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Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.

Low-level jets (LLJs) are examples of non-logarithmic wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant to wind energy than a falloff definition. The shear definition is demonstrated and validated through the development of a European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) LLJ climatology for six sites. Identification of LLJs and their morphology, frequency, and intensity is critically dependent on the (i) vertical window of data from which LLJs are extracted and (ii) the definition employed.

Windstorms are a major natural hazard in many countries. The objective of this study is to identify and characterize intense windstorms during the last 4 decades in the US Northeast and determine both the sources of cyclones responsible for these events and the manner in which those cyclones differ from the cyclone climatology. The windstorm detection is based on the spatial extent of locally extreme wind speeds at 100 m height from the ERA5 reanalysis database. During the top 10 windstorms, wind speeds exceed their local 99.9th percentile over at least one-third of land-based ERA5 grid cells in this high-population-density region of the USA. Maximum sustained wind speeds at 100 m during these windstorms range from 26 to over 43 ms−1, with wind speed return periods exceeding 6.5 to 106 years (considering the top 5 % of grid cells during each storm). Property damage associated with these storms, with inflation adjusted to January 2020, ranges from USD 24 million to over USD 29 billion. Two of these windstorms are linked to decaying tropical cyclones, three are Alberta clippers, and the remaining storms are Colorado lows. Two of the 10 re-intensified off the east coast, leading to development of nor'easters. These windstorms followed frequently observed cyclone tracks but exhibit maximum intensities as measured using 700 hPa relative vorticity and mean sea level pressure that is 5–10 times the mean values for cyclones that followed similar tracks over this 40-year period. The time evolution of wind speeds and concurrent precipitation for those windstorms that occurred after the year 2000 exhibit good agreement with in situ ground-based and remote sensing observations, plus storm damage reports, indicating that the ERA5 reanalysis data have a high degree of fidelity for large, damaging windstorms such as these. A larger pool of the top 50 largest windstorms exhibit evidence of only weak serial clustering, which is in contrast to the relatively strong serial clustering of windstorms in Europe.

Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring in the contemporary climate. Low-level jets affect rotor plane aerodynamic loading, turbine structural loading and turbine performance, and thus accurate characterization and identification are pertinent. Analyses using a detection algorithm wherein the wind speed above and below the jet maximum must be below 80 % of the jet wind speed within a vertical window of approximately 20–530 m a.g.l. (above ground level) indicate the presence of an LLJ in at least one of the 14 700 4 km×4 km grid cells over Iowa on 98 % of nights. Nocturnal LLJs are most frequently associated with stable stratification and low turbulent kinetic energy (TKE) and hence are more frequent during the winter months. The spatiotemporal mean LLJ maximum (jet core) wind speed is 9.55 m s−1, and the mean height is 182 m. Locations of high LLJ frequency and duration across the state are seasonally varying, with a mean duration of 3.5 h. The highest frequency occurs in the topographically complex northwest of the state in winter and in the flatter northeast of the state in spring. Sensitivity of LLJ characteristics to the (i) LLJ definition and (ii) vertical resolution at which the WRF output is sampled is examined. LLJ definitions commonly used in the literature are considered in the first sensitivity analysis. These sensitivity analyses indicate that LLJ characteristics are highly variable with definition. Use of different definitions identifies both different frequencies of LLJs and different LLJ events. Further, when the model output is down-sampled to lower vertical resolution, the mean jet core wind speed height decreases, but spatial distributions of regions of high frequency and duration are conserved. Implementation of a polynomial interpolation to extrapolate down-sampled output to full-resolution results in reduced sensitivity of LLJ characteristics to down-sampling.

Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

An automated wind turbine wake characterization algorithm has been developed and applied to a data set of over 19 000 scans measured by a ground-based scanning Doppler lidar at Perdigão, Portugal, over the period January to June 2017. Potential wake cases are identified by wind speed, direction and availability of a retrieved free-stream wind speed. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, with 46 % having a clear and well-defined wake centre surrounded by a coherent area of lower wind speeds while 16 % have split centres or multiple lobes where the lower wind speed volumes are no longer in coherent areas but present as two or more distinct areas or lobes. Only 5 % of cases are not detected; the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to two rotor diameters, D, downstream) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub height and descends broadly following the terrain slope. However, this behaviour is strongly linked to the hour of the day and atmospheric stability. Overnight and in stable conditions, the average height of the wake centre is 10 m higher than in unstable conditions at 2 D downstream from the wind turbine and 17 m higher at 4.5 D downstream.

Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ = 6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme maximum θe. Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θe > 347 K. The median hit rate for maximum θe > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

Limited area (regional) models applied at high resolution over specific regions of interest are generally expected to more accurately capture the spatiotemporal variability of key meteorological and climate parameters. However, improved performance is not inevitable, and there remains a need to optimize use of numerical resources and to quantify the impact on simulation fidelity that derives from increased resolution. The application of regional models for climate forcing assessment is currently limited by the lack of studies quantifying the sensitivity to horizontal spatial resolution and the physical–dynamical–chemical schemes driving the simulations. Here we investigate model skill in simulating meteorological, chemical and aerosol properties as a function of spatial resolution, by applying the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem) over eastern North America at different resolutions. Using Brier skill scores and other statistical metrics it is shown that enhanced resolution (from 60 to 12 km) improves model performance for all of the meteorological parameters and gas-phase concentrations considered, in addition to both mean and extreme aerosol optical depth (AOD) in three wavelengths in the visible relative to satellite observations, principally via increase of potential skill. Some of the enhanced model performance for AOD appears to be attributable to improved simulation of meteorological conditions and the concentration of key aerosol precursor gases (e.g., SO2 and NH3). Among other reasons, a dry bias in the specific humidity in the boundary layer and a substantial underestimation of total monthly precipitation in the 60 km simulations are identified as causes for the better performance of WRF-Chem simulations at 12 km.

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Using both statistically simulated and observed data, this paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, the systematic error is negligible but the random error exceeds about 10 %.

Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

Assessing the ability of global and regional models to describe aerosol optical properties is essential to reducing uncertainty in aerosol direct radiative forcing in the contemporary climate and to improving confidence in future projections. Here we evaluate the performance of high-resolution simulations conducted using the Weather Research and Forecasting model with coupled with Chemistry (WRF-Chem) in capturing spatiotemporal variability of aerosol optical depth (AOD) and the Ångström exponent (AE) by comparison with ground- and space-based remotely sensed observations. WRF-Chem is run over eastern North America at a resolution of 12 km for a representative year (2008). A systematic positive bias in simulated AOD relative to observations is found (annual mean fractional bias (MFB) is 0.15 and 0.50 relative to MODIS (MODerate resolution Imaging Spectroradiometer) and AERONET, respectively), whereas the spatial variability is well captured during most months. The spatial correlation of observed and simulated AOD shows a clear seasonal cycle with highest correlation during summer months (r = 0.5–0.7) when the aerosol loading is large and more observations are available. The model is biased towards the simulation of coarse-mode aerosols (annual MFB for AE = −0.10 relative to MODIS and −0.59 for AERONET), but the spatial correlation for AE with observations is 0.3–0.5 during most months, despite the fact that AE is retrieved with higher uncertainty from the remote-sensing observations. WRF-Chem also exhibits high skill in identifying areas of extreme and non-extreme aerosol loading, and its ability to correctly simulate the location and relative intensity of extreme aerosol events (i.e., AOD > 75th percentile) varies between 30 and 70 % during winter and summer months, respectively.

