In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions.

@en

Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas. The deliverable offers an in-depth resource assessment for wind-wave-solar renewable energy resources along the European Atlantic region. The duration of information cover 1990-2021 (including 2021), resulting in 32 years. This deliverable uses state of the art high resolution data for wind and solar, and introduces the European Coasts High resolution Ocean WAVEs (ECHOWAVE) hindcast, a new open source database for wave conditions with superior accuracy. ECHOWAVE covers North Atlantic European waters within the coastal shelf, from intermediate to shallow water relative depths and is specially adjusted to improve the representation of sea states within the area of interest. This translates as a reduction of the uncertainties in the estimation of some of the most important wave parameters for wave energy applications.@en

Renewable energy project require long term climate information, offshore renewable energies in particular are in need of higher fidelity information as both power production, reliability and survivability rely on them. Existing open source datasets are too coarse, and often do not have the suitable physics based solutions to resolve high fidelity areas. While for power production different datasets may be needed wind speeds, solar radiation, ambient temperature, metocean conditions, the common thread is that all information provided by often open source free dataset carry large deviations that can be catastrophic or under-estimate power production significantly. This deliverable aims to bridge the gap and offer the EU-SCORES project three custom models wind, solar, wave and are then used for ultra high-fidelity assessment (sub 500m). The physical parametrisation are specifically calibrated with the need of EU-SCORES, and the models are intensively validated against in-situ and satellite information. This report describes the different models used for the construction of the open-source databases for wind-wave-solar information, from 1990-2021. All models have been developed, calibrated and validated against in-situ measurements, providing with the uncertainty levels for the hindcasts.@en

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation.

@en

Wind ramps, or rapid changes in wind speed, are a crucial aspect of atmospheric dynamics and have significant implications for various wind energy applications. For example, wind ramps tend to increase uncertainty in power output predictions. Furthermore, they also induce fatigue damage to wind turbines. In a recent study, DeMarco and Basu (2018; Wind Energy) used long-term observational data from four geographical locations to characterize the tails of the wind ramp probability distribution functions (pdfs). They showed that the pdfs from these various sites (ranging from offshore to complex terrain) portray quasi-universal behavior. The tails of the pdfs are much heavier than the Gaussian pdf and decay faster with increasing time increments. The tail-index statistics, computed via the so-called Hill plots, exhibited minimal height dependency up to approximately one hundred meters above the land or sea surface level. However, wind ramp statistics at higher altitudes at Cabauw (the Netherlands) were quite distinct. In the present study, we investigate if state-of-the-art reanalysis datasets capture the intrinsic traits of wind ramp pdfs. Specifically, we make use of the newly released Copernicus European Regional ReAnalysis (CERRA) dataset in conjunction with the popular fifth-generation ECMWF reanalysis (ERA5) dataset. These datasets allow us to describe the characteristics of wind ramp pdfs at high altitudes (up to 500 m). Given the disparity of the spatial resolution of CERRA (~5.5 km) and ERA5 (~32 km) datasets, we are also able to demonstrate the impact of spatial resolution on simulated tail index characteristics. Lastly, the influence of natural climate patterns such as El-Nino and La-Nina on wind ramp pdfs are examined.@en

This Deliverable, 6.2 Renewable Coarse Resource Assessment for the European Region, aims to offer a preliminary overview of the available wind, wave and solar resources across the European Continent. The coarse assessment aims to analyse and assess the current levels of these renewable resources, analysing and discussing the expected variations per regions.
The resource assessment, even at coarse level, can indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The estimated energy densities of wind, wave and solar, are partially the main indicators, we also discuss the impacts of variability, as this is expected to alter the performance of power production, when each resource is utilised by specific technologies.
This report also introduces some of the main statistical approaches and ways to estimate the resource potentials. They will be used and expanded upon in forthcoming Deliverables that will also look into power production, via coupling of high fidelity wind-wave-solar models with specific renewable converters.
Finally, in this Deliverable we discuss the role of open source coarse data and underline their limitations for operational renewable energy projects.@en

This Deliverable, 6.1 Renewable Correlation of offshore resources, aims to investigate the potential for correlation between parameters of different renewable energies.
The analysis is based on a first layer on coarse data, and will allow us to identify which resources have more “connectivity”. The resource assessment, even at coarse level, will indicate regions for further high resolution analysis, with better suited wind-wave-solar models. The correlation analysis is expected to showcase the potential of temporal overlaps by the different resources.
The Deliverable examines the overlap of stochastic conditions, the analysis will consider different “time windows” for the base resource, and assess its complementarity with other stochastic renewables. The aim of this deliverable is the estimation of overlap and production of mean maps that indicate to which extend each resource is connected. It is expected that the wind and wave resources will produce higher interest, due to their temporal variability. However, peak solar performance will also be analysed in terms of overlap with wind and/or wave.@en