# Weather Radar in Complex Orography
## 2 Practical Applications, Needs and Requirements
### The Scales of Interest  
The temporal and spatial scales of the phenomena at the centre of attention range from a few minutes to several hours, and from a few kilometres to several tens of kilometres. The variability of precipitation at larger scales is well captured by rain and river gauges and by numerical weather prediction models. The principal task of a radar is to capture the spatiotemporal variability of rainfall at small scales, that is, the peaks of precipitation, hail cells and thunderstorms, which are not captured well by point measurements, and to provide the basis for storm severity ranking and warning issuing. Precipitation also exhibits high spatial variability in flat regions, but the regions of maximum rainfall over complex orography are often stationary for a certain period of time, whereas precipitating clouds usually move in flat regions. Because of stationarity, rainfall accumulations can reach extreme values locally, which, in turn, can trigger flash floods, debris flows and landslides. Moreover, a small shift in location over complex orography can make an important difference for the hazards on the ground. A numerical weather prediction (NWP) model can often correctly predict the presence of convective storms. However, it is practically impossible to forecast a thunderstorm at the right location, time and intensity, due to the chaotic nature of convection. An NWP model provides the information necessary to issue a pre-alert that allows people in the affected region to prepare for an event. The level of precision in the location, time and intensity typically required by a user before taking action is so high that it can only be reached shortly before the event, when the presence of a storm is confirmed by the radar. This is the essence of nowcasting, which is primarily based on the latest observations and fast statistical processing. Nowcasting starts with a detailed description of the present weather, such as the quantification of the severity of a thunderstorm, and makes an assessment of the possible hazards. Lagrangian extrapolation of radar fields can help to estimate the location of storms over the following few hours and build the basis necessary to issue a warning. Figures 3 and 4 show the skill and predictable scale as a function of the lead time of a state-of-the-art NWP model and a Lagrangian radar extrapolation nowcast evaluated over the central European Alps against radar observations. The figures show that, in the context of forecasting, a Lagrangian extrapolation of radar precipitation fields provides unique information at a high spatial resolution for lead times of up to a few hours. For a detailed discussion, the reader can refer to [85; 86]. According to these results, the crossover lead time, up to which the Lagrangian extrapolation of radar fields has a better skill than NWP forecasts, lies between 2.5 and 3 hours. Interestingly, this result is similar to that found a decade ago for the same study area, but with earlier versions of the models, see [87]. More precisely, the crossover point depends on various factors, and in particular on the type of precipitation and the spatial scale [59]. Figure 4: The predictable scale of Lagrangian radar extrapolation nowcasts (solid lines) and COSMO-1 forecasts (dashed lines) as a function of the lead time. The COSMO-1 curves are shifted by 1.5 h to account for the computational requirements of numerical simulations. The computation time may be reduced in the future by increasing the update frequency of the NWP model. The evaluation spans data from the whole of 2018 and over a 640 km \(\times\) 710 km domain centred over the European Alps. (**a**) The scale-aware normalised root-mean-square error, RMSE\({}_{n}\), for all analysed scales separately. Considering an RMSE\({}_{n}\) of 1 as the limit of predictability, it is possible to identify the predictable scale for each lead time. This is shown in (**b**). Taken from [86].