Snow plays an essential role in the climatic and environmental challenges of the 21st century. The snow cover represents a key source of global water supply and climate regulation, and has shown high sensitivity to a warming climate. The amount of collected snow information is also constantly increasing due to novel automated methods for cheaper and easier measurements, especially imagery. During the last decades, instrumentation and measurement techniques, especially remote sensing, have advanced fast, providing significant amount of new information about the extent and properties of snow (e.g. snow water equivalent, (SWE), albedo, reflectance, microstructure, and impurities). In addition, novel technologies such as unmanned aerial vehicles (UAVs) and webcams provide new opportunities and challenges. Optimization and agreement on sampling strategies are important to get spatially distributed data at different scales, and ensure broad use of the acquired data. Data management has become an important issue after general open data policy, where data sets should be available and usable for other users. A large variety of NWP and hydrological models or operational applications routinely make use of snow data to improve their performance. Forecasting snow related hazards in Europe is mostly performed at the country or regional level, and heavily relies on the concurrent meteorological factors and snowpack properties, which are usually acquired from point measurements or physical models. A big challenge is bridging information from microstructural scales of the snowpack up to the grid resolution in models and then to provide knowledge-based information on potential impacts to society, economy and safety (e.g. hydro-power, water availability, transportation, tourism, flooding and avalanches). In this session we would like discuss recent developments and progresses on (1) Snow data collection, curation, and management including harmonized observation techniques for several snow parameters and remote sensing snow observations by applying novel techniques, (2) Snow models, satellite-derived snow products, and data assimilation including improved snow modelling and prediction at different scales taking into account macro and microscale snow properties and (3) Monitoring snow-related hazards and extreme events including latest reanalysis and satellite data sets and models to predict and forecast extreme events and snow-related natural hazards.

Co-organized by CL5/HS13/NH8
Convener: A.N. Arslan | Co-conveners: Leena LeppänenECSECS, Carlo De Michele, Jürgen Helmert
| Attendance Thu, 07 May, 16:15–18:00 (CEST)

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Chat time: Thursday, 7 May 2020, 16:15–18:00

Chairperson: Ali Nadir Arslan
D755 |
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin

In a context of climate change, assessing trends in hazards related to extreme events is urgent. Specifically current methods to compute European standards for snow load actions on structures do not account for the non-stationarity due to climate change. We present the first analysis of extreme ground snow load trends for the whole French Alps. Our method is based on non-stationary generalized extreme value (GEV) distribution, time derivative of return level and likelihood ratio test. Thanks to Météo France reanalysis and snowpack models, we study moutain massif scale data available every 300m of altitude from 1958 to 2017. We detect an overall decreasing trend for annual maxima of ground snow load between 900m and 2700m, which is significant in the Northwest of the French Alps until 1800m. Despite decreasing return levels, in 2017 half of massifs at altitude 1800m still exceeds standard return levels. We underline the importance of snowpack modelling and limitations of approaches relying on ground snow load computed with snow depth annual maxima and an hypothesis on snow density.

How to cite: Le Roux, E., Evin, G., Eckert, N., Blanchet, J., and Morin, S.: Non-stationarity of extreme ground snow load in the French Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-464, https://doi.org/10.5194/egusphere-egu2020-464, 2020.

D756 |
Leena Leppänen, Juan Ignazio Lopez-Moreno, Bartłomiej Luks, Ladislav Holko, Ghislain Picard, Alba Sanmiguel-Vallelado, Esteban Alonso-González, David Finger, Ali Nadir Arslan, Katalin Gillemot, Aynur Sensoy, Arda Sorman, Cansaran Ertaş, Charles Fierz, Steven Fassnacht, and Christoph Marty

Manually collected snow data can be considered as ground truth for many applications, such as climatological or hydrological studies. Water equivalent of snow cover (SWE) can be manually measured by using a snow tube or snow cylinder to extract a snow core and measure the bulk density of the core by weighing it. Different snow core samplers and scales are used, but they all use the same measurement principle. However, there are various sources of uncertainty that have not been quantified in detail. To increase the understanding of these errors, different manual SWE measurement devices used across Europe were evaluated within the framework of the COST Action ES1404 HarmoSnow. Two field campaigns were organized in different environments to quantify uncertainties when measuring snow depth, snow bulk density and SWE with core samplers. The 1st field campaign in 2017 in Iceland focused on measurement differences attributed to different instrumentation compared with the natural variability in the snowpack, and the 2nd field campaign in 2018 in Finland focused on device comparison and on the separation of the different sources of variability. To our knowledge, such a comparison has not previously been conducted in terms of the number of device and different environments.

