ERE2.1

The Working Group III contribution to the Sixth Intergovernmental Panel on Climate Change (IPCC) Assessment Report-- mitigation of climate change-- will be publically released on 4 April 2022. The sections on "Mitigation Options: Energy Sources and Energy Conversion", "Climate Change Impacts on the Energy System", and a BOX on "Energy Resilience" are highly relevant to the Energy Meteorology community. As a Chapter Lead Author, I will summarise the findings of Chapter 6, Energy System, and emphasise their relevance to Energy Meteorology and Climate. I will also discuss my experience as a lead author and the challenges of communication between such different research communities. 

How to cite: Hahmann, A.: The GWIII contribution to the IPCC AR6 and its relevance to Energy Meteorology and Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12936, https://doi.org/10.5194/egusphere-egu22-12936, 2022.

January 2007 was a remarkably stormy period in Europe with impacts on societal infrastructure and implications for energy meteorology.  A series of cyclones tracked across the North Atlantic and into Europe during the two week period 8-22 January 2007.  For many parts of Europe, Storm Kyrill on 18 January 2007 was the most important of these for the infrastructure damage that it caused.  It had the highest European storm-related insurance losses in recent history.  The storm spawned a high intensity derecho that started in western Germany and travelled into eastern Europe. It was associated with severe convection, lightning, several tornadoes, and strong wind gusts.  The storm caused over 50 fatalities, widespread disruption of transport and power networks, and a lot of forest damage.   Storm Hanno on the 14 January 2007 was the second most severe storm of the period with serious impacts in Norway and southern Sweden.  Wind gusts reached the level of the 20-50 year event.  There were 6 fatalities in southern Sweden, some building damage, power cuts, and forest damage.  Storm Franz on 12 January 2007 caused the highest surge in the southern North Sea for January.  However, its flooding impact was reduced because the monthly cycle of spring and neap tides was near a minimum.  By contrast, astronomical tides were highest near the end of the period on 20-22 January 2007.  The highest absolute water levels for the month for many tide gauge stations were registered during Storm Kyrill on 18 January 2007 and also during Storm Lancelot on 20 January 2007.  This contribution takes a closer look at the North Sea surge of two important storms of the period: Storm Franz and Storm Kyrill.  An analysis is presented of tide gauge data to elucidate the storm surge and wave field around the North Sea and to assess possible links with shipping accidents and offshore incidents.  An unusually large wave sequence had been registered at the FINO1 offshore wind energy research platform only a couple of months previously on 1 November 2006.  The water level data is analyzed to ascertain if there may have been a repeat of the wave event during the storm sequence on 8-22 January 2007.

How to cite: Kettle, A.: Kyrill, Franz, and the Societal Impacts of the Storms of January 2007, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2287, https://doi.org/10.5194/egusphere-egu22-2287, 2022.

Fighting global warming implies replacing fossil fuels by renewable energy sources. Wind has the benefit to be an easily accessible and infinitely renewable resource but is not evenly distributed in space and time. A solution to prevent energy scarcity in a decarbonised world would be the building of a global interconnected grid that provide populated regions with electricity generated in remote but resourceful areas. In this context, it has appeared in previous studies that Greenland and Europe have complementary wind regimes. In particular, the southern tip of Greenland, Cape Farewell, has gained increasing interest for wind farm development as it is one of the windiest places on Earth. In order to gain new insights about future wind speed variations over South Greenland, the Modèle Atmosphérique Régional (MAR), validated against in situ observations over the ice-free area where wind turbines are most likely to be installed, is used to built climate projections under the emission scenario SSP 5-8.5 by downscaling an ensemble of CMIP6 earth system models (ESMs). These projections enable to assess the long term wind speed and maximum wind power change between 1981 and 2100 over Cape Farewell, quantified with the help of a linear regression. It appears from this analysis that, during this period over the ice-free area, the annual wind speed is expected to decrease of ~-0.8 m/s at 100m a.g.l. This decrease is particularly marked in winter while in summer, wind speed acceleration occurs along the ice sheet margins. An analysis of two-dimensional wind speed change at different vertical levels indicates that this decrease is likely due to synoptic circulation change, while in summer, the katabatic winds gowing down the ice sheet are expected to increase due to an enhaced temperature contrast between the ice sheet and the surroundings. As for the mean annual maximum wind power a turbine can yield, a decrease of ~-300.5 W is to be expected over the ice-free area of Cape Farewell between 1981 and 2100 at 100m a.g.l. Again, the decrease is especially marked in winter. Considering the very high winter wind speeds occuring in South Greenland which can cut off wind turbines if too intense, the projected wind speed decrease might be beneficial for the establishment of wind farms near Cape Farewell.

