Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather- and safety-related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicles are more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic, especially in cases of heavy local precipitation by re-routing/route planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems’ sensitivity to harsh weather conditions allows for weather forecasting based on the real vulnerability of each vehicle.

Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with the use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather and safety related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicle is more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based Nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic especially in cases of heavy local precipitation by re-routing/route-planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems sensitivity to harsh weather conditions allows the weather forecasting based on real vulnerability of each vehicle.

We present probabilistic warning tools for flood hazard and risk based on deep learning techniques and weather radar observations. This work has been done in the EU-funded INLINE project. The scope is on nowcasting heavy rainfall and associated flash floods in short time ranges (0-3 hours) and at high spatial and temporal resolutions (1-2 km and 5-15 minutes). Pan-European precipitation nowcasts are produced from OPERA rain rate composites using a convolutional neural network based on the SimVP architecture. Training the network with alternative loss functions together with three post-processing techniques are applied to improve the utility of the nowcasts. Underestimation of localized heavy precipitation is reduced by using a cumulative distribution transformation. Stochastic post-processing is then applied to produce ensemble members that reproduce the lost spatial variability. The ensembles are generated by Fourier-filtering and transforming white noise fields to reproduce the spatiotemporal correlation structure and the distribution of forecast errors. Finally, exceedance probabilities estimated from the ensembles are calibrated by using logistic regression. We present a verification study to determine the maximum time ranges of useful skill across different spatial scales and accumulation periods and to show that the proposed methodology outperforms simple extrapolation-based techniques in most cases. We also give examples of verification metrics where this is not the case. Precipitation rates from deterministic nowcasts are translated into color-coded hazard levels by using user-specified thresholds. The hazard levels are further translated into flood risk levels by using exposure information (i.e. population or critical infrastructure). In addition, we present different methods to translate ensemble nowcasts into probability-aware predictions of hazard and risk and give discussion of their practical utility. Practical use of the proposed methodology is demonstrated by using major flood events during the years 2024 and 2025 that affected multiple European countries.

The INLINE project aims at advancing on EWS capabilities with tools to anticipate the impacts caused by storms, heavy rain and floods to support the decision-making workflows of various levels of Civil Protection Agencies (CPAs), including their coordination and cooperation.To achieve this, the project is developing impact-forecasting products and functionalities with European coverage, which are being tested in real time over a 15-month demonstration period. Results are co-evaluated with the participation of a number of end-users (both partners and stakeholders integrated in the INLINE Community of Interest) to assess their operational value.This study presents results form the first months of the demonstration (starting in September 2025) focusing on (i) the skill of the products to anticipate the occurrence of the most significant events, and the magnitude of the resulting impacts; and (ii) the first results obtained with end-users during recent high-impact events in their regions.

We present probabilistic warning tools for flood hazards and risks based on deep learning (DL) techniques. The scope is on nowcasting heavy rainfall and associated flash floods in short time ranges (0-3 hours) and at high spatial and temporal resolutions (2 km and 15 minutes). Rainfall nowcasts are produced from pan-European OPERA radar composites by using a convolutional neural network based on the SimVP architecture. Two post-processing techniques are applied to enhance the utility of the DL-based nowcasts. First, underestimation of heavy rainfall is reduced by applying quantile mapping. Second, ensembles that provide realistic estimates of forecast uncertainty are generated by utilizing a stochastic technique. With these enhancements, the DL-based nowcast is shown to outperform the traditional extrapolation-based nowcasting techniques. The rainfall nowcasts are translated into color-coded hazard levels by using user-specified thresholds and statistically optimized probability thresholds that maximize hits and minimize false alarms. These are further translated into flood risk levels by using exposure information. Real-time feed of the warning products is displayed in a web platform developed in the EU-funded INLINE project. Demonstrations of the proposed methodology are given using major flood events during the years 2024 and 2025 that affected multiple European countries.