During the 1st campaign, repeated measurements were taken along two 20 m long snow trenches to distinguish snow variability measured at the plot and at the point scale. The results revealed a much higher variability of SWE at the plot scale, resulting from both natural variability and instrument bias, compared to repeated measurements at the same spot, resulting mostly from error induced by observers or a high variability in the snow depth. Snow Micro Pen sampling showed that the snowpack was very homogeneous for the 2nd campaign, which allowed for the disregarding of the natural variability of the snowpack properties and the focus to be on separating between instrumental bias and error induced by observers. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even when observers performed measurements with snow core samplers they were not formally trained on. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. The results showed that the devices provided slightly different uncertainties since they were designed for different snow conditions. The aim of this comparison was not to provide a definitive estimation of uncertainty for manual SWE measurements, but to illustrate the role of the different uncertainty sources.

How to cite: Leppänen, L., Lopez-Moreno, J. I., Luks, B., Holko, L., Picard, G., Sanmiguel-Vallelado, A., Alonso-González, E., Finger, D., Arslan, A. N., Gillemot, K., Sensoy, A., Sorman, A., Ertaş, C., Fierz, C., Fassnacht, S., and Marty, C.: Measurement inter-comparison of bulk snow density and water equivalent of snow cover with snow core samplers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1824, https://doi.org/10.5194/egusphere-egu2020-1824, 2020.

D757 |
Zuhal Akyurek, Kenan Bolat, Cagri Hasan Karaman, and Matias Takala

Snow cover is an essential climate variable directly affecting the Earth’s energy balance, therefore estimating the snow parameters play an important role in hydrological, land surface, meteorological and climate models. Remote sensing provides a good understanding of snow cover monitoring thus several satellite snow products have been developed and disseminated so far.  In this study, Jupyter Notebook as an open source interactive satellite snow products retrieval, visualization and analysis tool has been developed by using Python language. Jupyter Notebook allows easy and straightforward data analysis with the possibility of live interaction and requires little programming knowledge.

The developed tool provides the capabilities of downloading the satellite snow products, georeferencing them and performing spatial analysis like zonal statistics. In this study EUMETSAT HSAF snow products, namely H10 (Snow detection), H13 (Snow Water Equivalent) and H34 (Snow cover) are used. The tool allows user to upload their own region in ESRI shapefile format for spatial and temporal analysis and the uploaded region can be visualized on interactive map via custom interactive widget like ipyleaflet. The cloud percentage for the snow cover product can be selected and daily snow covered area or snow water equivalent change for the uploaded region can be calculated for the selected period. With this tool, it is aimed to retrieve the satellite snow products easily and perform spatial and temporal analysis of snow cover for the area of interest without getting lost in data formats. Therefore, users with little or no knowledge about programming can interact easily with EUMETSAT HSAF snow products. Furthermore, with the high extensibility of Jupyter Notebook, it can also be improved or modified in accordance with the need of the end users.

How to cite: Akyurek, Z., Bolat, K., Karaman, C. H., and Takala, M.: Using the Jupyter Notebook as a Tool for analyzing the EUMETSAT HSAF snow products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8028, https://doi.org/10.5194/egusphere-egu2020-8028, 2020.