How to cite: Lambin, C., Fettweis, X., Ernst, D., and Kittel, C.: Long term wind speed and wind power change analysis over South Greenland using the regional climate model MAR., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10440, https://doi.org/10.5194/egusphere-egu22-10440, 2022.

In 2020, the North Sea already had 19.8 GW or 79% of the European offshore wind installations. The size and number of wind farms in this region are expected to increase substantially to reach climate mitigation targets, with forecasts of offshore wind commitments across Europe adding up to 111 GW of offshore wind by 2030. However, governments base their climate mitigation plans on past historical wind resources data. Still, there is a probable threat that these will change in the future due to climate change during the lifetime of a wind farm. 

The study of future changes in wind resources is not a new subject. A systematic literature search with the keywords "Wind Resources" and "Climate Change" returned over 80 peer-reviewed articles that assessed future wind resources at the global, regional and local scale. Most of these studies used the 10-m wind speed output from the climate or regional model to directly estimate a wind turbine's power production, using the power law and sometimes an idealised power curve. As far as we know, only two studies explored the possible implications of changes in wind direction. 

In this presentation, we explore the implications of the various assumptions. We use the example of the North Sea and Northern Europe and the CMIP6 climate model archive to demonstrate that some assumptions can exaggerate future wind resource changes. We also consider the consequences of the changes in boundary layer stability, wind direction and vegetation changes to the future wind resources in Northern Europe. 

How to cite: Hahmann, A. N., Peña, A., García-Santiago, O., and Pryor, S. C.: Evaluation of the assumptions used in the assessment of future wind resources - A case for CMIP6 in Northern Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5232, https://doi.org/10.5194/egusphere-egu22-5232, 2022.

Wind farms extract kinetic energy from the flow to generate electricity. Thereby, they modify the wind and turbulence fields upwind, at the side and especially downwind of the farm. Due to the induced enhanced mixing, other meteorological variables such as temperature and humidity are also affected by the presence of wind farms. With the massive growth of installed capacity both on- and offshore, these wind farm effects play an increasing role in numerical weather forecasts. This study will investigate the impact of currently installed wind farms in Europe on the weather forecast accuracy.

We performed forecasts for central and northern Europe with and without wind farm parameterizations. We used the operational mesoscale model HARMONIE-AROME equipped with the wind farm parameterization (WFP) by Fitch et al. (2012) as implemented by van Stratum et al. (2021). We added another WFP, the explicit wake parameterization (EWP, Volker et al. 2015). We created a European wind turbine data set by combining different data sets and using a machine learning gap-filling approach. This data set includes turbine locations and their characteristics. Different scenarios were tested using this data set: (A) including only offshore turbines in the German Bight and surrounding Denmark, (B) including all on- and offshore turbines present in the European wind turbine data set.

The simulation results from HARMONIE-AROME indicate that wind farms affect near-surface wind speed, temperature and humidity. The magnitude of these differences decreases with increasing distance from the farm, but still amounts to ±0.5 m/s in 10-m-wind or ±0.25 K in 2-m-temperature at a non-negliable number of locations in Denmark for an investigated exemplary summer day compared to the scenario without wind farms. The impact of onshore turbines is generally smaller than that of offshore turbines. However, the response to scenarios (A) and (B) differ, indicating that it is necessary to include both on- and offshore turbines to capture the full effect of wind farms in Europe. The wind farm effect also depends on the chosen wind farm parameterization, and both schemes provide plausible results. Future studies are necessary to better evaluate the two parameterizations and derive possible fine-tuning or combinations of the schemes. Overall, an ensemble consisting of both wind farm parameterizations could give a more reliable forecast in the future.

A. C. Fitch, J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad. Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Mon Weather Rev, 140(9):3017–3038, sep 2012. ISSN 00270644. doi:10.1175/MWR-D-11-00352.1

B. van Stratum, N. E. Theeuwes, J. Barkmeijer, B. van Ulft, and I. Wijnant. A year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME. Earth and Space Science Open Archive, page 29, 2021. doi: 10.1002/essoar.10509415.1. URL doi:10.1002/essoar.10509415.1

P. J. H. Volker, J. Badger, A. N. Hahmann, and S. Ott. The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF. Geoscientific Model Development, 8(11):3715–3731, 2015. ISSN 1991-959X. doi:10.5194/gmd-8-3715-2015.

