Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Sentinel-1 Synthetic Aperture Radar (SAR) is a multi-purpose monitoring satellite suite that, among many applications, provides sea surface wind speeds at high spatial resolution. The overall aim of the study is to quantify the accuracy of the SAR wind products from Copernicus Ocean Wind, called OCN OWI, and from the Technical University of Denmark (DTU) Department of Wind and Energy Systems' product called DTU SAR. Both products serve as a basis for offshore wind resource mapping for offshore wind energy planning. With the growth in offshore wind farms, offshore wind resource information is highly relevant. However, a comparison between the two products is lacking. This study fills this gap by presenting a comprehensive validation of the two Sentinel-1 wind speed products using wind speed measurements from 18 weather buoys and 10 floating wind lidars in the European Seas. It is the first time a comprehensive wind lidar dataset has been used for SAR wind validation. Key findings: OCN OWI vs. lidar (buoy) shows R2 = 0.93 (0.84), root mean square error (RMSE) = 1.18 m/s (1.61 m/s), mean absolute error (MAE) = 0.86 m/s (1.24 m/s), and bias = -0.5 m/s (-0.6 m/s). DTU SAR vs. lidar (buoy) shows R2 = 0.88 (0.84), RMSE = 1.3 m/s (1.6 m/s), MAE = 0.92 m/s (1.22 m/s), and bias = 0.02 m/s (-0.04 m/s). OCN OWI provides a filtered dataset and validation vs. lidar shows R2 = 0.95 and RMSE = 0.88 m/s; however, this is achieved at the expense of discarding more than 50% of all data. The lidar vs. SAR wind speed statistics outperformed the buoy comparison statistics for all metrics studied. Lidar wind speed data are more accurate than buoy data and give a more trustworthy validation of SAR wind speeds than buoy data. Lidar data are recommended for validation studies on Geophysical Model Functions on SAR winds.

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product provides high-resolution, near-global satellite precipitation estimates, critical for assessing rainfall characteristics in regions with complex terrain or limited ground observations. Accurate representation of precipitation is essential for evaluating rainfall-driven impacts on wind turbine blades. This study evaluates the performance of IMERG V06B and V07B products over Northern Spain using observations from 28 rain gauge stations spanning elevations from 125 to 1353 m, with a focus on rainfall magnitude, phase (solid/liquid), and intensity. IMERG V07B shows modest improvements over V06B in capturing daily (R=0.56) and monthly (R=0.83) precipitation and better represents frozen and mixed-phase precipitation at high-elevation stations (60–199 mm for V07B versus 37–205 mm for V06B) compared to in situ totals (127–496 mm). At sub-daily scales, V07B slightly overestimates rainfall (median PBIAS at 30 min: 29.6% versus 15.8% for V06B) but more accurately resolves rainfall intensity, particularly for light events (55%–70%), while moderate events are slightly overestimated. Relative to in situ observations, both IMERG versions depict a wetter climate, with only minor differences between V07B and V06B in intensity and phase representation. A joint wind–rain analysis identifies the most erosive conditions under wind speeds of 4–10 m s−1 and rainfall amounts of 0–1 mm/30 min. Blade lifetime estimates derived from satellite precipitation range from 2.9–21 years for V07B and 4.4–23 years for V06B, with in situ estimates ranging from 3.6–24 years. The shortest lifetimes occur at high-wind exposed coastal and mountainous sites, while the longest are found at sheltered mid-elevation stations. Overall, IMERG V07B provides a realistic, high-resolution representation of rainfall suitable for assessing rainfall-driven blade erosion and enabling large-scale erosion risk mapping in complex terrain.

