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As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses., Atmospheric Remote Sensing

Item does not contain fulltext, BACKGROUND: Several reports have demonstrated the impact of the COVID-19 pandemic on the management and outcome of patients with ST-segment elevation myocardial infarction (STEMI). The aim of the current analysis is to investigate the potential gender difference in the effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI patients within the ISACS-STEMI COVID-19 Registry. METHODS: This retrospective multicenter registry was performed in high-volume primary percutaneous coronary intervention (PPCI) centers on four continents and included STEMI patients undergoing PPCIs in March-June 2019 and 2020. Patients were divided according to gender. The main outcomes were the incidence and timing of the PPCI, (ischemia time ≥ 12 h and door-to-balloon ≥ 30 min) and in-hospital or 30-day mortality. RESULTS: We included 16683 STEMI patients undergoing PPCIs in 109 centers. In 2020 during the pandemic, there was a significant reduction in PPCIs compared to 2019 (IRR 0.843 (95% CI: 0.825-0.861, p < 0.0001). We did not find a significant gender difference in the effects of the COVID-19 pandemic on the numbers of STEMI patients, which were similarly reduced from 2019 to 2020 in both groups, or in the mortality rates. Compared to prepandemia, 30-day mortality was significantly higher during the pandemic period among female (12.1% vs. 8.7%; adjusted HR [95% CI] = 1.66 [1.31-2.11], p < 0.001) but not male patients (5.8% vs. 6.7%; adjusted HR [95% CI] = 1.14 [0.96-1.34], p = 0.12). CONCLUSIONS: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a 16% reduction in PPCI procedures similarly observed in both genders. Furthermore, we observed significantly increased in-hospital and 30-day mortality rates during the pandemic only among females. Trial registration number: NCT 04412655.

Contains fulltext : 291566.pdf (Publisher’s version ) (Open Access), BACKGROUND: The constraints in the management of patients with ST-segment elevation myocardial infarction (STEMI) during the COVID-19 pandemic have been suggested to have severely impacted mortality levels. The aim of the current analysis is to evaluate the age-related effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI within the registry ISACS-STEMI COVID-19. METHODS: This retrospective multicenter registry was performed in high-volume PPCI centers on four continents and included STEMI patients undergoing PPCI in March-June 2019 and 2020. Patients were divided according to age (< or ≥75 years). The main outcomes were the incidence and timing of PPCI, (ischemia time longer than 12 h and door-to-balloon longer than 30 min), and in-hospital or 30-day mortality. RESULTS: We included 16,683 patients undergoing PPCI in 109 centers. In 2020, during the pandemic, there was a significant reduction in PPCI as compared to 2019 (IRR 0.843 (95%-CI: 0.825-0.861, p < 0.0001). We found a significant age-related reduction (7%, p = 0.015), with a larger effect on elderly than on younger patients. Furthermore, we observed significantly higher 30-day mortality during the pandemic period, especially among the elderly (13.6% vs. 17.9%, adjusted HR (95% CI) = 1.55 [1.24-1.93], p < 0.001) as compared to younger patients (4.8% vs. 5.7%; adjusted HR (95% CI) = 1.25 [1.05-1.49], p = 0.013), as a potential consequence of the significantly longer ischemia time observed during the pandemic. CONCLUSIONS: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a 16% reduction in PPCI procedures, with a larger reduction and a longer delay to treatment among elderly patients, which may have contributed to increase in-hospital and 30-day mortality during the pandemic.

Een voedselverspillingshotspot is een plek in het voedselsysteem waar voedselverspilling ontstaat. In deze presentatie wordt verspilling in de vleespluimveesector (kipketen) uitgelegd. (Dit document is een interactieve powerpoint: er wordt aangeraden deze in “dia-modus” te bekijken.). Dit document is tot stand gekomen vanuit een onderzoek van praktijkgericht onderzoek op het voedselsystemen in Nederland. Er is gebruik gemaakt van interviews met bedrijven, experts, co-creatiesessies en discussies met praktijkonderzoekers. Onderdeel van het SIA-project Voorkomen Voedselverspilling.