How to cite: Fischereit, J., Olsen, B. T., Imberger, M., Vedel, H., Guo Larsén, X., Hahmann, A., Giebel, G., and Kaas, E.: Wind farm effects on weather forecast using the operational model HARMONIE-AROME, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2373, https://doi.org/10.5194/egusphere-egu22-2373, 2022.

Accurately forecasting short-term wind power production is a challenging task. As the share of wind power in the electrical system is rapidly growing, this task is becoming increasingly important not only for power production companies but also for transmission system operators. By applying post-processing methods to forecasts of wind speed from numerical weather prediction (NWP) models, power production forecasts can be improved. In this study, we used two years of lidar measurements of the wind speed from a coastal site in the Baltic Sea to calculate a theoretical power production and evaluated forecasts from the NWP model HARMONIE-AROME. Six post-processing methods of varying degree of complexity were implemented and tested in order to mimic how they could be used operationally. The performance of the methods in different weather situations was analysed in terms of the mean absolute error (MAE) skill score. For the test period it was found that, in general, the simple method of temporally smoothing the wind speed forecast by applying a low-pass filter (moving average) with a window of ±1 h outperformed the other methods tested. The main reason for this being a reduced risk of double penalty due to small time shifts in wind speed variations in the forecast compared to the observations. However, under weak synoptic forcing the best skill score was achieved using a mix of the forecast from the previous and the current day. Additionally, when low-level jets were forecasted, the best result was achieved using the machine learning random forest algorithm.

How to cite: Hallgren, C., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Improving 0-24 h offshore wind power forecasts over the Baltic Sea: comparing post-processing methods of varying complexity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13481, https://doi.org/10.5194/egusphere-egu22-13481, 2022.

We present an intercomparison of a one year of atmospheric simulations performed with a numerical atmospheric model system based on the WRF model with tall mast observations. We employ the nestinga capabilities of the WRF model to run up to high resolution large-eddy simulations (WRF-LES). The simulations  aim at describing the wind climatology and the turbulence characteristics at Østeild, Denmark. There, DTU established the National Test Site for Wind Turbines, where some of the largest wind turbines prototypes are under testing. We evaluate the goodness of the simulations using the WRF-LES system by comparison with high-quality mean wind and turbulence observations from a 250-m meteorological mast. The main objective of the work is to demostraste that the WRF model does not only provide long-term time series of wind speed and direction but also turbulence characteristics and parameters, which are needed for the evaluation of the site conditions, and turbine design and peformance.

The WRF-LES based simulations are performed using four nested telescopic domains centered at the Østerild mast position. The outermost and largest domain has a horizontal resolution of 6250 m, whereas the innermost and smallest domain a horizontal resolution of 50 m. By modeling at these scales, we intend to resolve most of the turbulent scales.  We run the two outermost domains in a traditional mesoscale fashion, which means we use a commmonly used planetary boundary layer (PBL) scheme, whereas the two innermost domains are run in large-eddy simulation mode, i.e., without a PBL scheme. A complete year is simulated through parallel ten day long simulations. The output for the innermost domain is produced at the model grid point closest to Østerild every 12 s, whereas that of the other domains is produced every 10-min.

After computing the 10-min statistics for the full year on the model output of the innermost domain output and the 1-Hz data of the cup anemometers at Østerild that cover the range of the mast, we find very good agreement between the observed and simulated wind climatology. Turbulence estimates from both observations and simulations are also in good agreement, even though from the observations the site shows a wide variety of atmospheric stability and turbulence conditions. The turbulence intensity changes with wind speed in a similar way both in the simulations and the measurements. Our work shows that numerical models can be used as a tool to describe turbulent site conditions required, among others, for the efficient siting of wind turbines.

How to cite: Peña, A. and Mirocha, J.: Intercomparing WRF-LES based turbulence simulations with measurements from a 250-m tall meteorological mast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-800, https://doi.org/10.5194/egusphere-egu22-800, 2022.

