Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometrescale ensemble data assimilation system

Finn, Tobias Sebastian; Geppert, Gernot; Ament, Felix

doi:10.1080/16000870.2020.1764307

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…These profiles can be derived from various remote-sensing instruments, including radiosondes, dropsondes, wind profiler radar, ground and space-based lidars, and microwave radiometers. Assimilating profiles into atmospheric models using techniques like ensemble Kalman filtering (EnKF) has shown a significant impact on improving the accuracy and performance of global models [1], mesoscale models [2], and even small-scale models [3]; however, the solution for mitigating typhoon disasters through the use of these models, considering the observation cost and forecast benefit, is still under-explored. Therefore, the cost accounting of profile observations used to improve numerical forecasting is of great significance for both practical numerical weather forecasting and observation network construction.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Shao,

Nerger

2024

Remote Sensing

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Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26–27%. This may be useful information to the meteorological agencies and researchers.

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Section: Introductionmentioning

confidence: 99%

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Shao,

Nerger

2024

Remote Sensing

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“…There are a number of studies showing the benefits of data assimilation (DA) for low‐level wind forecasts using real observations from a single wind‐profiling instrument (Sawada et al ., 2015; Pichugina et al ., 2017; Finn et al ., 2020; Li et al ., 2020; Hristova‐Veleva et al ., 2021). For operational DA, however, it is beneficial to build a dense network of remote‐sensing sites capable of continuous profiling of wind in the atmospheric boundary layer (ABL).…”

Section: Introductionmentioning

confidence: 99%

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

Nomokonova

Griewank

Löhnert

et al. 2022

Quart J Royal Meteoro Soc

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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 boldB$$ \mathbf{B} $$ 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.

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“…Ground-, airborne-, and space-based wind observations are the backbone for reliable wind information. These measurements are utilized to initialize the models but also to improve them through the assimilation procedure (Finn et al, 2020;Gregow, 2018;Gustafsson et al, 2001;Rennie & Isaksen, 2020). Thus, the quality and accuracy of various model forecasts are directly affected by the accuracy and availability of the observations.…”

Section: Introductionmentioning

confidence: 99%

Evaluating modelled winds over an urban area using ground‐based Doppler lidar observations

Filioglou

Preißler²,

Troiville³

et al. 2022

Meteorological Applications

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Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone-related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large-eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long-term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially-averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to Associated post doc with Vaisala through PoDoCo program.

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Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometrescale ensemble data assimilation system

Cited by 6 publications

References 32 publications

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

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

Evaluating modelled winds over an urban area using ground‐based Doppler lidar observations

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