Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5049-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5049-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
School of Architecture, Civil and Environmental Engineering, Ècole Polytechnique Fèdèrale de Lausanne, Lausanne, Switzerland
Ethan Gutmann
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Bert Kruyt
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Subzero Research Laboratory, Montana State University, Bozeman, MT, USA
Michael Haugeneder
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Tobias Jonas
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Franziska Gerber
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
School of Architecture, Civil and Environmental Engineering, Ècole Polytechnique Fèdèrale de Lausanne, Lausanne, Switzerland
Michael Lehning
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
School of Architecture, Civil and Environmental Engineering, Ècole Polytechnique Fèdèrale de Lausanne, Lausanne, Switzerland
Rebecca Mott
Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Related authors
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
Short summary
Short summary
Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Mahdi Jafari and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2025-3035, https://doi.org/10.5194/egusphere-2025-3035, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We studied how air moves within snow in Arctic regions and how this affects the snow's structure. Using a new method that links two computer models, we found that cold weather can trigger air movement inside the snow, creating vertical channels and changing the snow's density and temperature. These changes are not captured by traditional models, so our work helps improve how snow and climate processes are simulated in cold environments.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
Short summary
Short summary
How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Sean P. Burns, Vincent Humphrey, Ethan D. Gutmann, Mark S. Raleigh, David R. Bowling, and Peter D. Blanken
EGUsphere, https://doi.org/10.5194/egusphere-2025-1755, https://doi.org/10.5194/egusphere-2025-1755, 2025
Short summary
Short summary
We compared two techniques that are affected by the amount of liquid water in a forest canopy. One technique relies on remote sensing (a pair of GPS systems) and the other uses tree motion generated by the wind. Though completely different, these two techniques show strikingly similar changes when rain falls on an evergreen forest. We combine these measurements with eddy-covariance fluxes of water vapor to provide some insight into the evaporation of canopy-intercepted precipitation.
Marit van Tiel, Matthias Huss, Massimiliano Zappa, Tobias Jonas, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2025-404, https://doi.org/10.5194/egusphere-2025-404, 2025
Short summary
Short summary
The summer of 2022 was extremely warm and dry in Europe, severely impacting water availability. We calculated water balance anomalies for 88 glacierized catchments in Switzerland, showing that glaciers played a crucial role in alleviating the drought situation by melting at record rates, partially compensating for the lack of rain and snowmelt. By comparing 2022 with past extreme years, we show that while glacier meltwater remains essential during droughts, its contribution is declining.
Francesca Carletti, Carlo Marin, Chiara Ghielmini, Mathias Bavay, and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2025-974, https://doi.org/10.5194/egusphere-2025-974, 2025
Short summary
Short summary
This work presents the first high-resolution dataset of wet snow properties for satellite applications. With it, we validate links between Sentinel-1 backscattering and snowmelt stages, and investigate scattering mechanisms through a radiative transfer model. We disclose the influence of liquid water content and surface roughness at different melting stages and address future challenges, such as capturing large-scale scattering mechanisms and enhancing radiative transfer modules for wet snow.
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andy Wood
Hydrol. Earth Syst. Sci., 29, 1117–1133, https://doi.org/10.5194/hess-29-1117-2025, https://doi.org/10.5194/hess-29-1117-2025, 2025
Short summary
Short summary
There is a perceived mismatch between the spatial scales on which global climate models can produce data and those needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We assessed the credibility of a set of water management decision metrics in the Community Earth System Model v2 (CESM2). CESM2 shows potentially greater use of its output in long-range water management decisions.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Christoph Marty, Adrien Michel, Tobias Jonas, Cynthia Steijn, Regula Muelchi, and Sven Kotlarski
EGUsphere, https://doi.org/10.5194/egusphere-2025-413, https://doi.org/10.5194/egusphere-2025-413, 2025
Short summary
Short summary
This work presents the first long-term (since 1962), daily, 1 km gridded dataset of snow depth and water storage for Switzerland. Its quality was assessed by comparing yearly, monthly, and weekly values to a higher-quality model and in-situ measurements. Results show good overall performance, though some limitations exist at low elevations and short timescales. Despite this, the dataset effectively captures trends, offering valuable insights for climate monitoring and elevation-based changes.