D758 |
Jürgen Helmert, Aynur Şensoy Şorman, Rodolfo Alvarado Montero, Carlo De Michele, Patricia De Rosnay, Marie Dumont, Samantha Pullen, David Christian Finger, Martin Lange, Ghislain Picard, Vera Potopová, Dagrun Vikhamar-Schuler, and Ali Nadir Arslan
Snow as a major part of the cryosphere is an important component of Earth’s hydrological cycle and energy balance. Understanding the microstructural, macrophysical, thermal and optical properties of the snowpack is essential for integration into numerical models and there is a great need for accurate snow data at different spatial and temporal resolutions to address the challenges of changing snow conditions.
Physical snow properties are currently determined by traditional ground-based measurements as well as remote sensing, over a range of temporal and spatial scales, following considerable developments in instrument technology over recent years. 
Data assimilation (DA) methods are widely used to combine data from different observations with numerical model using uncertainties of observed and modeled variables  to produce an optimal estimate. DA provides a reliable improvement of the initial states of the numerical model and a benefit for hydrological and snow model forecasts.
European efforts to harmonize approaches for validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation techniques were coordinated by the European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) .
One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models, and show its benefit for weather and hydrological forecasting as well as other applications.” 
One key result from COST HarmoSnow is a better knowledge about the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. The main parts of this knowledge are retrieved from a COST HarmoSnow survey exploring the common practices on the use of snow observations in different modeling environments. We will show results from the survey and their implications towards standardized and improved usage of snow observations in various data assimilation applications.

How to cite: Helmert, J., Şensoy Şorman, A., Alvarado Montero, R., De Michele, C., De Rosnay, P., Dumont, M., Pullen, S., Finger, D. C., Lange, M., Picard, G., Potopová, V., Vikhamar-Schuler, D., and Arslan, A. N.: Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from COST HarmoSnow (2014-2018), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8348, https://doi.org/10.5194/egusphere-egu2020-8348, 2020.

D759 |
Alexey Ekaykin, Natalia Tebenkova, Vladimir Lipenkov, Arina Veres, Kirill Tchikhatchev, and Andreas Richter

We demonstrate that the accumulation-stake measurements in central Antarctica systematically underestimate the value of the snow build-up due to the compaction of snow layer between the stake base and the snow surface. We have developed two methods to define the corresponding correction to the snow build-up measurements at the stake farm near Vostok station. The first method is based on "Sorge's law" to calculate the rate of thinning of the snow layers using the vertical snow density profile. The second method consists of direct instrumental measurements of this thinning in the field. We have also involved the data of other two independent methods to estimate the snow accumulation rate in the vicinity of Vostok - first, geodetic data on the rate of snow layer sinking and, second, glaciological data from snow pits. The most reliable estimate of the snow accumulation rate in this region is 2.26±0.10 g cm-2 year-1, that is 8±4 % higher than initial (not corrected) value from the accumulation-stake measurements.

How to cite: Ekaykin, A., Tebenkova, N., Lipenkov, V., Veres, A., Tchikhatchev, K., and Richter, A.: Systematic underestimation of snow accumulation rate by stake measurements in central Antarctica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9891, https://doi.org/10.5194/egusphere-egu2020-9891, 2020.

D760 |
Ali Arda Sorman and Mustafa Cansaran Ertas

Water has an essential effect on climate change, global warming, drought, flood and all kinds of living life as a result of the continuous movement between earth and atmosphere. In high latitude and elevated regions of the world, most of the annual total precipitation occurs in the form of snow and snow melting provides the majority of usable water. Due to the large impact of snow cover on water/energy balance, the quantity, spatial and temporal distribution of the snow is very important in the hydrological system.

Turkey is the 4th highest country in Europe, after Andorra, Georgia and Switzerland, with an average elevation of 1140 m. Therefore, snow frequently occurs and may stay on the ground more than half of the year especially in the north, east and central regions. Snowmelt runoff in the mountainous eastern part of Turkey, where large dams are located, is of great importance as it constitutes 2/3 in volume of the yearly total runoff during spring and early summer months. Therefore, determining the amount and timing of snowmelt is of utmost value in order to use the water resources of the country in an optimal manner.

In this study; conceptual snowpack model SNOW-17, which has a common usage in the literature, is applied in a fully distributed manner in the Upper Euphrates Basin. SNOW-17 is a conceptual model using air temperature as the sole index to determine the energy exchange across the snow-air interface. The model results of snowpack components, such as height of snow (HS) and snow water equivalent (SWE) are evaluated with independent pointwise in-situ measurements and spatially distributed satellite images. The snow model results show an average success of 0.81 and 0.66 in terms of Nash-Sutcliffe Efficiency (NSE) for the calibration and validation periods, respectively. In addition, the extreme snowfall and early snowmelt event that occurred in 2004 snow season is further evaluated by the snow model and satellite products.

