<p>The snowpack over mountains represents an important source of water both in these areas and in adjacent lowlands. It also has a large impact on their economy since it affects tourism, communications, logistics and risks associated with its recreational use. Snow cover in mid elevations is experimenting a significant decrease as a consequence of climate change (IPCC-2021) and it is becoming an important issue in the water management agenda. Despite its importance there is a lack of understanding of its dynamics, due to the scarcity of properly distributed temporally and spatially mountain snowpack observations and the availability of specific simulation tools. With the aim of overcoming this scarcity, we present a new processing chain that couples ERA5 atmospheric reanalysis (ECMWF) with the Intermediate Atmospheric Research model (ICAR, from NCAR) and the Flexible Snow Model (FSM2, University of Edinburgh) in order to assess the snowpack in a small area in Penalara Massif, a mountain region in Central Spain. The 2021-2022 winter season have been simulated with a resolution of 2 m for the snowpack output. Several sensitivity experiments have been conducted in order to assess the impact of the uncertainties on the input forcing data. Also, automatic and manual meteorological observations have been used to validate the model and draw future lines of improvement. First results are very promising. This system has been able to explain the main features of the dynamics of the snowpack and future improvements are foreseen like the impact of snow redistribution of fresh snow due to wind drift and a better knowledge of the transformation of the snow pack and melting process.</p>

<p>The snowpack is a fundamental element of the cryosphere and understanding its dynamics is crucial for regions where runoff  is the main source of freshwater. Snowpack variations in very short periods of time can have important security and logistical consequences. On the other hand, snowpack height measurements are complex due to its high spatial variability. The thermodynamic and physical processes that the snowpack undergoes are complex and are dominated by meteorological forcings which are also complex, specially in mountain regions. The most important forcing in terms of snowpack height variation is precipitation. It is well known how precipitation in the form of rain decreases the height of the snowpack almost immediately, while precipitation in the form of snow,  increases its height. The problem is that precipitation occurs with a variety of populations of phases, so this mixed precipitation makes the conversion between precipitation and snow height increase not straightforward.  Disdrometers are instruments capable of determining the size and speed at which precipitation falls very precisely. The population of different sizes and terminal velocities is known as the spectrogram. This map of velocities and sizes makes it possible to estimate the phase since their terminal fall velocities of rain and snow are very different. These instruments are very useful to determine the intensity of mixed precipitation and are widely spreaded in airports, highways and mountain areas. In this work we analyse the possibility of developing a relatively simple algorithm that from the size and velocity distributions detected by a disdrometer we could predict the variation of the snowpack in the next few hours. Several techniques have been tested in this work, some of them simple correlations. But the method that really outstanded was the one based on a reduction of the dimensions of the spectrograms applying a principal component analysis which is then used to search analogue situations. Although the available data is still very small, the results encourage to refine this technique when more data will be available in the next winters.</p>

<p>The snowpack over mountains represents an important source of water both in these areas and in adjacent lowlands. It also has a large impact on their economy since it affects tourism, communications, logistics and risks associated with its recreational use.  Snow cover in mid elevations is experiencing a significant decrease as a consequence of climate change (IPCC-2021) and it is becoming an important issue in the water management agenda. Despite its importance, there is a lack of understanding of its dynamics, due to the scarcity of properly distributed temporally and spatially mountain snowpack observations and the availability of specific simulation tools. This gap is even more pronounced in mediterranean mountainous regions, where the complex processes involved in snowpack growth and ablation, together with its high spatial variability, pose a challenge for the models. To overcome these challenges, a hyper-high resolution state-of-the-art chain model (SnowCast) has been developed and validated in Penalara Massif (Sierra de Guadarrama, Central Spain). It couples ERA5 atmospheric reanalysis (ECMWF) with the Intermediate Atmospheric Research model (ICAR, NCAR) and the Flexible Snow Model (FSM2, University of Edinburgh) along with own developed parametrizations and high resolution topographic forcing models. A multi-year simulation has been performed for this area and sensitivity tests have been performed with different resolutions and topographically induced air and soil forcings. Results after validation using intensive field work, automatic snowpack monitoring and satellite imagery look very promising. A temporal and spatial realistic representation of the snow cover will be presented along with an analysis of the performance of the model and a discussion about new processes to be implemented, additional validation techniques and future coupling with a hydrological model.</p> <p> </p>

Snow precipitation in mountains surrounded by semi-arid regions represents an important reservoir of fresh water during the melting season. The snow cover helps to compensate for the scarce precipitation that occurs during their long summer droughts. Knowing the phenomenology that leads to winter precipitation and snow at these areas becomes even more relevant in a context of climate change. Precipitation in Sierra de Guadarrama, a medium size mountain range in the middle of the Iberian Plateau, is the main source of fresh water for millions of inhabitants living under its area of influence, for an active industry and for agriculture and farming. In addition, scarce but heavy snow events affect logistics, transport and security in an area with abundant ground and air traffic. This work analyses the links between large scale atmospheric patterns and the complex winter precipitation and snow cover dynamics observed at local scale. Applying principal component analysis and K-means clustering on geopotential height field, a set of circulation weather types are obtained. The contribution of each circulation weather type to precipitation, snow and heavy snow events is analysed, and favouring conditions leading to snowfalls are identified. Results from this work can be useful as a framework for future modelling exercises, statistical downscaling of climate change scenarios, or even for the development of early warning systems.

<p>The Factorial Snowpack Model (FSM, Essery, 2015) has been applied for the winters ranging from 2008 to 2021 to predict snow height in a location at 1800 m of altitude in Peñalara Massif (Sierra de Guadarrama, Central Spain). Data from an automatic meteorological station is used as input after a thorough validation and completion using different methods. Several configurations of the model have been tested and sensitivity runs regarding long-wave and short-wave radiative flux, air temperature, liquid and solid precipitation rate, surface pressure, relative humidity and wind velocity, have been performed. Comparison of predictions versus automatic and manual in-situ measurements show a coherent evolution of the snow height. A satisfactory degree of precision regarding the beginning and end of the snow cover has been found but also a high sensitivity to radiative flux, mainly long-wave, air temperature and total solid precipitation rates that need further research. Future work will be carried out testing other snowpack models, developing new parametrizations and performing predictions for the  whole basin considering side effects and other factors.</p>