# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 4 Road Weather Forecasts and Precipitation Nowcasting  
FMI produces road weather forecasts during the project for the Oulu pilot area (Figure 3). The forecasts are generated with the FMI road weather model RoadSurf [13]. This is one-dimensional heat balance model that forecasts road surface temperature, friction, and road condition (wet, icy, snowy...). As input, the road weather model requires atmospheric forecast data from a numerical weather prediction model. Data from an MEPS (MetCoOp Ensemble Prediction System) [14] control member are used as the input to the model in this project. The road weather forecast points are in 50 m intervals along the major roads. The forecasts are updated every hour.  
Laser scanning data produced by national land survey of Finland were used to determine the sky view factors to each forecast point. The sky view factor means the area of the sky that is visible from the road point. Road points on open fields have larger sky view factors than road points surrounded by forests. RoadSurf uses sky view factors to adjust the incoming and outgoing radiation [15]. In addition, laser scanning data were used to determine local horizon angles to each direction in the forecast points. These were used by  
Figure 3: Piloting area for road weather forecasts (red routes in the map). Made with Natural Earth ([[https://www.naturalaearthdata.com/](https://www.naturalaearthdata.com/)]([https://www.naturalaearthdata.com/](https://www.naturalaearthdata.com/))).  
the shadowing algorithm in the road weather model. If the local horizon angle was larger than the sun elevation angle at the direction of the sun, then the road point was in shadow, and the direct solar radiation was set to zero in the model. Figure 4 shows an example of friction forecast to the pilot area on 29 March 2024. Snow on the road causes low friction values, except in the southern part of the area.  
Future work in this project includes developing road weather forecast products to meet the needs of different autonomous vehicles. Some autonomous vehicles might rely mostly on camera data, while others can have LiDARs and/or RADARs installed. Weather affects different instruments in different ways ([PERSON] et al., 2018), so the forecasts should be tailored for each type of autonomous vehicle. The forecast products will use an estimate of the vehicles' ability to detect the surroundings in certain weather conditions together with road friction to give suggestions regarding lowering speed and whether a human should take over.  
Precipitation nowcasts (concept better known as nowcasting) were produced by Pysteps, which is an open-source Python library (Pysteps, 2018). The nowcasts were based on extrapolations of radar observations along the motion field estimated from past observations.  
Nowcasting based on radar extrapolation provides better results than NWP-based models using short time ranges, that is, 0-6 h. Nowcasting models can produce reliable predictions of large-scale stratiform rainfall for up to 6 h and convective rainfall for up to the next 30-60 min. However, forecasting snowfall by using weather radars is still a challenging task. It is important to provide accurate information about precipitation for autonomous vehicles, as snowfall and heavy rainfall affect road conditions and the sensors' ability to observe the surroundings. In this project, we are developing a precipitation nowcasting web application to show results from a radar extrapolation algorithm in the Linnanmaa region in Oulu. The precipitation nowcast will be also given as input data for the road weather model to produce better road condition forecasts. Figure 5 shows an example of a precipitation nowcast for southern Finland.  
Figure 4: Example friction forecasts for pilot routes for 29 March 2024 12 UTC. Forecasts were created as hindcasts with historical data. Forecast start time was 9 UTC. Made with Natural Earth ([[https://www.naturalearthdata.com/](https://www.naturalearthdata.com/)]([https://www.naturalearthdata.com/](https://www.naturalearthdata.com/))).