With the ongoing expansion of wind energy onshore and offshore, large-scale wind-farm-flow effects in a temporally- and spatially-heterogeneous atmosphere become increasingly relevant. Mesoscale models equipped with a wind-farm parametrization (WFP) can be used to study these effects. Here, we conduct a systematic literature review on the existing WFPs for mesoscale models, their applications and findings. In total, 10 different explicit WFPs have been identified. They differ in their description of the turbine-induced forces, and turbulence-kinetic-energy production. The WFPs have been validated for different target parameters through measurements and large-eddy simulations. The performance of the WFP depends considerably on the ability of the mesoscale model to simulate the background meteorological conditions correctly as well as on the model set-up. The different WFPs have been applied to both onshore and offshore environments around the world. Here, we summarize their findings regarding (1) the characterizations of wind-farm-flow effects, (2) the environmental impact of wind farms, and (3) the implication for wind-energy planning. Since wind-farm wakes can last for several tens of kilometres downstream depending on stability, surface roughness and terrain, neighbouring wind farms need to be taken into account for regional planning of wind energy. Their environmental impact is mostly confined to areas close to the farm. The review suggests future work should include benchmark-type validation studies with long-term measurements, further developments of mesoscale model physics and WFPs, and more interactions between the mesoscale and microscale community.

Abstract:In cities, social well-being faces obstacles posed by globalization, demographic and climate change, new forms of social organization, and the fragmentation of lifestyles. These changes affect the vulnerability of city societies and impact their health-related urban well-being (UrbWellth). The conceptual model introduced in this paper systematizes the relevant variables while considering previous research, and establishes the target value UrbWellth. The model differs from existing approaches mainly in the analytical distinctions it suggests. These allow us to group the relevant urban influence variables into four sectors and enable a more general and abstract consideration of health-related urban relations. The introduction of vulnerability as a filter and transfer function acts as an effect modifier between UrbWellth and the various urban variables.

Abstract. While the wind farm parameterization by Fitch et al. (2012) in the Weather Research and Forecasting (WRF) model has been used and evaluated frequently, the explicit wake parameterization (EWP) by Volker et al. (2015) is less well explored. The openly available high-frequency flight measurements from Bärfuss et al. (2019a) provide an opportunity to directly compare the simulation results from the EWP and Fitch scheme with in situ measurements. In doing so, this study aims to complement the recent study by Siedersleben et al. (2020) by (1) comparing the EWP and Fitch schemes in terms of turbulent kinetic energy (TKE) and velocity deficit, together with FINO 1 measurements and synthetic aperture radar (SAR) data, and (2) exploring the interactions of the wind farm with low-level jets (LLJs). This is done using a bug-fixed WRF version that includes the correct TKE advection, following Archer et al. (2020). Both the Fitch and the EWP schemes can capture the mean wind field in the presence of the wind farm consistently and well. TKE in the EWP scheme is significantly underestimated, suggesting that an explicit turbine-induced TKE source should be included in addition to the implicit source from shear. The value of the correction factor for turbine-induced TKE generation in the Fitch scheme has a significant impact on the simulation results. The position of the LLJ nose and the shear beneath the jet nose are modified by the presence of wind farms.

Abstract:The local climate in cities differs from the one in rural areas, most prominently characterized by increased surface and air temperatures, known as the "(surface) urban heat island". As climate has changed and continues to change in all areas of the world, the question arises whether the effects that are noticeable in urban areas are "homemade", or whether some of them originate from global and regional scale climate changes. Identifying the locally induced changes of urban meteorological parameters is especially relevant for the development of adaptation and mitigation measures. This study aims to distinguish global and regional climate change signals from those induced by urban land cover. Therefore, it provides a compilation of observed and projected climate changes, as well as urban influences on important meteorological parameters. It is concluded that evidence for climate change signals is found predominantly in air temperature. The effect of urban land cover on local climate can be detected for several meteorological parameters, which are air and surface temperature, humidity, and wind. The meteorology of urban areas is a mixture of signals in which the influencing parameters cannot be isolated, but can be assessed qualitatively. Blending interactions between local effects and regional changes are likely to occur.

Several approaches have been used to assess potential human exposure to environmental stresses and achieve optimal results under various conditions, such as for example, for different scales, groups of people, or points in time. A thorough literature review in this paper identifies the research gap regarding modeling approaches for assessing human exposure to environment stressors, and it indicates that microsimulation tools are becoming increasingly important in human exposure assessments of urban environments, in which each person is simulated individually and continuously. The paper further describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision-making processes can be included for each individual based on rule-based behavior in order to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A basic modeling scenario of the ABM framework in Hamburg, Germany, demonstrates its utility in various urban environments and individual activity patterns, as well as its portability to other models, programs, and frameworks. The prototype model can potentially be extended to support environmental incidence management through exploring the daily routines of different groups of citizens, and comparing the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

A thermally comfortable design of outdoor spaces favors social interaction and outdoor activities and thus contributes to the overall well-being of urban dwellers. To assess such a design, obstacle-resolving models (ORM) combined with thermal indices may be used. This paper reviews existing thermal indices to identify those suitable for thermal comfort assessment with ORMs. For the identification, 11 criteria and six index features are derived from literature analysis focusing on the characteristics of human environmental heat exchange, of outdoor urban environments, and of ORMs. An air temperature weighted world population distribution is calculated to derive the minimal air temperature range; a thermal index should cover to be applicable to 95% of the world population. The criteria are applied to 165 thermal indices by reviewing their original publications. Results show that only four thermal indices are suitable to be applied globally in their current form to various outdoor urban environments and also fulfill the requirements of ORMs. The evaluation of the index features shows that they differ with respect to the comprehensiveness of the thermophysiological model, the assessed human response, the treatment of clothing and activity, and the computational costs. Furthermore, they differ in their total application frequency in past ORM studies and in their application frequency for different climatic zones, as a systematic literature analysis of thermal comfort studies employing ORMs showed. By depicting the differences of the thermal indices, this paper provides guidance to select an appropriate thermal index for thermal comfort studies with ORMs.Electronic supplementary materialThe online version of this article (10.1007/s00484-018-1591-6) contains supplementary material, which is available to authorized users.

Abstract. Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

Abstract. Numerical wind resource modelling across scales from the mesoscale to the turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore wind farm wakes can last several tens of kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed but it remains unclear how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that both WRF + FIT (BIAS = 0.52 m s−1) and WRF + EWP (BIAS = 0.73 m s−1) compare well with wind farm affected mast measurements. Compared with the RANS simulations, baseline intra-farm variability, i.e. the wind speed deficit in between turbines, can be captured reasonably well with WRF + FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF + EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate peak intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared with those of PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.