Abstract This work investigates leading edge erosion risk from the combined statistics of wind and rain at three sites (two offshore and one onshore). Coating lifetime for a reference 15 MW wind turbine is estimated using a rain erosion model driven by measured wind data and alternative rainfall inputs. Measurements include site-specific disdrometer and anemometer records, buoy-mounted offshore LiDAR, and satellite precipitation (IMERG-F). These datasets are systematically compared with ERA5 reanalysis to quantify meteorological discrepancies and their impact on erosion predictions. The overall objective is to evaluate the accuracy of largescale global rainfall datasets for leading edge erosion prediction, since reliable erosion forecasts are essential for effective predictive maintenance and precipitation-reactive control strategies [1], and disdrometric measurements are rarely available at wind farm sites.

Abstract. Leading-edge erosion of wind turbine blades due to hydrometeor impacts is observed across various climate zones. This study focuses on uncertainties in the assessment of end of incubation (EoI) and erosion-safe operation (ESO) efficiency using different types of meteorological observations. At the Risø site in Denmark the following data are retrieved: a 19-year-long time series from a rain gauge and a cup anemometer, up to 3-year long time series from disdrometers and a sonic, and 10-year-long satellite-based product IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement) and modeled wind speed from NEWA (New European Wind Atlas). The Spearman coefficient between joint precipitation and wind speed time series consistently takes positive values across the time series. Three reference turbines are assumed to have been installed at Risø. An impingement damage model derived from a rain erosion test is used to assess EoI and ESO efficiency. Key results on the uncertainty in assessing EoI are: 1) a long series is recommended, as EoI from 1-year time series varies by ~170 % between the minimum and maximum EoI. Uncertainty in EoI due to the choice of droplet slowdown is ~24 % when comparing an advanced model and the DNV Recommended Practice. Disdrometers estimate longer EoI than rain gauge due to under catch and missing data. The intuitive perception that similar EoI will occur across all blades of a turbine or within a wind farm exposed to the same weather is rarely observed in the field. Probabilistic variations in EoI are assessed using Monte Carlo simulations of the Pareto fronts for weather and materials. It is found that the uncertainty due to the material is larger than that due to the weather. The combined effect explains why the intuitive perception does not hold for EoI. ESO efficiencies are assessed from Pareto fronts. A flat Pareto front indicates high ESO efficiency, with only a small amount of AEP lost to ensure a long EoI extension. High-frequency disdrometer data (1-minute) with good measurement resolution (0.1 mm h−1) show flatter Pareto fronts than IMERG data (30-minute, 0.1 mm h−1). Resampling disdrometer data from 1-minute to 30-minute intervals results in steeper Pareto fronts, as expected, like those of IMERG. Rain gauge observations are inadequate due to limited measurement resolution (1.2 mm h−1). ESO efficiency is affected by the choice of droplet slowdown model, with the advanced model yielding flatter Pareto fronts. A recommendation is to assess the relevance of EoI from long-term time series and, in synergy with high-frequency observations, assess ESO efficiency for operational decision making.

The accurate characterisation of offshore wind resources is crucial for the efficient planning and design of wind energy projects. However, the scarcity of in situ observations in marine environments requires the exploration of alternative and reliable data sources. In response to this challenge, this study presents a comprehensive comparison between wind profiles derived from the Advanced Scatterometer (ASCAT) satellite observations and the ECMWF Reanalysis fifth-generation (ERA5) dataset against ship-based lidar measurements in the northern Baltic Sea. The aim is to investigate the applicability of ship-based lidar measurements for validating these datasets and to better understand the reliability, accuracy, and limitations of ASCAT- and ERA5-derived wind statistics for offshore wind characterisation at wind turbine operating heights. To extrapolate ASCAT observations from sea level to turbine rotating heights, a mean correction of atmospheric stability effects based on ERA5 and a probabilistic adaptation of the Monin-Obukhov similarity theory were implemented. The comparison between the two gridded datasets, extrapolated ASCAT and ERA5, reveals an overall good agreement in average wind speeds at 100 m height, with ASCAT exhibiting overall mean wind speeds approximately 0.6 ms-1 higher than ERA5 across the entire study region. However, excluding regions within 40 km of the coastline reduces this bias to around 0.4 ms-1, highlighting the negative impact of coastal contamination in ASCAT measurements and the difficulties ERA5 faces in accurately capturing wind conditions in complex coastal areas due to its coarse resolution. The validation against the ship-based lidar measurements shows a comparable performance of both datasets, with bias below +/- 0.2 ms-1 at heights between 90-170 m, with an overestimation by ASCAT and underestimation by ERA5. Both datasets show deteriorating performance with height, which is particularly notable in ASCAT profiles, with rapidly increasing biases above 170 m, peaking at around 0.5 ms-1 at 270 m.

