Intercalibration of the Meteosat‐7 water vapor channel with SSM/T‐2

Sohn, Byung-Ju; Schmetz, Johannes; Tjemkes, Stephen; Koenig, Marianne; Lutz, Hans-Joachim; Arriaga, A.; Chung, Eunhyea

doi:10.1029/2000jd900188

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…New measurements have been added in recent years with hyperspectral sounders including the Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-Track Infrared Sounder (CrIS). Efforts in developing upper tropospheric water vapor datasets from different satellite sensors established the climatology of UTH [17,[23][24][25][26][27], and further studies advanced our Remote Sens. 2018, 10, 1188 3 of 15 understanding of its variability [16,21,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events

2018

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As part of the activities for the Global Energy and Water Exchanges (GEWEX) water vapor assessment project, the consistency of satellite-observed upper tropospheric humidity (UTH) and total column water vapor (TCWV) is examined. The examination is focused on their respective patterns during major El Niño events. The analysis shows that the two datasets, consisting of one measurement of vertically averaged relative humidity in the upper troposphere and one of absolute water vapor integrated over the atmospheric vertical column with dominant contribution from the lower troposphere, are consistent over the equatorial central-eastern Pacific, both showing increases of water vapor during major El Niño events as expected. However, the magnitude of drying in the TCWV field over the western Pacific is much weaker than that of moistening over the central-eastern Pacific, while the UTH field exhibits equivalent magnitude of drying and moistening. Furthermore, the drying in the UTH field covers larger areas in the tropics. The difference in their patterns results in an opposite phase in the time series during a major El Niño event when a tropical average is taken. Both UTH and TCWV are closely correlated with major climate indices. However, they have significantly different lag correlations with the Niño 3.4 index in both the sign (positive or negative) and lag time over tropical oceans.

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Section: Introductionmentioning

confidence: 99%

Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events

2018

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“…Inter-satellite calibration is a useful method that has been used in many studies (e.g., Sohn et al, 2000;Heidinger et al, 2002;Minnis et al, 2002a, b;Wu and Sun, 2005;. Measured radiances (or reflectances) by the target sensor are compared with a well-calibrated reference sensor under ray-matched conditions with the same solar and viewing geometries.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R

Ham

Sohn

2010

Atmos. Chem. Phys.

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and MTSAT-1R visible channel reflectances. In the simulation, cloud three-dimensional (3-D) radiative effect associated with subgrid variations is taken into account using the lognormal-independent column approximation (LN-ICA) to minimize the simulation bias caused by the plane-parallel homogeneous assumption. Third, an independent method uses the typical optical properties of deep convective clouds (DCCs) to simulate reflectances of selected DCC targets.Although all three methods are not in perfect agreement, the results suggest that calibration coefficients of Meteosat-8/9 0.640-µm channels are underestimated by 6-7%. On the other hand, the calibration accuracy of MTSAT-1R visible channel appears to be variable with the target reflectance itself because of an underestimate of calibration coefficient (up to 20%) and a non-zero space offset. The results further suggest that the solar channel calibration scheme combining the Correspondence to: B. J. Sohn (sohn@snu.ac.kr) three methods in this paper can be used as a tool to monitor the calibration performance of visible sensors that are particularly not equipped with an onboard calibration system.

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“…It has been shown in Sohn et al [41] that the weighting functions of METEOSAT IR and the microwave UTH measurements lie in a similar altitude range and therefore this should not have caused the phase difference between the IR and the microwave diurnal cycles. They looked at the Special Sensor Microwave Water Vapor Profiler, SSM/T-2 where the upper tropospheric humidity channel is the same as of AMSU-B and MHS.…”

Section: Differences Between Ir and Microwave Diurnal Cyclementioning

confidence: 95%

Evaluating the Diurnal Cycle of Upper Tropospheric Humidity in Two Different Climate Models Using Satellite Observations

Kottayil

John

Buehler³

et al. 2016

Remote Sensing

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Abstract:The diurnal cycle of upper tropospheric humidity (UTH) is known to be influenced by such processes as convection and the formation of clouds which are parameterized in current global climate models. In this study, we evaluate the performance of two climate models, the Community Atmospheric Model version 5 (CAM-5) and the Global Atmosphere 3.0 (GA-3) model in simulating the diurnal cycle of UTH (represented by a combination of sinusoids of 12 and 24 h periods) by comparing with microwave and infrared (IR) measurements (where available). These comparisons were made over two convective land regions in South America and Africa, and over oceanic regions in the Atlantic, Indian and West Pacific for the month of January 2007. We analyzed how the diurnal cycles from IR and microwave instruments differ, and the reason for the differences. Our study suggests that the differences in the diurnal cycles of IR and microwave UTH result from sampling differences due to the presence of clouds. As noted by earlier studies, the models exhibit considerable discrepancies in diurnal amplitude and phase relative to observations, and these discrepancies have different magnitudes over land and ocean.

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Intercalibration of the Meteosat‐7 water vapor channel with SSM/T‐2

Cited by 28 publications

References 17 publications

Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events

Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events

Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R

Evaluating the Diurnal Cycle of Upper Tropospheric Humidity in Two Different Climate Models Using Satellite Observations

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