Recent laboratory chamber studies indicate a significant role for highly oxidized low-volatility organics in new particle formation (NPF), but the actual role of these highly oxidized low-volatility organics in atmospheric NPF remains uncertain. Here, particle size distributions (PSDs) measured in nine forest areas in North America are used to characterize the occurrence and intensity of NPF and to evaluate model simulations using an empirical formulation in which formation rate is a function of the concentrations of sulfuric acid and low-volatility organics from alpha-pinene oxidation (Nucl-Org), and using an ion-mediated nucleation mechanism (excluding organics) (Nucl-IMN). On average, NPF occurred on ~ 70 % of days during March for the four forest sites with springtime PSD measurements, while NPF occurred on only ~ 10 % of days in July for all nine forest sites. Both Nucl-Org and Nucl-IMN schemes capture the observed high frequency of NPF in spring, but the Nucl-Org scheme significantly overpredicts while the Nucl-IMN scheme slightly underpredicts NPF and particle number concentrations in summer. Statistical analyses of observed and simulated ultrafine particle number concentrations and frequency of NPF events indicate that the scheme without organics agrees better overall with observations. The two schemes predict quite different nucleation rates (including their spatial patterns), concentrations of cloud condensation nuclei, and aerosol first indirect radiative forcing in North America, highlighting the need to reduce NPF uncertainties in regional and global earth system models.

Ultrafine particle size distributions through a deciduous forest canopy indicate that nucleation mode particle concentrations decline with depth into the canopy, such that number concentrations at the bottom of the canopy are an average of 16% lower than those at the top. However, growth rates of nucleation mode particles (diameters 6–30 nm) are invariant with height within the canopy, which implies that the semi-volatile gases contributing to their growth are comparatively well-mixed through the canopy. Growth rates of nucleation mode particles during a meteorological drought year (2012) were substantially lower than during a meteorologically normal year with high soil water potential (2013). This may reflect suppression of actual biogenic volatile organic compound (BVOC) emissions by drought and thus a reduction in the production of condensable products during the drought-affected vegetation season. This hypothesis is supported by evidence that growth rates during the normal year exhibit a positive correlation with emissions of BVOC modeled on observed forest composition, leaf area index, temperature and photosynthetically active radiation (PAR), but particle growth rates during the drought-affected vegetation season are not correlated with modeled BVOC emissions. These data thus provide indirect evidence that drought stress in forests may reduce BVOC emissions and limit growth of nucleation mode particles to climate-relevant sizes.

The Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen (BEACHON) project seeks to understand the feedbacks and inter-relationships between hydrology, biogenic emissions, carbon assimilation, aerosol properties, clouds and associated feedbacks within water-limited ecosystems. The Manitou Experimental Forest Observatory (MEFO) was established in 2008 by the National Center for Atmospheric Research to address many of the BEACHON research objectives, and it now provides a fixed field site with significant infrastructure. MEFO is a mountainous, semi-arid ponderosa pine-dominated forest site that is normally dominated by clean continental air but is periodically influenced by anthropogenic sources from Colorado Front Range cities. This article summarizes the past and ongoing research activities at the site, and highlights some of the significant findings that have resulted from these measurements. These activities include - soil property measurements; - hydrological studies; - measurements of high-frequency turbulence parameters; - eddy covariance flux measurements of water, energy, aerosols and carbon dioxide through the canopy; - determination of biogenic and anthropogenic volatile organic compound emissions and their influence on regional atmospheric chemistry; - aerosol number and mass distributions; - chemical speciation of aerosol particles; - characterization of ice and cloud condensation nuclei; - trace gas measurements; and - model simulations using coupled chemistry and meteorology. In addition to various long-term continuous measurements, three focused measurement campaigns with state-of-the-art instrumentation have taken place since the site was established, and two of these studies are the subjects of this special issue: BEACHON-ROCS (Rocky Mountain Organic Carbon Study, 2010) and BEACHON-RoMBAS (Rocky Mountain Biogenic Aerosol Study, 2011).

New particle formation has been observed at a number of ground-based measurement sites. Prior research has provided evidence that this new particle formation, while observed in the near-surface layer, is actually occurring in atmospheric layers above the surface and appears to be focused in or close to the residual layer formed by the nocturnal inversion. Here, we present both observations and modeling for southern Indiana which support this postulate. Based on simulations with a detailed aerosol dynamics model and the Weather Research and Forecasting model, along with data from ground-based remote sensing instruments and detailed gas and particle phase measurements, we show evidence that (i) the maximum rate change of ultrafine particle concentrations as observed close to the surface is always preceded by breakdown of the nocturnal inversion and enhancement of vertical mixing and (ii) simulated particle size distributions exhibit greatest accord with surface observations during and subsequent to nucleation only when initialized with a particle size distribution representative of clear atmospheric conditions, rather than the in situ (ground-level) particle size distribution.