Wind fields are known to be extremely variable in space and time over a wide range of scales. Universal Multifractals (UM) are a common tool used to model and simulate such features. This parsimonious framework is based only on 3 parameters (α , C₁ and H) with physical interpretation, while the 4th, the power a of a conservative flux, is absorbed by the empirical estimation of the mean singularity over a non-conservative field. Obviously, in the context of wind power production, these properties are transferred to wind turbine torque and ultimately power.

Here, we investigate this transfer through modelling of wind turbine torque. For this purpose, 2 different modelling chains have been developed. The first one takes into account the spatial distribution of the wind velocities and the rotation of the blade considering an integral torque along the blades, this is in contrast with traditional approach which uses the average wind speed at hub height and blade radius to compute the torque. The second one is based on TurbSim for wind input computation, and OpenFAST for torque computation, which are tools developed by National Renewable Energy Laboratory (NREL). The main challenge is to input a space-time varying wind because, although it is possible to know the wind data at isolated points, where high resolution anemometers can be located, obtaining the wind speed in all points over a given area is rather complex. Using uniform wind fields in space creates too strong artificial correlations.

In this work, we suggest the reconstruction of wind fields from a point measurement by relying on well established scaling laws. More precisely, the wind field at any location is obtained by adding to the data at the available anemometer point, the product of a prefactor, a random UM field and distance increment raised to power av and Hv respectively. The exponents are obtained in the literature using purely dimensional arguments. Data from 2 high resolution 3D sonic anemometers located on a meteorological mast in a wind farm situated approximately 110 km south-east of Paris, with approx. 33 m vertical distance, are used to tune parameters of UM field and the prefactor according to the event. Data is collected in the framework of the RW-Turb measurement campaign (https://hmco.enpc.fr/portfolio-archive/rw-turb/); which is supported by the French National Research Agency (ANR-19-CE05-0022)

UM analysis over numerous events (more than 1-year data is available) was carried out to confirm good agreement between UM parameters retrieved on anemometer data and simulation data. A comparison between torque obtained with the traditional approach and both modelling chains using simulated fields and UM analysis of the outputs was also performed to observe the differences focusing on the small scales.

How to cite: García Gago, Á., Gires, A., Veers, P., Schertzer, D., and Tchiguirinskaia, I.: Transfer of small scales space-time fluctuations of wind fields to wind turbines torque computation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10583, https://doi.org/10.5194/egusphere-egu22-10583, 2022.

As today’s wind farm clusters can be as large as thousands of kilometers squared and individual turbines hundreds of meters tall, we are challenged when applying classical wind and turbulence models for corresponding wind energy-related calculations.  Typical boundary-layer turbulence models are applicable to time scales smaller than ~1 h, or as denoted by Högström et al. (2002) the Kolmogoroff inertial subrange, the shear production range, and for ranges within the spectral gap region. 

This study revisits some key characteristics of the atmospheric boundary-layer turbulence, covering frequencies from 1/year, over the energy-containing range, to synoptic- and mesoscales, to the gap region and to the 3D turbulence range. This study aims at investigating the following fundamental questions: How to characterize the full-scale spectral behaviors and what are the mechanisms behind them? To which extent is the condition of stationarity fulfilled? What are the 2D-isotropy characteristics? How are numerical modeling abilities in capturing these characteristics? We also show how these findings have been used in wind energy applications, e.g. for generating time series of wind speed including meso-scale variability, for investigating meandering, for extreme wind calculation and for improving turbulence intensity calculation in the presence of organized atmospheric phenomena.

The study includes literatures as well as a series of our studies in recent years (e.g. Larsén et al. 2013, 2016, 2019, 2021).  We combined measurements and modeling in the analysis. The primary datasets are from several met stations over Denmark and the North Sea region, including both 10-min and sonic measurements from about 10 m up to 240 m. The investigations include both statistical and numerical modeling.

References:

Högström U, Hunt J, Smedman AS (2002) Theory and measurements for turbulence spectra and variances in the atmospheric neutral surface layer. Boundary-Layer Meteorol 103:101–124

Larsén, X. G., Larsen, S. E., Petersen, E. L., & Mikkelsen, T. K. (2021). A Model for the Spectrum of the Lateral Velocity Component from Mesoscale to Microscale and Its Application to Wind-Direction Variation. Boundary-Layer Meteorology, 178, 415-434. https://doi.org/10.1007/s10546-020-00575-0

Larsén X., Larsen S., Petersen E. and Mikkelsen T. 2019: Turbulence Characteristics of Wind-Speed Fluctuations in the Presence of Open Cells: A Case Study. Boundary-Layer Meteorology, https://doi.org/10.1007/s10546-019-00425-8, (171), 191 – 212.

