@article{alonso-gonzalezMuSAMultiscaleSnow2022,
  title = {{{MuSA}}: {{The Multiscale Snow Data Assimilation System}} (v1.0)},
  shorttitle = {{{MuSA}}},
  author = {{Alonso-Gonz{\'a}lez}, Esteban and Aalstad, Kristoffer and Baba, Mohamed Wassim and Revuelto, Jes{\'u}s and {L{\'o}pez-Moreno}, Juan Ignacio and Fiddes, Joel and Essery, Richard and Gascoin, Simon},
  year = {2022},
  month = aug,
  journal = {Geoscientific Model Development Discussions},
  pages = {1--43},
  publisher = {{Copernicus GmbH}},
  issn = {1991-959X},
  doi = {10.5194/gmd-2022-137},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e. the number of particles and their covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including the particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost.{$<$}/p{$>$}},
  langid = {english},
  keywords = {assimilation,fsm,musa,snow},
  file = {/home/simonfi/Zotero/storage/BR8W5QWP/Alonso-González et al. - 2022 - MuSA The Multiscale Snow Data Assimilation System.pdf;/home/simonfi/Zotero/storage/E24RKBLD/gmd-2022-137.html}
}

@article{alonso-gonzalezSnowpackDynamicsLebanese2021,
  title = {Snowpack Dynamics in the {{Lebanese}} Mountains from Quasi-Dynamically Downscaled {{ERA5}} Reanalysis Updated by Assimilating Remotely Sensed Fractional Snow-Covered Area},
  author = {{Alonso-Gonz{\'a}lez}, Esteban and Gutmann, Ethan and Aalstad, Kristoffer and Fayad, Abbas and Bouchet, Marine and Gascoin, Simon},
  year = {2021},
  month = aug,
  journal = {Hydrology and Earth System Sciences},
  volume = {25},
  number = {8},
  pages = {4455--4471},
  publisher = {{Copernicus GmbH}},
  issn = {1027-5606},
  doi = {10.5194/hess-25-4455-2021},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} The snowpack over the Mediterranean mountains constitutes a key water resource for the downstream populations. However, its dynamics have not been studied in detail yet in many areas, mostly because of the scarcity of snowpack observations. In this work, we present a characterization of the snowpack over the two mountain ranges of Lebanon. To obtain the necessary snowpack information, we have developed a 1 km regional-scale snow reanalysis (ICAR\_assim) covering the period 2010\textendash 2017. ICAR\_assim was developed by means of an ensemble-based data assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow-covered area (fSCA) through an energy and mass snow balance model, the Flexible Snow Model (FSM2), using the particle batch smoother (PBS). The meteorological forcing data were obtained by a regional atmospheric simulation from the Intermediate Complexity Atmospheric Research model (ICAR) nested inside a coarser regional simulation from the Weather Research and Forecasting model (WRF). The boundary and initial conditions of WRF were provided by the ERA5 atmospheric reanalysis. ICAR\_assim showed very good agreement with MODIS gap-filled snow products, with a spatial correlation of \emph{R}=0.98 in the snow probability (\emph{P}(snow)) and a temporal correlation of \emph{R}=0.88 on the day of peak snow water equivalent (SWE). Similarly, ICAR\_assim has shown a correlation with the seasonal mean SWE of \emph{R}=0.75 compared with in situ observations from automatic weather stations (AWSs). The results highlight the high temporal variability in the snowpack in the Lebanese mountain ranges, with the differences between Mount Lebanon and the Anti-Lebanon Mountains that cannot only be explained by hypsography as the Anti-Lebanon Mountains are in the rain shadow of Mount Lebanon. The maximum fresh water stored in the snowpack is in the middle elevations, approximately between 2200 and 2500 m a.s.l. (above sea level). Thus, the resilience to further warming is low for the snow water resources of Lebanon due to the proximity of the snowpack to the zero isotherm.{$<$}/p{$>$}},
  langid = {english},
  keywords = {assimilation,downscaling},
  file = {/home/simonfi/Zotero/storage/PABD2T2P/Alonso-González et al. - 2021 - Snowpack dynamics in the Lebanese mountains from q.pdf;/home/simonfi/Zotero/storage/PSZQYKXH/2021.html}
}