Elizaveta Sharaborova, Michael Lehning, Nander Wever, Marcia Phillips, and Hendrik Huwald
EGUsphere, https://doi.org/10.5194/egusphere-2024-4174, https://doi.org/10.5194/egusphere-2024-4174, 2025
Short summary
Short summary
Global warming provokes permafrost to thaw, damaging landscapes and infrastructure. This study explores methods to slow this thawing at an alpine site. We investigate different methods based on passive and active cooling system. The best approach mixes both methods and manages heat flow, potentially allowing excess energy to be used locally.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
Short summary
Short summary
We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Hongxiang Yu, Michael Lehning, Guang Li, Benjamin Walter, Jianping Huang, and Ning Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2458, https://doi.org/10.5194/egusphere-2024-2458, 2024
Short summary
Short summary
Cornices are overhanging snow accumulations that form on mountain crests. Previous studies focused on how cornices collapse, little is known about why they form in the first place, specifically how snow particles adhere together to form the front end of the cornice. This study looked at the movement of snow particles around a developing cornice to understand how they gather, the speed and angle at which the snow particles hit the cornice surface, and how this affects the shape of the cornice.
Sonja Wahl, Benjamin Walter, Franziska Aemisegger, Luca Bianchi, and Michael Lehning
The Cryosphere, 18, 4493–4515, https://doi.org/10.5194/tc-18-4493-2024, https://doi.org/10.5194/tc-18-4493-2024, 2024
Short summary
Short summary
Wind-driven airborne transport of snow is a frequent phenomenon in snow-covered regions and a process difficult to study in the field as it is unfolding over large distances. Thus, we use a ring wind tunnel with infinite fetch positioned in a cold laboratory to study the evolution of the shape and size of airborne snow. With the help of stable water isotope analyses, we identify the hitherto unobserved process of airborne snow metamorphism that leads to snow particle rounding and growth.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
Short summary
Short summary
Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Johanna Teresa Malle, Giulia Mazzotti, Dirk Nikolaus Karger, and Tobias Jonas
Earth Syst. Dynam., 15, 1073–1115, https://doi.org/10.5194/esd-15-1073-2024, https://doi.org/10.5194/esd-15-1073-2024, 2024
Short summary
Short summary
Land surface processes are crucial for the exchange of carbon, nitrogen, and energy in the Earth system. Using meteorological and land use data, we found that higher resolution improved not only the model representation of snow cover but also plant productivity and that water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the Earth system.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
Short summary
Short summary
Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
Short summary
Short summary
Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Daniela Brito Melo, Armin Sigmund, and Michael Lehning
The Cryosphere, 18, 1287–1313, https://doi.org/10.5194/tc-18-1287-2024, https://doi.org/10.5194/tc-18-1287-2024, 2024
Short summary
Short summary
Snow saltation – the transport of snow close to the surface – occurs when the wind blows over a snow-covered surface with sufficient strength. This phenomenon is represented in some climate models; however, with limited accuracy. By performing numerical simulations and a detailed analysis of previous works, we show that snow saltation is characterized by two regimes. This is not represented in climate models in a consistent way, which hinders the quantification of snow transport and sublimation.
