A New Snow Density Parameterization for Land Data Initialization

Abstract Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.

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What is the Trade-Off between Snowpack Stratification and Simulated Snow Water Equivalent in a Physically-Based Snow Model?

Water ◽

10.3390/w12123449 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3449

Author(s):

Julien Augas ◽

Kian Abbasnezhadi ◽

Alain N. Rousseau ◽

Michel Baraer

Keyword(s):

Snow Water Equivalent ◽

Melting Process ◽

Snow Density ◽

Major Step ◽

Trade Off ◽

Radiation Coefficient ◽

Modeling Accuracy ◽

Physically Based ◽

Snow Water ◽

Snow Model

In Nordic watersheds, estimation of the dynamics of snow water equivalent (SWE) represents a major step toward a satisfactory modeling of the annual hydrograph. For a multilayer, physically-based snow model like MASiN (Modèle Autonome de Simulation de la Neige), the number of modeled snow layers can affect the accuracy of the simulated SWE. The objective of this study was to identify the maximum number of snow layers (MNSL) that would define the trade-off between snowpack stratification and SWE modeling accuracy. Results indicated that decreasing the MNSL reduced the SWE modeling accuracy since the thermal energy balance and the mass balance were less accurately resolved by the model. Nevertheless, from a performance standpoint, SWE modeling can be accurate enough with a MNSL of two (2), with a substantial performance drop for a MNSL value of around nine (9). Additionally, the linear correlation between the values of the calibrated parameters and the MNSL indicated that reducing the latter in MASiN increased the fresh snow density and the settlement coefficient, while the maximum radiation coefficient decreased. In this case, MASiN favored the melting process, and thus the homogenization of snow layers occurred from the top layers of the snowpack in the modeling algorithm.

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A 7-year dataset for driving and evaluating snow models at an arctic site (Sodankylä, Finland)

10.5194/gi-2015-35 ◽

2016 ◽

Cited By ~ 1

Author(s):

R. Essery ◽

A. Kontu ◽

J. Lemmetyinen ◽

M. Dumon ◽

C. B. Ménard

Keyword(s):

Wind Speed ◽

Atmospheric Pressure ◽

Snow Water Equivalent ◽

Longwave Radiation ◽

Snow Density ◽

Evaluation Data ◽

Physically Based ◽

Snow Water ◽

Snow Model ◽

Snow Temperature

Abstract. Datasets derived from measurements at Sodankylä in the Finnish Arctic that can be used for driving and evaluating snow models are presented. The driving datasets comprise all of the meteorological variables required as inputs for physically-based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically-based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees extends the duration of snow cover on the ground by several days a year.

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Seasonal prediction and predictability of Eurasian spring snow water equivalent in NCEP Climate Forecast System version 2 reforecasts

Climate Dynamics ◽

10.1007/s00382-017-3611-3 ◽

2017 ◽

Vol 50 (1-2) ◽

pp. 339-348 ◽

Cited By ~ 5

Author(s):

Qiong He ◽

Zhiyan Zuo ◽

Renhe Zhang ◽

Ruonan Zhang

Keyword(s):

Snow Water Equivalent ◽

Seasonal Prediction ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Snow Water ◽

Ncep Climate Forecast System

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Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽

10.1190/geo2015-0121.1 ◽

2016 ◽

Vol 81 (1) ◽

pp. WA183-WA193 ◽

Cited By ~ 9

Author(s):

W. Steven Holbrook ◽

Scott N. Miller ◽

Matthew A. Provart

Keyword(s):

Ground Penetrating Radar ◽

Snow Water Equivalent ◽

Ground Surface ◽

Snow Accumulation ◽

Accurate Estimation ◽

Snow Density ◽

Snow Stratigraphy ◽

Flood Estimation ◽

Snow Water ◽

Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

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Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

10.5194/egusphere-egu21-1325 ◽

2021 ◽

Author(s):

Colleen Mortimer ◽

Lawrence Mudryk ◽

Chris Derksen ◽

Kari Luojus ◽

Pinja Venalainen ◽

...

Keyword(s):

Spatial Resolution ◽

Snow Depth ◽

Snow Water Equivalent ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Snow Density ◽

Snow Season ◽

Snow Water ◽

Snow Mass

The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.Previous GlobSnow SWE products used a constant snow density of 0.24 kg m-3 to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m-3 early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.

