Water Vapor Continuum Absorption in the Microwave

Payne, Vivienne H.; Mlawer, E. J.; Cady‐Pereira, Karen; Moncet, Jean-Luc

doi:10.1109/tgrs.2010.2091416

Cited by 77 publications

(90 citation statements)

References 35 publications

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“…There is thus an inconsistency between two large sets of experimental data, namely laboratory (together with surface path measurements) and radiometric measurements. This is confirmed by Payne et al (2011) who concluded that for atmospheric path lengths the combination of MPM (Millimeter-wave Propagation Model, Liebe, 1989;Rosenkranz, 1998) foreign and self-continuum (solid lines in Fig. 3) is inconsistent with the radiometric measurements (looking up) at high column water vapor amounts.…”

Section: Spectroscopy Statusmentioning

confidence: 53%

“…More recently, Chen et al (2010) have shown that, on a sample of diverse atmospheric profiles, fast and line-by-line RTMs agree within 0.1 K for MHS channels, and that this result was highly stable under humid conditions. In their evaluation of SAPHIR using RAOBs, Clain et al (2015) have shown that the three RTMs, RT-TOV.v10, ARTS and MonoRTM (Monochromatic Radiative Transfer Model; Clough et al, 2005;Payne et al, 2011), provide fairly consistent BTs on a common set of tropical profiles, the differences being in the range −1.50 K / 0.78 K, with the largest differences observed for the central channel (183 ± 0.2 GHz). These three RTMs rely on the currently most widely accepted model MT_CKD (MlawerTobin_CloughKneisysDavies;Mlawer et al, 2012) for the parametrization of the absorption due to the water vapor continuum.…”

Section: Behavior Of Radiative Transfer Modelsmentioning

confidence: 99%

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A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

et al. 2016

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Abstract. Several recent studies have observed systematic differences between measurements in the 183.31 GHz water vapor line by space-borne sounders and calculations using radiative transfer models, with inputs from either radiosondes (radiosonde observations, RAOBs) or short-range forecasts by numerical weather prediction (NWP) models. This paper discusses all the relevant categories of observation-based or model-based data, quantifies their uncertainties and separates biases that could be common to all causes from those attributable to a particular cause. Reference observations from radiosondes, Global Navigation Satellite System (GNSS) receivers, differential absorption lidar (DIAL) and Raman lidar are thus overviewed. Biases arising from their calibration procedures, NWP models and data assimilation, instrument biases and radiative transfer models (both the models themselves and the underlying spectroscopy) are presented and discussed. Although presently no single process in the comparisons seems capable of explaining the observed structure of bias, recommendations are made in order to better understand the causes.

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Section: Spectroscopy Statusmentioning

confidence: 53%

Section: Behavior Of Radiative Transfer Modelsmentioning

confidence: 99%

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

et al. 2016

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“…The corrected and uncorrected RH data from the 144 RS92 radiosondes launched during RHUBC-II were used as input into version 4.1 of the MonoRTM radiative transfer model (Payne et al, 2008(Payne et al, , 2011Clough et al, 2005) to compute monochromatic downwelling radiance at high spectral resolution (10 MHz) from 168 to 185 GHz. Since the Cerro Toco site almost always has clear skies, the model was run to compute clear-sky radiances (methodology for identifying cases with environmental inhomogeneity or clouds is described in the next paragraph).…”

Section: A M Dzambo Et Al: Comparing Radiosonde Humidity Correctiomentioning

confidence: 99%

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016

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Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS).The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from groundbased microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the groundbased retrievals.The two closure activities -done for clear-sky scenesuse the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG-and MILOcorrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover -a condition not accounted for in the radiance closure experiments.

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“…The MUSICA MetOp/IASI retrieval is based on a nadir version of the retrieval code PROFFIT (PROFile FIT; Hase et al, 2004) and on the corresponding radiative transfer model PRFFWD (PRoFit ForWarD model; Hase et al, 2004 by including water continuum calculations according to the model "MT_CKD" v2.5.2 (Mlawer et al, 2012;Delamere et al, 2010;Payne et al, 2011 fitted during the retrieval process whereby the spectroscopic parameters are taken from the HITRAN database (Gordon et al, 2017) with small modifications for HDO parameters (similar to Schneider et al, 2016, the line intensity parameters of HDO have been increased by 10%).…”

Section: The Musica Retrieval Setupmentioning

confidence: 99%

“…We hypothetically assume that calculations based on the model "MT_CKD" v2.5.2 (Mlawer et al, 2012;Delamere et al, 2010;Payne et al, 2011) only partly capture the full water vapour continuum effect. For the respective Jacobian calculation we perform forward calculations without considering the water vapour continuum (F noWVC (x, p)).…”

Section: Water Vapour Continuummentioning

confidence: 99%

Evaluation of MUSICA MetOp/IASI tropospheric water vapour profiles by theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements

Borger¹,

Schneider²,

Ertl³

et al. 2017

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The IASI water vapour profiles have been compared to 100 individual coinciding GRUAN water vapour profiles. The systematic difference between the IASI and GRUAN data is within 12% at all altitudes. The scatter is largest close to the surface and close to the tropopause, but does never exceed 30%. The study documents that the MUSICA MetOp/IASI retrieval proces-15 sor provides H 2 O profiles with good accuracy and captures the variations in H 2 O volume mixing ratio profiles from 1 −2 km above ground up to altitudes close to the tropopause with a precision that is in accordance to the theoretical error assessment.

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Water Vapor Continuum Absorption in the Microwave

Cited by 77 publications

References 35 publications

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

Evaluation of MUSICA MetOp/IASI tropospheric water vapour profiles by theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements

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Water Vapor Continuum Absorption in the Microwave

Cited by 77 publications

References 35 publications

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

Evaluation of MUSICA MetOp/IASI tropospheric water vapour profiles by theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements

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A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz