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At least 307 records · Page 17

All sources NASA NTRS DOE OSTI

NASA NTRS2014

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

data assimilation↗

NASA NTRS2017

NASA's SnowEx Campaign: Observing Seasonal Snow in a Forested Environment

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1(2016-17) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site was Grand Mesa and the secondary site was the Senator Beck Basin, both in western Colorado, USA. Nine sensors on five aircraft made observations using a broad range of sensing techniques - active and passive microwave, and active and passive optical/infrared - to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also included an extensive range of ground truth measurements - in-situ manual samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some preliminary results will be presented.

cryosphere↗

NASA NTRS2018

Snow in the Changing Sea-Ice Systems

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

Snow↗

NASA NTRS2018

Estimation of Seasonal Snow Water Equivalent Using Landsat Observations

This work presents a methodology for estimating seasonal snow water equivalent (SWE) from the use of remotely sensed Visible and Near Infrared observations from the Landsat mission. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2016). 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 correlations with in situ SWE observations of greater than 0.95. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (32 years), and accuracy provide a unique dataset for investigating snow processes. In particular, this presentation illustrates how the reanalysis dataset was used to provide climatology of the seasonal snowfall accumulation rates, distributions, and variability over the last three decades.

SWE↗

NASA NTRS2020

Evaluation of long-term Northern Hemisphere snow water equivalent products

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.

Northern Hemisphere snow water equivalent (SWE) pr↗

DOE OSTI2022

[Retracted] Research on Application Experience Design of Ice and Snow Sports Equipment Based on Bee Colony Model

Sports equipment is the key to the smooth development of ice and snow sports. With the rapid development of social economy and the improvement of people’s living standards, the demand for ice and snow sports equipment is increasing day by day. This article presents an improved method based on the chaos theory and the bee colony algorithm to quantify the application experience design of ice and snow sports equipment and reduce the influence of uncertain factors on the design results. First, the chaos theory can establish the dataset of application experience design and analyze the discreteness of the set. According to the bee colony algorithm, the dataset is divided into several groups, and each group obtains the best application experience design by using the design optimization strategy. Finally, the results are mixed to obtain the final experience design results. Through MATLAB simulation analysis and verification, the improved bee colony model can improve the accuracy of application experience design of ice and snow sports equipment in an uncertain environment, shorten the overall design time, and meet the requirements of application experience design of different ice and snow sports equipment. Therefore, the model proposed in this paper is suitable for the application experience design of ice and snow sports equipment.

Li, Yuanjing (ORCID:000000018276647X)↗

DOE OSTI2024

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Determination of Ground Subsidence Around Snow Fences in the Arctic Region

In this study, we analyzed the effects of snow cover changes caused by snow fences (SFs) installed in 2017 in the Alaskan tundra to examine ground subsidence. Digital surface model data obtained through LiDAR-based remote sensing in 2019 and 2022, combined with a field survey in 2021, revealed approximately 0.2 m of ground subsidence around the SF. To investigate the relationship between SF-induced snow cover changes and ground subsidence, geophysical methods, electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), were applied in 2023 to analyze subsurface characteristics. The increased snow cover due to the SF-enhanced insulation, delaying the penetration of winter cold into the subsurface. This delay caused subsurface temperatures to decrease more slowly, melting the upper permafrost and increasing the thickness of the active layer. ERT and GPR surveys well delineated the boundary between the active layer and permafrost, confirming that the increased snow cover thickened the active layer. This thickening led to the melting of pore ice, causing water runoff and ground compaction, which resulted in subsidence. The runoff also formed channels flowing eastward over the SF. This study highlights how changes in snow cover can influence active layer properties, leading to localized environmental changes and ground subsidence.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

Survey of the seasonal snow cover of Alaska

The author has identified the following significant results. The delineation of the transient snow cover can generally be made directly from any of the RBV of MSS images. On many glaciers, however, there seems to be a relatively small difference between the reflectivities of snow and ice in the visible portion of the spectrum (corresponding to MSS bands 4 and 5). On the other hand, the reflectivity is distinctly lower for ice than for snow in the near infrared (MSS bands 6 and 7). By applying the sensors of the latter spectral bands the position of the transient snowline, indicative of the mass balance, can in many cases be determined with what appears to be a satisfactory accuracy. This circumstance should prove especially useful in studies of regional variations of glacier mass balances. It had been found that the effect of winds early in the fall seasons can be seen on the arctic slope when a light dusting of snow is present. Winds channeled by topography redistribute the snow and actually remove it from regions. After freeze up has occurred and a continuous snow cover exists it is possible to identify open water reaches on streams flowing through the region. Such cases are identifiable on the ERTS images especially on the MSS 7 images.

Weller, G. E.↗

NASA NTRS1973

Survey of the seasonal snow cover in Alaska

The author has identified the following significant results. ERTS-1 data are used together with synoptic-climatological data to describe the buildup of the seasonal snow and ice covers in a north-south transect of a total length of about 1250 km across Alaska. It has been demonstrated that the ERTS-1 data may, under favorable conditions, be used for accurate mapping of snow lines in high mountain regions. The analysis shows that especially in the Brooks Range and on the Arctic Slope where snow covers generally are relatively thin, the ERTS-1 scenes can be useful for qualitative descriptions of the snow and ice covers over wide expanses. The onset and retreat of the seasonal snow cover are sensitive indicators of climatic fluctuations and the ERTS-1 data offers a possibility to record variations of the snow and ice buildup from year to year in a practical and informative way, which should be especially useful for studies of climatic trends. This is particularly true in Alaska where the density of the station network is too low to permit interpolations between the stations.

