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

• Published in: Quarterly journal of the Royal Meteorological Society 2023-01, Vol.149 (750), p.192-210
• Main Authors: Nomokonova, Tatiana, Griewank, Philipp J., Löhnert, Ulrich, Miyoshi, Takemasa, Necker, Tobias, Weissmann, Martin
• Format: Article
• Language: English
• Subjects: covariance; data assimilation; Doppler sonar; Ensemble forecasting; ensemble sensitivity analysis; Impact assessment; Instruments; Lidar; Lidar measurements; Localization; low‐level wind forecasts; Metropolitan areas; network of Doppler lidars; observing system; Renewable energy; Sensitivity analysis; Wind; Wind observation
• ISSN: 0035-9009; 1477-870X
• DOI: 10.1002/qj.4402

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 B$$ \mathbf{B} $$ 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. This study presents the potential of a Doppler lidar network to improve short‐term low‐level wind forecasts. The approach used in this study does not require real observations and can provide valuable information for designing an operational network. The study is based on a convective‐scale 1,000‐member ensemble simulation over Germany. The results show that Doppler lidars lead to considerable variance reduction and should be considered for future observational networks.