Evaluating the risk of leading edge erosion on wind turbine blades requires accurate characterization of wind and rain climates, as they determine the amount of rain impinging the blades. However, existing fatigue lifetime models often assume statistical independence between wind speed and rain intensity, potentially underestimating erosion risks. This study introduces a copula-based framework to explicitly model their dependence using historical data from the British Isles and surrounding seas. The methodology is used to generate a probabilistic erosion parameter atlas containing site-specific marginal distributions and copula parameters. The parameter atlas enables fast and tailored estimates of the probabilistic incubation period, allowing for site-specific reliability analysis based on custom turbine operation characteristics and blade coating materials. The framework also supports the assessment of erosion-safe operation feasibility by quantifying the trade-off between fatigue life extension and energy production loss. Results reveal that neglecting the dependence structure can lead to overestimation of the incubation period by up to 90% in regions where the wind-rain correlation is high, particularly in offshore and coastal areas. A clear positive correlation between wind speed and rain intensity is observed, with the effect being even more pronounced in dry conditions, where wind speed on average is 50% higher when it rains 10 mm/hr compared to no rain, highlighting the critical need to incorporate wind-rain dependence into erosion risk assessments.

Offshore wind energy is rapidly growing due to high wind speeds and low visual impact at sea. In the North-Western Mediterranean, the Gulf of Lion is one area where floating offshore wind turbines will be installed. A de-tailed analysis of synthetic aperture radar (SAR) scenes revealed wind flow patterns induced by coastal orography, such as flow channeling and horizontal lee waves, often unresolved in model wind fields, thus quantifying the local winds of the Tramontane and the Mistral. High-accuracy wind speeds retrieved at a 10-m level from satellite-borne SAR offer significant insight for wind resource estimation. Copolarized SAR wind speed retrievals depend on wind direction input. Our motivation is to investigate the importance of reliable wind direction input in the SAR wind retrieval based on 1 year of wind direction data from the Global Forecast System (GFS), fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERAS), and the New European Wind Atlas (NEWA). We validated these against buoy data to assess their accuracy. While ERAS showed the most accurate wind direction, this did not translate into high-precision SAR wind speeds. The three derived SAR wind speeds showed correlation coefficients of R2 between 0.90 (0.81) and 0.94 (0.84) against the Lion (Begur) buoy datasets with the best agreement being with ERAS and NEWA at the Lion and Begur buoys, respectively. These results suggest no preferred numerical model wind direction input for SAR retrievalks.

An enhanced understanding of the mechanisms responsible for wind turbine blade leading-edge erosion (LEE) and advancing technology readiness level (TRL) solutions for monitoring its environmental drivers, reducing LEE, detecting LEE evolution, and mitigating its impact on power production are a high priority for all wind farm owners/operators and wind turbine manufacturers. Identifying and implementing solutions has the potential to continue historical trends toward lower Levelized Cost of Energy (LCoE) from wind turbines by reducing both energy yield losses and operations and maintenance costs associated with LEE. Here, we present results from the first Phenomena Identification and Ranking Tables (PIRT) assessment for wind turbine blade LEE. We document the LEE-relevant phenomena/processes that are deemed by this expert judgment assessment tool to be the highest priorities for research investment within four themes: atmospheric drivers, damage detection and quantification, material response, and aerodynamic implications. The highest priority issues, in terms of importance to LEE but where expert judgment indicates that there is a lack of fundamental knowledge, and/or implementation in measurement, and modeling is incomplete include the accurate quantification of hydrometeor size distribution (HSD) and phase, the translation of water impingement to material loss/stress, the representation of operating conditions within rain erosion testers, the quantification of damage and surface roughness progression through time, and the aerodynamic losses as a function of damage morphology. We discuss and summarize examples of research endeavors that are currently being undertaken and/or could be initiated to reduce uncertainty in the identified high-priority research areas and thus enhance the TRLs of solutions to mitigate/reduce LEE.

