Saharan dust infrared optical depth and altitude retrieved from AIRS: a focus over North Atlantic – comparison to MODIS and CALIPSO

Peyridieu, S.; Chédin, A.; Tanré, D.; Capelle, V.; Pierangelo, Clémence; Lamquin, Nicolas; Armante, R.

doi:10.5194/acpd-9-21199-2009

Cited by 18 publications

(34 citation statements)

References 31 publications

(30 reference statements)

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“…The various dust height retrieval algorithms used in this study are summarized in Table 1 and described in more detail below. Peyridieu et al (2010Peyridieu et al ( , 2013, from Volz (1972Volz ( , 1973 layer height Capelle et al (2014) The Mineral Aerosol Profiling from Thermal Infrared (MAPIR) retrieval algorithm is an extensive technical and scientific improvement of the algorithm first published by Vandenbussche et al (2013). Version 3.5 of the algorithm, fully described in Vandenbussche and De Mazière (2017), is used in this study.…”

Section: Passive Instrument Dust Height Retrievalsmentioning

confidence: 99%

“…The LMD (Laboratoire de Météorologie Dynamique) method for the retrieval of dust characteristics from IASI observations was originally developed for application to the Atmospheric Infrared Sounder (AIRS) (Pierangelo et al, 2004(Pierangelo et al, , 2005 and then slightly modified as described in detail in Peyridieu et al (2010Peyridieu et al ( , 2013 and in Capelle et al (2014) for application to IASI. The method used to derive dust characteristics from IASI observations is a three-step physical algorithm based on a look-up table (LUT) approach.…”

Section: Iasi Lmd Algorithmmentioning

confidence: 99%

“…Earlier studies (Capelle et al, 2014;Peyridieu et al, 2010Peyridieu et al, , 2013 compared monthly averaged and gridded data, or investigated a specific episode (Vandenbussche et al, 2013;Cuesta et al, 2015). We perform a point-by-point comparison for selected episodes.…”

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confidence: 99%

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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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Section: Passive Instrument Dust Height Retrievalsmentioning

confidence: 99%

Section: Iasi Lmd Algorithmmentioning

confidence: 99%

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

et al. 2018

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“…However, for improved dust monitoring, physical parameters, such as aerosol optical thickness (AOT) which provides a qualitative measure of dust aerosol intensity, would likely be more beneficial, and retrieval of nighttime VIS-like AOTs should be possible. Recent studies have demonstrated that high spectral resolution infrared sounders (e.g., AIRS and IASI) are able to retrieve dust altitude and IR optical depth (Pierangelo et al 2004;Peyridieu et al 2010;Yao et al 2011). In other words, IR-based AOTs can be converted into corresponding VIS-based AOTs if the spectral dependence of dust-influenced extinction efficiencies is known.…”

Section: Introductionmentioning

confidence: 99%

Nighttime AOT Retrieval for Asian Dusts from MODIS IR Measurements: An Artificial Neural Network Approach

Lee

Sohn²

2012

Journal of the Meteorological Society of Japan

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In order to estimate MODIS-equivalent aerosol optical thicknesses (AOTs) for dust particles during the nighttime over East Asia, an Artificial Neural Network (ANN) model approach was used to combine MODIS-measured infrared (IR) brightness temperatures (BTs) and visible (VIS) AOTs. For training the ANN model, IR BTs were used together with surface type and geometrical information as inputs to predict MODIS-derived AOTs as target data during the daytime when VIS-based AOTs are available. The training was done exclusively over dust-laid pixels during the spring (MarchMay) of 2006 over the East Asian domain (20It should be noted that the obtained daytime AOTs from the ANN model are in good agreement with MODIS-derived AOTs, with a correlation coefficient of 0.77 over the analysis domain. Although a weaker correlation is found during the nighttime when derived AOTs are compared against AOTs from CALIPSO, the case study indicates that the developed ANN method appears to effectively depict both the evolutionary features and intensity of the Asian dust plume during the nighttime. Results further indicate that nighttime VIS-like AOTs can be readily used for monitoring dust movement and its intensity during the nighttime, filling the gap between consecutive daytime AOT distributions.

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“…Some of the recent sensors enable discrimination 254 of the contribution of fine and coarse modes to the total AOT (e.g., MODIS), which, for 255 example, helps to distinguish fine pollution aerosols from coarse mineral dust. Aerosol retrieval 256 from the POLDER instrument can be used to distinguish spherical and non-spherical aerosols 257 within the coarse mode (Herman et al 2005), and thus further refine the detection of mineral 258 dust (Tanré et al 2011;Peyridieu et al 2010). Several parameters (Angström exponent, size9 the different types of aerosols (Kaufman et al 2005;Ginoux et al 2012).…”

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confidence: 99%