Een voedselverspillingshotspot is een plek in het voedselsysteem waar voedselverspilling ontstaat. In deze presentatie wordt verspilling in de aardappelketen uitgelegd. (Dit document is een interactieve powerpoint: er wordt aangeraden deze in “dia-modus” te bekijken.). Dit document is tot stand gekomen vanuit een onderzoek van praktijkgericht onderzoek op het voedselsystemen in Nederland. Er is gebruik gemaakt van interviews met bedrijven, experts, co-creatiesessies en discussies met praktijkonderzoekers. Onderdeel van het SIA-project Voorkomen Voedselverspilling.

Een voedselverspillingshotspot is een plek in het voedselsysteem waar voedselverspilling ontstaat. In deze presentatie worden verspillingshotspots in 4 voedselketens vergeleken: de melkketen, de kipketen (vleespluimveesector), de aardappelketen en de kasgroentenketen. (Dit document is een interactieve powerpoint: er wordt aangeraden deze in “dia-modus” te bekijken.). Dit document is tot stand gekomen vanuit een onderzoek van praktijkgericht onderzoek op het voedselsystemen in Nederland. Er is gebruik gemaakt van interviews met bedrijven, experts, co-creatiesessies en discussies met praktijkonderzoekers. Onderdeel van het SIA-project Voorkomen Voedselverspilling.

Een voedselverspillingshotspot is een plek in het voedselsysteem waar voedselverspilling ontstaat. In deze presentatie wordt verspilling in de melkketen uitgelegd. (Dit document is een interactieve powerpoint: er wordt aangeraden deze in “dia-modus” te bekijken.). Dit document is tot stand gekomen vanuit een onderzoek van praktijkgericht onderzoek op het voedselsystemen in Nederland. Er is gebruik gemaakt van interviews met bedrijven, experts, co-creatiesessies en discussies met praktijkonderzoekers. Onderdeel van het SIA-project Voorkomen Voedselverspilling.

In the next few decades climate mitigation efforts will transform the North Sea into one of the most important energy sources. The present wind energy capacity on the North Sea is expected to increase by almost a factor 5 in 2030 and almost a factor 10 in 2050. It is therefore of paramount importance to know how wind farms influence the atmosphere. Wind farms extract kinetic energy from the atmosphere and in doing so decrease the wind speed and increase turbulence levels. More turbulence means more mixing of vertical layers in the atmosphere and a change in humidity and temperature profiles. This may lead to cloud forming or dissipation. Wind farms are also an obstacle to the flow, which is what is called the blockage effect, as opposed to the wake effect behind the wind farm. This report is about the wake effect, mainly on wind, but we also analysed temperature and humidity profiles. In order to assess and quantify the wake effect, we compared two high resolution re-analyses for the year 2019 on a 2000 by 2000 km North Sea domain. The high resolution re-analyses with a 2.5 km horizontal grid spacing is based on global re-analysis ERA5 and downscaled with mesoscale weather model HARMONIE-AROME which is used operationally at KNMI. One of the re-analyses is without the effect of wind farms (referred to as control or HarmCY43-CTL in this report) and one with the Fitch wind farm parametrization that was recently incorporated in HARMONIE-AROME (HarmCY43-WFP). From the differences between the two we can isolate the wind speed deficits, or wakes, from the wind farms. Earlier validation studies have shown that a previous version of the HARMONIE-AROME model (HarmCY40) produces accurate wind climatology for undisturbed wind fields (period 2008-2018) and validates well against disturbed tower, aircraft and lidar measurements from 2016. In these studies the wind climatology is not validated for different stability regimes. In this study we do make t, Atmospheric Remote Sensing