Wind energy is facing two major problems, recyclability of wind turbine blades, primarily made from fiberglass, and rain erosion on the blade’s leading edges. Here, we show that flax fiber reinforced epoxy composites have less impact fatigue damage than glass fiber (GF) composites made with the same resin. The novel treatment of flax with non-toxic nanodiamonds even boosts its outstanding performance. Nanodiamond-treated flax fiber (FFND) composites exhibit a damage incubation period up to 17 times as long as GF composites and have at least 74% less mass loss. This is connected to lower initial impact pressure, less shock wave reflections and better impact absorption of flax composites. The nanodiamonds act as fiber sizing, strengthening the fibers and their matrix interface. This delays fracturing and results in less erosion, making the biodegradable FFND a promising replacement for GF towards a fabrication of more sustainable and longer lasting wind turbine blades.

The planning of offshore wind farms relies on information about the offshore wind climate. The annual energy production of wind farms is based on the winds occurring at the wind farm site. Wind measurements are taken from floating buoy systems equipped with wind lidars. Wind lidars measure the winds at several heights including the wind turbine hub height. Atmospheric models complement the assessment of offshore wind resources at the global scale. During operations of wind farms, the forecasting of winds and other meteorological parameters play a vital role. Weather phenomena such as typhoons, low-level jets, and tornados may exhibit particular impact to wind turbines. Furthermore, rain and hail may erode the leading edge of wind turbine blades. Climate change may impact the atmospheric conditions relevant for the future offshore wind farms.

Aeolus is the first satellite mission focusing on wind profile detection from near the surface to about 30 km in height on a global scale. This study evaluates the contribution of Aeolus winds to sea surface wind forecasts geographically by further analyzing the Observing System Experiments from the European Centre for Medium-Range Weather Forecasts (ECMWF) with scatterometer winds from the meteorological operational satellites (assimilated into the model) and the Haiyang-2B satellite (not assimilated into the model). The findings indicate that Aeolus has the ability to reduce the root-mean-square difference between scatterometer winds and background forecasts (short-range) by about 0.05%-0.16% on average for climatic regions, except for the meridional wind component in the tropics. Also, Aeolus can generally reduce zonal biases of the background forecasts, while its beneficial impact on meridional biases mainly occurs in the Northern Hemisphere extratropics and tropics. For medium-range forecast assessments, as the forecast step extends up to day 5, the positive impact of Aeolus on sea surface wind forecasts becomes more evident and is even greater than 3%, especially for extratropical ocean regions in the Southern Hemisphere. Furthermore, the impact of Aeolus shows seasonal variation, with a substantial positive impact from September 2019 to February 2020 and a negative impact mainly in March, April, and May 2020. Wind information plays a vital role in understanding atmosphere dynamics and improving weather forecasts. Aeolus is the first satellite to detect global wind profiles from near the surface to about 30 km in height. These wind profiles can be used to assist in Numerical Weather Prediction (NWP). In this study, we investigate whether the Aeolus winds can benefit the sea surface wind forecasts in the global NWP model at ECMWF by exploiting satellite-observed ocean winds. The finding is that Aeolus can generally reduce background forecast (i.e., short-range forecast) biases for the east-west wind component, while the bias reductions for the north-south wind component are mainly found in the Northern Hemisphere extratropics and tropics. Moreover, Aeolus can slightly reduce the difference between satellite observations and forecasts of vector winds for both short-range and medium-range forecasts up to day 5, especially in the Southern Hemisphere extratropics. In addition, the effects of Aeolus on medium-range sea surface wind forecasts vary with season. Generally, Aeolus has a more positive impact between September 2019 and February 2020. However, its impact tends to be negative for many regions during March, April, and May 2020. Aeolus can slightly improve background forecasts in global sea surface vector wind by similar to 0.11% on average Aeolus can slightly reduce zonal and meridional wind biases of background forecasts for most climatic ocean regions As the forecast step extends to day 5, the positive impact of Aeolus becomes more evident and varies with seasons

