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The Alps are a natural barrier for avian broad‐front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2‐year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year‐round on‐site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps. [ABSTRACT FROM AUTHOR]

Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data. [ABSTRACT FROM AUTHOR]

We present a procedure to detect landslide events by analyzing in-sequence data acquired from regional broadband seismic networks and spaceborne radar imagery. The combined used of these techniques is meant to exploit their complementary elements and mitigate their limitations when used singularly. To test the method, we consider a series of six slope failures associated to the Piz Cengalo rock avalanche that recently occurred in the Swiss Alps, a region where we can benefit from high spatial density and quality of seismic data, as well as from the high spatial and temporal resolution of the European Space Agency (ESA) Copernicus Sentinel-1 radar satellites. The operational implementation of the proposed approach, in combination with the future increase in availability of seismic and satellite data, can offer a new and efficient solution to build and/or expand landslide catalogues based on quantitative measurements and, thus, help in hazard assessments and the definition of early warning systems at regional scales. [ABSTRACT FROM AUTHOR]

The representation of precipitation variability over mountainous regions by ground-based sensors is an open problem in hydrometeorological applications that necessitates the use of satellite-based precipitation products (SPPs). An extended network of ground-based X-band radar (GR) deployments over complex terrain areas, including the northeastern Italian Alps, North Carolina, Olympic Mountain, and the southern tip of Vancouver Island, is used in this study as a benchmark rainfall data set for error characterization and modeling of Level 2 PMW retrievals (Goddard profiling (GPROF) V05 algorithm) for the different sensors: the Microwave Humidity Sounder (MHS), the Special Sensor Microwave Imager/Sounder (SSMIS), the Global Precipitation Measurement Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Matchups of Level 2 PMW/GR rainfall are extracted based on a matching methodology that identifies GR volume scans with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. The error model is the nonparametric machine learning tree-based quantile regression forest (QRF), which we developed using matchups of PMW/GR rainfall data from the different study areas. Validation of the error model is conducted using three cross-validation techniques: the k-fold, leave-one region out, and enforced. All validations showed that the error model-based corrections can significantly reduce both the mean relative error and the random component of PMW products. Moreover, the error reduction demonstrated with the leave-one region out cross-validation technique indicated that the error model is transferable among complex terrain regions. Algorithm developers may find this error model useful to integrate in the Level 3 products. [ABSTRACT FROM AUTHOR]

Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation and the amplification of upper-level ridges. This study presents a case study that involves aircraft, lidar and radar observations in a WCB ascending from western Europe towards the Baltic Sea during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) and T-NAWDEX-Falcon in October 2012, a preparatory campaign for the THORPEX North Atlantic Waveguide and Downstream Impact Experiment (T-NAWDEX). Trajectories were used to link different observations along the WCB, that is, to establish so-called Lagrangian matches between observations. To this aim, an ensemble of wind fields from the global analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble of Data Assimilations (EDA) system were used, which allowed for a probabilistic quantification of the WCB occurrence and the Lagrangian matches. Despite severe air traffic limitations for performing research flights over Europe, the German Aerospace Center (DLR) Falcon successfully sampled WCB air masses during different phases of the WCB ascent. The WCB trajectories revealed measurements in two distinct WCB branches: one branch ascended from the eastern North Atlantic over southwestern France, while the other had its inflow in the western Mediterranean. Both branches passed across the Alps, and for both branches Lagrangian matches coincidentally occurred between lidar water vapour measurements in the inflow of the WCB south of the Alps, radar measurements during the ascent at the Alps and in situ aircraft measurements by Falcon in the WCB outflow north of the Alps. An airborne release experiment with an inert tracer could confirm the long pathway of the WCB from the inflow in the Mediterranean boundary layer to the outflow in the upper troposphere near the Baltic Sea several hours later. The comparison of observations and ensemble analyses reveals a moist bias in the analyses in parts of the WCB inflow but a good agreement of cloud water species in the WCB during ascent. In between these two observations, a precipitation radar measured strongly precipitating WCB air located directly above the melting layer while ascending at the southern slopes of the Alps. The trajectories illustrate the complexity of a continental and orographically influenced WCB, which leads to (i) WCB moisture sources from both the Atlantic and Mediterranean, (ii) different pathways of WCB ascent affected by orography, and (iii) locally steep WCB ascent with high radar reflectivity values that might result in enhanced precipitation where the WCB flows over the Alps. The linkage of observational data by ensemble-based WCB trajectory calculations, the confirmation of the WCB transport by an inert tracer and the model evaluation using the multi-platform observations are the central elements of this study and reveal important aspects of orographically modified WCBs. [ABSTRACT FROM AUTHOR]