@article{fiddesHyperresolutionEnsemblebasedSnow2019,
  title = {Hyper-Resolution Ensemble-Based Snow Reanalysis in Mountain Regions Using Clustering},
  author = {Fiddes, Joel and Aalstad, Kristoffer and Westermann, Sebastian},
  year = {2019},
  month = nov,
  journal = {Hydrology and Earth System Sciences},
  volume = {23},
  number = {11},
  pages = {4717--4736},
  publisher = {{Copernicus GmbH}},
  issn = {1027-5606},
  doi = {10.5194/hess-23-4717-2019},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1\textendash 2 orders of magnitude of computational cost due to ensemble simulations, we save 4\textendash 5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.{$<$}/p{$>$}},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/E5XGCNFU/Fiddes et al. - 2019 - Hyper-resolution ensemble-based snow reanalysis in.pdf;/home/simonfi/Zotero/storage/ZYG6H4AI/2019.html}
}

@article{fiddesHyperresolutionEnsemblebasedSnow2019a,
  title = {Hyper-Resolution Ensemble-Based Snow Reanalysis in Mountain Regions Using Clustering},
  author = {Fiddes, Joel and Aalstad, Kristoffer and Westermann, Sebastian},
  year = {2019},
  month = nov,
  journal = {Hydrology and Earth System Sciences},
  volume = {23},
  number = {11},
  pages = {4717--4736},
  publisher = {{Copernicus GmbH}},
  issn = {1027-5606},
  doi = {10.5194/hess-23-4717-2019},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1\textendash 2 orders of magnitude of computational cost due to ensemble simulations, we save 4\textendash 5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.{$<$}/p{$>$}},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/ZC5FQZV2/Fiddes et al. - 2019 - Hyper-resolution ensemble-based snow reanalysis in.pdf;/home/simonfi/Zotero/storage/HBCKHCIB/2019.html}
}

@article{fiddesTopoSCALEDownscalingGridded2014,
  title = {{{TopoSCALE}} v.1.0: Downscaling Gridded Climate Data in Complex Terrain},
  shorttitle = {{{TopoSCALE}} v.1.0},
  author = {Fiddes, J. and Gruber, S.},
  year = {2014},
  month = feb,
  journal = {Geoscientific Model Development},
  volume = {7},
  number = {1},
  pages = {387--405},
  publisher = {{Copernicus GmbH}},
  issn = {1991-959X},
  doi = {10.5194/gmd-7-387-2014},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). {$<$}br{$><$}br{$>$} The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. {$<$}br{$><$}br{$>$} We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. {$<$}br{$><$}br{$>$} This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).{$<$}/p{$>$}},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/P68N4PGQ/Fiddes and Gruber - 2014 - TopoSCALE v.1.0 downscaling gridded climate data .pdf;/home/simonfi/Zotero/storage/TZEWX2IR/2014.html}
}

@article{fiddesTopoSUBToolEfficient2012,
  title = {{{TopoSUB}}: A Tool for Efficient Large Area Numerical Modelling in Complex Topography at Sub-Grid Scales},
  shorttitle = {{{TopoSUB}}},
  author = {Fiddes, J. and Gruber, S.},
  year = {2012},
  month = oct,
  journal = {Geoscientific Model Development},
  volume = {5},
  number = {5},
  pages = {1245--1257},
  publisher = {{Copernicus GmbH}},
  issn = {1991-959X},
  doi = {10.5194/gmd-5-1245-2012},
  abstract = {{$<$}p{$><$}strong class="journal-contentHeaderColor"{$>$}Abstract.{$<$}/strong{$>$} Mountain regions are highly sensitive to global climate change. However, large scale assessments of mountain environments remain problematic due to the high resolution required of model grids to capture strong lateral variability. To alleviate this, tools are required to bridge the scale gap between gridded climate datasets (climate models and re-analyses) and mountain topography. We address this problem with a sub-grid method. It relies on sampling the most important aspects of land surface heterogeneity through a lumped scheme, allowing for the application of numerical land surface models (LSMs) over large areas in mountain regions or other heterogeneous environments. This is achieved by including the effect of mountain topography on these processes at the sub-grid scale using a multidimensional informed sampling procedure together with a 1-D lumped model that can be driven by gridded climate datasets. This paper provides a description of this sub-grid scheme, TopoSUB, and assesses its performance against a distributed model. We demonstrate the ability of TopoSUB to approximate results simulated by a distributed numerical LSM at around 10\textsuperscript{4} less computations. These significant gains in computing resources allow for: (1) numerical modelling of processes at fine grid resolutions over large areas; (2) efficient statistical descriptions of sub-grid behaviour; (3) a "sub-grid aware" aggregation of simulated variables to coarse grids; and (4) freeing of resources for computationally intensive tasks, e.g., the treatment of uncertainty in the modelling process.{$<$}/p{$>$}},
  langid = {english},
  keywords = {climate,downscaling},
  file = {/home/simonfi/Zotero/storage/J7CJPR5M/Fiddes and Gruber - 2012 - TopoSUB a tool for efficient large area numerical.pdf;/home/simonfi/Zotero/storage/8J93ZKXG/2012.html}
}