Wind turbine blades are mainly made from E-glass fiber (GF) epoxy composites, because of their good ratio of strength to weight and costs. With the increase in blade length and tip speed, the problem of leading edge erosion is becoming more severe, reducing annual energy production and raising maintenance cost. It was recently shown that nanodiamond-treated flax fiber (FFND) composites have significantly less erosion than GF composites and could be an alternative for GF in the turbine blade aeroshells. However, FFND alone might not be suitable for manufacturing turbine blades at the large scale of modern wind turbines. Here, we show that a hybrid composite with a thin layer of only 1.5 mm of FFND on a GF base, can achieve the same superior results as bulk material FFND composite. In addition, we show and explain why aramid fibers, that are known for impact resistance, do not perform well as erosion protection. Our research shows the great potential of this technology to be implemented as a low-cost, lightweight skin layer on the leading edge. Acting as damage-tolerant failsafe layer, negligible ∼0.04% extra weight of the FFND could increase the blade’s base erosion resistance by a factor of 60±20 compared to plain GF, expanding the repair window, reducing costs, and enhancing reliability.

Leading edge erosion on wind turbine blades can reduce aerodynamic efficiency and cause increased maintenance costs, potentially impacting the overall economic viability. Erosion-safe operation is the concept of reducing the blade tip speed during periods of heavy rain, thereby significantly reducing the erosion development and progression. This study explores the application of reinforcement learning, namely using a double deep Q-network, to implement erosion-safe operation. The proposed methodology involves learning a policy for tip speed control that maximizes revenue over a specific period of time. We demonstrate the concept based on 5 years of simulation of the DTU 10MW reference turbine and mesoscale weather simulation from Horns Rev. The trained model was found to increase the cumulative revenue by 1.6 % compared to not using erosion-safe operation. The model was able to effectively adapt to varying weather conditions and stochastic damage progression. Based on 10,000 random simulations, the trained model outperforms two baseline models in more than 98 % of the simulations.

