The manuscript compares ground-based measurements of ultraviolet (UV) radiation from 17 German stations with modelled UV maps in which the influence of clouds is taken into account using information from satellite images. While for clear sky conditions the agreement is good, uncertainties increase for cloudy conditions. The issue of representativeness of satellite pixel values versus ground measurements is discussed and an approach to combine measurement and UV map is presented.

We present a fast an easy method to incorporate clouds from satellite imagery into a model for calculating surface UV index maps in near-real time. To judge the quality of the model, we compare our results to measurements from ground based detectors. We discuss in detail where variations in either of the values come from and why satellite and ground values might not necessarily be comparable in every situation.

Large-scale retrievals of the ultraviolet index (UVI) in real time by exploiting the modern Earth observation data and techniques are capable of forming operational early warning systems that raise awareness among citizens of the health implications of high UVI doses. In this direction a novel UVI operating system, the so-called UVIOS, was introduced for massive outputs, while its performance was tested against ground-based measurements revealing a dependence on the input quality and resolution.

When measuring the aerosol optical depth of the atmosphere, clouds in front of the sun lead to erroneously high values. Therefore, measurements that are potentially affected by clouds need to be removed from the dataset by an automatic process. As the currently used algorithm cannot reliably identify thin clouds, we developed a new one based on a method borrowed from machine learning. Tests with 10 years of data show improved performance of the new routine and therefore higher data quality.

A major phenomenon in the stratosphere is the Quasi Biennial Oscillation (QBO). Although a feature of the equatorial stratosphere, its influence extends to surface weather at both equatorial and mid latitudes. Improved knowledge of mechanisms of influence should help to improve weather forecasts. In this paper, QBO impacts at the surface are characterized and dominant mechanisms explored. Three pathways are identified, referred to as the tropical, subtropical and polar routes.