Blended high‐resolution sub‐hourly precipitation fields are increasingly needed for nowcasting and automatic warnings systems. Here we present a 7‐year (2012–2018) assessment of (5‐min) precipitation characteristics for the topographically complex region of Switzerland. We use CombiPrecip, a blended radar—rain‐gauge product that provides high resolution (1 km2) hourly precipitation fields. Five‐minute fields (CPCD), fine enough to disentangle even the shortest convective events from longer‐lasting precipitation, are obtained by temporally disaggregating these hourly fields. A rain‐gauge verification of the sub‐hourly precipitation fields shows that CPCD out‐performs the radar‐only estimates both in terms of quantitative error and in detecting wet/extreme periods, making CPCD sufficiently accurate to be used with confidence as input fields for hydrological modelling or for regional model validation. The quantitative error of CombiPrecip at the 60 (10)‐min time‐scale, as expressed by the scatter, is 40% (22%) lower than that of the radar‐only product. Radar‐gauge discrepancies are large and the quantitative error of CPCD increases by 30% when decreasing aggregation times from 60 to 10 min. Although summertime 5‐min precipitation extremes are strongest and more frequent on average south of the Alps, where they contribute to approximately 10% of the seasonal total precipitation, the most intense extremes are observed over the Jura Mountains in the afternoon and the highest wet 5‐min frequency in the Prealps around the same time. Intense precipitation is more evenly distributed throughout the day south of the Alps. Extreme intensity and frequency peaks 2 hr earlier in the Prealps, the Jura Mountains and the Alps (~1600 UTC) compared to the Swiss Plateau (~1800 UTC). Extremes are less intense and frequent at high elevations (>2,000 MASL) than over the Swiss Plateau (<800 MASL). [ABSTRACT FROM AUTHOR]

Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m 2 for TSX and 2000 m 2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018. [ABSTRACT FROM AUTHOR]

The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. When extrapolating the radar measurements aloft for quantitative precipitation estimation (QPE) at the ground, these must be corrected for the vertical change of the radar echo caused by the growth and transformation of precipitation. Building on the availability of polarimetric data and a hydrometeor classification algorithm, this work explores the potential of machine learning methods to study the vertical structure of precipitation in Switzerland and to propose a more localised vertical profile correction. It first establishes the ground work for the use of machine learning methods in this context: from volumetric data of 30 precipitation events, vertical cones with 500 m vertical resolution are extracted. It is shown that these cones can well represent the vertical structure of different types of precipitation events (stratiform, convective, snowfall). The reflectivity data and the hydrometeor proportions from the extracted cones constitute the input for the training of artificial neural networks (ANNs), which are used to predict the vertical change in reflectivity. Lower height levels are gradually removed in order to test the ANN's ability to extrapolate the radar measurements to the ground level. It is found that ANN models using the information on hydrometeor proportions can predict from altitudes between 500 and 1000 m higher than the ANN based on only reflectivity data. In comparison to more traditional vertical profile correction techniques, the ANNs show less prediction errors made from all height levels up to 4000 m a.s.l., above which the ANNs lose predictive skill and the performance levels off to a constant value. [ABSTRACT FROM AUTHOR]

The influence of mountain ranges on broad-front bird migration has been investigated for several decades. However, almost all the studies have focused on flight behaviour, and have rarely considered the quantitative effect of mountain ranges as a barrier. The present study investigates the spatial and temporal pattern of autumnal bird migration across the eastern part of the Alpine Arc (Austria). Migration intensity was recorded continuously using two dedicated bird radar systems during a full autumn migration season. One radar was located to the north in the Alpine Foreland region for the whole observation period, serving as a reference for the number of birds approaching the Alps. The second radar was moved between four locations across the Alps, operating for 21–26 days at each site. Our findings clearly show that topography significantly influences the spatial distribution of bird migration. Most birds flew lower than the main ridges, with only a small proportion detected above such altitudes (~ 3000 m above sea level). The daily pattern of migration was similar across sites, and there was no accumulation of birds ("Zugstau") in front of the Eastern Alps. At only one of the inner-Alpine sites did the day-to-day pattern of migration intensities differ considerably from the pattern in the foreland of the Alps. This may be influenced by the wind conditions, causing birds to enter valleys of different geographic orientation. At a large scale, we did not find any evidence of a major barrier effect. In contrast to the south-west oriented central part of the Alpine Arc, where birds concentrate along the mountain ranges, the flux of autumnal bird migration appears to flow over and through the nearly perpendicularly oriented (with respect to their migratory direction) Eastern Alps. This demonstrates how the influence of mountain ranges as barriers depends on their spatial context. [ABSTRACT FROM AUTHOR]

Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanches reports from a network of observers. During bad weather, however, information about occurred avalanches can be scarce or even be missing completely. To asses the potential of weather independent radar satellites we compared manual and automatic avalanche mapping results from high resolution TerraSAR-X (TSX) stripmap images and from medium resolution Sentinel-1 (S1) interferometric wide swath images. Within a selected test site in the central Swiss Alps the TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other but with TSX about 40 % more, mainly smaller avalanches were detected. However, S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 days with freely available acquisitions. With costly SPOT-6 images the Alps can be even covered in a single day at meter-resolution, at least for clear sky conditions. For the SPOT-6 and TSX mapping results we found a fair agreement but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, with radar, mainly the avalanche deposition zone was detected, whereas the release zone was well visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of the manually mapped new avalanches in the same image pair, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single orbit TSX change detection results. In a multiorbital S1 moasic covering entire Switzerland, we detected 7361 new avalanches which occurred during an extreme avalanche period around Jan 4th 2018. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization. [ABSTRACT FROM AUTHOR]

The use of radar for precipitation measurement in mountainous regions is complicated by many factors, especially beam shielding by terrain features, which, for example, reduces the visibility of the shallow precipitation systems during the cold season. When extrapolating the radar measurements aloft for quantitative precipitation estimation (QPE) at the ground, these must be corrected for the vertical change of the radar echo caused by the growth and transformation of precipitation. Building on the availability of polarimetric data and a hydrometeor classification algorithm, this work explores the potential of machine learning methods to study the vertical structure of precipitation in Switzerland and to propose a more localised vertical profile correction. It first establishes the ground work for the use of machine learning methods in this context: from volumetric data of 30 precipitation events vertical cones with 500 m vertical resolution are extracted. It is shown that these cones can well represent the vertical structure of different types of precipitation events (stratiform, convective, snowfall). The reflectivity data and the hydrometeor proportions from the extracted cones constitute the input for the training of artificial neural networks (ANN), which are used to predict the vertical change in reflectivity. Lower height levels are gradually removed in order to test the ANN's ability to extrapolate the radar measurements to the ground level. It is found that ANN models using the information on hydrometeor proportions can predict from altitudes between 500 and 1000 metres higher than the ANN based on only reflectivity data. In comparison with more traditional vertical profile correction techniques the ANNs show less prediction errors made from all height levels up to 4000 m a.s.l., above which the ANNs lose predictive skill and the performance levels off to a constant value. [ABSTRACT FROM AUTHOR]

In this paper we present an analysis of a large dataset of lightning and polarimetric weather radar data collected in the course of a lightning measurement campaign that took place in the summer of 2017 in the area surrounding Säntis, in the northeastern part of Switzerland. For this campaign and for the first time in the Alps, a lightning mapping array (LMA) was deployed. The main objective of the campaign was to study the atmospheric conditions leading to lightning production with a particular focus on the lightning discharges generated due to the presence of the 124 m tall Säntis telecommunications tower. In this paper we relate LMA very high frequency (VHF) sources data with co-located radar data in order to characterise the main features (location, timing, polarimetric signatures, etc.) of both the flash origin and its propagation path. We provide this type of analysis first for all of the data and then we separate the datasets into intra-cloud and cloud-to-ground flashes (and within this category positive and negative flashes) and also upward lightning. We show that polarimetric weather radar data can be helpful in determining regions where lightning is more likely to occur but that lightning climatology and/or knowledge of the orography and man-made structures is also relevant. [ABSTRACT FROM AUTHOR]

