Characterisation of offshore winds for energy applications

Offshore wind energy is regarded as a key component of future energy systems. However, rolling out wind energy on a large scale requires scientific, technological and economic breakthroughs. Wind power forecasting, resource assessment, wind farm optimization, and turbine load assessments all rely on engineering models that simulate the interaction between the wind and the turbine(s). These models often represent the wind in an idealised fashion, which introduces uncertainties that translate into financial risk for investors. This thesis addresses these uncertainties by re-evaluating common assumptions about the (offshore) wind field, studying the physics that governs winds in coastal areas, evaluating the representation of offshore winds in numerical weather prediction models, and proposing alternative methods to represent the offshore wind climate in engineering models. To characterize uncertainties in the simulated wind climate, the concept of anomalous wind events is introduced. An important and illustrative example is the low-level jet. During a low-level jet event, the wind speed does not increase monotonically with height, as is often assumed. Instead, it reaches a maximum in the lower atmosphere (typically around wind turbine hub height), and then decreases with height. As such, low-level jets can substantially impact power production and wind loads on the turbine. This research finds that low-level jets occur often over the North Sea. Moreover, numerical weather prediction models struggle to adequately represent this phenomenon. A climatology based on observations is also biased, because the observations are limited in time and space. This work shows that a reliable climatology combines field observations with output of reanalysis products. Examination of the physics governing low-level jet characteristics reveals the complexity of the wind field in coastal areas, and over the North Sea in particular. The thermal contrast between land and sea plays a pivotal role in the manifestation of the wind field. This feature is not unique for the North Sea, but the relatively small scale and complex coastlines in this area pose an extra challenge in describing the local climatology. It is shown that the newest generation of downscaled reanalysis datasets, represented by the Dutch Offshore Wind Atlas and the New European Wind Atlas represent this complex wind field with reasonable accuracy. As the resolution and quality of weather models and reanalysis products increase, direct coupling between meteorological forcing data and wind energy engineering models becomes an appealing alternative to the use of idealized inflow fields. However, this paradigm shift will come with a substantial increase in computational demands and calls for innovative case selection, or dimensionality reduction. The results presented in this thesis provide a starting point to select relevant parameters, and possible directions are extensively discussed.</p

Quality of wind characteristics in recent wind atlases over the North Sea

Offshore wind energy production is rapidly growing as an essential element in the sustainable energy share. Wind energy siting studies require accurate wind data, and in particular the knowledge of extreme wind events (low-level jets, wind ramps, extreme shear and high wind speeds) is crucial for resource and load assessment. This study evaluates the skill of three relatively new wind atlases, i.e. ERA-5, DOWA and NEWA on the representation of extreme wind events using observations taken at the Met Mast IJmuiden over the North Sea. Overall, DOWA appears to best represent the wind speed profile with virtually no bias. ERA-5 underestimates the mean wind speed profile though the wind shear is well represented, while NEWA correctly represents the near surface wind but underestimates the wind shear. The frequency of low-level jets are also best represented by DOWA. Wind speed ramps and direction ramps are best represented by ERA-5, while DOWA appears to outperform the others concerning wind shear.</p

A maximum entropy approach to the parametrization of subgrid processes in two-dimensional flow

In numerical models of geophysical fluid systems parametrization schemes are needed to account for the effect of unresolved processes on processes that are resolved explicitly. Usually, these parametrization schemes require tuning of their parameters to achieve optimal performance. We propose a new type of parametrization that requires no tuning as it is based on an assumption that is not specific to any particular model. The assumption is that the unresolved processes can be represented by a probability density function that has maximum information entropy under the constraints of zero average time-derivatives of key integral quantities of the unresolved processes. In the context of a model of a simple fluid dynamical system it is shown that this approach leads to definite expressions of the mean effect that unresolved processes have on the processes that are resolved. The merits of the parametrization, regarding both short-range forecasting and long-term statistics, are demonstrated by numerical experiments in which a low-resolution version of the model is used to simulate the results of a high-resolution version of the model. For the fluid dynamical system that is studied, the proposed parametrization turns out to be related to the Anticipated Potential Vorticity Method with definite values of its parameters

An observational climatology of anomalous wind events at offshore meteomast IJmuiden (North Sea)