@article{hersbachERA5GlobalReanalysis2020,
  title = {The {{ERA5}} Global Reanalysis},
  author = {Hersbach, Hans and Bell, Bill and Berrisford, Paul and Hirahara, Shoji and Hor{\'a}nyi, Andr{\'a}s and {Mu{\~n}oz-Sabater}, Joaqu{\'i}n and Nicolas, Julien and Peubey, Carole and Radu, Raluca and Schepers, Dinand and Simmons, Adrian and Soci, Cornel and Abdalla, Saleh and Abellan, Xavier and Balsamo, Gianpaolo and Bechtold, Peter and Biavati, Gionata and Bidlot, Jean and Bonavita, Massimo and De Chiara, Giovanna and Dahlgren, Per and Dee, Dick and Diamantakis, Michail and Dragani, Rossana and Flemming, Johannes and Forbes, Richard and Fuentes, Manuel and Geer, Alan and Haimberger, Leo and Healy, Sean and Hogan, Robin J. and H{\'o}lm, El{\'i}as and Janiskov{\'a}, Marta and Keeley, Sarah and Laloyaux, Patrick and Lopez, Philippe and Lupu, Cristina and Radnoti, Gabor and {de Rosnay}, Patricia and Rozum, Iryna and Vamborg, Freja and Villaume, Sebastien and Th{\'e}paut, Jean-No{\"e}l},
  year = {2020},
  journal = {Quarterly Journal of the Royal Meteorological Society},
  volume = {146},
  number = {730},
  pages = {1999--2049},
  issn = {1477-870X},
  doi = {10.1002/qj.3803},
  abstract = {Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA-Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3-hourly at half the horizontal resolution). This paper describes the general set-up of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re-forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA-Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global-mean correlation with monthly-mean GPCP data is increased from 67\% to 77\%. In general, low-frequency variability is found to be well represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA-Interim, MERRA-2 and JRA-55 reanalyses.},
  langid = {english},
  keywords = {climate reanalysis,Copernicus Climate Change Service,data assimilation,ERA5,historical observations},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3803},
  file = {/home/simonfi/Zotero/storage/2WEZ3DQP/Hersbach et al. - 2020 - The ERA5 global reanalysis.pdf;/home/simonfi/Zotero/storage/GNYEJ8DK/qj.html}
}