Larsén X. Larsen S. and Petersen E. (2016): Full-scale spectrum of the boundary layer wind. Boundary-Layer Meteorology, Vol 159, p 349-371

Larsén X., Vincent C. and Larsen S.E. (2013): Spectral structure of mesoscale winds over the water, Q. J. R. Meteorol. Soc., DOI:10.1002/qj.2003, 139, 685-700.  

How to cite: Larsén, X., Larsen, S., Petersen, E., and Mikkelsen, T.: Full-scale turbulence structure of wind and applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4572, https://doi.org/10.5194/egusphere-egu22-4572, 2022.

Nocturnal Low-Level Jets (NLLJ) are maxima in vertical profiles of the horizontal wind speed in the lowest hundreds of meters of the troposphere. NLLJs therefore influence the winds at typical rotor heights. However, due to rare measurements with a sufficient precision and resolution, the occurrence frequency and spatio-temporal characteristics of NLLJs on the mesoscale are still poorly understood. The present work uses new measurements of wind profiles for June to August 2020 from Doppler wind lidars that were installed as a part of the Field Experiment on Submesoscale Spatio-Temporal Variability (FESTVaL) campaign, in Lindenberg and Falkenberg (Germany), at about 6 km of distance from each other. The aim of our NLLJ assessment is to characterize their mesoscale properties and evaluate their potential impacts on wind power production. The vertical profiles of the 10-minute mean winds from the lidar measurements were statistically analysed using automated detection tools for NLLJs. These allowed the determination of the frequency of occurrence, height and wind speed in the core of NLLJs as well as the vertical wind shear with a high temporal resolution. First, we intercompared the results from the two sites in order to analyse the temporal and spatial variability of NLLJs on the mesoscale. Our automatic detection identified NLLJs in about 64% to 74% of the summer nights in 2020, showing that they were a common phenomenon during that summer. About half of the NLLJ events were longer than 3 hours, with Lindenberg having more often shorter events of less than 1 hour. If very long NLLJ events (> 6 hours) occurred, they typically affected both places simultaneously, an indicative of their mesoscale character. Our results further suggest that very long NLLJ events are generated by the classical inertial oscillations, influenced by a large-scale horizontal pressure gradient and intermittent turbulent mixing, while shorter NLLJ events are more strongly dependent on local conditions or are driven by shorter-living density currents. Regarding their potential impact on wind turbines, we simulated wind power production for two different turbine types of different height and capacity. Both simulations indicate that NLLJs clearly increase the power production compared to nights without NLLJs. The quantitative NLLJ impacts on power production strongly depend on the height of the wind turbines: during NLLJ events the average wind production was 80% higher for a hub height of 135 m and only 53% higher for 94 m. At the same time, NLLJs increased the wind shear across the rotor layer. Extreme shear in the rotor layer was often associated with NLLJs, with 37% of all NLLJs leading to extreme shear and 48% of all extreme shear cases being caused by NLLJs. We infer from our assessment that particularly long NLLJ events strong influence wind power production, while shorter NLLJs can increase the temporal and spatial variability in power production, causing power ramps.

How to cite: Weide Luiz, E. and Fiedler, S.: Assessment of nocturnal low-level jets and their implication for wind power from FESST@home measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3177, https://doi.org/10.5194/egusphere-egu22-3177, 2022.

Unique airborne in-situ measurements were evaluated to investigate the influence of offshore wind farms on the latent heat flux in the marine boundary layer. 21 of the total 42 measurement flights carried out in the frame work of the WIPAFF project over the German Bight in the years 2016 and 2017  enabled such an evaluation under different atmospheric conditions. The measurements of 15 flights showed a significant increase of the vertical upward latent heat flux over the offshore wind farm clusters Amrumbank West, Nordsee Ost, Meerwind Süd/Ost or the wind farm Godewind. Under thermally stable conditions, all except one of the measurement flights showed an increase of latent heat flux over or in the wake of the wind farms, with an heat flux up to 17 times higher compared to the undisturbed flow. During flights under unstable thermal stratification, the phenomenon was observed in 8 out of 13 cases. The results also suggest that not only thermal stratification but also moisture stratification plays a decisive role in whether the influence of the wind farm becomes noticeable in the latent heat flux.  Considering the absolute amount of the increase of the upward latent heat flux, a maximum increase of +400 W/m² was measured in unstable conditions. 

