The simulated sea ice thermal microwave emission at window and sounding frequencies

2010 ◽  
Author(s):  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Microwave Emission

scholarly journals Optical properties of ice and snow

2019 ◽  
Vol 377 (2146) ◽  
pp. 20180161 ◽  
Author(s):  
Stephen G. Warren
Keyword(s):  
Sea Ice ◽  
Sheet Thickness ◽  
Open Water ◽  
Microwave Emission ◽  
Radiative Properties ◽  
Radio Waves ◽  
Lake Ice ◽  
Near Ultraviolet ◽  
Glacier Ice ◽  
Natural Snow

The interactions of electromagnetic radiation with ice, and with ice-containing media such as snow and clouds, are determined by the refractive index and absorption coefficient (the ‘optical constants’) of pure ice as functions of wavelength. Bulk reflectance, absorptance and transmittance are further influenced by grain size (for snow), bubbles (for glacier ice and lake ice) and brine inclusions (for sea ice). Radiative transfer models for clouds can also be applied to snow; the important differences in their radiative properties are that clouds are optically thinner and contain smaller ice crystals than snow. Absorption of visible and near-ultraviolet radiation by ice is so weak that absorption of sunlight at these wavelengths in natural snow is dominated by trace amounts of light-absorbing impurities such as dust and soot. In the thermal infrared, ice is moderately absorptive, so snow is nearly a blackbody, with emissivity 98–99%. The absorption spectrum of liquid water resembles that of ice from the ultraviolet to the mid-infrared. At longer wavelengths they diverge, so microwave emission can be used to detect snowmelt on ice sheets, and to discriminate between sea ice and open water, by remote sensing. Snow and ice are transparent to radio waves, so radar can be used to infer ice-sheet thickness.This article is part of the theme issue ‘The physics and chemistry of ice: scaffolding across scales, from the viability of life to the formation of planets’.

scholarly journals The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output

2020 ◽  
Vol 14 (7) ◽  
pp. 2369-2386 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Subsurface Layer ◽  
Microwave Emission ◽  
The Arctic ◽  
Model Output ◽  
Linear Temperature ◽  
Observation Operator ◽  
Brightness Temperatures ◽  
Climate Model Output

Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in cold conditions from a simplified linear temperature profile and a simplified salinity profile as a function of depth in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In warm conditions, the simplified salinity profiles lead to brine volume fractions that are too high in the subsurface layer. To overcome this limitation, we suggest using a constant brightness temperature for the ice during warm conditions and treating melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of wet snow properties. As periods of snow with intermediate moisture content, typically occurring in spring and fall, locally last for less than a month, our approach allows one to estimate realistic brightness temperatures at 6.9 GHz from climate model output for most of the year.

scholarly journals Remote sensing of sea ice: advances during the DAMOCLES project

2012 ◽  
Vol 6 (6) ◽  
pp. 1411-1434 ◽  
Author(s):  
G. Heygster ◽  
V. Alexandrov ◽  
G. Dybkjær ◽  
W. von Hoyningen-Huene ◽  
F. Girard-Ardhuin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Estimation Method ◽  
Satellite Observations ◽  
Microwave Emission ◽  
The Arctic ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Microwave Emissivity

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

scholarly journals Variability of the Arctic sea ice microwave emission at 89 GHz under winter conditions

2018 ◽  
Vol 15 (3) ◽  
pp. 139-147
Author(s):  
E.V. Zabolotskikh ◽  
◽  
M.A. Zhivotovskaya‎ ◽  
N.Yu.‎ Zakhvatkina‎ ◽  
B. Chapron ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Microwave Emission ◽  
The Arctic ◽  
Winter Conditions ◽  
Arctic Sea

scholarly journals The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

2013 ◽  
Vol 7 (6) ◽  
pp. 5711-5734 ◽  
Author(s):  
S. Willmes ◽  
M. Nicolaus ◽  
C. Haas
Keyword(s):  
Sea Ice ◽  
Early Summer ◽  
Weddell Sea ◽  
Microwave Emission ◽  
Late Winter ◽  
Emission Model ◽  
Regional Variability ◽  
Sea Ice Concentration ◽  
Regional Patterns ◽  
Microwave Emissivity

Abstract. Satellite observations of microwave brightness temperatures between 19 GHz and 85 GHz are the main data source for operational sea-ice monitoring. However, the sea ice microwave emissivity is subject to pronounced seasonal variations and shows significant hemispheric contrasts that mainly arise from differences in the rate and strength of snow metamorphism and melt. We use the thermodynamic snow model SNTHERM and the microwave emission model MEMLS to identify the contribution of regional patterns in atmospheric energy fluxes to surface emissivity variations on Arctic and Antarctic sea ice between 2000 and 2009. The obtained emissivity data reveal a pronounced seasonal cycle with a large regional variability. The emissivity variability increases from winter to early summer and is more pronounced in the Antarctic. In the pre-melt period (January–May, July–November) the variations in surface microwave emissivity due to diurnal, regional and inter-annual variability of atmospheric forcing reach up to 3.4%, 4.3%, and 9.7% for 19 GHz, 37 GHz and 85 GHz channels, respectively. Small but significant emissivity trends can be observed in the Weddell Sea during November and December as well as in Fram Strait during February. The obtained emissivity data lend themselves for an assessment of sea-ice concentration and snow-depth algorithm accuracies.

Towards improving radiometry-derived snow depths with SNOWPACK and SMRT

2020 ◽  
Author(s):  
Robbie Mallett ◽  
Julienne Stroeve ◽  
Michel Tsamados ◽  
Glen Liston
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Model Development ◽  
Light Flux ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Gradient Ratio ◽  
Strong Control

<p>The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness.</p><p>Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy.</p><p>We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional “gradient ratio” method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths.</p><p> </p><p>References</p><p>Wever, Nander, et al. "Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model." <em>Geoscientific Model Development</em> 13.1 (2020): 99-119.</p><p>Picard, Ghislain, Melody Sandells, and Henning Löwe. "SMRT: An active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0)." <em>Geoscientific Model Development </em>11.7 (2018): 2763-2788.</p><p>Markus, Thorsten, and Donald J. Cavalieri. "Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data." <em>Antarctic sea ice: physical processes, interactions and variability </em>74 (1998): 19-39.</p>

scholarly journals Aircraft measurements of microwave emission from Arctic Sea ice

1971 ◽  
Vol 2 ◽  
pp. 129-139 ◽  
Author(s):  
T WILHEIT ◽  
W NORDBERG ◽  
J BLINN ◽  
W CAMPBELL ◽  
A EDGERTON
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Microwave Emission ◽  
Aircraft Measurements ◽  
Arctic Sea
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