@article{jenningsSpatialVariationRain2018,
  title = {Spatial Variation of the Rain\textendash Snow Temperature Threshold across the {{Northern Hemisphere}}},
  author = {Jennings, Keith S. and Winchell, Taylor S. and Livneh, Ben and Molotch, Noah P.},
  year = {2018},
  month = mar,
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {1148},
  publisher = {{Nature Publishing Group}},
  issn = {2041-1723},
  doi = {10.1038/s41467-018-03629-7},
  abstract = {Despite the importance of precipitation phase to global hydroclimate simulations, many land surface models use spatially uniform air temperature thresholds to partition rain and snow. Here we show, through the analysis of a 29-year observational dataset (n\,=\,17.8\,million), that the air temperature at which rain and snow fall in equal frequency varies significantly across the Northern Hemisphere, averaging 1.0\,\textdegree C and ranging from \textendash 0.4 to 2.4\,\textdegree C for 95\% of the stations. Continental climates generally exhibit the warmest rain\textendash snow thresholds and maritime the coolest. Simulations show precipitation phase methods incorporating humidity perform better than air temperature-only methods, particularly at relative humidity values below saturation and air temperatures between 0.6 and 3.4\,\textdegree C. We also present the first continuous Northern Hemisphere map of rain\textendash snow thresholds, underlining the spatial variability of precipitation phase partitioning. These results suggest precipitation phase could be better predicted using humidity and air temperature in large-scale land surface model runs.},
  copyright = {2018 The Author(s)},
  langid = {english},
  keywords = {Climate change,Hydrology,Water resources},
  file = {/home/simonfi/Zotero/storage/3PGH3GEU/Jennings et al. - 2018 - Spatial variation of the rain–snow temperature thr.pdf;/home/simonfi/Zotero/storage/9QPSP5HJ/s41467-018-03629-7.html}
}

@misc{PerformanceAirTemperature,
  title = {Performance of Air Temperature from {{ERA5-Land}} Reanalysis in Coastal Urban Agglomeration of {{Southeast China}} | {{Elsevier Enhanced Reader}}},
  doi = {10.1016/j.scitotenv.2022.154459},
  howpublished = {https://reader.elsevier.com/reader/sd/pii/S0048969722015522?token=3C96980E8D7C230CC7CFBCD8F96D594CF7F83118DA117E13489D6DEBB6D3C5559B9B11F8FB30276B6C6C1584422C8345\&originRegion=eu-west-1\&originCreation=20220316183343},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/ES65C6Z8/S0048969722015522.html}
}

@article{sheridanComparisonStationObservations2020,
  title = {A {{Comparison Between Station Observations}} and {{Reanalysis Data}} in the {{Identification}} of {{Extreme Temperature Events}}},
  author = {Sheridan, Scott C. and Lee, Cameron C. and Smith, Erik T.},
  year = {2020},
  journal = {Geophysical Research Letters},
  volume = {47},
  number = {15},
  pages = {e2020GL088120},
  issn = {1944-8007},
  doi = {10.1029/2020GL088120},
  abstract = {While many studies comparing atmospheric reanalysis and surface observations have focused on the similarity of mean fields, trends, or frequencies of extreme events, very few have assessed how similar surface observations and reanalysis data sets are in terms of their specific identification of extreme temperature event days. Here, we assess the similarity between surface observations and three reanalysis products: ERA5, ERA5-LAND, and NARR, in terms of the days on which they identify extreme heat and cold events for the period 1979\textendash 2016 at 230 locations in the United States and Canada. Cold events have a greater match than heat events. ERA5 has the greatest match percentage with station data across the study region. Match percentage is greatest in midlatitude, continental locations, with poorer performance in coastal areas, and the Arctic.},
  langid = {english},
  keywords = {climate,cold waves,extreme temperature events,heat waves,North America,reanalysis},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020GL088120},
  file = {/home/simonfi/Zotero/storage/MQPTSAED/Sheridan et al. - 2020 - A Comparison Between Station Observations and Rean.pdf;/home/simonfi/Zotero/storage/5UA5BGLR/2020GL088120.html}
}

