This layer shows a weather radar-based nowcasting product to highlight areas (shown as ellipses) where intense precipitation may cause flash flood impacts. Each ellipse is shaded according to the potential level (yellow, orange or red) of the flash flood hazard, with red being the highest level. Grey ellipses show where no flash flood hazard is expected, white ellipses are where the flash flood hazard level could not be computed. The ellipses begin to fade out with increasing lead time, this represents the spread of the ellipses as the forecast lead time is increased, typically ellipses will disappear after 2-3 hours lead time. An example of the layer is given in Figure 1 below.

Figure 1 below shows an example of precipitation hazard layer. The hazard classes have colours white (no data), grey (no hazard), yellow (moderate hazard), orange (severe hazard) and red (extreme hazard). The three nonzero classes correspond to 10%, 50% and 75% percentiles of storm hazard levels in the pan-European training dataset consisting of a five-year period. The products shown in this example are based on the maximum level of each hazard type (heavy rainfall, wind gusts, hail and lightning). In addition, the uncertainty in the storm location is represented by the increasing transparency and .

The layer was originally developed by FMI (Finnish Meteorological Institute) during the UCPM (European Commission Union Civil Protection Mechanism) funded EDERA project.

Figure 1. Example of the Radar-based precipitation hazard layer shown on the EFAS web interface for a forecast on the 11th February 2025 at 09:00 UTC. 

These products combine cell-based storm nowcasts (Rossi et al. 2015) with a machine learning (ML) model to predict storm hazard levels. The hazard levels are storm-estimated based on historical meteorological observations and weather impact reports following the concepts described by Rossi et al. (2013) and Tervo et al. (2019).

The storm impact layers combine meteorological data from various sources such as weather radar, Numerical Weather Prediction (NWP) models and ERA5 re-analyses. The weather impact reports are obtained from the European Severe Weather Database (ESWD). Probabilistic nowcasts for the future location of the classified thunderstorms for the coming 5-60 minutes are being produced by using a Kalman filter model (Rossi et al. 2015).

Methodology

The product generation process is illustrated in Figure 2. Storm cells are identified and tracked from OPERA radar reflectivity composites where the reflectivity exceeds a value of 35 dBZ (step 1). Hazard levels of the cells are then predicted with the ML (Machine Learning) model which is trained on observations of flash flood impacts, radar reflectivity and meteorological information (specifically the Convective Available Potential Energy - CAPE, and Convective INhibition - CIN). Ellipses are fitted to the storm cells, and probabilistic nowcasts of the storm ellipse locations over the next 3 hours are produced by applying the Kalman filter-based model (step 2). The predicted hazard levels are combined with the storm locations, which gives the precipitation hazard nowcasts (step 3). Here we assume that the hazard levels remain constant during the forecast time range. 

Figure 2. Different stages of generating the multi-hazard product for thunderstorms.

References

Rossi, P.J., Hasu, V., Halmevaara, K., Makela, A., Koistinen, J., Pohjola, H. 2013. Real-time hazard approximation of long-lasting convective storms using emergency data. Journal of Atmospheric and Oceanic Technology. 30, pp 538-555. https://doi.org/10.1175/JTECH-D-11-00106.1

Rossi, P. J., V. Chandrasekar, V. Hasu, and D. Moisseev, 2015: Kalman Filtering–Based Probabilistic Nowcasting of Object-Oriented Tracked Convective Storms. J. Atmos. Oceanic Technol., 32, 461–477, https://doi.org/10.1175/JTECH-D-14-00184.1.