The Mediterranean Sea heavily relies on fossil fuels for its energy supply, but the shift toward green energy is imminent. Offshore wind farms represent a pivotal step in this transition. However, deploying these wind farms effectively requires an accurate assessment of the wind potential in offshore areas.Unlike Northern Europe, research on Mediterranean wind energy is sparse due to its intricate topography and bathymetry. The Mediterranean's coastlines, hemmed in by mountain ranges, create unique wind climates, posing challenges for modeling offshore wind conditions. Additionally, existing in-situ observations are limited to specific points.Sentinel-1 satellite Synthetic Aperture Radar (SAR) wind fields can resolve wind variability at sub-km scales. The strength of satellite wind fields lies in the observation of large spatial domains over broad temporal periods. Therefore, this technology holds promise for uncovering the wind resource potential.This study focuses on the Gulf of Lion, located in the NW Mediterranean Sea and currently the Mediterranean's most promising area for floating wind turbine installation. Here, two dominant local winds—the Mistral and Tramontane—prevail. The Mistral, a northerly wind, forms between the Alps and the Massif Central, while the Tramontane, a northwesterly wind, sweeps through the Aude valley between the Massif Central and the Pyrénées.SAR records images of the radar backscatter from the Earth’s surface, which is commonly known as the Normalized Radar Cross Section (NRCS). NRCS values can be used as inputs in a Geophysical Model Function (GMF), along with other radar parameters, to retrieve the SAR ocean wind fields. For the SAR wind speed retrieval, a necessary input is the wind direction, commonly provided by numerical models. This study utilizes SAR wind speed retrievals driven by three numerical model wind directions—GFS and ERA-5 with 27 km spatial resolution, and New European Wind Atlas - NEWA (WRF) with 3 km spatial resolution—over a year-long period. We first compare the three model wind directions against in situ measurements from two buoys located in the greater area of the Gulf of Lion, which are provided by the Copernicus Marin Service In Situ TAC data platform.Then, we compare the in-situ wind speeds against the three SAR wind speeds and the three model wind speeds. Our objective is to determine which SAR-model data synergy best reflects true wind conditions in the area. This study also aims to highlight the benefits of involving satellite-derived wind fields for wind resource estimations in upcoming wind farm areas characterized by complex wind climates.This work is supported by the ESAWAAI project, funded by the European Space Agency under the grant agreement 4000142170/23/DT.

Leading edge erosion on wind turbine blades is a common issue, particularly for wind turbines placed in regions characterized by high wind speeds and precipitation. This study presents the development of a rain erosion atlas for Scandinavia and Finland, based on ERA5 reanalysis and NORA3 mesoscale model data on rainfall intensity and wind speed over five years. The IEA 15 MW reference wind turbine is used as an example to evaluate impingement water impact and erosion onset time for a commercial coating material. The damage progression is modeled by combining the wind speed and rainfall data with an empirical damage model that relates impinged water (H) as a function of impact velocity to the time of erosion onset. Comparative analyses at two weather station locations show that NORA3 data more accurately aligns with measurements in terms of power spectral density, mean wind speed, rainfall, and erosion prediction than ERA5. NORA3-based atlas layers offer finer spatial detail and predict shorter erosion onset times over land compared to ERA5, particularly in complex terrain. Conversely, the ERA5-based atlas suggests a shorter onset of erosion offshore. Based on NORA3 data, erosion onset time is estimated at 5 years on average for Baltic Sea wind farm sites and 3.2 years for sites in the North Sea.

Wind turbine blade erosion poses a significant challenge to the durability and performance of wind turbines. Modeling of rain erosion damage, considering atmospheric conditions, improves our understanding of the progression of leading-edge erosion on wind turbine blades. In this study, we investigate the impact of varying raindrop characteristics on rain erosion damage development. We analyse 2.5 years of data from a disdrometer, which measures the size and velocity of falling rain droplets, at Riso campus. Various post-processing methods of the disdrometer data are used for estimating representative droplet diameters and fall velocities for each rain event. We compare measured droplet fall velocities with theoretical terminal velocities, revealing a necessity for revising theoretical approaches to raindrop fall velocity for erosion damage modeling. The measured rain rates and representative fall velocities are used to calculate the liquid water content in the air. We introduce a bin-wise summation method for estimating the liquid water content, circumventing the need for representative droplet assumptions. As this method provides the most accurate input for the damage model, we benchmark the other post-processing methods against it and employ it to evaluate bias estimates of associated damage predictions. The largest bias (22%) in accumulated damage is found with an arithmetic mean droplet diameter approach and the smallest bias (-2%) with the median volume estimation method for damage model input. Furthermore, we demonstrate that, for a given rainfall volume, smaller droplets contribute to larger accumulated damage compared to larger droplets.