High-resolution, regularly gridded air-temperature maps are frequently used in climatology, hydrology, and ecology. Within the Netherlands, 34 official automatic weather stations (AWSs) are operated by the National Met Service according to World Meteorological Organization (WMO) standards. Although the measurements are of high quality, the spatial density of the AWSs is not sufficient to reconstruct the temperature on a 1-km-resolution grid. Therefore, a new methodology for daily temperature reconstruction from 1990 to 2017 is proposed, using linear regression and multiple adaptive regression splines. The daily 34 AWS measurements are interpolated using eight different predictors: diurnal temperature range, population density, elevation, albedo, solar irradiance, roughness, precipitation, and vegetation index. Results are cross-validated for the AWS locations and compared with independent citizen weather observations. The RMSE of the reference method ordinary kriging amounts to 2.6 °C whereas using the new methods the RMSE drops below 1.0 °C. Especially for cities, a substantial improvement of the predictions is found. Independent predictions are on average 0.3 °C less biased than ordinary kriging at 40 high-quality citizen measurement sites. With this new method, we have improved the representation of local temperature variations within the Netherlands. The temperature maps presented here can have applications in urban heat island studies, local trend analysis, and model evaluation., Atmospheric Remote Sensing

This research thesis investigated the automatisation of the air temperature Siting Classification (SC) according to its World Meteorological Organization (WMO) guidelines. Instead of going into the field, the Automated SC (ASC) developed in this research thesis uses the Actueel Hoogtebestand Nederland (AHN) and Basisregistratie Grootschalige Topografie (BGT) geospatial open data datasets to determine the classifications for air temperature. The algorithm was first tested at the Automatic Weather Station (AWS) in De Bilt, after which it was applied to all the other 33 AWS sites owned and maintained by the Royal Netherlands Meteorological Institute (KNMI). The ASC model was developed using the programming language of R and made use of a decision tree to determine an air temperature SC Class 1 through 5 for each AWS. The usability of the model was tested through visual validations based on photos or satellite imagery to check on its accuracy, and comparison validations based on the current Manual Siting Classification (MSC) procedure done by the KNMI. Results shows that an ASC model corresponds for 55.9% with the MSC values and that the model can be applied for a reproducible and consistent SC if the data is determined to be correct and if a manual validation of the outcomes is executed. The ability of performing an ASC can contribute for further research in improving the air temperature SC of existing, or determining completely new locations. Furthermore, the model can also be used to determine the air temperature SC of the vast of other sensors of third-party networks. Moreover, the model further substantiates the need to specify the different air temperature SC guideline criteria even more so that the surroundings are taken better into account.

The first 3D sonar field measurements of ripples in the intertidal area are presented. The measurements were conducted during a 6 week field campaign in Autumn 2014 at the Sand Motor near Den Hague, The Netherlands. Ripple profile, hydrodynamic and suspension data were collected along a cross-shore transect. By means of a 2D Fourier analysis the 3D sonar images of the bedforms were analysed. Key parameters such as ripple height, length and angle were obtained. These parameters correlate to one another as well as to hydrodynamic data. During the measurement period the wave conditions were mostly calm and most waves arrived shore normal. A correlation between relative wave height and ripple length is found. The estimated ripple index, of 17.1, corresponds well to values found in literature. The FFT-routines are a powerful tool for analysing the enormous amount of sonar data, 1100 3D-images, automatically. A rotational diagram method was used to analyse 3D sonar images. A ripple regularity index (RRI) is proposed, as an objective and quantitative way to classify a ripple pattern as ‘regular’ or ‘irregular’.

A model for peak height distribution using the bivariate normal distribution is proposed to deal with some flaws of the already established model using the gamma distribution. This new model is compared to the "Gamma model" via artificially generated test data using a model that simulates the PCR process. In addition the performance of these models is compared using data from an actual case. To efficiently execute the computations necessary, techniques using bayesian networks are utilized.