We show results from two unique measurement campaigns aimed at better understanding effects of large wind turbines on radar returns by deploying a mobile X-band weather radar system in the proximity of a small wind park. Measurements were taken in 24/7 operation with dedicated scan strategies to retrieve the variability and most extreme values of reflectivity and radar cross-section of the wind turbines. The findings are useful for wind turbine interference mitigation measures in radar systems.

In this work, we present a novel quantitative precipitation estimation method for Switzerland that uses random forests, an ensemble-based machine learning technique. The estimator has been trained with a database of 4 years of ground and radar observations. The results of an in-depth evaluation indicate that, compared with the more classical method in use at MeteoSwiss, this novel estimator is able to reduce both the average error and bias of the predictions.

To obtain reliable snowfall estimates in high mountain remains a challenge. This study uses daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glaciers to assess three readily-available high-quality precipitation products. We find a large bias between in situ SWE and snowfall, which differs among the precipitation products, the two sites, the winter seasons and in situ meteorological conditions. All products have great potential for various applications in the Alps.