How to cite: Sorman, A. A. and Ertas, M. C.: Assessment of Distributed Snow Modeling using Ground and Remote Sensing Data in Mountainous Eastern Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12877, https://doi.org/10.5194/egusphere-egu2020-12877, 2020.

D761 |
Semih Kuter and Zuhal Akyurek

Spatial extent of snow has been declared as an essential climate variable. Accurate modeling of snow cover is crucial for the better prediction of snow water equivalent and, consequently, for the success of general circulation and weather forecasting models as well as climate change and hydrological studies. This presentation mainly focuses on the representation of the latest findings of our efforts in fractional snow cover mapping on MODIS images by data-driven machine learning methodologies. For this purpose, a dataset composed of 20 MODIS - Landsat 8 image pairs acquired between Apr 2013 and Dec 2016 over European Alps were employed. Artificial neural networks (ANN), multivariate adaptive regression splines (MARS), support vector regression (SVR) and random forest (RF) models were trained and tested by using reference FSC maps generated from higher spatial resolution Landsat 8 binary snow maps. ANN, MARS, SVR and RF models exhibited quite good performance with average R ≈ 0.93, whereas the agreement between the reference FSC maps and the MODIS’ own product MOD10A1 (C5) was slightly poorer with R ≈ 0.88.

How to cite: Kuter, S. and Akyurek, Z.: Latest Advances in Fractional Snow Cover Mapping on MODIS Data by Machine Learning Algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13193, https://doi.org/10.5194/egusphere-egu2020-13193, 2020.

D762 |
David Pulido-Velazquez, Antonio-Juan Collados-Lara, Eulogio Pardo-Igúzquiza, Pedro Ossorio, Leticia Baena-Ruiz, Juan Ignacio Lopez-Moreno, Crisanto Martín-Montañés, Antonio Navarro, and Rosa Maria Mateos

The spatial distribution of snow thickness in extensive alpine regions can be estimated with high spatial resolution from LIDAR data capture. However, due to the significant economic cost of these activities, its application is limited, especially to cover large areas. Traditionally, snow thickness has been estimated by applying interpolation methods and/or hydrological models that approximate the distribution of snow from fieldwork data. In some places there is permanent infrastructure that facilitates the collection of data. In Spain, within the framework of the national snow depth monitoring program (ERHIN, initiated in 1981), an infrastructure consisting of a network of poles distributed over various mountain ranges was generated to obtain measurements by direct observation from helicopter flights (1-3 per year). This monitoring activity has been drastically reduced and even cancelled in some mountain ranges in recent years as a result of budget cuts. In order to maintain the observation of snow, we propose a novel approach to involve groups of volunteers to take advantage of the existing (or optimally increased) infrastructure that are underutilized or in disuse, to gather photos of poles as a means of monitoring snow thickness. The proposal is being applied in the Sierra Nevada (Southern Spain), and may be extended in the future across the whole Spain. The tasks performed to achieve the objective are: 1) Inventory of infrastructures and potential volunteer: The 23 poles installed by the ERHIN program in Sierra Nevada are being marked to facilitate their identification in photos sent in by volunteers. An inventory of potential volunteer and user groups has been compiled; 2) Optimum design of snowpole network for monitoring snow depth: The aim is to identify the optimal location of new poles in order to minimize uncertainty in estimates of snow depth. To solve this problem, we have employed a regression model that estimates the spatial distribution of snow depth and its uncertainty 3) Generation of tools to supply and display information: Mobile application and web platform. For the success of the activity, we need user-friendly applications that include relevant local information (e.g., location of the poles) and an adequate link to the project web for supplying and storing information; 4) Maximization of participation. Program of incentives. We have involved key institutions, such as Sierra Nevada Natural Park and Andalusian Mountaineering Federation (FAM), which predict a high participation rate by its members. To encourage collaboration, participation by volunteers will be published, including ranking and honorary awards to the most active. As stated by the Andalusian Federation of Mountaineers, this ‘competition’ will be an additional incentive to increase participation by mountaineers. The task will have a significant impact with a low economic cost; 5) Dissemination and communication of relevant: It includes the design and distribution of posters and brochures to the main potential groups of volunteers.