Florian Zellweger, Eric Sulmoni, Johanna T. Malle, Andri Baltensweiler, Tobias Jonas, Niklaus E. Zimmermann, Christian Ginzler, Dirk Nikolaus Karger, Pieter De Frenne, David Frey, and Clare Webster
Biogeosciences, 21, 605–623, https://doi.org/10.5194/bg-21-605-2024, https://doi.org/10.5194/bg-21-605-2024, 2024
Short summary
Short summary
The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 m spatial resolution across Switzerland using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
Short summary
Short summary
Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary
Short summary
This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Hongxiang Yu, Guang Li, Benjamin Walter, Michael Lehning, Jie Zhang, and Ning Huang
The Cryosphere, 17, 639–651, https://doi.org/10.5194/tc-17-639-2023, https://doi.org/10.5194/tc-17-639-2023, 2023
Short summary
Short summary
Snow cornices lead to the potential risk of causing snow avalanche hazards, which are still unknown so far. We carried out a wind tunnel experiment in a cold lab to investigate the environmental conditions for snow cornice accretion recorded by a camera. The length growth rate of the cornices reaches a maximum for wind speeds approximately 40 % higher than the threshold wind speed. Experimental results improve our understanding of the cornice formation process.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
Short summary
Short summary
Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
Short summary
Short summary
Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
Short summary
Short summary
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
Short summary
Short summary
High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Joel Fiddes, Kristoffer Aalstad, and Michael Lehning
Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, https://doi.org/10.5194/gmd-15-1753-2022, 2022
Short summary
Short summary
This study describes and evaluates a new downscaling scheme that addresses the need for hillslope-scale atmospheric forcing time series for modelling the local impact of regional climate change on the land surface in mountain areas. The method has a global scope and is able to generate all model forcing variables required for hydrological and land surface modelling. This is important, as impact models require high-resolution forcings such as those generated here to produce meaningful results.
Adrien Michel, Bettina Schaefli, Nander Wever, Harry Zekollari, Michael Lehning, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 26, 1063–1087, https://doi.org/10.5194/hess-26-1063-2022, https://doi.org/10.5194/hess-26-1063-2022, 2022
Short summary
Short summary
This study presents an extensive study of climate change impacts on river temperature in Switzerland. Results show that, even for low-emission scenarios, water temperature increase will lead to adverse effects for both ecosystems and socio-economic sectors throughout the 21st century. For high-emission scenarios, the effect will worsen. This study also shows that water seasonal warming will be different between the Alpine regions and the lowlands. Finally, efficiency of models is assessed.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
Short summary
Short summary
Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Geosci. Model Dev., 14, 1657–1680, https://doi.org/10.5194/gmd-14-1657-2021, https://doi.org/10.5194/gmd-14-1657-2021, 2021
Short summary
Short summary
This process-based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found to be effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Rebecca Mott, Ivana Stiperski, and Lindsey Nicholson
The Cryosphere, 14, 4699–4718, https://doi.org/10.5194/tc-14-4699-2020, https://doi.org/10.5194/tc-14-4699-2020, 2020
Short summary
Short summary
The Hintereisferner Experiment (HEFEX) investigated spatial and temporal dynamics of the near-surface boundary layer and associated heat exchange processes close to the glacier surface during the melting season. Turbulence data suggest that strong changes in the local thermodynamic characteristics occur when westerly flows disturbed prevailing katabatic flow, forming across-glacier flows and facilitating warm-air advection from the surrounding ice-free areas, which potentially promote ice melt.
Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
Hydrol. Earth Syst. Sci., 24, 5423–5438, https://doi.org/10.5194/hess-24-5423-2020, https://doi.org/10.5194/hess-24-5423-2020, 2020
Short summary
Short summary
Low river flows affect societies and ecosystems. Here we study how precipitation and potential evapotranspiration shape low flows across a network of 380 Swiss catchments. Low flows in these rivers typically result from below-average precipitation and above-average potential evapotranspiration. Extreme low flows result from long periods of the combined effects of both drivers.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
Short summary
Short summary
We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Cited articles
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R.,
Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A.,
Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and
Manikin, G. S.: A North American Hourly Assimilation and Model Forecast
Cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694,
https://doi.org/10.1175/MWR-D-15-0242.1, 2016. a, b
Bonekamp, P. N. J., Collier, E., and Immerzeel, W. W.: The Impact of Spatial
Resolution, Land Use, and Spinup Time on Resolving Spatial Precipitation
Patterns in the Himalayas, J. Hydrometeorol., 19, 1565–1581,
https://doi.org/10.1175/JHM-D-17-0212.1, 2018. a, b
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model
with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation
and Sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a
Chow, F. K., Schär, C., Ban, N., Lundquist, K. A., Schlemmer, L., and Shi, X.:
Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale
Simulation over Complex Terrain, Atmosphere, 10, 274, https://doi.org/10.3390/atmos10050274,
2019. a
Collados-Lara, A.-J., Pardo-Igúzquiza, E., Pulido-Velazquez, D., and
Jiménez-Sánchez, J.: Precipitation fields in an alpine Mediterranean
catchment: Inversion of precipitation gradient with elevation or undercatch
of snowfall?, Int. J. Climatol., 38, 3565–3578,
https://doi.org/10.1002/joc.5517, 2018. a
Collier, E., Mölg, T., Maussion, F., Scherer, D., Mayer, C., and Bush, A. B. G.: High-resolution interactive modelling of the mountain glacier–atmosphere interface: an application over the Karakoram, The Cryosphere, 7, 779–795, https://doi.org/10.5194/tc-7-779-2013, 2013. a
Comola, F., Giometto, M. G., Salesky, S. T., Parlange, M. B., and Lehning, M.:
Preferential Deposition of Snow and Dust Over Hills: Governing Processes and
Relevant Scales, J. Geophys. Res.-Atmos., 124,
7951–7974, https://doi.org/10.1029/2018JD029614, 2019. a
Crameri, F.: Scientific colour maps (8.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.8035877, 2023. a
Daly, C., Neilson, R. P., and Phillips, D. L.: A Statistical-Topographic Model
for Mapping Climatological Precipitation over Mountainous Terrain, J.
Appl. Meteorol. Clim., 33, 140–158,
https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994. a
Dujardin, J. and Lehning, M.: Wind-Topo: Downscaling near-surface wind fields
to high-resolution topography in highly complex terrain with deep learning,
Q. J. Roy. Meteor. Soc., 148, 1368–1388,
https://doi.org/10.1002/qj.4265, 2022. a
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002JD003296, 2003. a
European Environmental Agency: CORINE Land Cover (CLC) 2006 raster data, Version 13,
https://www.eea.europa.eu/data-and-maps/data/clc-2006-raster (last access: 21 December 2022), 2006. a
Forthofer, J.: Modeling wind in complex terrain for use in fire spread
prediction, mSc thesis, Colorado State University, Fort Collins, 2007. a
Forthofer, J., Butler, B., and Wagenbrenner, N.: A comparison of three
approaches for simulating fine-scale surface winds in support of wildland
fire management. Part I. Model formulation and comparison against
measurements, Int. J. Wildland Fire, 23, 969–981,
https://doi.org/10.1071/WF12089, 2014. a, b, c, d
Gal-Chen, T. and Somerville, R. C.: On the use of a coordinate transformation
for the solution of the Navier-Stokes equations, J. Comput.
Phys., 17, 209–228, https://doi.org/10.1016/0021-9991(75)90037-6, 1975. a
Gerber, F. and Lehning, M.: High resolution static data for WRF over
Switzerland, EnviDat [data set], https://doi.org/10.16904/envidat.233, 2021. a
Gerber, F. and Sharma, V.: Running COSMO-WRF on very-high resolution over
complex terrain, EnviDat [data set], https://doi.org/10.16904/envidat.35, 2018. a
Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, 2018. a, b, c, d, e, f, g, h
Gerber, F., Mott, R., and Lehning, M.: The Importance of Near-Surface Winter
Precipitation Processes in Complex Alpine Terrain, J.
Hydrometeorol., 20, 177–196, https://doi.org/10.1175/JHM-D-18-0055.1, 2019. a
Germann, U., Boscacci, M., Clementi, L., Gabella, M., Hering, A., Sartori, M.,
Sideris, I. V., and Calpini, B.: Weather Radar in Complex Orography, Remote
Sensing, 14, 503. https://doi.org/10.3390/rs14030503, 2022. a
Goger, B., Rotach, M. W., Gohm, A., Fuhrer, O., Stiperski, I., and Holtslag, A.