Measurements of aerosol particle physical and chemical properties, gas phase concentrations and meteorological parameters were made along a transect in southern Indiana during the Nucleation In ForesTs (NIFTy) experiment conducted in May 2008. These measurements indicate nucleation was observed at all three measurement sites on almost half of all sampling days. The intensity of the nucleation events, as measured by the increase in ≥10 nm aerosol particle number concentrations of approximately 2×104 cm−3 over a layer of at least 300 m depth, is in good agreement with recent model results for the Midwestern USA derived using PMCAMx-UF. During the hour after termination of nucleation approximately half of the number concentration reduction is due to coagulation, while the remainder is due in equal parts to dry deposition and entrainment of relatively ultra-fine aerosol particle free troposphere air. Clear nucleation with continuous subsequent growth is only observed on days when the morning fractional cloud cover was less than 30%. It is associated with a clear transition from a strongly stratified atmosphere with low turbulence intensity and weak vertical velocities, to much a weaker vertical gradient of wind speed, increased turbulence intensity and stronger downwards vertical velocities, consistent with growth of the mixed layer and entrainment of air from the residual layer. Nucleation intensity is not very strongly determined by the prevailing condensational sink. However, there is a strong correlation between both a modified version of the Nucleation Parameter from Boy and Kulmala (2002) and ultrafine aerosol particle number concentrations, and mean morning H2SO4 concentrations and ultrafine aerosol particle number concentrations. Five A-class event days during NIFTy were characterized by values of the dimensionless nucleation parameter of Kuang et al. (2010) that are below 0.3, further indicating the applicability of their postulate that nucleation is favored by LΓ values below 0.7. Based on aerosol particle composition measurements it appears that aerosol particle formation and initial growth to approximately 30 nm diameter is dominated by ammonium and sulfate. Conservative estimates of the percent contribution of H2SO4 to aerosol particle growth (for sub-30 nm aerosol particles) on five A-class event days ranged from 23 to 85%.

Wind energy developments offshore focus on larger turbines to keep the relative cost of the foundation per MW of installed capacity low. Hence typical wind turbine hub-heights are extending to 100 m and potentially beyond. However, measurements to these heights are not usually available, requiring extrapolation from lower measurements. With humid conditions and low mechanical turbulence offshore, deviations from the traditional logarithmic wind speed profile become significant and stability corrections are required. This research focuses on quantifying the effect of humidity fluxes on stability corrected wind speed profiles. The effect on wind speed profiles is found to be important in stable conditions where including humidity fluxes forces conditions towards neutral. Our results show that excluding humidity fluxes leads to average predicted wind speeds at 150 m from 10 m which are up to 4% higher than if humidity fluxes are included, and the results are not very sensitive to the method selected to estimate humidity fluxes.

The exchange of Ammonia (NH3) between grassland and the atmosphere was determined using Relaxed Eddy Accumulation (REA) measurements. The use of REA is of special interest for NH3, since the determination of fluxes at one height permits multiple systems to be deployed to quantify vertical flux divergence (either due to effects of chemical production or advection). During the Braunschweig integrated experiment four different continuous-sampling REA systems were operated during a period of about 10 days and were compared against a reference provided by independent application of the Aerodynamic Gradient Method (AGM). The experiment covered episodes before and after both cutting and fertilizing and provided a wide range of fluxes −60–3600 ng NH3 m−2 s−1 for testing the REA systems. The REA systems showed moderate to good correlation with the AGM estimates, with r2 values for the linear regressions between 0.3 and 0.82. For the period immediately after fertilization, the REA systems showed average fluxes 20% to 70% lower than the reference. At periods with low fluxes REA and AGM can agree within a few %. Overall, the results show that the continuous REA technique can now be used to measure NH3 surface exchange fluxes. While REA requires greater analytical precision in NH3 measurement than the AGM, a key advantage of REA is that reference sampling periods can be introduced to remove bias between sampling inlets. However, while the data here indicate differences consistent with advection effects, significant improvements in sampling precision are essential to allow robust determination of flux divergence in future studies. Wet chemical techniques will be developed further since they use the adsorptive and reactive properties of NH3 that impedes development of cheaper optical systems.