This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).

How to cite: Pulido-Velazquez, D., Collados-Lara, A.-J., Pardo-Igúzquiza, E., Ossorio, P., Baena-Ruiz, L., Lopez-Moreno, J. I., Martín-Montañés, C., Navarro, A., and Mateos, R. M.: Designing and launching a citizen initiative to monitor snow depth in Sierra Nevada (South Spain), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13945, https://doi.org/10.5194/egusphere-egu2020-13945, 2020.

D763 |
Kari Luojus, Matias Takala, Jouni Pulliainen, Juha Lemmetyinen, Mikko Moisander, Pinja Venäläinen, Chris Derksen, Colleen Mortimer, Thomas Nagler, and Gabriele Schwaizer

Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km² of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability making satellite observations the only means for providing timely and complete observations of the global snow cover. The ESA Snow CCI project was initiated in 2018 to improve methodologies for snow cover extent (SE) and snow water equivalent (SWE) retrieval [1] using satellite data and construct long term data records of terrestrial snow cover for climate research purposes.

The first new long term SWE data record from the ESA Snow CCI project, spanning 1979 to 2018 has been constructed and assessed in terms of retrieval performance, homogeneity and temporal stability. The initial results show that the new SWE dataset is more robust, more accurate and more consistent over the 40-year time series, compared to the earlier ESA GlobSnow SWE v1.0 and v2.0 data records [1].

The improved SWE retrieval methodology incorporates a new emission model (within the retrieval scheme), an improved synoptic weather station snow depth data record (applied to support SWE retrieval), extension of the SWE retrieval to cover the whole Northern Hemisphere.

The new Snow CCI SWE data record has been used to assess changes in the long term hemispherical snow conditions and climatological trends in Northern Hemisphere, Eurasia and North America. The general finding is that the peak hemispherical snow mass during the satellite era has not yet decreased significantly but has remained relatively stable, with changes to lower and higher SWE conditions in different geographical regions.



[1] Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. Kärnä, J. Koskinen, B. Bojkov. 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 12, 3517-3529, doi:10.1016/j.rse.2011.08.014.

How to cite: Luojus, K., Takala, M., Pulliainen, J., Lemmetyinen, J., Moisander, M., Venäläinen, P., Derksen, C., Mortimer, C., Nagler, T., and Schwaizer, G.: Development of Long-Term Satellite-Based Snow Mass Records in the ESA Snow CCI Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14123, https://doi.org/10.5194/egusphere-egu2020-14123, 2020.

D764 |
Kristoffer Aalstad, Sebastian Westermann, Joel Fiddes, James McCreight, and Laurent Bertino

Accurately estimating the snow water equivalent (SWE) that is stored in the worlds mountains remains a challenging and important unsolved problem. The SWE reconstruction approach, where the remotely sensed seasonal depletion of fractional snow-covered area (fSCA) is used with a snow model to build up the snowpack in reverse, has been used for decades to help tackle this problem retrospectively. Despite some success, this deterministic approach ignores uncertainties in the snow model, the meteorological forcing, and the remotely sensed fSCA. A trade-off has also existed between the desired temporal and spatial resolution of the satellite-retrieved fSCA depletion. Recently, ensemble-based data assimilation techniques that can account for the uncertainties inherent in the reconstruction exercise have allowed for probabilistic snow reanalyses. In addition, new higher resolution optical satellite constellations such as Sentinel-2 and the PlanetScope cubesats have been launched into polar orbit, potentially eliminating the aforementioned trade-off.