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Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1593-2018 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1593-1614 ◽

Cited By ~ 21

Author(s):

Florian Hanzer ◽

Kristian Förster ◽

Johanna Nemec ◽

Ulrich Strasser

Keyword(s):

Climate Change ◽

21St Century ◽

Snow Water Equivalent ◽

Climate Change Impacts ◽

Future Climate Change ◽

Climate Projections ◽

Early 21St Century ◽

Physically Based ◽

Snow Water ◽

High Elevations

Abstract. A physically based hydroclimatological model (AMUNDSEN) is used to assess future climate change impacts on the cryosphere and hydrology of the Ötztal Alps (Austria) until 2100. The model is run in 100 m spatial and 3 h temporal resolution using in total 31 downscaled, bias-corrected, and temporally disaggregated EURO-CORDEX climate projections for the representative concentration pathways (RCPs) 2.6, 4.5, and 8.5 scenarios as forcing data, making this – to date – the most detailed study for this region in terms of process representation and range of considered climate projections. Changes in snow coverage, glacierization, and hydrological regimes are discussed both for a larger area encompassing the Ötztal Alps (1850 km2, 862–3770 m a.s.l.) as well as for seven catchments in the area with varying size (11–165 km2) and glacierization (24–77 %). Results show generally declining snow amounts with moderate decreases (0–20 % depending on the emission scenario) of mean annual snow water equivalent in high elevations (> 2500 m a.s.l.) until the end of the century. The largest decreases, amounting to up to 25–80 %, are projected to occur in elevations below 1500 m a.s.l. Glaciers in the region will continue to retreat strongly, leaving only 4–20 % of the initial (as of 2006) ice volume left by 2100. Total and summer (JJA) runoff will change little during the early 21st century (2011–2040) with simulated decreases (compared to 1997–2006) of up to 11 % (total) and 13 % (summer) depending on catchment and scenario, whereas runoff volumes decrease by up to 39 % (total) and 47 % (summer) towards the end of the century (2071–2100), accompanied by a shift in peak flows from July towards June.

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Physically Based Global Downscaling: Regional Evaluation

Journal of Climate ◽

10.1175/jcli3622.1 ◽

2006 ◽

Vol 19 (3) ◽

pp. 429-445 ◽

Cited By ~ 16

Author(s):

Steven J. Ghan ◽

Timothy Shippert ◽

Jared Fox

Keyword(s):

Upper Bound ◽

Snow Water Equivalent ◽

Grid Cell ◽

Surface Elevation ◽

Windward Side ◽

Late Winter ◽

Cold Bias ◽

Mountain Ranges ◽

Physically Based ◽

Snow Water

Abstract The climate simulated by a global atmosphere–land model with a physically based subgrid orography scheme is evaluated in 10 selected regions. Climate variables simulated for each of multiple elevation classes within each grid cell are mapped according to a high-resolution distribution of surface elevation in each region. Comparison of the simulated annual mean climate with gridded observations leads to the following conclusions. At low to moderate elevations the downscaling scheme correctly simulates increasing precipitation, decreasing temperature, and increasing snow with increasing elevation across distances smaller than 100 km. At high elevations the downscaling scheme correctly simulates decreasing precipitation with increasing elevation. The rain shadow of many mountain ranges is poorly resolved, with too little precipitation simulated on the windward side of mountain ranges and too much on the lee side. The simulated sensitivity of surface air temperature to surface elevation is too strong, particularly in valleys influenced by drainage circulations. Observations show little evidence of a “snow shadow,” so the neglect of the subgrid rain shadow does not produce an unrealistic simulation of the snow distribution. Summertime snow area, which is a proxy for land ice, is much larger than observed, mostly because of excessive snowfall but in some places because of a cold bias. Summertime snow water equivalent is far less than the observed thickness of glaciers because a 1-m upper bound on snow water is applied to the simulations and because snow transport by slides is neglected. The 1-m upper bound on snow water equivalent also causes an underestimate of seasonal snow water during late winter, compared with gridded station measurements. Potential solutions to these problems are discussed.

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Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Journal of Hydrometeorology ◽

10.1175/2008jhm981.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1416-1426 ◽

Cited By ~ 45

Author(s):

Naoki Mizukami ◽

Sanja Perica

Keyword(s):

United States ◽

Snow Depth ◽

Snow Water Equivalent ◽

Winter Season ◽

Western United States ◽

Densification Rate ◽

Snow Density ◽

Mountainous Regions ◽

Spatiotemporal Characteristics ◽

Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

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A Process-Based Energy Balance Snowmelt Model II: Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.3346 ◽

2013 ◽

Vol 726-731 ◽

pp. 3346-3352

Author(s):

Gao Jie

Keyword(s):

Energy Balance ◽

Snow Water Equivalent ◽

Sensible Heat Flux ◽

Colorado Front Range ◽

Wave Radiation ◽

Snow Pack ◽

Front Range ◽

Snow Density ◽

Column Model ◽

Snow Water

Based on the process-based energy balance snow melting model Snow Column Model, several scenarios are set to study the response of snow pack to climate change according to site-based data in snowpit 006, Niwot Ridge, Colorado, Front Range of Rocky Mountains. Based on an introduction and validation of Snow Column Model by data of 1996, a further validation is made on data during 1997 and 1999. Scenarios are set based on observations of solar radiation, long-wave radiation, air temperature, latent and sensible heat flux during 1996 and 1999. The responses of snow pack to an average temperature fluctuation within 6.2°C are analyzed. The results illustrate that snow density and snow water equivalent accelerated decreases while the variance in snow density does not increase monotonically over time.

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