the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland
Alessandro Hering
Urs Germann
Olivia Martius
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nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
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We investigated the potential of radio occultation (RO) data for climate-oriented wind field monitoring, focusing on the equatorial band within ±5° latitude. In this region, the geostrophic balance breaks down, and the equatorial balance approximation takes over. The study encourages the use of RO wind fields for mesoscale climate monitoring for the equatorial region, showing a small improvement in the troposphere when including the meridional wind in the zonal-mean total wind speed.
The sodar model is a complement to forecasting methods because it is useful due to its simplicity and speed of calculations. It does not require emission data, for which it is difficult to quickly verify temporal and spatial variability.
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