We combine these two developments, namely ensemble-based data assimilation and the emerging remotely sensed data streams, to see if snow reanalyses can be improved at the hillslope (100 m) scale in complex terrain. As a first step, we develop accurate high-resolution binary snow-cover maps using a terrestrial automatic camera system installed on a mountaintop near Ny-Ålesund (Svalbard, Norway). These maps are used to validate fSCA retrieved from various satellite sensors (MODIS, Sentinel-2 MSI, and Landsat 8 OLI) using algorithms ranging from simple thresholding of the normalized difference snow index to spectral unmixing. Through the validation, we demonstrate that the spectral unmixing technique can obtain unbiased fSCA retrievals at the hillslope scale. Next, we move to the Mammoth Lakes basin in the Californian Sierra Nevada, USA, where we have access to independent validation data retrieved from several Airborne Snow Observatory (ASO) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flights. Using these airborne retrievals as a reference, we show that fSCA can be retrieved at the hillslope scale with reasonable accuracy at an unprecedented near daily revisit period using a combination of the Landsat, Sentinel-2 MSI, RapidEye, and PlanetScope satellite constellations. In a series of data assimilation experiments we show how the combination of these constellations can lead to significant improvements in hillslope scale snow reanalyses as gauged by various evaluation metrics. Furthermore, it is suggested that an iterative ensemble smoother data assimilation scheme can provide more robust SWE estimates than other smoothers that have previously been proposed for snow reanalysis. We briefly conclude with thoughts as to the current impediments to conducting a global hillslope scale snow reanalysis and propose avenues for further research, such as how snow reanalyses can help in the prediction exercise.

How to cite: Aalstad, K., Westermann, S., Fiddes, J., McCreight, J., and Bertino, L.: Retrieving, validating, and assimilating fractional snow-covered area from emerging optical satellites for snow reanalysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14969, https://doi.org/10.5194/egusphere-egu2020-14969, 2020.

D765 |
Aynur Sensoy, Gokcen Uysal, and Rodolfo Alvarado Montero

Modeling streamflows is challenging in snow dominated high altitude regions due to limited observations, harsh topographic conditions and complex snow physics. Different uncertainties arise from multiple sources in modeling and forecasting. The uncertainties of the initial conditions are mainly tackled with data assimilation techniques. On the other hand, the uncertainty of the model structure should also be considered since assimilation techniques can only use same model and parameter sets in each implementation. Generally, this uncertainty can be taken into account using multi-modelling methods that can produce ensemble set of parameters. In order to make use of this approach, this study aims the realization of a novel method that generates a probabilistic estimate of initial states using a multi-parametric modelling method with deterministic Variational Data Assimilation, as referred to the multi-parametric variational data assimilation, MP-VarDA.  The study is accomplished for runoff predictions over the mountainous Eastern part of Turkey concerning the importance of snowmelt and the limited availability of observed data. The model pool is generated with Generalized Likelihood Uncertainty Estimation (GLUE) method with a calibrated hydrological model using HBV. The implementation of MP-VarDA assimilates both discharge and satellite snow observations on snow cover. The preliminary results having 3 model instances are promising to set a model pool for MP-VarDA method which can reduce model uncertainty. The model is also tested via hindcast experiments under close-loop mode in order to assimilate discharge and satellite snow data, and model results showed that runoff and snow state predictions are improved compared to conditional assimilation techniques.

How to cite: Sensoy, A., Uysal, G., and Alvarado Montero, R.: Multi-parametric Variational Data Assimilation of MODIS snow cover data through HBV Model in Mountainous Upper Euphrates River Catchment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17823, https://doi.org/10.5194/egusphere-egu2020-17823, 2020.

D766 |
Daniele Casella, Andrea Camplani, Paolo Sanò, Giulia Panegrossi, and Mark Kulie