Precipitation in complex terrain is subject to orographic enhancement which leads to identifiable signatures in the space-time precipitation distribution over the long term, for instance the presence of long-term persistence. Here we quantify long-term persistence by the scaling exponent H of the thcrease in the spatial variance of accumulated precipitation with time from high resolution radar data over a 2 year period in a section of the Swiss-Italian Alps with relief greater than 4000 m. We find that the long-term persistence signal is strong and seasonal with mean H = 0.87- 0.99 in the cold season (autumn-winter), when stratiform precipitation is dominant, and H = 0.73-0.81 in the warm season (spring-summer), when convective events are dominant. H is also significantly correlated with mean altitude and surface variance, suggesting that singularities in the deposition process are partly driven by topography. The correlation is negative in the warm season and positive in the cold season, indicating the different physical origin of precipitation formation, atmospheric stability, and orographic effects in those seasons. By analyzing the binary precipitation occurrence process, we find that a large part of the long-term persistence signal comes from the space-time distribution of precipitation occurrence and clustering, regardless of precipitation intensity. [ABSTRACT FROM AUTHOR]

Summary: A Mesoscale Convective System in North-Western Slovenia produced up to 350–400mm in 12h on 18 September, 2007. The region impacted by the storm shows significant differences in climatic and geologic properties at short distances. Owing to such variability, extreme flooding concentrated over the Selška Sora watershed at Železniki (103.3km2), outside the area which received the highest precipitation. Hydrometeorological analyses of the storm are based on accurate analysis of C-band weather-radar observations and data from a rain gauge network. Detailed surveys of high-water marks and channel/floodplain geometry, carried out two months after the flood, are used for hydrologic analyses of the Selška Sora flood. These include estimation of peak discharge at 21 sites. Unit peak discharges range from 5 to 7m3 s−1 km−2 in basins characterised by size up to approximately 25km2. Higher unit peak discharges (>10m3 s−1 km−2), estimated in a few smaller basins, are influenced by intense sediment transport. Observed rainfall, estimated peak discharges, and observer notes on timing of peak discharge are used along with a distributed hydrologic model to reconstruct hydrographs at multiple locations. Examination of the rainfall distribution and flood response shows that the extent and the position of the karst terrain provided a major control on flood response in the region impacted by the storm. Use of the distributed hydrological model together with the post-flood survey observations is shown to provide an accurate description of the flood. Water balance and response time characteristics are examined for selected catchments, showing that event runoff coefficient ranged between 17% and 24% for different catchments. The quality of the peak discharge simulation at the 21 surveyed sites is substantially degraded when using spatially-uniform rainfall over the area covering all the surveyed sub-catchments, mainly due to rainfall volume errors introduced by using the spatially uniform value. On the other hand, the influence of rainfall spatial averaging at the scale of the sub-catchments generally has a very limited effect on runoff modelling, showing that rainfall spatial organisation was not able to overcome the catchment dampening effect for this flood. [Copyright &y& Elsevier]

A computer program for meteorological radar siting, previously developed for pencil-beam antenna, long-range, C-band radars, has been adapted for fan-beam antenna, short-range, X-band radars. The simulator uses topographic information in the form of a raster digital elevation model and a surface backscattering cross-section per unit area at grazing angles derived from the literature. It is independent of specific radar sites and radar systems. Its use will enhance optimized scanning strategies, antenna design tailored to mountainous terrain and knowledge of land-clutter measurements at low grazing angles. The raster-based approach used in implementing the program makes it compatible with any desired spatial resolution. Simplicity and short simulation times have been pursued as primary goals during the planning and implementation phases of the routines. A preliminary validation of the simulator using backscattering data acquired in the Alps is presented in this letter. [ABSTRACT FROM AUTHOR]

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. [ABSTRACT FROM AUTHOR]