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  • 1
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    Synoptic conditions and atmospheric moisture pathways associated with virga and precipitation over coastal Adélie Land in Antarctica (2020)
    Copernicus
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    Publication Date: 2020-05-27
    Description: Precipitation falling over the coastal regions of Antarctica often experiences low-level sublimation within the dry katabatic layer. The amount of water that reaches the ground surface is thereby considerably reduced. This paper investigates the synoptic conditions and the atmospheric transport pathways of moisture that lead to either virga – when precipitation is completely sublimated – or actual surface precipitation events over coastal Adélie Land, East Antarctica. For this purpose, the study combines ground-based lidar and radar measurements at Dumont d'Urville station (DDU), Lagrangian back trajectories, Eulerian diagnostics of extratropical cyclones and fronts, and moisture source estimations. It is found that precipitating systems at DDU are associated with warm fronts of cyclones that are located to the west of Adélie Land. Virga – corresponding to 36 % of the hours with precipitation above DDU – and surface precipitation cases are associated with the same precipitating system but they correspond to different phases of the event. Virga cases more often precede surface precipitation. They sometimes follow surface precipitation in the warm sector of the cyclone's frontal system, when the associated cyclone has moved to the east of Adélie Land and the precipitation intensity has weakened. On their way to DDU, the air parcels that ultimately precipitate above the station experience a large-scale lifting across the warm front. The lifting generally occurs earlier in time and farther from the station for virga than for precipitation. It is further shown that the water contained in the snow falling above DDU during pre-precipitation virga has an oceanic origin farther away (about 30∘ more to the west) from Adélie Land than the one contained in the snow that precipitates down to the ground surface.
    Print ISSN: 1994-0416
    Electronic ISSN: 1994-0424
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 2
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    Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps (2020)
    Copernicus
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    Publication Date: 2020-05-19
    Description: 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.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
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    Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification (2020)
    Copernicus
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    Publication Date: 2020-06-05
    Description: The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    Microphysics and dynamics of snowfall associated with a warm conveyor belt over Korea (2020)
    Copernicus
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    Publication Date: 2020-06-25
    Description: On 28 February 2018, 57 mm of precipitation associated with a warm conveyor belt (WCB) fell within 21 h over South Korea. To investigate how the large-scale circulation influenced the microphysics of this intense precipitation event, we used radar measurements, snowflake photographs and radiosounding data from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018). The WCB was identified with trajectories computed with analysis wind fields from the Integrated Forecast System global atmospheric model. The WCB was collocated with a zone of enhanced wind speed of up to 45 m s−1 at 6500 m a.s.l., as measured by a radiosonde and a Doppler radar. Supercooled liquid water (SLW) with concentrations exceeding 0.2 g kg−1 was produced during the rapid ascent within the WCB. During the most intense precipitation period, vertical profiles of polarimetric radar variables show a peak and subsequent decrease in differential reflectivity as aggregation starts. Below the peak in differential reflectivity, the specific differential phase shift continues to increase, indicating early riming of oblate crystals and secondary ice generation. We hypothesise that the SLW produced in the WCB led to intense riming. Moreover, embedded updraughts in the WCB and turbulence at its lower boundary enhanced aggregation by increasing the probability of collisions between particles. This suggests that both aggregation and riming occurred prominently in this WCB. This case study shows how the large-scale atmospheric flow of a WCB provides ideal conditions for rapid precipitation growth involving SLW production, riming and aggregation. Future microphysical studies should also investigate the synoptic conditions to understand how observed processes in clouds are related to large-scale circulation.
    Print ISSN: 1680-7316
    Electronic ISSN: 1680-7324
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
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    Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing (2020)
    Copernicus
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    Publication Date: 2020-08-14
    Description: Air mass factors (AMFs) are used in passive trace gas remote sensing for converting slant column densities (SCDs) to vertical column densities (VCDs). AMFs are traditionally computed with 1D radiative transfer models assuming horizontally homogeneous conditions. However, when observations are made with high spatial resolution in a heterogeneous atmosphere or above a heterogeneous surface, 3D effects may not be negligible. To study the importance of 3D effects on AMFs for different types of trace gas remote sensing, we implemented 1D-layer and 3D-box AMFs into the Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres (MYSTIC), a solver of the libRadtran radiative transfer model (RTM). The 3D-box AMF implementation is fully consistent with 1D-layer AMFs under horizontally homogeneous conditions and agrees very well (
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 6
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    Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera (2020)
    Copernicus
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    Publication Date: 2020-01-30
    Description: A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image, and their size and geometry to classify each individual image. The classification task is achieved with a two-component Gaussian mixture model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labeling the subset of images on which the model was fitted. An overall accuracy and a Cohen kappa score of 99.4 % and 98.8 %, respectively, are achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antarctica and during a winter in Davos, respectively, is presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.