Within the development of passive microwave precipitation retrieval techniques, and, in
particular, of snowfall detection and retrieval techniques, the possibility to characterize the
frozen background surface (snowcover and sea ice conditions) at the time of the overpass
appears to be a relevant task. As demonstrated by many recent studies (e.g., Tabkiri et al.,
2019, Ebtehaj and Kummerow 2017, Panegrossi et al., 2017), the microwave signal
related to snowfall is strongly influenced by the surface conditions, and the response of the
observed brightness temperatures to the presence and intensity of snowfall depends on
complex interconnections between environmental conditions (surface temperature, water
vapor content, snow water path, cloud depth, presence of supercooled droplets) and the
different surface conditions (wet or dry snow cover, sea ice concentration and type, etc.).
The use of surface classification climatological datasets results inadequate for the purpose
because of the extreme variability of the frozen surface conditions. It is therefore
necessary to be able to identify the background surface condition as close as possible (in
space and time) to that of the observation. The conically scanning GPM Microwave Imager
(GMI) and cross-track the Advanced Technology Microwave Sounder (ATMS) are the most
advanced currently available microwave radiometers. They are both equipped with
channels at several different frequencies that can be exploited both for the identification of
the frozen surface conditions and for snowfall detection and retrieval at the time of the
overpass over a precipitation event (i.e., Rysman et al., 2018). Moreover, they can be
used to analyze the potentials of future radiometers with similar characteristics such as the
EPS-SG Microwave Sounder (MWS) and Microwave Imager (MWI), which represent the
future in terms of European operational radiometers that can be exploited for precipitation
retrieval at all latitudes (including the Polar Regions). In the last years we have developed
two frozen surface classification schemes based on the use of GMI and ATMS low
frequency channels (from 10 GHz up to 36 GHz) and on ancillary near-surface
temperature and columnar water vapor data (obtained from ECMWF global ERA5
reanalysis). The algorithm is able to identify 9 classes of soil including different type of
snow and sea ice. The results of such classification have been compared with other
products, such as the NASA-GPROF soil type classification, and with snowcover and sea
ice global datasets (such as GMASI- Autosnow, and SNODAS from NOAA, and ECMWF
ERA5). In particular, the comparison with SNODAS over Northern America region shows
that the probability of detection of snow-covered surfaces varies between 86% - 98%
(79%-95%) for GMI (ATMS) with a relatively small false alarm ratio (10%-30%). The
analysis evidenced the main factors limiting the detection capability, such as the moisture
content, the presence of orography, the snow cover beam filling and the snow depth.

How to cite: Casella, D., Camplani, A., Sanò, P., Panegrossi, G., and Kulie, M.: Frozen Surface Classification Scheme for ATMS and GMI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18844, https://doi.org/10.5194/egusphere-egu2020-18844, 2020.

D767 |
Marketa Souckova and Roman Juras

Snow avalanche hazard in the Czech Republic is mainly constrained to Krkonoše mountains. Approximately twenty snow avalanches release every year during winter season, usually lasting from November to May. Although Krkonoše belongs to mid-mountain range (highest peak 1602 m a.s.l.) due to its prevailing influence of moist, cold air masses and mean annual temperature about 0 °C climate conditions are similar to high-elevation or subpolar climate. Despite the low altitude, Krkonoše experiences considerable avalanche activity at fifty-five permanent paths and even cause fatalities. The monitoring of avalanche releases has been conducted since 1961, therefore most of the avalanches were reported to the extensive database (> 1100 events). The land use of the mountain range is changing and many areas are being deforested resulting in new avalanche prone areas. The presented research is based on results of previous project focused on the avalanche hazard assessment in the Krkonoše mountains and thus results are extended, and monitoring methods and creation of avalanche susceptibility maps are improved.

It is clear, from abovementioned, that existing avalanche hazard maps have to be reassessed. Therefore, release zones and avalanche susceptibility map must be determined.

Mapping of avalanche activity includes continous monitoring of the avalanche paths by unmanned aerial systems photogrammetry (UAS-P) and ground GPS measurements of the avalanche path. This data will be used for evaluation of the mass movement dynamic model (RAMMS).  Such a monitoring covers:

The result of RAMMS model for entire Krkonoše mountain range including czech and polish side will be parametrised and compared to newly obtained data of avalanche releases. The results can be then used by public authorities such as: Krkonoše National Park administrations, Mountain rescue service of the Czech Republic or Institute of Forest Management.


How to cite: Souckova, M. and Juras, R.: Mapping snow avalanche releases by unmanned aerial vehicles (UAV) in Krkonoše mountain range, the Czech Republic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19307, https://doi.org/10.5194/egusphere-egu2020-19307, 2020.

