Matching radiative transfer models and radiosonde data from the EPS/Metop Sodankylä campaign to IASI measurements

Calbet, Xavier; Kivi, Rigel; Tjemkes, Stephen; Montagner, Francois; Stuhlmann, R.

doi:10.5194/amt-4-1177-2011

Cited by 20 publications

(29 citation statements)

References 22 publications

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“…Note the very low standard deviation, below 1σ IASI instrument noise, for some regions of the spectrum for the "Interpolated" and "RS92 Corr." profiles, as already acknowledged in Calbet et al (2011). Also recall that there is only one parameter retrieved from IASI radiances when obtaining the calculated radiances, which is the surface skin temperature.…”

Section: Atmospheric Profile Errorsmentioning

confidence: 96%

“…In this paper, it is taken as the best estimate of the atmosphere for this hyperspectral observation. See Calbet et al (2011) for more details.…”

Section: Raw Datamentioning

confidence: 99%

“…"), which are complemented in the upper layers, where the sonde instruments reach their limit, with the "ECMWF" profile. See Calbet et al (2011) for more details.…”

Section: Observed Minus Calculated Radiancesmentioning

confidence: 99%

“…Radiosonde data are from the EPS/Metop Sodankylä campaign, which took place during the time period 4 June to 5 September 2007 (for more details see Calbet et al, 2011). Also, ECMWF analyses have been used either on its own or to complement the radiosonde data.…”

Section: Raw Datamentioning

confidence: 99%

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Assessment of adequate quality and collocation of reference measurements with space-borne hyperspectral infrared instruments to validate retrievals of temperature and water vapour

Calbet

2016

Atmos. Meas. Tech.

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Abstract.A method is presented to assess whether a given reference ground-based point observation, typically a radiosonde measurement, is adequately collocated and sufficiently representative of space-borne hyperspectral infrared instrument measurements. Once this assessment is made, the ground-based data can be used to validate and potentially calibrate, with a high degree of accuracy, the hyperspectral retrievals of temperature and water vapour.

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Section: Atmospheric Profile Errorsmentioning

confidence: 96%

“…In this paper, it is taken as the best estimate of the atmosphere for this hyperspectral observation. See Calbet et al (2011) for more details.…”

Section: Raw Datamentioning

confidence: 99%

“…"), which are complemented in the upper layers, where the sonde instruments reach their limit, with the "ECMWF" profile. See Calbet et al (2011) for more details.…”

Section: Observed Minus Calculated Radiancesmentioning

confidence: 99%

Section: Raw Datamentioning

confidence: 99%

See 2 more Smart Citations

Assessment of adequate quality and collocation of reference measurements with space-borne hyperspectral infrared instruments to validate retrievals of temperature and water vapour

Calbet

2016

Atmos. Meas. Tech.

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“…The RS92 has participated in a number of campaigns and intercomparisons Nash et al, 2006Nash et al, , 2011Calbet et al, 2011;Leblanc et al, 2011;Bock et al, 2013). Campaigns have identified error sources for the RS92 such as radiation dry bias (Vömel et al, 2007b), sensor time-lag (Miloshevich et al, 2004), and a temperaturedependent calibration error for the humidity sensors (Vömel et al, 2007b;Miloshevich et al, 2009).…”

Section: Description Of the Rs92 Radiosondementioning

confidence: 99%

Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde

et al. 2014

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Abstract. The GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) data processing for the Vaisala RS92 radiosonde was developed to meet the criteria for reference measurements. These criteria stipulate the collection of metadata, the use of well-documented correction algorithms, and estimates of the measurement uncertainty. An important and novel aspect of the GRUAN processing is that the uncertainty estimates are vertically resolved. This paper describes the algorithms that are applied in version 2 of the GRUAN processing to correct for systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, as well as how the uncertainties related to these error sources are derived. Currently, the RS92 is launched on a regular basis at 13 out of 15 GRUAN sites. An additional GRUAN requirement for performing reference measurements with the RS92 is that the manufacturerprescribed procedure for the radiosonde's preparation, i.e. heated reconditioning of the sensors and recalibration during ground check, is followed. In the GRUAN processing however, the recalibration of the humidity sensors that is applied during ground check is removed. For the dominant error source, solar radiation, laboratory experiments were performed to investigate and model its effect on the RS92's temperature and humidity measurements. GRUAN uncertainty estimates are 0.15 K for night-time temperature measurements and approximately 0.6 K at 25 km during daytime. The other uncertainty estimates are up to 6 % relative humidity for humidity, 10-50 m for geopotential height, 0.6 hPa for pressure, 0.4-1 m s −1 for wind speed, and 1 • for wind direction. Daytime temperature profiles for GRUAN and Vaisala processing are comparable and consistent within the estimated uncertainty. GRUAN daytime humidity profiles are up to 15 % moister than Vaisala processed profiles, of which two-thirds is due to the radiation dry bias correction and onethird is due to an additional calibration correction. Redundant measurements with frost point hygrometers (CFH and NOAA FPH) show that GRUAN-processed RS92 humidity profiles and frost point data agree within 15 % in the troposphere. No systematic biases occur, apart from a 5 % dry bias for GRUAN data around −40 • C at night.

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Validation practices for satellite‐based Earth observation data across communities

Loew

Bell

Brocca

et al. 2017

Reviews of Geophysics

180

137

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Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state‐of‐the‐art methods of satellite validation and documents their similarities and differences. First, the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given.

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Matching radiative transfer models and radiosonde data from the EPS/Metop Sodankylä campaign to IASI measurements

Cited by 20 publications

References 22 publications

Assessment of adequate quality and collocation of reference measurements with space-borne hyperspectral infrared instruments to validate retrievals of temperature and water vapour

Assessment of adequate quality and collocation of reference measurements with space-borne hyperspectral infrared instruments to validate retrievals of temperature and water vapour

Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde

Validation practices for satellite‐based Earth observation data across communities

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