Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010

2015 ◽  
Vol 28 (20) ◽  
pp. 8037-8051 ◽  
Author(s):  
L. R. Mudryk ◽  
C. Derksen ◽  
P. J. Kushner ◽  
R. Brown
Keyword(s):  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Temporal Consistency ◽  
Meteorological Forcing ◽  
Temporal Correlations ◽  
Snow Water

Abstract Five, daily, gridded, Northern Hemisphere snow water equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical snow models, and reanalyses. While the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with one another and show better correlations with the satellite product [the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow)] than do those using older reanalyses. Finally, a comparison of eight reanalysis datasets over the 2001–10 period shows that land surface model differences control the majority of spread in the climatological value of SWM, while meteorological forcing differences control the majority of the spread in temporal correlations of SWM anomalies.

scholarly journals Evaluation of long-term Northern Hemisphere snow water equivalent products

2020 ◽  
Vol 14 (5) ◽  
pp. 1579-1594 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Ross Brown ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Temporal Correlations ◽  
Surface Snow ◽  
Snow Water

Abstract. Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

scholarly journals Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

2009 ◽  
Vol 10 (1) ◽  
pp. 130-148 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Energy Fluxes ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

scholarly journals Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

2017 ◽  
Vol 18 (9) ◽  
pp. 2425-2452 ◽  
Author(s):  
Rachel R. McCrary ◽  
Seth McGinnis ◽  
Linda O. Mearns
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Dominant Role ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Observational Uncertainty ◽  
Snow Water ◽  
Noah Land Surface Model

Abstract This study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.

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

2013 ◽  
Vol 14 (1) ◽  
pp. 203-219 ◽  
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.

scholarly journals Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation

2014 ◽  
Vol 27 (9) ◽  
pp. 3318-3330 ◽  
Author(s):  
T. Nitta ◽  
K. Yoshimura ◽  
K. Takata ◽  
R. O’ishi ◽  
T. Sueyoshi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Model ◽  
Snow Cover Fraction ◽  
Snow Covered Area ◽  
Covered Area ◽  
Global Land

Abstract Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly for the accumulation season and/or regions with relatively small amounts of snowfall; snow cover fraction was typically underestimated in the simulation without SSNOWD. In the Northern Hemisphere, the daily snow-covered area was validated using Interactive Multisensor Snow and Ice Mapping System (IMS) snow analysis datasets. In the simulation with SSNOWD, snow-covered area largely agreed with the IMS snow analysis and the seasonal cycle in the Northern Hemisphere was improved. This was because SSNOWD formulates the snow cover fraction differently for the accumulation season and ablation season, and represents the hysteresis of the snow cover fraction between different seasons. The effects of including SSNOWD on hydrological properties and snow mass were also examined.

scholarly journals Evaluation of long term Northern Hemisphere snow water equivalent products

2019 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Ross Brown ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Temporal Correlations ◽  
Surface Snow ◽  
Snow Water ◽  
Surface Observations

Abstract. Seven gridded northern hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Inter-comparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS; the European Centre for Medium-Range Forecasts interim land surface reanalysis – ERA-land; the NASA Modern-Era Retrospective Analysis for Research and Applications – MERRA; the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) standalone passive microwave retrievals (NASA AMSR-E historical and operational algorithms) which do not utilize surface snow observations. Evaluation included comparisons against independent surface observations from Russia, Finland, and Canada, and calculation of spatial and temporal correlations in SWE anomalies. The standalone passive microwave SWE products (AMSR-E historical and operational SWE algorithms) exhibit low spatial and temporal correlations to other products, and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides comparable performance to the reanalysis-based products; RMSEs over Finland and Russia for all but the AMSR-E products is ~50 mm or less. Using a four-dataset ensemble that excluded the standalone passive microwave products reduced the RMSE by 10 mm (20%) and increased the correlation by 0.1; ensembles that contain Crocus and/or MERRA perform better than those that do not. The observed RMSE of the best performing datasets is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

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

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.

scholarly journals Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

2010 ◽  
Vol 11 (2) ◽  
pp. 352-369 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Paul R. Houser ◽  
Kristi R. Arsenault ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
A Priori ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Coarse Scale ◽  
Spatial Error ◽  
Snow Water

Abstract Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

scholarly journals Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent

2006 ◽  
Vol 7 (3) ◽  
pp. 443-457 ◽  
Author(s):  
Michael Durand ◽  
Steven A. Margulis
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Model ◽  
True Value ◽  
Snow Water ◽  
Snowpack Properties ◽  
Radiometric Data

Abstract A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a snow depth of around 3 m and the presence of liquid water in the snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the snowpack properties and the observations, and exploring the implications of these relationships for the inversion of remote sensing measurements for estimating snowpack properties. The contributions of each channel to recovering the true SWE are computed, and it was found that the low-frequency 10.67-GHz AMSR-E channels contain information even for very deep snow. The effect of vegetation thickness on assimilation results is explored. Results from the assimilation are compared to those from a pure modeling approach and from a remote sensing inversion approach, and the effects of measurement error and ensemble size are investigated.

scholarly journals Biases in Reanalysis Snowfall Found by Comparing the JULES Land Surface Model to GlobSnow

2014 ◽  
Vol 27 (2) ◽  
pp. 624-632 ◽  
Author(s):  
Steven Hancock ◽  
Brian Huntley ◽  
Richard Ellis ◽  
Robert Baxter
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Gauge Data

Abstract Snow exerts a strong influence on weather and climate. Accurate representation of snow processes within models is needed to ensure accurate predictions. Snow processes are known to be a weakness of land surface models (LSMs), and studies suggest that more complex snow physics is needed to avoid early melt. In this study the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow) snow water equivalent and NASA’s “MOD10C1” snow cover products are used to assess the accuracy of snow processes within the Joint U.K. Land Environment Simulator (JULES). JULES is run “offline” from a general circulation model and so is driven by meteorological reanalysis datasets: “Princeton,” Water and Global Change–Global Precipitation Climatology Centre (WATCH–GPCC), and WATCH–Climatic Research Unit (CRU). This reveals that when the model achieves the correct peak accumulation, snow does not melt early. However, generally snow does melt early because peak accumulation is too low. Examination of the meteorological reanalysis data shows that not enough snow falls to achieve observed peak accumulations. Thus, the earlier studies’ conclusions may be as a result of weaknesses in the driving data, rather than in model snow processes. These reanalysis products “bias correct” precipitation using observed gauge data with an undercatch correction, overriding the benefit of any other datasets used in their creation. This paper argues that using gauge data to bias-correct reanalysis data is not appropriate for snow-affected regions during winter and can lead to confusion when evaluating model processes.

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