A. M.: The Impact of Three-Dimensional Effects on the Simulation of
Turbulence Kinetic Energy in a Major Alpine Valley, Bound.-Lay.
Meteorol., 168, 1–27, https://doi.org/10.1007/s10546-018-0341-y, 2018. a
Goger, B., Stiperski, I., Nicholson, L., and Sauter, T.: Large-eddy simulations
of the atmospheric boundary layer over an Alpine glacier: Impact of synoptic
flow direction and governing processes, Q. J. Roy.
Meteorol. Soc., 148, 1319–1343, https://doi.org/10.1002/qj.4263, 2022. a, b, c
Gómez-Navarro, J. J., Raible, C. C., and Dierer, S.: Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain, Geosci. Model Dev., 8, 3349–3363, https://doi.org/10.5194/gmd-8-3349-2015, 2015. a
Goodin, W. R., McRae, G. J., and Seinfeld, J. H.: An Objective Analysis
Technique for Constructing Three-Dimensional Urban-Scale Wind Fields, J. Appl. Meteorol. Clim., 19, 98–108,
https://doi.org/10.1175/1520-0450(1980)019<0098:AOATFC>2.0.CO;2, 1980. a
Groot Zwaaftink, C. D., Mott, R., and Lehning, M.: Seasonal simulation of
drifting snow sublimation in Alpine terrain, Water Resour. Res., 49,
1581–1590, https://doi.org/10.1002/wrcr.20137, 2013. a
Grünewald, T. and Lehning, M.: Are flat-field snow depth measurements
representative? A comparison of selected index sites with areal snow depth
measurements at the small catchment scale, Hydrol. Process., 29,
1717–1728, https://doi.org/10.1002/hyp.10295, 2015. a
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and Lehning, M.: Statistical modelling of the snow depth distribution in open alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, 2013. a
Gutmann, E., Barstad, I., Clark, M., Arnold, J., and Rasmussen, R.: The
Intermediate Complexity Atmospheric Research Model (ICAR), J.
Hydrometeorol., 17, 957–973, https://doi.org/10.1175/JHM-D-15-0155.1, 2016. a
Homicz, G. F.: Three-Dimensional Wind Field Modeling: A Review, United States,
https://doi.org/10.2172/801406, 2002. a, b
Hong, S.-Y. and Pan, H.-L.: Nonlocal Boundary Layer Vertical Diffusion in a
Medium-Range Forecast Model, Mon. Weather Rev., 124, 2322–2339,
https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2, 1996. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an
Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134,
2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Horak, J., Hofer, M., Maussion, F., Gutmann, E., Gohm, A., and Rotach, M. W.: Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography, Hydrol. Earth Syst. Sci., 23, 2715–2734, https://doi.org/10.5194/hess-23-2715-2019, 2019. a, b
Horak, J., Hofer, M., Gutmann, E., Gohm, A., and Rotach, M. W.: A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1, Geosci. Model Dev., 14, 1657–1680, https://doi.org/10.5194/gmd-14-1657-2021, 2021. a, b
Jenness, J.: Topographic Position Index extension for ArcView 3.x, v. 1.2, http://www.jennessent.com/arcview/tpi.htm (last access: 6 December 2022), 2006. a
Khadka, A., Wagnon, P., Brun, F., Shrestha, D., Lejeune, Y., and Arnaud, Y.:
Evaluation of ERA5-Land and HARv2 Reanalysis Data at High Elevation in the
Upper Dudh Koshi Basin (Everest Region, Nepal), J. Appl.
