Reconstructing solid precipitation from snow depth measurements and a land surface model

Abstract A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catchment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. These results are similar to gauge biases estimated by a water budget approach. Reconstructed snowfall is consistently higher than snowfall from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) but does not have a consistent relationship with snowfall derived from the WMO Solid Precipitation Intercomparison Project correction algorithms. Advantages of the PASR approach include that 1) the assimilation of snow depth observations captures blowing snow where it is deposited and 2) the modeling approach takes into account physical snowpack evolution. These advantages suggest that the PASR product could be a valuable alternative to statistical gauge corrections and that arctic ground-based solid precipitation observing networks might emphasize snow depth measurements over gauges.

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Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic Games

Journal of Hydrometeorology ◽

10.1175/2011jhm1250.1 ◽

2011 ◽

Vol 12 (4) ◽

pp. 508-530 ◽

Cited By ~ 26

Author(s):

Natacha B. Bernier ◽

Stéphane Bélair ◽

Bernard Bilodeau ◽

Linying Tong

Keyword(s):

High Resolution ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Near Surface ◽

Elevation Changes ◽

Forecast Models ◽

Operational Models ◽

Paralympic Games

Abstract A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

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Impacts of snow and organic soils parameterization on North-Eurasian soil temperature profiles simulated by the ISBA land surface model

The Cryosphere Discussions ◽

10.5194/tcd-9-6733-2015 ◽

2015 ◽

Vol 9 (6) ◽

pp. 6733-6790

Author(s):

B. Decharme ◽

E. Brun ◽

A. Boone ◽

C. Delire ◽

P. Le Moigne ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Temperature ◽

Soil Properties ◽

Temperature Profile ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Temperature Profiles

Abstract. In this study we analysed how an improved representation of snowpack processes and soil properties in the multi-layer snow and soil schemes of the ISBA land surface model impacts the simulation of soil temperature profiles over North-Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over Northern-Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

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Optimization of snow density parameter of Noah Land Surface Model using micro-genetic algorithm for estimating snow depth

10.5194/egusphere-egu21-7848 ◽

2021 ◽

Author(s):

Ebony Lee ◽

Seon Ki Park

Keyword(s):

Genetic Algorithm ◽

Land Surface ◽

Snow Depth ◽

Fitness Function ◽

Land Surface Model ◽

Surface Model ◽

Density Parameter ◽

Snow Density ◽

Noah Land Surface Model ◽

Micro Genetic Algorithm

<p>The Noah Land Surface Model (Noah LSM) estimates snow depth using snow water equivalent and snow density. The snow density is determined by snow compaction, snowmelt water storing, and density of fresh snowfall. The Noah LSM usually underestimates snow depth compared to the ground observations in Korea, which occurs from the beginning of snowfall. We performed an optimal estimation of parameters related to the density of fresh snowfall, using micro-genetic algorithm (&#956;-GA) that uses the evolution process concept through natural selection and mutation mechanism. Ground observations from 36 sites of the Korea Meteorological Administration, for the recent 10 years (May 2009 &#8211; April 2019), are used for offline forcing of the Noah LSM and evaluating the fitness function in &#956;-GA. Optimized parameters reduced the density of fresh snowfall, and improved the simulated snow depth. The root-mean-square error of snow depth decreased from 8.1 cm to 7.1 cm.</p>

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ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3515-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3515-3532 ◽

Cited By ~ 74

Author(s):

Clement Albergel ◽

Emanuel Dutra ◽

Simon Munier ◽

Jean-Christophe Calvet ◽

Joaquin Munoz-Sabater ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Model Simulations ◽

In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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Impact of Surface Albedo Assimilation on Snow Estimation

Remote Sensing ◽

10.3390/rs12040645 ◽

2020 ◽

Vol 12 (4) ◽

pp. 645 ◽

Cited By ~ 1

Author(s):

Sujay Kumar ◽

David Mocko ◽

Carrie Vuyovich ◽

Christa Peters-Lidard

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Surface Albedo ◽

Land Surface Model ◽

Snow Accumulation ◽

High Plains ◽

Surface Model ◽

Fractional Snow Cover ◽

The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

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Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-012.1 ◽

2013 ◽

Vol 14 (1) ◽

pp. 203-219 ◽

Cited By ~ 79

Author(s):

Eric Brun ◽

Vincent Vionnet ◽

Aaron Boone ◽

Bertrand Decharme ◽

Yannick Peings ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Snow Water Equivalent ◽

Land Surface Model ◽

Northern Eurasia ◽

Surface Model ◽

Monthly Precipitation ◽

Blowing Snow ◽

Snow Water

Abstract The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

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Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0249.1 ◽

2021 ◽

Author(s):

Gonzalo Leonardini ◽

François Anctil ◽

Vincent Vionnet ◽

Maria Abrahamowicz ◽

Daniel F. Nadeau ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Snow Water Equivalent ◽

Weather Prediction ◽

Land Surface Model ◽

Late Winter ◽

Surface Model ◽

Lower Performance ◽

Snow Water

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

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On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model

Hydrology ◽

10.3390/hydrology7020020 ◽

2020 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

Michael Weber ◽

Moritz Feigl ◽

Karsten Schulz ◽

Matthias Bernhardt

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Water Cycle ◽

Land Surface Model ◽

Surface Model ◽

Discretization Scheme ◽

Dominant Component ◽

Alpine Research ◽

Alpine Catchments

To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, where the variability of topography and meteorology over short distances strongly influences the distribution of snow cover, the dominant component in the alpine water cycle. For the high alpine Research Catchment Zugspitze (RCZ) a new method for objective delineation of hydrological response units (HRUs) using a time series of high resolution LIDAR derived snow depth maps and the physiographic properties of the RCZ is introduced. Via principle component analysis (PCA) of these maps, a dominant snow depth pattern, that turned out to be largely defined during the (winter) accumulation period was identified. This dominant pattern serves as a reference for HRU delineations on the basis of cluster analyses of the catchment’s physiographic properties. The method guarantees for an appropriate, objective, spatial discretization scheme, which allows for a reliable and meaningful reproduction of snow cover variability with the Cold Regions Hydrological Model — at the same time avoiding significant increase of computational demands. Different HRU schemes were evaluated with measured snow depth and the comparison of their model results identified significant differences in model output and best performance of the scheme which best represents measured snow depth distribution.

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