scholarly journals An Evaluation of Snow Initializations in NCEP Global and Regional Forecasting Models

2016 ◽  
Vol 17 (6) ◽  
pp. 1885-1901 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng ◽  
Michael Leuthold ◽  
Michael Barlage ◽  
...  
Keyword(s):  
United States ◽  
Air Force ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
The United States ◽  
Regional Models ◽  
Forecasting Models ◽  
Snow Water ◽  
Operational Models

Abstract Snow plays a major role in land–atmosphere interactions, but strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ measurements. First, a new method is developed to upscale point measurements into gridded datasets that is superior to other tested methods. It is then utilized to generate daily SD and SWE datasets for water years 2012–14 using measurements from two networks (COOP and SNOTEL) in the United States. These datasets are used to evaluate daily SD and SWE initializations in NCEP global forecasting models (GFS and CFSv2, both on 0.5° × 0.5° grids) and regional models (NAM on 12 km × 12 km grids and RAP on 13 km × 13 km grids) across eight 2° × 2° boxes. Initialized SD from three models (GFS, CFSv2, and NAM) that utilize Air Force Weather Agency (AFWA) SD data for initialization is 77% below the area-averaged values, on average. RAP initializations, which cycle snow instead of using the AFWA SD, underestimate SD to a lesser degree. Compared with SD errors, SWE errors from GFS, CFSv2, and NAM are larger because of the application of unrealistically low and globally constant snow densities. Furthermore, the widely used daily gridded SD data produced by the Canadian Meteorological Centre (CMC) are also found to underestimate SD (similar to GFS, CFSv2, and NAM), but are worse than RAP. These results suggest an urgent need to improve SD and SWE initializations in these operational models.

scholarly journals Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

2010 ◽  
Vol 11 (6) ◽  
pp. 1380-1394 ◽  
Author(s):  
Matthew Sturm ◽  
Brian Taras ◽  
Glen E. Liston ◽  
Chris Derksen ◽  
Tobias Jonas ◽  
...  
Keyword(s):  
United States ◽  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Estimation Procedure ◽  
The United States ◽  
Practical Applications ◽  
Continental Scale ◽  
Depth Data ◽  
Snow Water

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

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

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

<p>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.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> 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.</p><p>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<sup>-3</sup> 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.</p>

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

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
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.

scholarly journals Climate sensitivity of snow water equivalent and snowmelt runoff in a Himalayan catchment

2016 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F.P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model ◽  
Increasing Temperature

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However this provides no information on the actual amount of water stored in a snowpack i.e. the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ meteorological observations and a modified version of the seNorge snow model to estimate climate sensitivity of SWE and snowmelt runoff in the Langtang catchment in Nepal. Landsat 8 and MOD10A2 snow cover maps were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 % and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an Ensemble Kalman filter. The approach of modelling snow depth in a Kalman filter framework allows for data-constrained estimation of SWE rather than snow cover alone and this has great potential for future studies in the Himalayas. Climate sensitivity tests with the optimized snow model show a strong decrease in SWE in the valley with increasing temperature. However, at high elevation a decrease in SWE is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature. Finally the climate sensitivity study revealed that snowmelt runoff increases in winter and early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature.

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

scholarly journals CONVERSE TRENDS OF THE TERRESTRIAL AND GROUND WATER STORAGE CHANGES IN CANADA AND THE UNITED STATES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1793-1796
Author(s):  
H. Wang ◽  
L. Xiang ◽  
H. Steffen ◽  
P. Wu ◽  
L. Jiang ◽  
...  
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
High Water ◽  
The United States ◽  
Water Usage ◽  
Grace Data ◽  
Terrestrial Water ◽  
Snow Water ◽  
Satellite Altimetry Data

<p><strong>Abstract.</strong> We study the terrestrial water storage (TWS) and groundwater storage (GWS) changes in Canada and United States. We employ the separation approach from Wang et al. (2013) together with the improved GRACE data of Release 6 for a longer time span until December, 2016. The TWS signals from lake levels are derived from satellite altimetry data over the lakes while TWS signals due to soil moisture (SM) and snow water equivalent (SWE) changes from hydrology models. There are four significant trend anomalies in North America for both TWS and GWS changes. Two positive anomalies are found in Canada with their centers in the provinces of Saskatchewan and Quebec, respectively, due to increased precipitation and/or increased runoff in their surroundings. Two negative anomalies are shown in the United States with their centers in California and the northwest of Texas, respectively, which are due to decreased precipitation and, especially for California, high water usage for agriculture.</p>

scholarly journals Estimation of Daily Spatial Snow Water Equivalent from Historical Snow Maps and Limited In-Situ Measurements

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Sami A. Malek ◽  
Roger C. Bales ◽  
Steven D. Glaser
Keyword(s):  
Snow Depth ◽  
Spatial Interpolation ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Optimal Interpolation ◽  
Novel Approach ◽  
Tuolumne River ◽  
Snow Water ◽  
Iot Devices

We present a scheme aimed at estimating daily spatial snow water equivalent (SWE) maps in real time and at high spatial resolution from scarce in-situ SWE measurements from Internet of Things (IoT) devices at actual sensor locations and historical SWE maps. The method consists of finding a background SWE field, followed by an update step using ensemble optimal interpolation to estimate the residuals. This novel approach allowed for areas with parsimonious sensors to have accurate estimates of spatial SWE without explicitly discovering and specifying the spatial-interpolation features. The scheme is evaluated across the Tuolumne River basin on a 50 m grid using an existing LiDAR-based product as the historical dataset. Results show a minimum RMSE of 30% at 50 m resolutions. Compared with the operational SNODAS product, reduction in error is up to 80% with historical LiDAR-measured snow depth as input data.

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