Estimating the benefit of Doppler wind lidars for short-term low-level wind ensemble forecasts

This work focuses on the potential of a network of Doppler lidars for the improvement of short-term forecasts of low-level wind. For the impact assessment, we developed a new methodology that is based on ensemble sensitivity analysis (ESA). In contrast to preceding network design studies using ESA, we calculate the explicit sensitivity including the inverse of the background covariance (Formula presented.) matrix to account directly for the localization scale of the assimilation system. The new method is applied to a pre-existing convective-scale 1,000-member ensemble simulation to mitigate effects of spurious correlations. We evaluate relative changes in the variance of a forecast metric, that is, the low-level wind components averaged over the Rhein–Ruhr metropolitan area in Germany. This setup allows us to compare the relative variance change associated with the assimilation of hypothetical observations from a Doppler wind lidar with respect to the assimilation of surface-wind observations only. Furthermore, we assess sensitivities of derived variance changes to a number of settings, namely observation errors, localization length scale, regularization factor, number of instruments in the network, and their location, as well as data availability of the lidar measurements. Our results demonstrate that a network of 20–30 Doppler lidars leads to a considerable variance reduction of the forecast metric chosen. On average, an additional network of 25 Doppler lidars can reduce the 1–3 hr forecast error by a factor of 1.6–3.3 with respect to 10-m wind observations only. The results provide the basis for designing an operational network of Doppler lidars for the improvement of short-term low-level wind forecasts that could be especially valuable for the renewable energy sector.