@article{siirila-woodburnLowtonoSnowFuture2021,
  title = {A Low-to-No Snow Future and Its Impacts on Water Resources in the Western {{United States}}},
  author = {{Siirila-Woodburn}, Erica R. and Rhoades, Alan M. and Hatchett, Benjamin J. and Huning, Laurie S. and Szinai, Julia and Tague, Christina and Nico, Peter S. and Feldman, Daniel R. and Jones, Andrew D. and Collins, William D. and Kaatz, Laurna},
  year = {2021},
  month = nov,
  journal = {Nature Reviews Earth \& Environment},
  volume = {2},
  number = {11},
  pages = {800--819},
  issn = {2662-138X},
  doi = {10.1038/s43017-021-00219-y},
  abstract = {Anthropogenic climate change is decreasing seasonal snowpacks globally, with potentially catastrophic consequences on water resources, given the long-h eld reliance on snowpack in water management. In this Review, we examine the changes and trickle-d own impacts of snow loss in the western United States (WUS). Across the WUS, snow water equivalent declines of \textasciitilde 25\% are expected by 2050, with losses comparable with contemporary historical trends. There is less consensus on the time horizon of snow disappearance, but model projections combined with a new low-to-no snow definition suggest \textasciitilde 35\textendash 60 years before low-t o-no snow becomes persistent if greenhouse gas emissions continue unabated. Diminished and more ephemeral snowpacks that melt earlier will alter groundwater and streamflow dynamics. The direction of these changes are difficult to constrain given competing factors such as higher evapotranspiration, altered vegetation co\- m\-pos\- i\-tion and changes in wildfire behaviour in a warmer world. These changes undermine conventional WUS water management practices, but through proactive implementation of soft and hard adaptation strategies, there is potential to build resilience to extreme, episodic and, eventually, persistent low-t o-no snow conditions. Federal investments offer a timely opportunity to address these vulnerabilities, but they require a concerted portfolio of activities that cross historically siloed physical and disciplinary boundaries.},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/5D5GU8CJ/Siirila-Woodburn et al. - 2021 - A low-to-no snow future and its impacts on water r.pdf}
}

@article{smithLinearTheoryOrographic2004,
  title = {A {{Linear Theory}} of {{Orographic Precipitation}}},
  author = {Smith, Ronald B. and Barstad, Idar},
  year = {2004},
  month = jun,
  journal = {Journal of the Atmospheric Sciences},
  volume = {61},
  number = {12},
  pages = {1377--1391},
  publisher = {{American Meteorological Society}},
  issn = {0022-4928, 1520-0469},
  doi = {10.1175/1520-0469(2004)061<1377:ALTOOP>2.0.CO;2},
  abstract = {Abstract A linear theory of orographic precipitation is developed, including airflow dynamics, condensed water advection, and downslope evaporation. The formulation extends the widely used ``upslope'' model. Vertically integrated steady-state governing equations for condensed water are solved using Fourier transform techniques. Closed form expressions are derived for special cases. For more general cases, the precipitation field is computed quickly by multiplying the terrain transform by a wavenumber-dependent transfer function. Five length scales are included in the model: mountain width, a buoyancy wave scale, the moist layer depth, and two condensed water advection distances. The efficiency of precipitation in the model is sensitive to the decay of the forced ascent through the moist layer and to the advection of condensed water downwind into the region of descent. The strong influence of horizontal scale on precipitation pattern and amount predicted by the model is discussed. The model is illustrated by applying it to the Olympic Mountains in Washington State.},
  chapter = {Journal of the Atmospheric Sciences},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/KLSYY53L/Smith and Barstad - 2004 - A Linear Theory of Orographic Precipitation.pdf;/home/simonfi/Zotero/storage/LFEHU3EH/1520-0469_2004_061_1377_altoop_2.0.co_2.html}
}

@article{sturmRevisitingGlobalSeasonal2021,
  title = {Revisiting the {{Global Seasonal Snow Classification}}: {{An Updated Dataset}} for {{Earth System Applications}}},
  shorttitle = {Revisiting the {{Global Seasonal Snow Classification}}},
  author = {Sturm, Matthew and Liston, Glen E.},
  year = {2021},
  month = sep,
  journal = {Journal of Hydrometeorology},
  issn = {1525-755X, 1525-7541},
  doi = {10.1175/JHM-D-21-0070.1},
  abstract = {Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land cover. When introduced in 1995, the finest-resolution global datasets of these variables were on a 0.58 3 0.58 latitude\textendash longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arc-s 3 10-arc-s latitude\textendash longitude grid (approximately 300 m). We downscaled 0.18 3 0.18 latitude\textendash longitude (approximately 10 km) gridded meteorological climatologies [1981\textendash 2019, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 5th Generation Land (ERA5-Land)] using MicroMet, a spatially distributed, high-resolution, micrometeorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth's terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification, Version 1) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).},
  langid = {english},
  file = {/home/simonfi/Zotero/storage/LPJI2R9L/Sturm and Liston - 2021 - Revisiting the Global Seasonal Snow Classification.pdf}
}