Uncertainty reduction in offshore wind systems heavily relies on meteorological advances. A detailed characterization of the wind climate at a given site is indispensable for site assessment, and its accurate representation in load assessment models can reduce costs of turbine design and the risk of failure. While regular wind conditions are reasonably described by established methods, some atypical wind conditions are poorly understood and represented, although they contribute substantially to load on turbines. In this study, 4 years of high-quality observations gathered up to 300 m are analyzed to characterize the wind climate at the IJmuiden tower, focusing on these ill-defined conditions. Following a systematic approach, six ‘anomalous wind events’ are identified and described: low-level jets, extreme wind speeds, shear, veer, turbulence and wind ramps. In addition, we identify typical weather conditions that favour their formation. Stable stratification in spring and summer leads to low-level jets (up to 12% of the time) for moderate wind conditions, and to extreme wind shear for stronger wind regimes. Typical wind ramps lead to a change in wind speed of 2 m s−1 in one hour. The applicability of turbulence intensity as a measure of turbulence and gusts is found to be questionable

i-VRESSE bartender

Bartender is a middleware web service to schedule jobs on various infrastructures. Initial release Added Run jobs for multiple applications (#1) Submit job by uploading a zip file containing configuration file and data files. (#6) OpenID authentication (#7) Run jobs on in-memory queue (#15) Slurm batch scheduler, locally or via ssh/sftp (#17) </ul

i-VRESSE bartender

Bartender is a middleware web service to schedule jobs on various infrastructures. Added arq scheduler, a scheduler that uses Redis as a queue and multiple workers (https://github.com/i-VRESSE/bartender/issues/21) Download output files in background (#26) Routes to retrieve job output files and directories (https://github.com/i-VRESSE/bartender/issues/44 https://github.com/i-VRESSE/bartender/issues/55) Create readthedocs/sphinx site (https://github.com/i-VRESSE/bartender/issues/13) Markdown linter to CI and/or pre-commit (https://github.com/i-VRESSE/bartender/issues/28) Code of conduct and contributing guidelines ((https://github.com/i-VRESSE/bartender/issues/#27) Allow consumer web application to intercept social logins (https://github.com/i-VRESSE/bartender/pull/52) Require role for submitting job (#22) Changed Update dependencies, most notably fastapi and sqlalchemy (https://github.com/i-VRESSE/bartender/pull/64) </ul

Springtime

The Springtime Python package helps to streamline workflows for doing machine learning with phenological datasets

Evaluation of the Weather Research and Forecasting model in the Durance Valley complex terrain during the KASCADE field campaign

In the winter of 2012-2013, the KASCADE observational campaign wascarried out in southeast France in order to characterize the wind and thermodynamic structure of the (stable) planetary boundary layer (PBL). Data werecollected with two micro-meteorological towers, a SODAR, a tethered balloonand radiosoundings. Here, we use this dataset to evaluate the representationof the boundary layer in the WRF model. Generally, we find that diurnaltemperature range (DTR) is largely underestimated, there is a strong negativebias in both longwave radiation components, and evapotranspiration is overestimated. An illustrative case is subjected to a thorough model-physics evaluation. First, five PBL parameterization schemes and two land surface schemes are employed. We find a marginal sensitivity to PBL parameterization, while the sophisticated Noah land-surface model represents the extremes in skintemperature better than a more simple thermal diffusion scheme. In a secondstage, we performed sensitivity tests regarding land-surface-atmosphere coupling (through parameterization of z0h=z0m), initial soil moisture content and radiation parameterization. Relatively strong surface coupling and low soilmoisture content results in a larger sensible heat flux, deeper PBL and a largerDTR. However, the larger sensible heat flux is not supported by the observations.It turns out that for the selected case, a combination of subsidence andwarm air advection is not accurately simulated, but this cannot fully explainthe discrepancies found in the WRF simulations. The results of the sensitivityanalysis reiterate the important role of initial soil moisture values.<br/

EUCP Storyboard viewer

First release This repository contains the source code for a storyboard website. Developed in the context of the European Climate Prediction system (EUCP) Horizon2020 project, it contains a collection of stories showcasing the outputs of this project. For information on how to (re-)use, see the README.</p