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

Tonboe, Rasmus

doi:10.1111/j.1600-0870.2010.00434.x

Cited by 40 publications

(64 citation statements)

References 28 publications

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“…Emissivity at 18, 36 and 89 GHz, its temporal variability, the gradient ratio at 18 and 36 GHz (GR 18/36 = (T 36v − T 18v )/(T 36v + T 18v )), and the polarisation ratio at 18 GHz (PR 18 = (T 18v − T 18h )/(T 18v + T 18h )) are comparable to typical signatures derived from satellite measurements (Tonboe, 2010).…”

Section: Emissivity Modelling; Combination With Thermodynamic Modelsupporting

confidence: 50%

“…The purpose is not necessarily to reproduce a particular situation in time and space but rather to provide a realistic characterisation of the daily to seasonal variability. In DAMOCLES the microwave emission processes from sea ice have been simulated using the combination of a one dimensional thermodynamic sea ice model and a microwave emission model (Tonboe, 2010;. The emission model is the sea ice version of the Microwave Emission Model for Layered Snow-packs (MEMLS) Mätzler et al, 2006) It uses the theoretical improved Born approximation for estimating scattering, which validates for a wider range of frequencies and scatterer sizes than the empirical formulations .…”

Section: Emissivity Modelling; Combination With Thermodynamic Modelmentioning

confidence: 99%

“…Beneath the snow surface there is a strong temperature gradient with increasing temperatures towards the ice-water interface temperature at the freezing point around −1.8 • C. With the thermodynamic model presented in Tonboe (2010) and in , the sea ice surface temperature and the thermal microwave brightness temperature were simulated using a combination of thermodynamic and microwave emission models .…”

Section: Snow and Sea Ice Temperaturesmentioning

confidence: 99%

“…Summer melt is therefore not included in this study. The thermodynamic model is further described in Tonboe (2010) and .…”

Section: Emissivity Modelling; Combination With Thermodynamic Modelmentioning

confidence: 99%

“…As an attempt to predict the sea ice emissivity from the meteorological history, Sect. 2.3 combines a sea ice emissivity model with a thermodynamic model, driven with ECMWF atmospheric model data (Tonboe, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing of sea ice: advances during the DAMOCLES project

et al. 2012

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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 DAMO-CLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer AS-CAT (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.

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Section: Emissivity Modelling; Combination With Thermodynamic Modelsupporting

confidence: 50%

Section: Emissivity Modelling; Combination With Thermodynamic Modelmentioning

confidence: 99%

Section: Snow and Sea Ice Temperaturesmentioning

confidence: 99%

“…Summer melt is therefore not included in this study. The thermodynamic model is further described in Tonboe (2010) and .…”

Section: Emissivity Modelling; Combination With Thermodynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote sensing of sea ice: advances during the DAMOCLES project

et al. 2012

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Modeling Microwave Emission and Scattering from Snow‐Covered Sea Ice

Tonboe¹

2023

Sea Ice

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Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface‐sensitive microwave channels

Kang

Sohn

Tonboe³

et al. 2023

Quart J Royal Meteoro Soc

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Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface‐sensitive microwave‐sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e. snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically driven snow/ice growth model. We conducted 1D‐Var experiments in order to examine whether this approach can help to use the surface‐sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface‐sensitive microwave channels can be used in the 1D‐Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface‐sensitive microwave‐sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.

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

Cited by 40 publications

References 28 publications

Remote sensing of sea ice: advances during the DAMOCLES project

Remote sensing of sea ice: advances during the DAMOCLES project

Modeling Microwave Emission and Scattering from Snow‐Covered Sea Ice

Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface‐sensitive microwave channels

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