Meteorol. Clim., 61, 931–954, https://doi.org/10.1175/JAMC-D-21-0091.1,
2022. a
Kruyt, B., Mott, R., Fiddes, J., Gerber, F., Sharma, V., and Reynolds, D.: A
Downscaling Intercomparison Study: The Representation of Slope- and
Ridge-Scale Processes in Models of Different Complexity, Front. Earth
Sci., 10, 789332, https://doi.org/10.3389/feart.2022.789332, 2022. a, b, c, d, e, f, g
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous
precipitation distribution and snow transport in steep terrain, Water
Resour. Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008. a
Liston, G. E. and Elder, K.: A Meteorological Distribution System for
High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol.,
7, 217–234, https://doi.org/10.1175/JHM486.1, 2006. a
Liu, C., Ikeda, K., Thompson, G., Rasmussen, R., and Dudhia, J.:
High-Resolution Simulations of Wintertime Precipitation in the Colorado
Headwaters Region: Sensitivity to Physics Parameterizations, Mon. Weather
Rev., 139, 3533–3553, https://doi.org/10.1175/MWR-D-11-00009.1, 2011. a
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our Skill in Modeling
Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks,
B. Am. Meteorol. Soc., 100, 2473–2490,
https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a
Lundquist, J. D., Minder, J. R., Neiman, P. J., and Sukovich, E.: Relationships
between Barrier Jet Heights, Orographic Precipitation Gradients, and
Streamflow in the Northern Sierra Nevada, J. Hydrometeorol., 11,
1141–1156, https://doi.org/10.1175/2010JHM1264.1, 2010. a
Lundquist, K. A., Chow, F. K., and Lundquist, J. K.: An Immersed Boundary
Method Enabling Large-Eddy Simulations of Flow over Complex Terrain in the
WRF Model, Mon. Weather Rev., 140, 3936–3955,
https://doi.org/10.1175/MWR-D-11-00311.1, 2012. a, b
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation of
point SWE data into a distributed snow cover model comparing two contrasting
methods, Water Resour. Res., 50, 7816–7835,
https://doi.org/10.1002/2014WR015302, 2014. a, b, c
Marks, D., Winstral, A., and Seyfried, M.: Simulation of terrain and forest
shelter effects on patterns of snow deposition, snowmelt and runoff over a
semi-arid mountain catchment, Hydrol. Process., 16, 3605–3626,
https://doi.org/10.1002/hyp.1237, 2002. a, b, c
Menke, R., Vasiljević, N., Mann, J., and Lundquist, J. K.: Characterization of flow recirculation zones at the Perdigão site using multi-lidar measurements, Atmos. Chem. Phys., 19, 2713–2723, https://doi.org/10.5194/acp-19-2713-2019, 2019. a
MeteoCH: MeteoSwiss: Daily Precipitation (final analysis): RhiresD,
http://www.meteoswiss.admin.ch/content/dam/meteoswiss/de/ (last access: 12 May 2022), 2013. a
METI/NASA: 2009, ASTER Global Digital Elevation Model V002, NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center [data set], Sioux Falls, South Dakota, https://doi.org/10.5067/ASTER/ASTGTM.002, 2009. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on
the Development of Trailing Stratiform Precipitation in a Simulated Squall
Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137,
991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Mott, R., Schirmer, M., Bavay, M., Grünewald, T., and Lehning, M.: Understanding snow-transport processes shaping the mountain snow-cover, The Cryosphere, 4, 545–559, https://doi.org/10.5194/tc-4-545-2010, 2010. a, b
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., Berne, A., and Lehning, M.:
Orographic effects on snow deposition patterns in mountainous terrain,
J. Geophys. Res.-Atmos., 119, 1419–1439,
https://doi.org/10.1002/2013JD019880, 2014. a
Mott, R., Stiperski, I., and Nicholson, L.: Spatio-temporal flow variations driving heat exchange processes at a mountain glacier, The Cryosphere, 14, 4699–4718, https://doi.org/10.5194/tc-14-4699-2020, 2020. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N.,
Magnusson, J., Mazzotti, G., Quéno, L.,
Schirmer, M., Webster, C., and Jonas, T.:
Operational snow-hydrological
modeling for Switzerland,
Front. Earth Sci., 11, 1228158,
https://doi.org/10.3389/feart.2023.1228158, 2023. a, b
Moussiopoulos, N., Flassak, T., and Knittel, G.: A refined diagnostic wind
model, Environ. Softw., 3, 85–94, https://doi.org/10.1016/0266-9838(88)90015-9,
1988. a, b, c
Prein, A. F., Holland, G. J., Rasmussen, R. M., Done, J., Ikeda, K., Clark,
M. P., and Liu, C. H.: Importance of Regional Climate Model Grid Spacing for
the Simulation of Heavy Precipitation in the Colorado Headwaters, J.