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.

Conditional sampling is used herein to examine the effect of fetch, stability, and surface roughness changes on wind speeds in the coastal zone. Using data from an offshore wind farm it is shown that at a distance of 1.2–1.7 km from the coast, up to a height of 20 m above the surface, differences in wind speed distributions from onshore and offshore masts are statistically significant for flow moving offshore under all stability conditions. In contrast, differences between the distribution of wind speeds at 38 and 48 m at masts located at the coast and in the coastal zone are not significant for flow moving offshore, indicating that flow at these heights is not fully adjusted to the change in surface roughness (land to sea). These findings are in accordance with calculations of the internal boundary layer (IBL) height which indicate that the IBL would frequently be below the two upper measurement heights at 1.2–1.6 km from the coast. The analyses presented here indicate that the wind speed distribution at a potential offshore wind farm site is not solely dependent on fetch (distance from the coast) but also depends on the stability climate.Key words. Meteorology and atmospheric dynamics · Mesoscale meteorology · Ocean-atmosphere interaction · Marine meteorology

This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific-North American pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climatemodes. Substantial variability in skill scoresmanifests across realizations fromindividual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM-PNA first-order interaction and underestimate the NAM-AMO connection. These results suggest that the choice of ESMand ESMrealizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term. SIGNIFICANCE STATEMENT: Internal climate variability occurs over multiple spatial and temporal scales and is encapsulated in a series of internal climate modes. The representation of such modes in climate models is a critically important aspect of model fidelity. Analyses presented herein uses several skill scores to evaluate both the spatial and temporal manifestations of these climate modes in the CMIP6 generation of Earth system models (ESMs). There is marked variability in model fidelity for these modes and this variability in credibility within the current climate has important implications for the choice of specific ESMs and ESM realizations in making climate projections. [ABSTRACT FROM AUTHOR]

Wind speeds over the Baltic significantly increased over the second half of the 20th century (C20th), with the majority of the increase being focused on the upper quartile of the wind speed distribution and in the southwest of the region. These changes have potentially profound implications for the wind energy resource. For example, based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data it is shown that, owing to this non‐stationarity, using the normalization period of 1987–98 to determine the wind resource (as in the Danish wind index) leads to overestimation of the wind energy index (and hence the wind energy resource) in western Denmark relative to 1958–2001 by approximately 10%. To address whether the increased prevalence of high wind speeds at the end of the C20th will be maintained in the future, we provide a first prognosis of annual wind indices from the HadCM3 coupled atmosphere–ocean general circulation model. The results suggest the 21st century (C21st) will be similar to the 1958–2001 period with respect to the wind energy density, but that the northeastern Baltic will exhibit slightly higher wind energy indices over the course of the C21st relative to the latter half of the C20th, whereas the southwest of the Baltic exhibits some evidence of declining wind indices towards the end of the C21st. These changes may indicate a tendency in HadCM3 towards more northerly tracking of mid‐latitude cyclones in the future, possibly due to evolution of the North Atlantic oscillation. As a caveat to this finding, it should be noted that the NCEP–NCAR and European Centre for Medium‐Range Weather Forecasts reanalysis data sets and HadCM3 simulations, although exhibiting commonalities during the period of overlap, differ quantitatively in terms of the spatial fields and empirical cumulative probability distributions at individual grid cells. Copyright © 2005 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)–National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP–NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP–NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets. Copyright © 2003 Royal Meteorological Society [ABSTRACT FROM AUTHOR]