How to cite: Platis, A., Büchau, Y., and Bange, J.: Influence of offshore wind farms on the latent heat flux in the marine boundary layer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2602, https://doi.org/10.5194/egusphere-egu22-2602, 2022.

The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to its predecessor, MASC-3, it can automatically takeoff and land on small patches of land while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows for deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.

Before its first offshore mission, MASC-V underwent a full system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UAS data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UAS for vertical profiling as well as to operate tens of kilometers away from ground personell over open water.

How to cite: Weber, I., Platis, A., zum Berge, K., Schön, M., Boventer, J., Djath, B., Schulz-Stellenfleth, J., and Bange, J.: An unmanned aircraft system for (offshore) wind energy research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11858, https://doi.org/10.5194/egusphere-egu22-11858, 2022.

The largest part of the global population without reliable access to electricity lives in Africa. Here, renewable energy is a sustainable, cost efficient, and climate-friendly solution, especially given the large untapped renewable energy potential existing over the African continent. However, most renewable energy-related studies over Africa typically use input datasets at relatively coarse spatial resolutions (e.g., ERA5 at about 30km). Our objective is to produce a prototypical high-resolution dataset over southern Africa from dedicated atmospheric simulations. The data will be used with renewable energy assessment models, to eventually evaluate the renewables potentials. The hypothesis is that the high-resolution datasets provide more realistic and accurate renewable energy potential estimates. The ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model is run as the operational forecast model at the German Weather Service (DWD); and we employ the same model in its Limited Area Mode (ICON-LAM) in this project. The study domain over southern Africa is chosen due to its high solar and wind energy potential. ICON-LAM dynamically downscales the global deterministic ICON-NWP forecasts dataset from a spatial grid spacing of 13km to a convection-permitting resolution of 3.3km, without convection parameterization. This southern Africa ICON-LAM implementation is novel and has not been run before. Simulations cover the time span from 2017 to 2019 with contrasting meteorological conditions. The high-resolution triangulated grid cells of the 3.3km domain are exactly inscribed in the 13km global grid cells, following the sub-triangle generation rule of the ICON model mesh. To keep the ICON-LAM close to the observed atmospheric state the model atmosphere is reinitialized every 5 days, with one day spinup. The land surface and subsurface are run transient. In a very initial evaluation step, simulated 10m wind speed, global solar radiation, 2m air temperature, and precipitation from the coarser driving model, the ERA5 reanalysis as well as our ICON-LAM setup are validated using satellite data and in situ observations from the two local meteorological networks (SASSCAL and TAHMO). Initial results point to an added value of the convection-permitting simulations.

How to cite: Chen, S., Poll, S., Heinrichs, H., Hendricks-Franssen, H.-J., and Görgen, K.: Convection-permitting ICON-LAM simulations as input to evaluate renewable energy potentials over southern Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6339, https://doi.org/10.5194/egusphere-egu22-6339, 2022.

Wind and solar energy have emerged as the one of the most popular and successful sources of renewable energy in combating environmental degradation and climate change. Countries around the world are developing policy mechanisms for increasing the share of renewable energy technologies for fulfilling their energy demands. Both wind and solar have proved their potential as clean and efficient sources of energy generation. Therefore, transitioning into a sustainable future requires a shift from fossil fuels to renewable energy technologies. The main goal of this study is to compare wind and solar energy potential for different climate regions of Morocco, India and Kenya using standard methodologies.

In this study we have used the wind profile power law relationship for estimating the wind speed and power at 100 m level. We are analysing long term synoptic datasets from 2 to 4 synop stations in arid and humid regions of North India, Morocco and Kenya based on the Meteomanz standard meteorological database. Stability dependent power law profile approximations were used and comparisons made with ERA5 reanalysis data. Estimation of wind energy production for different continental wind generators were also provided. Using the connection between the wind speed and profile law we demonstrated how wind energy can vary using different values of power law exponents for different climatic regions.

Standard meteorological measurements (temperature, humidity and cloudiness) gave the opportunity for estimation of global irradiance which was also compared with the ERA5 dataset. Applicability of widely used direct and diffuse irradiance parameterizations for different climate regions were also investigated.

