The Improved Initialization Inversion Method: A High Resolution Physical Method for Temperature Retrievals from Satellites of the TIROS-N Series

Chédin, A.; Scott, N. A.; Wahiche, C.; Moulinier, P.

doi:10.1175/1520-0450(1985)024<0128:tiiima>2.0.co;2

Cited by 364 publications

(175 citation statements)

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“…Here we provide some examples rather than a complete review. LST can be retrieved from a single infrared channel through an accurate radiative transfer model if surface emissivity is known and temperature/water vapor profile is given by either satellite soundings or conventional radiosonde data (Price, 1983;Susskind, Rosenfield, Reuter, & Chahine, 1984;Chedin, Scott, Wahiche, & Moulinier, 1985;Ottlé & Vidal-Madjar, 1992). Split-window LST methods require known surface emissivities to make corrections for the atmospheric and surface emissivity effects based on the differential atmospheric absorption in the 10-13 Am split window without knowledge of the atmospheric temperature/water vapor profile although column water vapor is used in some split-window LST algorithms to improve the accuracy of LST retrieval (Price, 1984;Becker, 1987;Wan & Dozier, 1989;Becker & Li, 1990;Sobrino, Coll, & Caselles, 1991;Vidal, 1991;Kerr, Lagouarde, & Imbernon, 1992;Ottlé & Stoll, 1993;Prata, 1994;Wan & Dozier, 1996).…”

Section: Heritage For Lst Remote Sensingmentioning

confidence: 99%

Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data

Wan¹,

Zhang²,

Zhang³

et al. 2002

Remote Sensing of Environment

771

488

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Section: Heritage For Lst Remote Sensingmentioning

confidence: 99%

Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data

Wan¹,

Zhang²,

Zhang³

et al. 2002

Remote Sensing of Environment

771

488

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“…Section 2 describes the AIRS-LMD cloud property retrieval algorithm, which makes use of retrieved atmospheric temperature and water vapour profiles of the AIRS L2 data (Susskind et al, 2003(Susskind et al, , 2006 and of atmospheric spectral transmissivity profiles which have been simulated for atmospheric profiles of the Thermodynamic Initial Guess Retrieval (TIGR) data base (Chédin et al, 1985;Chevallier et al, 1998). AIRS data have been collocated with CALIPSO data and then with the Radar -Lidar GEOPROF data.…”

Section: Introductionmentioning

confidence: 99%

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

et al. 2010

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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thicknessCorrespondence to: C. J. Stubenrauch (stubenrauch@lmd.polytechnique.fr) optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.

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“…Entries to the model include AOD, height, surface pressure, surface temperature and emissivity, viewing angle, two refractive indices from Volz (1972Volz ( , 1973 and Balkanski et al (2007) and a set of 2311 atmospheric situations. These were selected using statistical methods from 80 000 radiosonde reports and stored in the Thermodynamic Initial Guess Retrieval (TIGR) climatological database (Chédin et al, 1985;Chevallier et al, 1998). The PSD is modelled by a monomodal log-normal distribution described by the effective radius (R eff ) and the standard deviation of the distribution σ g .…”

Section: Iasi Lmd Algorithmmentioning

confidence: 99%

Comparison of dust-layer heights from active and passive satellite sensors

et al. 2018

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Abstract. Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosollayer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosollayer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOPderived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5-0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

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The Improved Initialization Inversion Method: A High Resolution Physical Method for Temperature Retrievals from Satellites of the TIROS-N Series

Cited by 364 publications

References 0 publications

Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data

Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Comparison of dust-layer heights from active and passive satellite sensors

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