Climate, 26, 4848–4857, https://doi.org/10.1175/JCLI-D-12-00727.1, 2013. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475,
2015. a
Raderschall, N., Lehning, M., and Schär, C.: Fine-scale modeling of the
boundary layer wind field over steep topography, Water Resour. Res.,
44, W09425, https://doi.org/10.1029/2007WR006544, 2008. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu,
R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow: The
NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am.
Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1,
2012. a
Rasmussen, S., Gutmann, E., Friesen, B., Rouson, D., Filippone, S., and
Moulitsas, I.: Development and Performance Comparison of MPI and Fortran
Coarrays within an Atmospheric Research Model, Presented at the Workshop
2018 IEEE/ACM Parallel Applications Workshop, Alternatives To MPI (PAW-ATM),
Dallas, TX, USA, 2018. a
Ratto, C., Festa, R., Romeo, C., Frumento, O., and Galluzzi, M.:
Mass-consistent models for wind fields over complex terrain: The state of the
art, Environ. Softw., 9, 247–268, https://doi.org/10.1016/0266-9838(94)90023-X,
1994. a
Reynolds, D.: HICAR-Model/HICAR: v1.1, Zenodo [code], https://doi.org/10.5281/zenodo.7920422, 2023. a
Ross, D. G. and Fox, D. G.: Evaluation of an Air Pollution Analysis System for
Complex Terrain, J. Appl. Meteorol. Clim., 30, 909–923, https://doi.org/10.1175/1520-0450(1991)030<0909:EOAAPA>2.0.CO;2, 1991. a, b
Ross, D. G., Smith, I. N., Manins, P. C., and Fox, D. G.: Diagnostic Wind Field
Modeling for Complex Terrain: Model Development and Testing, J.
Appl. Meteorol., 27, 785–796,
http://www.jstor.org/stable/26183717 (last access: 30 November 2022), 1988. a
Sasaki, Y.: An ObJective Analysis Based on the Variational Method, J. Meteorol. Soc. Jpn. Ser. II 36, 77–88,
https://doi.org/10.2151/jmsj1923.36.3_77, 1958. a
Schär, C., Leuenberger, D., Fuhrer, O., Lüthi, D., and Girard, C.: A New
Terrain-Following Vertical Coordinate Formulation for Atmospheric Prediction
Models, Mon. Weather Rev., 130, 2459–2480,
https://doi.org/10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2, 2002. a, b, c, d
Seifert, A., Baldauf, M., Stephan, K., Blahak, U., and Beheng, K.: The
challenge of convective-scale quantitative precipitation forecasting, 15th
Int. Conf. on Clouds and Precipitation, Cancun, Mexico, Centro de Ciencias de
la Atmósfera, Universidad Nacional Autónoma de México (CCA-UNAM), 2008. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F.,
Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model,
Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023. a, b
Sherman, C. A.: A Mass-Consistent Model for Wind Fields over Complex Terrain,
J. Appl. Meteorol. Clim., 17, 312–319,
https://doi.org/10.1175/1520-0450(1978)017<0312:AMCMFW>2.0.CO;2, 1978. a, b
Shin, H. H. and Hong, S.-Y.: Representation of the Subgrid-Scale Turbulent
Transport in Convective Boundary Layers at Gray-Zone Resolutions, Mon.
Weather Rev., 143, 250–271,
https://doi.org/10.1175/MWR-D-14-00116.1, 2015. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., and Duda,
M. G., and Powers, J. G.: A Description of the Advanced Research WRF Version
3, University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH,
2008. a
Smith, R. B. and Barstad, I.: A Linear Theory of Orographic Precipitation,
J. Atmos. Sci., 61, 1377–1391,
https://doi.org/10.1175/1520-0469(2004)061<1377:ALTOOP>2.0.CO;2, 2004. a
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., and Dosio, A.: Will drought
events become more frequent and severe in Europe?, Int. J.
