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.@en

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean.

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To better understand and quantify the impact of clouds on the Greenland Ice Sheet surface mass balance (SMB), we study the spatiotemporal variability of the cloud radiative effect (CRE). The total CRE is separated in short-term and long-term impacts by performing multiple simulations with the SNOWPACK model for 2001-+2010. The annual total CRE is 16.8 ± 4.5 W m⁻², reducing the SMB with −157 ± 3.8 Gt yr⁻¹. Summer cloud radiative cooling is −6.4 ± 5.7 W m⁻² in the ablation area, increasing the SMB with 121 ± 2.2 Gt yr⁻¹. The annual integrated impact is cloud-reduced SMB of −36 Gt yr⁻¹. The short-term effect dominates the opposing long-term effects through the albedo-melt feedback. A long-term warming effect decreases the albedo and so preconditions the surface for enhanced (summer) melt. The impact of the CRE, determined by spatial, temporal and initial conditions, explains existing conflicted views on the role of cloud radiation and emphasizes the need for accurate cloud and albedo representations in future studies.

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Continuous measurements of blowing snow are scarce, both in time and space. Satellites now provide the opportunity to derive blowing snow occurrences, transport and sublimation rates over Antarctica. These products are extremely valuable and offer a continental-wide assessment of blowing snow, which is an important but unknown component of the surface mass balance of the Antarctic ice sheet. However, little ground truth is available to validate these retrievals. The recent application of ceilometers for detection of blowing snow frequencies provides an opportunity to validate the satellite retrievals of blowing snow. A routine to detect blowing snow occurrence from ground-based remote sensing ceilometers has been developed at two coastal locations in East Antarctica for the 2011–2016 time period. Thanks to their ground-based location, ceilometers are able to detect blowing snow events in the presence of clouds and precipitation, which can be missed by the satellite, since optically thick clouds impede the penetration of the signal. In coastal areas, more than 90% of blowing snow occurs under cloudy conditions and represent 30 to 56% of all cloudy conditions at Princess Elisabeth and Neumayer III (Neumayer hereafter) stations, respectively. For cloud-free conditions, 8% of the measurements at Princess Elisabeth (and none at Neumayer) are identified as blowing snow by the satellite but not by the ceilometer, likely due to differences in sensors, limitation of the surface identification by the satellite, or the spatial inhomogeneity of the blowing snow event. While the satellite blowing snow retrieval is a useful product, further investigation is needed to reduce the uncertainties on blowing snow frequencies associated with clouds.

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Local surface mass balance (SMB) measurements are crucial for understanding changes in the total mass of the Antarctic Ice Sheet, including its contribution to sea level rise. Despite continuous attempts to decipher mechanisms controlling the local and regional SMB, a clear understanding of the separate components is still lacking, while snowfall measurements are almost absent. In this study, the different terms of the SMB are quantified at the Princess Elisabeth (PE) station in Dronning Maud Land, East Antarctica. Furthermore, the relationship between snowfall and accumulation at the surface is investigated. To achieve this, a unique collocated set of ground-based and in situ remote sensing instrumentation (Micro Rain Radar, ceilometer, automatic weather station, among others) was set up and operated for a time period of 37 months. Snowfall originates mainly from moist and warm air advected from lower latitudes associated with cyclone activity. However, snowfall events are not always associated with accumulation. During 38% of the observed snowfall cases, the freshly fallen snow is ablated by the wind during the course of the event. Generally, snow storms of longer duration and larger spatial extent have a higher chance of resulting in accumulation on a local scale, while shorter events usually result in ablation (on average 17 and 12h respectively). A large part of the accumulation at the station takes place when preceding snowfall events were occurring in synoptic upstream areas. This fresh snow is easily picked up and transported in shallow drifting snow layers over tens of kilometres, even when wind speeds are relatively low ( < 7ms-1). Ablation events are mainly related to katabatic winds originating from the Antarctic plateau and the mountain ranges in the south. These dry winds are able to remove snow and lead to a decrease in the local SMB. This work highlights that the local SMB is strongly influenced by synoptic upstream conditions.

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Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on numerical weather prediction, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the type of input dataset. We then optimise the configuration and show that false alarms also contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

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Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30ĝ€m height. Results show that 78ĝ€% of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36ĝ€% of the time at Neumayer (2011-2015) and 13ĝ€% at PE station (2010-2016). Blowing snow occurrence peaks during the austral winter and shows around 5ĝ€% interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92ĝ€% of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300ĝ€mĝ€†a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