For instance, in Marrakech the six Pasquill-Gifford stability classes were determined by estimating the global solar irradiance for cloudy and clear sky conditions as well as the wind speed. Analysis of the data showed that windspeed at 10 m varied between 1.8 m/s in the early morning (UTC 06:00) to 3.5 m/s in the evening (UTC 18:00) while the windspeed at 100 m varied between 2.6 m/s and 5 m/s at the same time periods.  The estimated wind energy at 100 m level for rural areas was more than that of urban areas The wind energy at 100 m varied between 47.2 KW in the early morning (UTC 06:00) to 573 KW in the evening (UTC 18:00) for the rural areas while in urban areas the variation was between 83.8 KW to 670.5 KW during the same time periods. The annual average global solar radiation was found to be maximum during the afternoon with a value more than 970 W/m².

How to cite: Mendyl, A., Gandhi, A., Musyimi, P. K., Székely, B., and Weidinger, T.: Comparative analysis of wind and solar energy potential from differnet  climate regions, case studies  of Morocco , India and  Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-380, https://doi.org/10.5194/egusphere-egu22-380, 2022.

 The transition towards decarbonized power systems requires to account for the impacts of the climate variability and climate change on renewable energy sources. With the growing share of wind and solar power in the European power system and their strong weather dependence, balancing the energy demand and supply becomes a great challenge. In this study, we assess energy compound events, defined as periods of simultanous low renewable production of wind and solar power, and high electricity demand. Using a country-based logistic regression approach, we model the binary occurrence of energy compound events and we examine the effects of meterological and weather regimes. Then, we quantify the meteorological conditions resulting in the highest probability of occurrence of energy compound events. We found that the combination of extremely low temperatures (below the 5th percentile) and low wind speed (below the 10th percentile), along with moderate-high solar radiation (above the 50th percentile), lead to the highest probability of occurrence of energy compound events over most European countires. Furthermore, we show that blocked weather regimes lead to the weather conditions that can have a major risk in the European power system. In particular, the Greenland blocking and the European blocking were associated with widespread energy compound events that affected multiple countries at the same time. Our results highlight the importance of the weather regimes that result in spatially compounding energy events, which might a major impact within a potential fully interconnected European grid.

How to cite: Otero, N., Martius, O., Allen, S., Bloomfield, H., and Schaefli, B.: Quantifing the influence of meteorological and large-scale atmopsheric drivers on energy compound events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1837, https://doi.org/10.5194/egusphere-egu22-1837, 2022.

Accurate irradiance data is necessary for model estimates of expected photovoltaic (PV) power production. Such data is freely available from reanalysis and satellite products with a high temporal and spatial resolution, including locations without ground-based measurements. Gridded irradiance data is therefore used for the characterization of solar resources at specific locations and larger areas, e.g. by power system modellers. Past assessments of irradiance data for PV modelling often relied on the evaluation of global horizontal irradiance (Q_GHI). However, the direct and diffuse irradiance components as well as differences in seasonal characteristics can strongly affect the PV capacity factors (C) potentially leading to larger biases in C than for Q_GHI. We therefore systematically assess differences in Q_GHI, direct and diffuse horizontal irradiance (Q_dir  and Q_dif) and quantify the subsequent bias propagation from individual radiation components to C in a contemporary PV power model. Our PV model simulations use seven different gridded irradiance data sets, namely ERA5, COSMO-REA6, COSMO-REA6pp, COSMO-REA2, CAMS radiation service, SARAH-2 and CERES Syn1Deg. All data sets provide Q_dir and Q_dif as separate time series spanning seven to 43 years and with a temporal resolution of 15 minutes to one hour. The results are compared against seven years of simulations based on reference measurements from 30 weather stations of the German Weather Service. We compute metrics characterizing biases in seasonal and annual spatial means, day-to-day variability and extremes in C, considering single stations and a simulated PV fleet. Our results highlight biases of -1.4 % (COSMO-REA6) to +8.2 % (ERA5) in annual and spatial means of C at single stations across Germany, while the bias in Q_GHI is -3 % for COSMO-REA6 to +3.6 % for ERA5. We also show the bias on days of very low PV production, relevant for extreme event analysis: The days within the lowest ten percent of daily PV production in a PV fleet show a bias of +70.2 % in ERA5, while it is only +4 % in the post-processed COSMO-REA6 data (COSMO-REA6pp). SARAH-2 and COSMO-REA6pp outperform the other products for many metrics, but also cause some biases in C. For instance, SARAH-2 yields good results in summer, but overestimates C in winter by 16 % averaged across all stations. COSMO-REA6pp represents day-to-day variability in C of a simulated PV fleet very well and has a relatively small bias of +0.5 % in the annual spatial means, but this is partly due to compensating biases from individual stations. Our results suggest that gridded irradiance data should be used with caution for site assessments and should ideally be complemented by local measurements. For power system modellers, our results may provide guidance for the quantification of uncertainties caused by gridded irradiance data.

