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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Electromagnetic reflection from multi-layered snow models

The remote sensing of snow-pack characteristics with surface installations or an airborne system could have important applications in water-resource management and flood prediction. To derive some insight into such applications, the electromagnetic response of multilayered snow models is analyzed in this paper. Normally incident plane waves at frequencies ranging from 1 MHz to 10 GHz are assumed, and amplitude reflection coefficients are calculated for models having various snow-layer combinations, including ice layers. Layers are defined by thickness, permittivity, and conductivity; the electrical parameters are constant or prescribed functions of frequency. To illustrate the effect of various layering combinations, results are given in the form of curves of amplitude reflection coefficients versus frequency for a variety of models. Under simplifying assumptions, the snow thickness and effective dielectric constant can be estimated from the variations of reflection coefficient as a function of frequency.

Linlor, W. I.↗
NASA NTRS2021

Modeling Snow Ablation over the Western United States Mountains: Patterns and Controlling Factors

When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

Snow↗
NASA NTRS1994

The development and validation of a simple snow model for the GISS GCM

Five years of meteorological and hydrological data from a typical New England watershed where winter snow cover is significant were used to drive and validate two off-line land surface schemes suitable for use in the Goddard Institute for Space Studies (GISS) general circulation model (GCM): a baseline scheme that does not model the physics of a snowpack and therefore, neglects the insulating properties of snow cover; and a modified scheme in which a three-layer snowpack is modeled. Comparing baseline model results with validation data reveals several model deficiencies. Surface radiation temperatures could not adequately be modeled and the ground froze to unreasonable depths. Furthermore, because of ground cooling resulting from large surface heat fluxes to the atmosphere from the uninsulated surface, deeper model layers did not unfreeze until midsummer. As such, the normal hydrologic processes of runoff, ground water infiltration, and movement, etc., are compromised for a good part of the year. With the inclusion of a simple three-layer snow model into the baseline model, not only are the ground and surface radiation temperatures adequately modeled but all the features of snowpack ripening that characterize pack growth/ablation are simulated.

Lynch-Stieglitz, Marc↗
NASA NTRS2011

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to-7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Toure, Ally M.↗
NASA NTRSDate not supplied

Improving Modeled Snow Mass in GEOS with Snow Cover Fraction Assimilation

The NASA Goddard Earth Observing System (GEOS) Forward Processing (FP) global weather analysis often considerably underestimates near-surface air temperature during the spring snow melt season, which can be caused by excessive simulated snow cover. To mitigate such errors, we developed an empirical snow cover fraction (SCF) analysis to assimilate SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) into the GEOS Catchment land surface model. Results obtained with a land-only version of the system indicate that the SCF analysis reduces errors in snow mass and snow depth by ~10% when compared to in situ measurements from the Snow Telemetry (SNOTEL), Canadian historical Snow Water Equivalent dataset (CanSWE), and Global Historical Climatology Network - Daily (GHCN-Daily) networks.

Rolf Reichle↗
NASA NTRS1994

Analysis of snow feedbacks in 14 general circulation models

Snow feedbacks produced by 14 atmospheric general circulation models have been analyzed through idealized numerical experiments. Included in the analysis is an investigation of the surface energy budgets of the models. Negative or weak positive snow feedbacks occurred in some of the models, while others produced strong positive snow feedbacks. These feedbacks are due not only to melting snow, but also to increases in boundary temperature, changes in air temperature, changes in water vapor, and changes in cloudiness. As a result, the net response of each model is quite complex. We analyze in detail the responses of one model with a strong positive snow feedback and another with a weak negative snow feedback. Some of the models include a temperature dependence of the snow albedo, and this has significantly affected the results.

Randall, D. A.↗
NASA NTRS2005

Earth Observing System (EOS) Snow and Ice Products for Observation and Modeling

Snow and ice are the key components of the Earth's cryosphere, and their influence on the Earth's energy balance is very significant due at least in part to the large areal extent and high albedo characterizing these features. Large changes in the cryosphere have been measured over the last century and especially over the past decade, and remote sensing plays a pivotal role in documenting these changes. Many of NASA's Earth Observing System (EOS) products derived from instruments on the Terra, Aqua, and Ice, Cloud and land Elevation Satellite (ICESat) satellites are useful for measuring changes in features that are associated with climate change. The utility of the products is continually enhanced as the length of the time series increases. To gain a more coherent view of the cryosphere and its historical and recent changes, the EOS products may be employed together, in conjunction with other sources of data, and in models. To further this goal, the first EOS Snow and Ice Products Workshop was convened. The specific goals of the workshop were to provide current and prospective users of EOS snow and ice products up-to-date information on the products, their validation status and future enhancements, to help users utilize the data products through hands-on demonstrations, and to facilitate the integration of EOS products into models. Oral and poster sessions representing a wide variety of snow and ice topics were held; three panels were also convened to discuss workshop themes. Panel discussions focused on data fusion and assimilation of the products into models. Approximately 110 people attended, representing a wide array of interests and organizations in the cryospheric community.

Hall, D.↗
NASA NTRS2008

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

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, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order 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 both during the snow season and, in some regions, on into the following spring.

Zaitchik, Benjamin F.↗
NASA NTRS1991

Interpretation of snow-climate feedback as produced by 17 general circulation models

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Cess, R. D.↗
NASA NTRS2006

An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

Kim, Edward J.↗
NASA NTRS2006

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Kim, E.↗