even less pronounced. A comparison of the novel setup to the operational forecast by DWD agrees within ±2 units of UV-Index for almost all data points with the exception of larger differences in mountainous areas.

References:
[1] Mohammed Ahmed Sadeq et al. Causes of death among patients with cutaneous melanoma: a US population-based study. Scientific Reports 2023 13:1, 13(1):1–11, 6 2023.

[2] Report of the WMO-WHO Meeting of Experts on Standardization of UV Indices and their Dissemination to the Public. Technical report, 1997.

[3] Günther Zängl et al. The ICON(ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M : Description of the non-hydrostatic dynamical core. Quarterly Journal of the Royal Meteorological Society, 141(687):563–579, 1 2015.

[4] Henning Staiger et al. UV index forecasting on a global scale. Meteorologische Zeitschrift, 14(2):259–270, 4 2005.

[5] C. A. McLinden et al. Stratospheric ozone in 3-D models: A simple chemistry and the cross-tropopause flux. Journal of Geophysical Research: Atmospheres, 105(D11):14653–14665, 6 2000.

[6] M. J. Prather. Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c. Geoscientific Model Development, 8(8):2587–2595, 8 2015.

[7] Jennifer Schröter et al. ICON-ART 2.1: A flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations. Geoscientific Model Development, 11(10):4043–4068, 10 2018.