    Print ISSN: 1994-0416
    Electronic ISSN: 1994-0424
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
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    Gravity Wave Excitation during the Coastal Transition of an Extreme Katabatic Flow in Antarctica (2020)
    American Meteorological Society
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    Publication Date: 2020-03-25
    Description: The offshore extent of Antarctic katabatic winds exerts a strong control on the production of sea ice and the formation of polynyas. In this study, we make use of a combination of ground-based remotely sensed and meteorological measurements at Dumont d’Urville (DDU) station, satellite images, and simulations with the Weather Research and Forecasting Model to analyze a major katabatic wind event in Adélie Land. Once well developed over the slope of the ice sheet, the katabatic flow experiences an abrupt transition near the coastal edge consisting of a sharp increase in the boundary layer depth, a sudden decrease in wind speed, and a decrease in Froude number from 3.5 to 0.3. This so-called katabatic jump manifests as a turbulent “wall” of blowing snow in which updrafts exceed 5 m s−1. The wall reaches heights of 1000 m and its horizontal extent along the coast is more than 400 km. By destabilizing the boundary layer downstream, the jump favors the trapping of a gravity wave train—with a horizontal wavelength of 10.5 km—that develops in a few hours. The trapped gravity waves exert a drag that considerably slows down the low-level outflow. Moreover, atmospheric rotors form below the first wave crests. The wind speed record measured at DDU in 2017 (58.5 m s−1) is due to the vertical advection of momentum by a rotor. A statistical analysis of observations at DDU reveals that katabatic jumps and low-level trapped gravity waves occur frequently over coastal Adélie Land. It emphasizes the important role of such phenomena in the coastal Antarctic dynamics.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 8
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    R2D2 - A Region-based Recursive Doppler Dealiasing Algorithm for Operational Weather Radar (2020)
    American Meteorological Society
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    Publication Date: 2020-11-05
    Description: Region-based Recursive Doppler Dealiasing (R2D2) is a novel dealiasing algorithm to unfold Doppler velocity fields obtained by operational radar measurements. It specializes in resolving issues when the magnitude of the gate-to-gate velocity shear approaches or exceeds the Nyquist velocity. This occurs either in highly sheared situations, or when the Nyquist velocity is low. Highly sheared situations, such as convergence lines or mesocyclones, are of particular interest for nowcasting and warnings. R2D2 masks high-shear areas and adds a spatial buffer around them. The areas between the buffers are then identified as continuous regions that lie within the same Nyquist interval. Each region subsequently is assigned its most likely Nyquist interval by applying vertical and temporal continuity constraints, as well as supplemental wind information from an operational mesoscale model. The shear zones are then resolved using 2D continuity in azimuth and range. This 4D procedure is repeated until no further improvement can be achieved. Each iteration with fewer folds identifies fewer, but larger continuous regions and less shear zones until an optimum is reached. Residual errors, often related to shear greater than the Nyquist velocity, are contained to small areas within the buffer zones. This approach maximizes operational performance in high-shear situations and restricts errors to minimal areas to mitigate error propagation.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 9
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    Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (2021)
    Copernicus
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    Publication Date: 2021-02-15
    Description: This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehring et al., 2020a). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before.
    Print ISSN: 1866-3508
    Electronic ISSN: 1866-3516
    Topics: Geosciences
    Published by Copernicus
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  • 10
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    RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland (2021)
    Copernicus
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    Publication Date: 2021-04-29
    Description: Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising 4 years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyperparameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid or mixed precipitation. In weak precipitation, however, and despite a posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 min QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth than the original non-polarimetric QPE and offers an improved performance for five out of six events.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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