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  • Meteorology and Climatology; Life Sciences (General); Computer Programming and Software  (1)
  • ensemble sensitivity analysis  (1)
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  • 1
    Publication Date: 2024-02-12
    Description: 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 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.
    Description: 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.
    Description: Hans‐Ertel‐Centre for Weather Research funded by the German Federal Ministry for Transportation and Digital Infrastructure
    Description: https://doi.org/10.5281/zenodo.6331758
    Keywords: ddc:551.6 ; covariance ; data assimilation ; ensemble sensitivity analysis ; localization ; low‐level wind forecasts ; network of Doppler lidars ; observing system
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2019-07-13
    Description: Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as UN population projections. This makes current models likely to miss important feedbacks in the real Earth-Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth-Human system models for devising effective science-based policies and measures to benefit current and future generations.
    Keywords: Meteorology and Climatology; Life Sciences (General); Computer Programming and Software
    Type: GSFC-E-DAA-TN39220 , National Science Review (ISSN 2095-5138) (e-ISSN 2053-714X); 3; 4; 470-494
    Format: application/pdf
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