scholarly journals A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

2015 ◽  
Vol 16 (4) ◽  
pp. 1752-1772 ◽  
Author(s):  
Steven A. Margulis ◽  
Manuela Girotto ◽  
Gonzalo Cortés ◽  
Michael Durand
Keyword(s):  
Data Assimilation ◽  
Sierra Nevada ◽  
Forest Cover ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Covered Area ◽  
Covered Area ◽  
Sensitivity Tests ◽  
American River ◽  
Snow Water

Abstract This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

scholarly journals A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015)

2016 ◽  
Vol 17 (4) ◽  
pp. 1203-1221 ◽  
Author(s):  
Steven A. Margulis ◽  
Gonzalo Cortés ◽  
Manuela Girotto ◽  
Michael Durand
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Reanalysis Dataset ◽  
In Situ Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Fully Bayesian ◽  
Mean Square Errors

Abstract A newly developed state-of-the-art snow water equivalent (SWE) reanalysis dataset over the Sierra Nevada (United States) based on the assimilation of remotely sensed fractional snow-covered area data over the Landsat 5–8 record (1985–2015) is presented. The method (fully Bayesian), resolution (daily and 90 m), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The reanalysis dataset was used to characterize the peak SWE climatology to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years. The pixel-wise peak SWE volume over the domain was found to be 20.0 km3 on average with a range of 4.0–40.6 km3. The ongoing drought in California contains the two lowest snowpack years (water years 2014 and 2015) and three of the four driest years over the examined record. It was found that the basin-average peak SWE, while underestimating the total water storage in snowpack over the year, accurately captures the interannual variability in stored snowpack water. However, the results showed that the assumption that 1 April SWE is representative of the peak SWE can lead to significant underestimation of basin-average peak SWE both on an average (21% across all basins) and on an interannual basis (up to 98% across all basin years).

scholarly journals Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Modeling the spatial distribution of snow water equivalent, taking into account changes in snow-covered area

2013 ◽  
Vol 54 (62) ◽  
pp. 305-313 ◽  
Author(s):  
T. Skaugen ◽  
F. Randen
Keyword(s):  
Covariance Matrix ◽  
Snow Water Equivalent ◽  
Current Model ◽  
Distribution Model ◽  
Spatial Moments ◽  
Positive Elements ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Rainfall Runoff Model

AbstractA good estimate of the spatial probability density function (PDF) of snow water equivalent (SWE) provides the mean of the snow reservoir, but also enables modelling of the changes in snow-covered area (SCA), which is crucial for the runoff dynamics in spring. The spatial PDF of accumulated SWE is here modelled as a sum of correlated gamma-distributed variables, called units. The spatial variance of accumulated SWE is evaluated by the covariance matrix of the units. For accumulation events, there are only positive elements in the covariance matrix, whereas for melting events there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. After accumulation and melting events, the changes in the spatial moments are weighted by changes in SCA. Results from the model are in good agreement with observed spatial moments of SWE and SCA and found to provide better estimates of the spatial variability than the current model for snow distribution used in the Norwegian version of the Swedish rainfall–runoff model HBV. The parameters in the distribution model are estimated from observed historical precipitation, so no calibration parameters are introduced.

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 A Comparison of National Water Model Retrospective Analysis Snow Outputs at SNOTEL Sites Across the Western U.S.

2021 ◽  
Author(s):  
Irene Garousi-Nejad ◽  
David Tarboton
Keyword(s):  
Snow Water Equivalent ◽  
Water Model ◽  
Central Basin ◽  
Model Errors ◽  
Considerable Variability ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
The U.S ◽  
National Water

This study compares the U.S. National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at SNOTEL sites across the Western U.S. This was done to evaluate and identify opportunities for improving the modeling of snow in the NWM. SWE was obtained from SNOTEL sites, while SCAF was obtained from MODIS observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modeled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under-predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under-modeling of SWE. There was also under-modeling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modeled and observed SCAF that hampered useful interpretation of these comparisons. This is in part due to the model grid SCAF essentially being binary (snow or no snow) while observations from MODIS are much more fractional. However, when SCAF was aggregated across all sites and years, modeled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.

scholarly journals Ensemble-based assimilation of fractional snow covered area satellite retrievals to estimate snow distribution at a high Arctic site

2017 ◽  
Author(s):  
Kristoffer Aalstad ◽  
Sebastian Westermann ◽  
Thomas Vikhamar Schuler ◽  
Julia Boike ◽  
Laurent Bertino
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Coefficient Of Variation ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
High Arctic ◽  
Sensing Applications ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Model

Abstract. Snow, with high albedo, low thermal conductivity and large water holding capacity strongly modulates the surface energy and water balance, thus making it a critical factor in high-latitude and mountain environments. At the same time, already at medium spatial resolutions of 1 km, estimating the average and subgrid variability of the snow water equivalent (SWE) is challenging in remote sensing applications. In this study, we demonstrate an ensemble-based data assimilation scheme to estimate peak SWE distributions at such scales from a simple snow model driven by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (that is accessible from satellite products) to pre-melt SWE, while at the same time obtaining the subgrid scale distribution. Subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fractional snow covered area (fSCA) by means of the snow model. Assimilation of satellite-derived fSCA hence facilitates the constrained estimation of the average SWE and coefficient of variation, while taking into account uncertainties in both the model and assimilated data sets. Our method makes use of the ensemble-smoother with multiple data assimilation (ES-MDA) combined with analytical Gaussian anamorphosis to assimilate time series of MODIS and Sentinel-2 fSCA retrievals. The scheme is applied to high-Arctic sites near Ny Ålesund (79° N, Svalbard, Norway) where in-situ observations of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak subgrid SWE distributions on most of the occasions considered. Through the ES-MDA assimilation, the root mean squared error (RMSE) for the fSCA, peak mean SWE and subgrid coefficient of variation is improved by around 75 %, 60 % and 20 % respectively when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13 respectively. By comparing the performance of the ES-MDA to that of other ensemble-based batch smoother schemes, it was found that the ES-MDA either outperforms or at least nearly matches the performance of the other schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

scholarly journals A seasonal algorithm of the snow-covered area fraction for mountainous terrain

2021 ◽  
Author(s):  
Nora Helbig ◽  
Michael Schirmer ◽  
Jan Magnusson ◽  
Flavia Mäder ◽  
Alec van Herwijnen ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
Ground Surface ◽  
Grid Cell ◽  
Mountainous Terrain ◽  
Fine Scale ◽  
Snow Covered Area ◽  
Covered Area ◽  
Mean Square Errors

Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is therefore an essential model parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation-ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires computing subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. For a spatiotemporal evaluation of modelled fSCA we compiled three independent fSCA data sets. Evaluating modelled 1 km fSCA seasonally with fSCA derived from airborne-acquired fine-scale HS data, satellite- as well as terrestrial camera-derived fSCA showed overall normalized root mean square errors of respectively 9 %, 20 % and 22 %, and represented seasonal trends well. The overall good model performance suggests that the seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.

Sign in / Sign up

Export Citation Format

Share Document