This article presents comprehensive validation of specific sea state parameters (SSPs) and synthetic aperture radar(SAR)-derived wind speeds (u(SAR)). The article introduces a novel approach to retrieving roughness length (z(0)) based on wave steep-ness, following the retrieval of the short wavelengths necessary to estimate z(0). The SAR onboard the Sentinel-1 (S1) satellite that was used specifically in the interferometric wide swath mode(IW) data. The data were processed using the extended version of CWAVE (CWAVE_EX) algorithm for SSPs and CMOD5 for u(SAR). CWAVE_EX was developed especially for coastal waters; the processing chain includes steps for SAR image denoising and eliminating image artifacts. SAR S-1 data inherently exhibit asubstantial azimuthal cutoff length due to the data's high satellite altitude and SAR IW resolution. That complicates the retrieving of short wavelengths prevalent in coastal zones and needed to retrieve z(0). The article focuses on the coastal seas of the USA, benefiting from the presence of an extensive network of ocean buoys for validation purposes. The complete SAR S1 A/B archive from 2014to 2022 was first processed to retrieve SSPs and uSAR. The validation for significant wave height (H-s), second moment wave period(T-m2), and uSAR was performed using in-situ measurements with about 6000 collocations. H-s and T-m2 were compared against the corresponding parameters from hind cast spectral numerical model data with about 380 000 collocations. The comparisons between the retrieved H-s and T-m2 against the in-situ observations and hind cast wave model data yielded a root mean square error (RMSE) of 0.46-0.50 m and 0.9-1.1 s. The RMSE of u(SAR )against in-situ observation was about 2 m/s with a bias of 0.78 m/s. The estimated z(0) values from satellite-driven wave parameters were highly correlated withthez0estimated from the in-situ observations, with an RMSE of 0.04x10(-3)m and a bias of-0.01x10(-3)m. The article highlights the possibility of using SAR remote sensing data for global mapping of z(0), including coastal effects of local variability in sea state and wind field gustiness

ABSTRACTSatellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface.

Accurate precipitation estimation is crucial for various meteorological and climatological applications, including renewable energy generation. The NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product provides precipitation data derived from microwave and infrared satellite sensors. In this study, we evaluate the performance of two successive versions, V6 and V7, of the IMERG Final Precipitation Satellite Product. The evaluation of IMERG V6 and V7 products involves comparing rainfall time series of 6 years (2015-2020) against rain gauge measurements from 28 weather stations in complex terrain in Navarra, Spain. We assess various statistical metrics such as correlation coefficient, bias, root mean square error, and probability of detection. We hypothesize that the V7 product displays improvements from the V6, particularly in terms of accuracy. The objective is to assess if the V7 product demonstrates enhanced performance in capturing precipitation events in a region with complex terrain, such as Navarra. We aim to provide evaluation results and valuable insights for users relying on IMERG precipitation data for hydrological, meteorological, and climate studies. We also investigate the utility of IMERG precipitation products in the context of wind energy applications. Precipitation, including rain and hail, can impact the structural integrity and performance of wind turbines over time. Surface erosion of wind turbine blades is caused by heavy precipitation and strong winds and represents a major challenge in the wind energy industry. Therefore, we focus on predicting the lifetime of wind turbine blades in the 28 stations by integrating IMERG precipitation in an erosion onset prediction model along with New European Wind Atlas (NEWA) wind speeds. Previous studies that have used IMERG V6 in a blade lifetime prediction model have shown that IMERG V6 is sufficient to predict erosion onset time in blades in selected European sites, but it requires further calibration and adjustments since it tends to overestimate orographic rainfall. We hereby explore any improvements in the prediction of erosion onset time by incorporating the newly published IMERG V7 product. Finally, we aim to highlight the usefulness of satellite data in monitoring leading-edge erosion in wind turbine blades.The proposed approach holds promise for improving the reliability and efficiency of wind turbines. The knowledge of erosion onset time in blades can optimize maintenance schedules, reduce downtime, and enhance the overall operational performance of wind farms. Our findings may offer valuable implications for the renewable energy sector and precipitation monitoring.This work is supported by the AIRE project, which has received funding from the European Union under the grant agreement 101083716.