Atmospheric 60-GHz oxygen spectrum: New laboratory measurements and line parameters

Liebe, Hans J.; Rosenkranz, P. W.; Hufford, George A.

doi:10.1016/0022-4073(92)90127-p

Cited by 235 publications

(190 citation statements)

References 37 publications

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“…This temperature dependence is given by the expression ( 300K /T air ) α . The numerical value of the exponent α is 1.5 in [18] and it is 0.8 in [19]. This means that the change of the absorption coefficient as function of air temperature when going from warm to cold temperatures has effectively been reduced in Version 5 by about 50% compared to prior versions.…”

Section: Oxygen Absorption Modelmentioning

confidence: 95%

“…These biases have been tracked back to the oxygen absorption model. It has been found that these biases can be significantly reduced by reverting to the oxygen absorption model of Liebe et al [19], which is based on laboratory measurements. The Version 5 algorithm uses the oxygen absorption model of [19].…”

Section: Oxygen Absorption Modelmentioning

confidence: 99%

“…It has been found that these biases can be significantly reduced by reverting to the oxygen absorption model of Liebe et al [19], which is based on laboratory measurements. The Version 5 algorithm uses the oxygen absorption model of [19]. The difference between the two oxygen absorption models is the dependence of the oxygen absorption coefficient of air temperature air T .…”

Section: Oxygen Absorption Modelmentioning

confidence: 99%

“…Figure 3 depicts the radiometric impact of the change in the O2 absorption by showing the difference in the correction (TOA TB minus surface TB) between the two absorption models as function of time and latitude. [19], which is used in the Version 5 algorithm and the Wentz Meissner O2 absorption model [18], which had been used in prior versions.…”

Section: Oxygen Absorption Modelmentioning

confidence: 99%

“…In actuality, bistatic scattering from a rough ocean will result in galactic radiation entering the mainlobe of the antenna from many different directions. In effect, a rough ocean surface tends to add additional spatial [19], which is used in the Version 5 algorithm and the Wentz Meissner O 2 absorption model [18], which had been used in prior versions.…”

Section: Geometric Optics Modelmentioning

confidence: 99%

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The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

2018

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Abstract:The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within ±0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

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Section: Oxygen Absorption Modelmentioning

confidence: 95%

Section: Oxygen Absorption Modelmentioning

confidence: 99%

Section: Oxygen Absorption Modelmentioning

confidence: 99%

Section: Oxygen Absorption Modelmentioning

confidence: 99%

Section: Geometric Optics Modelmentioning

confidence: 99%

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The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

2018

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Sources of discrepancies between satellite‐derived and land surface model estimates of latent heat fluxes

Lipton

Liang

Jiménez³

et al. 2015

JGR Atmospheres

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Monthly-average estimates of latent heat flux have been derived from a combination of satellite-derived microwave emissivities, day-night differences in land surface temperature (from microwave AMSR-E), downward solar and infrared fluxes from ISCCP cloud analysis, and MODIS visible and near-infrared surface reflectances. The estimates, produced with a neural network, were compared with data from the Noah land surface model, as produced for GLDAS-2, and with two alternative estimates derived from different datasets and methods. Areas with extensive, persistent, substantial discrepancies between the satellite and land surface model fluxes have been analyzed with the aid of data from flux towers. The sources of discrepancies were found to include problems with the model surface roughness length and turbulent exchange coefficients for midlatitude cropland areas in summer, inaccuracies in the precipitation data that were used as forcing for the land surface model, and model underestimation of transpiration in some forests during dry periods. At the tower sites analyzed, agreement with tower data was generally closer for our satellite-derived fluxes than for the land surface model fluxes, in terms of monthly averages.

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Atmospheric absorption model for dry air and water vapor at microwave frequencies below 100 GHz derived from spaceborne radiometer observations

Wentz

Meißner

2016

Radio Science

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The Liebe and Rosenkranz atmospheric absorption models for dry air and water vapor below 100 GHz are refined based on an analysis of antenna temperature (TA) measurements taken by the Global Precipitation Measurement Microwave Imager (GMI) in the frequency range 10.7 to 89.0 GHz. The GMI TA measurements are compared to the TA predicted by a radiative transfer model (RTM), which incorporates both the atmospheric absorption model and a model for the emission and reflection from a rough‐ocean surface. The inputs for the RTM are the geophysical retrievals of wind speed, columnar water vapor, and columnar cloud liquid water obtained from the satellite radiometer WindSat. The Liebe and Rosenkranz absorption models are adjusted to achieve consistency with the RTM. The vapor continuum is decreased by 3% to 10%, depending on vapor. To accomplish this, the foreign‐broadening part is increased by 10%, and the self‐broadening part is decreased by about 40% at the higher frequencies. In addition, the strength of the water vapor line is increased by 1%, and the shape of the line at low frequencies is modified. The dry air absorption is increased, with the increase being a maximum of 20% at the 89 GHz, the highest frequency considered here. The nonresonant oxygen absorption is increased by about 6%. In addition to the RTM comparisons, our results are supported by a comparison between columnar water vapor retrievals from 12 satellite microwave radiometers and GPS‐retrieved water vapor values.

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Atmospheric 60-GHz oxygen spectrum: New laboratory measurements and line parameters

Cited by 235 publications

References 37 publications

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

Sources of discrepancies between satellite‐derived and land surface model estimates of latent heat fluxes

Atmospheric absorption model for dry air and water vapor at microwave frequencies below 100 GHz derived from spaceborne radiometer observations

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