Assimilating WIVERN winds in WRF model: an application to the outstanding case of the Medicane Ianos

Abstract. Accurate weather forecasts are important to our daily lives. Wind, cloud and precipitation are key drivers of the Earth's water and energy cycles, and they can also pose weather-related threats, making the task of numerical weather prediction (NWP) models particularly challenging and important.

The Wind Velocity Radar Nephoscope (WIVERN) mission will be the first space-based mission to provide global in-cloud wind measurements, and also the first to deliver simultaneous observations of winds, clouds and precipitation. The mission is proposed as a candidate for the European Space Agency (ESA)'s Earth Explorer 11 within the Future Earth Observation (FutureEO) programme. It is currently in Phase A, with the recommendation decision expected in July 2025. If the mission is successfully selected for implementation, its data could be beneficial to several sectors: improving our knowledge of weather phenomena, validate climate statistics, and enhancing NWP performance. This paper aims to contribute to the last point by analyzing the impact that WIVERN would have in the case of a Tropical-like cyclone (TLC) event.

In this work, the impact of assimilating WIVERN Line of Sight (LoS) winds (retrieved from WIVERN Doppler measurements) on NWP performance is assessed, for the high-impact case study of Medicane Ianos, which occurred in mid-September 2020 in the central Mediterranean and made landfall on the west coast of Greece.

To this end, we generate WIVERN pseudo-observations, that are assimilated in the Weather Research and Forecasting (WRF) model run at moderate horizontal resolution (4 km).

Results show that assimilating WIVERN into the WRF model has a positive impact on the prediction of the Medicane trajectory. Specifically, assimilating WIVERN just once improves the trajectory forecast error by 43 %. The data assimilation of WIVERN pseudo-observations affects not only the storm's trajectory but also its physical characteristics. It is also shown that the assimilation improves the prediction of precipitation and surface winds, and has the potential to improve our resilience to severe weather events by enabling better forecasts of storm impacts. Finally, we present the results of two sensitivity experiments in which the background and observation errors were different. The results show greater sensitivity to changes in the background error matrix.