Surface Emissivity at Microwaves to Millimeter Waves over Polar Regions: Parameterization and Evaluation with Aircraft Experiments

Wang, Dié; Prigent, Catherine; Kilic, Lise; Fox, Stuart; Harlow, Chawn; Jiménez, Carlos; Aires, Filipe; Grassotti, Christopher; Karbou, Fatima

doi:10.1175/jtech-d-16-0188.1

Cited by 40 publications

(43 citation statements)

References 34 publications

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“…self-similar Rayleigh-Gans Approximation (Hogan & Westbrook, 2014;Hogan et al, 2017) Aires et al, 2011;Wang et al, 2017) has been implemented, which provides the emissivities based on geographic location and month derived from satellite measurements.…”

Section: The Pamtra Modelmentioning

confidence: 99%

Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

et al. 2020

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Within a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic‐wide scale is based on passive microwave satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N‐ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower‐frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher‐frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong.

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Section: The Pamtra Modelmentioning

confidence: 99%

Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

et al. 2020

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“…It depends on the frequency, the incidence angle, and the subsurface extinction and reflections between snow and sea ice layers (Tonboe, 2010). Therefore, estimating the surface contribution is particularly complicated over sea ice due to the layering and the vertical structure of the snowpack, which affect the microwave emission processes (Mathew et al, 2008;Rosenkranz and Mätzler, 2008;Harlow, 2009Harlow, , 2011Tonboe, 2010;Tonboe et al, 2011), and to the large spatial and temporal variability of sea ice and snow cover (English, 2008;Tonboe et al, 2013;Wang et al, 2017). The understanding of the relationship between T eff and the physical temperature profile is complicated, especially at microwave frequencies ≥ 18 GHz, when scattering occurs, but it has been shown that from 6 to 50 GHz there is a high correlation between the T eff and the T Snow−Ice (Tonboe et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

et al. 2019

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Abstract. Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature (TSnow−Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The TSnow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. Teff is estimated from the TSnow−Ice with a RMSE of less than 1 K.

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“…Without adequate maps of surface emissivity from observations or near-real-time LSE models, the retrievals of precipitation over land will always be affected by substantial errors, especially when using MW channels whose weighting functions peak close to the ground. Several groups have been working on this problem, and datasets are available (e.g., [195][196][197]). LSE models also exist for use in NWP applications, and are useful also for precipitation retrievals; two examples are the Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) [198] and the Ringerud et al [199] model.…”

Section: Land Surface Emissionmentioning

confidence: 99%

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

Levizzani

Cattani

2019

Remote Sensing

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The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

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Surface Emissivity at Microwaves to Millimeter Waves over Polar Regions: Parameterization and Evaluation with Aircraft Experiments

Cited by 40 publications

References 34 publications

Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

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