scholarly journals Longwave Radiation on Snow-Covered Mountainous Surfaces

1997 ◽  
Vol 36 (6) ◽  
pp. 818-824 ◽  
Author(s):  
Christian Plüss ◽  
Atsumu Ohmura

Abstract Longwave radiation in snow-covered alpine environments was investigated based on LOWTRAN7 calculations. The irradiance from the sky and from the surrounding topography were determined separately in order to detect the influence of the topography on longwave radiation balance. Sensitivity studies showed that the irradiance from the surrounding terrain is determined primarily by the atmospheric conditions within the investigated area and by the surface temperature of the surrounding terrain. In snow-covered environments, the air temperature is usually above the snow surface temperature and the effects of the air between the topography and the receiving surface may be relevant. Longwave irradiance from the surrounding terrain is an important component of the energy balance at the snow surface on inclined slopes and should be considered for areal investigations. A simple parameterization that accounts for the effects of the air is proposed for efficient calculation of longwave irradiance from snow-covered topography.

2009 ◽  
Vol 6 (3) ◽  
pp. 3863-3890 ◽  
Author(s):  
C. H. Luce ◽  
D. G. Tarboton

Abstract. The snow surface temperature is an important quantity in the snow energy balance, since it modulates the exchange of energy between the surface and the atmosphere as well as the conduction of energy into the snowpack. It is therefore important to correctly model snow surface temperatures in energy balance snowmelt models. This paper focuses on the relationship between snow surface temperature and conductive energy fluxed that drive the energy balance of a snowpack. Time series of snow temperature at the surface and through the snowpack were measured to examine energy conduction in a snowpack. Based on these measurements we calculated the snowpack energy content and conductive energy flux at the snow surface. We then used these estimates of conductive energy flux to evaluate formulae for the calculation of the conductive flux at the snow surface based on surface temperature time series. We use a method based on Fourier frequency analysis to estimate snow thermal properties. Among the formulae evaluated, we found that a modified force-restore formula, based on the superimposition of the force-restore equation capturing diurnal fluctuations on a gradually changing temperature gradient, had the best agreement with observations of heat conduction. This formula is suggested for the parameterization of snow surface temperature in a full snowpack energy balance model.


2013 ◽  
Vol 10 (12) ◽  
pp. 15071-15118 ◽  
Author(s):  
J. You ◽  
D. G. Tarboton ◽  
C. H. Luce

Abstract. \\label{sec:abstract} Snow surface temperature is a key control on energy exchanges at the snow surface, particularly net longwave radiation and turbulent energy fluxes. The snow surface temperature is in turn controlled by the balance between various external fluxes and the conductive heat flux, internal to the snowpack. Because of the strong insulating properties of snow, thermal gradients in snow packs are large and nonlinear, a fact that has led many to advocate multiple layer snowmelt models over single layer models. In an effort to keep snowmelt modeling simple and parsimonious, the Utah Energy Balance (UEB) snowmelt model used only one layer but allowed the snow surface temperature to be different from the snow average temperature by using an equilibrium gradient parameterization based on the surface energy balance. Although this procedure was considered an improvement over the ordinary single layer snowmelt models, it still resulted in discrepancies between modeled and measured snowpack energy contents. In this paper we examine the parameterization of snow surface temperature in single layer snowmelt models from the perspective of heat conduction into a semi-infinite medium. We evaluate the equilibrium gradient approach, the force-restore approach, and a modified force-restore approach. In addition, we evaluate a scheme for representing the penetration of a refreezing front in cold periods following melt. We also introduce a method to adjust effective conductivity to account for the presence of ground near to a shallow snow surface. These parameterizations were tested against data from the Central Sierra Snow Laboratory, CA, Utah State University experimental farm, UT, and Subnivean snow laboratory at Niwot Ridge, CO. These tests compare modeled and measured snow surface temperature, snow energy content, snow water equivalent, and snowmelt outflow. We found that with these refinements the model is able to better represent the snowpack energy balance and internal energy content while still retaining a parsimonious one layer format.


2010 ◽  
Vol 14 (3) ◽  
pp. 535-543 ◽  
Author(s):  
C. H. Luce ◽  
D. G. Tarboton

Abstract. The snow surface temperature is an important quantity in the snow energy balance, since it modulates the exchange of energy between the surface and the atmosphere as well as the conduction of energy into the snowpack. It is therefore important to correctly model snow surface temperatures in energy balance snowmelt models. This paper focuses on the relationship between snow surface temperature and conductive energy fluxes that drive the energy balance of a snowpack. Time series of snow temperature at the surface and through the snowpack were measured to examine energy conduction in a snowpack. Based on these measurements we calculated the snowpack energy content and conductive energy flux at the snow surface. We then used these estimates of conductive energy flux to evaluate formulae for the calculation of the conductive flux at the snow surface based on surface temperature time series. We use a method based on Fourier frequency analysis to estimate snow thermal properties. Among the formulae evaluated, we found that a modified force-restore formula, based on the superimposition of the force-restore equation capturing diurnal fluctuations on a gradually changing temperature gradient, had the best agreement with observations of heat conduction. This formula is suggested for the parameterization of snow surface temperature in a full snowpack energy balance model.