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Snowfall rate (SR) estimates over Antarctica are sparse and characterised by large uncertainties. Yet, observations by precipitation radar offer the potential to get better insight in Antarctic SR. Relations between radar reflectivity (Ze) and snowfall rate (Ze-SR relations) are however not available over Antarctica. Here, we analyse observations from the first Micro Rain Radar (MRR) in Antarctica together with an optical disdrometer (Precipitation Imaging Package; PIP), deployed at the Princess Elisabeth station. The relation Ze = A*SR^B was derived using PIP observations and its uncertainty was quantified using a bootstrapping approach, randomly sampling within the range of uncertainty. This uncertainty was used to assess the uncertainty in snowfall rates derived by the MRR. We find a value of A = 18 [11–43] and B = 1.10 [0.97–1.17]. The uncertainty on snowfall rates of the MRR based on the Ze-SR relation are limited to 40%, due to the propagation of uncertainty in both Ze as well as SR, resulting in some compensation. The prefactor (A) of the Ze-SR relation is sensitive to the median diameter of the snow particles. Larger particles, typically found closer to the coast, lead to an increase of the value of the prefactor (A = 44). Smaller particles, typical of more inland locations, obtain lower values for the prefactor (A = 7). The exponent (B) of the Ze-SR relation is insensitive to the median diameter of the snow particles. In contrast with previous studies for various locations, shape uncertainty is not the main source of uncertainty of the Ze-SR relation. Parameter uncertainty is found to be the most dominant term, mainly driven by the uncertainty in mass-size relation of different snow particles. Uncertainties on the snow particle size distribution are negligible in this study as they are directly measured. Future research aiming at reducing the uncertainty of Ze-SR relations should therefore focus on obtaining reliable estimates of the mass-size relations of snow particles.

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Surface mass balance (SMB) strongly controls spatial and temporal variations in the Antarctic Ice Sheet (AIS) mass balance and its contribution to sea level rise. Currently, the scarcity of observational data and the challenges of climate modeling over the ice sheet limit our understanding of the processes controlling AIS SMB. Particularly, the impact of blowing snow on local SMB is not yet constrained and is subject to large uncertainties. Tho assess the impact of blowing snow on local SMB, we investigate the 15-sec attenuated backscatter profiles from 910 nm ceilometers at two East Antarctic locations in Dronning Maud Land. Ceilometers are robust ground-based remote sensing instruments that can withstand harsh conditions unmanned and produce data continuously. In addition to yielding information on cloud base height and vertical structure, these instruments also provide information on the particles present in the boundary layer. We developed a new algorithm to detect blowing snow (snow particles lifted by the wind from the surface to substantial height) from the ceilometer attenuated backscatter. The algorithm routinely detects the presence of blowing snow if 1) a certain threshold is be exceeded at the range bin closest to the ground (signaling a large concentration of scatterers), and 2) if the intensity of the signal decreases with height (signature of the presence of a blowing snow layer). The algorithm successfully allows to detect strong blowing snow signal from layers thicker than 15 m at the Princess Elisabeth (PE, 72S, 23E) and Neumayer (70S, 8 W) stations in Dronning Maud Land, East Antarctica. Moreover, we combined the ceilometer with automatic weather stations to understand key conditions for blowing snow at the study locations. Results show a very good match between the blowing snow events detected by the new algorithm and visual observations at Neumayer station. Applying the algorithm to PE station, we retrieve the frequency and annual cycle of blowing snow. Finally, the analysis of the automatic weather station data enables to distinguish key meteorological parameters for blowing snow conditions: wind speed, wind direction, relative humidity and temperature inversion (boundary layer stability). These parameters can then be used as input in snow models resolving blowing snow and for validation of the blowing snow schemes in climate models.@en

Weather extremes have harmful impacts on communities around Lake Victoria, where thousands of fishermen die every year because of intense night-time thunderstorms. Yet how these thunderstorms will evolve in a future warmer climate is still unknown. Here we show that Lake Victoria is projected to be a hotspot of future extreme precipitation intensification by using new satellite-based observations, a high-resolution climate projection for the African Great Lakes and coarser-scale ensemble projections. Land precipitation on the previous day exerts a control on night-time occurrence of extremes on the lake by enhancing atmospheric convergence (74%) and moisture availability (26%). The future increase in extremes over Lake Victoria is about twice as large relative to surrounding land under a high-emission scenario, as only over-lake moisture advection is high enough to sustain Clausius–Clapeyron scaling. Our results highlight a major hazard associated with climate change over East Africa and underline the need for high-resolution projections to assess local climate change. @en

This study presents a data set of daily, 1 km resolution Greenland ice sheet (GrIS) surface mass balance (SMB) covering the period 1958–2015. Applying corrections for elevation, bare ice albedo and accumulation bias, the high-resolution product is statistically downscaled from the native daily output of the polar regional climate model RACMO2.3 at 11 km. The data set includes all individual SMB components projected to a down-sampled version of the Greenland Ice Mapping Project (GIMP) digital elevation model and ice mask. The 1 km mask better resolves narrow ablation zones, valley glaciers, fjords and disconnected ice caps. Relative to the 11 km product, the more detailed representation of isolated glaciated areas leads to increased precipitation over the southeastern GrIS. In addition, the downscaled product shows a significant increase in runoff owing to better resolved low-lying marginal glaciated regions. The combined corrections for elevation and bare ice albedo markedly improve model agreement with a newly compiled data set of ablation measurements.@en