Climatol., 38, 1718–1736, https://doi.org/10.1002/joc.5291, 2018. a
Thompson, G., Tewari, M., Ikeda, K., Tessendorf, S., Weeks, C., Otkin, J., and
Kong, F.: Explicitly-coupled cloud physics and radiation parameterizations
and subsequent evaluation in WRF high-resolution convective forecasts,
Atmos. Res., 168, 92–104, https://doi.org/10.1016/j.atmosres.2015.09.005,
2016. a
Umek, L., Gohm, A., Haid, M., Ward, H. C., and Rotach, M. W.: Large-eddy
simulation of foehn–cold pool interactions in the Inn Valley during PIANO
IOP 2, Q. J. Roy. Meteor. Soc., 147,
944–982, https://doi.org/10.1002/qj.3954, 2021. a
Vionnet, V., Martin, E., Masson, V., Guyomarc'h, G., Naaim-Bouvet, F., Prokop, A., Durand, Y., and Lac, C.: Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model, The Cryosphere, 8, 395–415, https://doi.org/10.5194/tc-8-395-2014, 2014.
a
Vionnet, V., Martin, E., Masson, V., Lac, C., Naaim Bouvet, F., and
Guyomarc'h, G.: High-Resolution Large Eddy Simulation of Snow Accumulation in
Alpine Terrain, J. Geophys. Res.-Atmos., 122,
11005–11021, https://doi.org/10.1002/2017JD026947, 2017. a
Wagenbrenner, N. S., Forthofer, J. M., Lamb, B. K., Shannon, K. S., and Butler, B. W.: Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja, Atmos. Chem. Phys., 16, 5229–5241, https://doi.org/10.5194/acp-16-5229-2016, 2016. a
Wang, H., Skamarock, W. C., and Feingold, G.: Evaluation of Scalar Advection
Schemes in the Advanced Research WRF Model Using Large-Eddy Simulations of
Aerosol–Cloud Interactions, Mon. Weather Rev., 137, 2547–2558,
https://doi.org/10.1175/2009MWR2820.1, 2009. a, b
Wang, Z. and Huang, N.: Numerical simulation of the falling snow deposition
over complex terrain, J. Geophys. Res.-Atmos, 122,
980–1000, https://doi.org/10.1002/2016JD025316, 2017. a
Westerhuis, S., Fuhrer, O., Bhattacharya, R., Schmidli, J., and Bretherton, C.:
Effects of terrain-following vertical coordinates on simulation of stratus
clouds in numerical weather prediction models, Q. J. Roy.
Meteor. Soc., 147, 94–105, https://doi.org/10.1002/qj.3907, 2021. a
Wicker, L. J. and Skamarock, W. C.: Time-Splitting Methods for Elastic Models
Using Forward Time Schemes, Mon. Weather Rev., 130, 2088–2097,
https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002. a
Winstral, A. and Marks, D.: Simulating wind fields and snow redistribution
using terrain-based parameters to model snow accumulation and melt over a
semi-arid mountain catchment, Hydrol. Process., 16, 3585–3603,
https://doi.org/10.1002/hyp.1238, 2002. a
Winstral, A., Marks, D., and Gurney, R.: Simulating wind-affected snow
accumulations at catchment to basin scales, Adv. Water Resour., 55,
64–79, https://doi.org/10.1016/j.advwatres.2012.08.011, 2013. a
Winstral, A., Jonas, T., and Helbig, N.: Statistical Downscaling of Gridded
Wind Speed Data Using Local Topography, J. Hydrometeorol., 18, 335–348, https://doi.org/10.1175/JHM-D-16-0054.1, 2017. a, b
Wyngaard, J. C.: Toward Numerical Modeling in the “Terra Incognita”,
J. Atmos. Sci., 61, 1816–1826,
https://doi.org/10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2, 2004. a
Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
The challenge of running geophysical models is often compounded by the question of where to...