D768 |
Cemal Melih Tanis, Ali Nadir Arslan, and Miina Rautiainen

Environmental camera networks are growing in usage in different parts of the globe. Time series of webcam imagery from the camera networks are used in estimating snow cover properties. Fractional snow cover (FSC) and snow depth (SD) are two important parameters which can be estimated from the webcam imagery using image processing algorithms. Monitoring of snow cover from webcam imagery has the potential to be used in gap filling and validation of satellite derived products. It can also be used as a data source for snow monitoring in remote areas where manual measurements and in-situ sensor installation and maintenance are costly, especially under forest canopy which retrieval of signal from ground by satellites is a challenge. In this paper, we have used multiple webcams from MONIMET Camera Network in Finland and Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) on the cloud to establish an automated processing chain which reports FSC and SD estimations in near real time, available in FMIPROT website. Image processing algorithms are implemented in the toolbox, the images from last year are also processed and the results are compared with ultrasonic in-situ measurements and values generated by visual inspections on images. In the website, estimations from the day-time images of the latest one month are visualized on interactive plots, along with time-lapse animations of images, with a latency of 3 hours.

How to cite: Tanis, C. M., Arslan, A. N., and Rautiainen, M.: Near real time monitoring of snow cover using webcam imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22347, https://doi.org/10.5194/egusphere-egu2020-22347, 2020.

D769 |
Outi Meinander, Ali Nadir Arslan, Leena Leppanen, and Jouni Pulliainen and the NordSnowNet

Snow cover is an essential climate variable directly affecting the earth energy balance. Proper description and assimilation of snow cover information into hydrological, land surface, meteorological and climate models are critical to address the impact of snow on various phenomena, to predict local snow water resources and to warn about snow-related natural hazards. This induces a challenging problem of bridging information from micro-structural scales of the snowpack up to the grid resolution in models. 

These questions are included in the actions of the Nordic Snow Network, NordSnowNet, http://nordsnownet.fmi.fi/, a recent project launched in 2019 under the Nordic Arctic Co-operation Programme of the Nordic Council of Ministers, which is administered by Nordregio and contributes to enhancing knowledge about the Arctic region (https://nordregioprojects.org/arctic-programme/nordic-snow-network-nordsnownet/). The point of view of the network will be focused on snow in Nordic and related Arctic areas: making existing Nordic-Arctic research and snow data from observations and models (forecasts and assimilated observations by weather and hydrology models, projections by the climate models) visible for the researcher, data user and education communities. It supports snow-related research and development of applications by the exchange of information and data, arranging workshops, training and supporting also informal Nordic researcher contacts and meetings. For example, citizen observations will be collected on color snow and ice, to estimate the appearance of dirty snow due to deposition of atmospheric aerosols and algae existing on snow and ice, in co-operation with the Ministry of Foreign Affairs of Finland’s project IBA-FIN-BCDUST (2019-2020, PI Meinander FMI, https://en.ilmatieteenlaitos.fi/iba-project). The color of snow and ice has effect on the snow and ice albedo, which in turn influences snow and ice melt.

The project is managed by the Finnish Meteorological Institute (FMI) by Ali Nadir Arslan, and the steering committee with representatives from each partner country, including Finland (Outi Meinander, FMI), Denmark (Kristian Pagh Nielsen, Danish Meteorological Institute), Estonia (Marko Kaasik, University of Tartu), Greenland (Kirsty Langley, Asiaq Greenland), Iceland (Pavla Dagsson-Waldhauserova, Agricultural Institute of Iceland), Sweden (Patrick Samuelsson, Swedish Meteorological Institute), and Norway (Mariken Homleid, Norwegian Meteorological Institute). The partners are Nordic meteorological institutes and collaborative universities and research institutes. The first field work activity will be in the Arctic Space Centre in Sodankylä, Finland, in early April 2020. The network is open and welcomes collaborators to join by contacting the corresponding country representative.

We gratefully acknowledge the Nordic Council of Ministers Arctic Co-operation Programme and Nordregio for their support to the NordSnowNet.

How to cite: Meinander, O., Arslan, A. N., Leppanen, L., and Pulliainen, J. and the NordSnowNet: The Nordic Snow Network (NordSnowNet): Arctic research and snow data from observations and models , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22350, https://doi.org/10.5194/egusphere-egu2020-22350, 2020.