Reference:

Kenny, D., and Fiedler, S., in press, Which gridded irradiance data is best for modelling photovoltaic power production in Germany?, Solar Energy.

How to cite: Kenny, D. and Fiedler, S.: Inter-comparison of PV power simulations from seven gridded irradiance data sets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5888, https://doi.org/10.5194/egusphere-egu22-5888, 2022.

The impacts of large scale climate variabilities on the solar energy resource are widely investigated around the world, however their effects are not yet clear for Mascarene Islands (southwest Indian Ocean, SWIO) and needs to be addressed. In this study, surface solar radiation (SSR) classification and anomalies at the diurnal scale from SARAH-E satellite product over Reunion Island are linked to the large scale climate variabilities in SWIO region. These climate variabilities include Tropical Cyclones (TCs) and the Tropical Temperate Troughs (TTTs) at the synoptic scale, the Madden–Julian Oscillation (MJO) at the intraseasonal scale, and the Indian Ocean Dipole (IOD), the Subtropical Indian Ocean Dipole (SIOD) and the El Niño–Southern Oscillation (ENSO) at the interannual scale.
We identified the variability of SSR at various time scales where both local processes and the large scale convective variabilities play important roles. At the synoptic and intraseasonal timescales, the local variability of SSR over Reunion shows a significant association with TCs, TTTs and the MJO. The sign and amplitude of SSR diurnal anomaly are found to be correlated with the enhanced- / depressed- convective phase and amplitude of these events. The SSR anomaly is strongly altered with the presence of nearby TCs, with a value of up to about 30% of climatology, although at low occurrence; TTTs and MJO have relatively weaker impact, with a value of about 13% and 5% respectively. At the interannual timescale, IOD, SIOD and ENSO have relatively much less importance on local SSR variability. The daily total solar energy density has been calculated for all these variabilities to provide useful information for energy applications. 

How to cite: Tang, C., Mialhe, P., Pohl, B., Morel, B., Koseki, S., Abiodun, B., and Bessafi, M.: Variability of the Surface Solar Radiation over Reunion island and its interaction with the synoptic, intraseasonal and interannual convective variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6831, https://doi.org/10.5194/egusphere-egu22-6831, 2022.

Regional meteorological models are becoming a generalized tool for solar energy production forecasting,  due to their capacity to simulate different types of cloud formations and their interaction with solar radiation. The greater demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting atmospheric model package designed to fulfill the needs of solar energy applications (WRF-Solar), with the solaR power plant model. This study focuses on the use and validation of this coupled tool in forecasting the energy production for two real solar plants, one in Spain and another in India. A period of one year for the Spanish emplacement and nine months for the Indian site are simulated with a daily operational forecasting set-up. Aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) are considered in the calculations, a new capability in WRF-Solar. Power predictions are obtained and compared with real data from the inverters of both plants provided by the operating company.

The results show that WRF-Solar obtains accurate forecasts of global, direct, and diffuse radiation and of the ambient temperature that solaR uses as input to predict the energy production of the solar plants. The normalized Mean Annual Errors (NMAE) is 5.18% in the Spanish and 5.59% in the Indian plant for the first day of predictions, demonstrating a reliable performance of the forecasting system in different climate locations. The skill scores for the second day of prediction are also promising, with practically the same errors as the previous day (5.19% and 6.17 for Spain and India respectively). By comparing the model predictions, with and without AOD input during the dustiest days in the Spanish site, the importance of the aerosol effect inclusion is demonstrated with an improvement up to 10% in the energy forecast. These results demonstrate the system’s potential both for solar plant operation and energy market applications.

How to cite: Prósper, M. A., Sosa-Tinoco, I., and Miguez-Macho, G.: Development of a solar energy forecasting system for two real solar plants based on WRF Solar with aerosol input and a solar plant model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9393, https://doi.org/10.5194/egusphere-egu22-9393, 2022.