2014 ◽  
Vol 18 (12) ◽  
pp. 5061-5076 ◽  
Author(s):  
J. You ◽  
D. G. Tarboton ◽  
C. H. Luce

Abstract. Snow surface temperature is a key control on and result of dynamically coupled energy exchanges at the snow surface. The snow surface temperature is the result of the balance between external forcing (incoming radiation) and energy exchanges above the surface that depend on surface temperature (outgoing longwave radiation and turbulent fluxes) and the transport of energy into the snow by conduction and meltwater influx. Because of the strong insulating properties of snow, thermal gradients in snow packs are large and nonlinear, a fact that has led many to advocate multiple layer snowmelt models over single layer models. In an effort to keep snowmelt modeling simple and parsimonious, the Utah Energy Balance (UEB) snowmelt model used only one layer but allowed the snow surface temperature to be different from the snow average temperature by using an equilibrium gradient parameterization based on the surface energy balance. Although this procedure was considered an improvement over the ordinary single layer snowmelt models, it still resulted in discrepancies between modeled and measured snowpack energy contents. In this paper we evaluate the equilibrium gradient approach, the force-restore approach, and a modified force-restore approach when they are integrated as part of a complete energy and mass balance snowmelt model. The force-restore and modified force-restore approaches have not been incorporated into the UEB in early versions, even though Luce and Tartoton have done work in calculating the energy components using these approaches. In addition, we evaluate a scheme for representing the penetration of a refreezing front in cold periods following melt. We introduce a method to adjust effective conductivity to account for the presence of ground near to a shallow snow surface. These parameterizations were tested against data from the Central Sierra Snow Laboratory, CA, Utah State University experimental farm, UT, and subnivean snow laboratory at Niwot Ridge, CO. These tests compare modeled and measured snow surface temperature, snow energy content, snow water equivalent, and snowmelt outflow. We found that with these refinements the model is able to better represent the snowpack energy balance and internal energy content while still retaining a parsimonious one layer format.


2014 ◽  
Vol 18 (9) ◽  
pp. 3553-3570 ◽  
Author(s):  
R. Sultana ◽  
K.-L. Hsu ◽  
J. Li ◽  
S. Sorooshian

Abstract. Noah (version 2.7.1), the community land-surface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the land-surface processes either in stand-alone or in coupled land-atmospheric model systems, is recognized to underestimate snow–water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 2002–2008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.


2016 ◽  
Vol 17 (8) ◽  
pp. 2175-2189 ◽  
Author(s):  
J. W. Pomeroy ◽  
R. L. H. Essery ◽  
W. D. Helgason

Abstract The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using finescale multilayer snow physics models that track changes in snow properties and internal energy; however, these models are heavily parameterized, have high predictive uncertainty, and require continuous simulation to estimate prognostic state variables. Here, a relatively simple model to estimate SST that is not reliant on prognostic state variables is proposed. The model assumes that the snow surface is poorly connected thermally to the underlying snowpack and largely transparent for most of the shortwave radiation spectrum, such that a snow surface energy balance among only sensible heat, latent heat, longwave radiation, and near-infrared radiation is possible and is called the radiative psychrometric model (RPM). The RPM SST is sensitive to air temperature, humidity, ventilation, and longwave irradiance and is secondarily affected by absorption of near-infrared radiation at the snow surface that was higher where atmospheric deposition of particulates was more likely to be higher. The model was implemented with neutral stability, an implicit windless exchange coefficient, and constant shortwave absorption factors and aerodynamic roughness lengths. It was evaluated against radiative SST measurements from the Canadian Prairies and Rocky Mountains, French Alps, and Bolivian Andes. With optimized and global shortwave absorption and aerodynamic roughness length parameters, the model is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dewpoint, or ice bulb calculation approaches.


2021 ◽  
Vol 877 (1) ◽  
pp. 012005
Author(s):  
Dahlia S. Abed-Zaid ◽  
Hussein A. M. Al-Zubaidi

Abstract Estimating heat budget factors are important to understand the many physical processes of large lakes and their reaction to the atmosphere. Some of these components are affected by water temperature, while the other depends on atmospheric conditions. This paper estimates the total heat flux for Lawrence lake via a code developed in MATLAB environment. The code can deal with different time resolutions if the lake water surface temperature data were at different time resolutions from the meteorological data. Results showed that solar energy peaks at 842 Watt/m2 at 540 Julian day, which is very normal for a sunny summer day, while the longwave radiation has 204 Watt/m2 as a min value. The back radiation did not make any reaction for the variation, but it revealed a small gradient. Furthermore, evaporation recorded - 67 Watt/m2 as a minimum value at 659 Julian day and 360 Watt/m2 as a maximum value at 578.43 Julian day close to the maximum water surface temperature event.


2001 ◽  
Vol 32 ◽  
pp. 217-222 ◽  
Author(s):  
Peter Höller

AbstractSnow surface temperature (Ts) plays an important role in the formation of surface hoar or near-surface faceted crystals The goal of this study was to obtain detailed information on Ts in different forest stands nelr the timberline. The investigations were conducted during clear nights and showed that the snow surface temperature is influenced very strongly by the forest canopy. While the air temperature was very similar on the different experimental sites, Ts was higher in the forest than in the open field; on the south-facing slope the difference between the forest and the open field was 3–4.5°C, and on the north-facing slope approximately 3–7°C. Taking into account that εair is 0.7 and εtree is 0.94, the incoming radiation (I ↓) for the different experimental sites was calculated by the equation of Brunt (the canopy density was estimated using photographs taken with an 8 mm fish-eye). To calculate Ts, air temperature and averaged values of the net radiation (because the net radiation (I) has only a small range of variation during clear nights) were used. The results show that the calculated values were higher than the measured values (by approximately 2°C). However, a better correlation was found by using lower values of the emissivity (εair0.67 and εtree0.91).


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