Weller, G. E.↗

NASA NTRS1973

Snow survey and vegetation growth in high mountains (Swiss Alps)

The author has identified the following significant results. A method for mapping snow over large areas was developed combining the possibilities of a Quantimet (QTM 72) to evaluate the exact density level of the snow cover for each individual image (or a selected section of the photo) with the higher resolution of photographic techniques. The density level established on the monitor by visual control is used as reference for the exposure time of a lithographic film, producing a clear tonal separation of all snow- and ice-covered areas from uncovered land in black and white. The data is projected onto special maps 1:500,000 or 1:100,000 showing the contour lines and the hydrographic features only. The areal extent of the snow cover may be calculated directly with the QTM 720 or on the map. Bands 4 and 5 provide the most accurate results for mapping snow. Using all four bands a separation of an old melting snow cover from a new one is possible. Regional meteorological studies combining ERTS-1 imagery and conventional sources describe synoptical evolution of meteorological systems over the Alps.

Haefner, H.↗

NASA NTRS1973

Facilitating the exploitation of ERTS-1 imagery utilizing snow enhancement techniques

The author has identified the following significant results. Snow cover in combination with low angle solar illumination has been found to provide increased tonal contrast of surface feature and is useful in the detection of bedrock fractures. Identical fracture systems were not as readily detectable in the fall due to the lack of a contrasting surface medium (snow) and a relatively high sun angle. Low angle solar illumination emphasizes topographic expressions not as apparent on imagery acquired with a higher sun angle. A strong correlation exists between the major fracture-lineament directions interpreted from multi-sensor imagery (including snow-free and snow cover ERTS) and the strike of bedrock joints recorded in the field indicating the structural origin of interpreted fracture-lineaments. A fracture-annotated ERTS-1 photo base map (1:250,000 scale) is being prepared for western Massachusetts. The map will document the utilization of ERTS-1 imagery for geological analysis in comparative snow-free and snow-covered terrain.

Wobber, F. J.↗

NASA NTRS1975

Measuring snow cover using satellite imagery during 1973 and 1974 melt season: North Santiam, Boise, and Upper Snake Basins, phase 1

Measurements are examined of snow coverage during the snow-melt season in 1973 and 1974 from LANDSAT imagery for the three Columbia River Subbasins. Satellite derived snow cover inventories for the three test basins were obtained as an alternative to inventories performed with the current operational practice of using small aircraft flights over selected snow fields. The accuracy and precision versus cost for several different interactive image analysis procedures was investigated using a display device, the Electronic Satellite Image Analysis Console. Single-band radiance thresholding was the principal technique employed in the snow detection, although this technique was supplemented by an editing procedure involving reference to hand-generated elevation contours. For each data and view measured, a binary thematic map or "mask" depicting the snow cover was generated by a combination of objective and subjective procedures. Photographs of data analysis equipment (displays) are shown.

Wiegman, E. J.↗

NASA NTRS1975

Snow survey from space, with emphasis on the results of the analysis of Skylab EREP S192 multispectral scanner data

The Skylab EREP S192 multispectral scanner data have provided an opportunity to examine the reflectance characteristics of snow cover in several spectral bands extending from the visible into the near infrared spectral region to about 2 microns. The analysis of the S192 imagery and digital tape data from five EREP passes, two from the SL-2 mission and three from the SL-4 mission, indicates a sharp drop in reflectance of snow in the near infrared, with snow becoming essentially nonreflective in Bands 11 (1.55-1.75 microns) and 12 (2.10-2.35 microns). The results are in good agreement with the results of laboratory experiments. Two potential applications to snow mapping of measurements in the near infrared spectral region are possible: (1) the use of a near infrared band in conjunction with a visible band to distinguish automatically between snow and water droplet clouds, and (2) the use of one or more near infrared bands to detect areas of melting snow.

Barnes, J. C.↗

NASA NTRS1980

The seasonal cycle of snow cover, sea ice and surface albedo

The paper examines satellite data used to construct mean snow cover caps for the Northern Hemisphere. The zonally averaged snow cover from these maps is used to calculate the seasonal cycle of zonally averaged surface albedo. The effects of meltwater on the surface, solar zenith angle, and cloudiness are parameterized and included in the calculations of snow and ice albedo. The data allows a calculation of surface albedo for any land or ocean 10 deg latitude band as a function of surface temperature ice and snow cover; the correct determination of the ice boundary is more important than the snow boundary for accurately simulating the ice and snow albedo feedback.

Robock, A.↗

NASA NTRS1981

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Rango, A.↗

NASA NTRS1985

Studies on Physical Properties of Snow Based on Multi Channel Microwave Radiometer

The analysis of the data observed over a snow field with a breadboard model of MSR (microwave scanning radiometer) to be installed in MOS-1 (Marine Observation Satellite-1) indicates that: (1) the influence of incident angle on brightness temperature is larger in horizontal polarization component than in vertical polarization component. The effect of incident angle depends upon the property of snow with larger value for dry snow; (2) the difference of snow surface configuration consisting of artifically made parallel ditches of 5 cm depth and 5 cm width with spacing of 10 and 30 cm respectively which are oriented normal to electrical axis do not affect brightness temperature significantly; and (3) there is high negative correlation between brightness temperature and snow depth up to the depth of 70 cm which suggests that the snow depth can be measured with a two channel microwave radiometer up to this depth.

Tsuchiya, K.↗