Evaluation of IASI-derived dust aerosol characteristics over the tropical belt

Abstract

Abstract.IASI (Infrared Atmospheric Sounder Interferometer)-derived monthly mean infrared (10 µm) dust aerosol optical depth (AOD) and altitude are evaluated against ground-based Aerosol RObotic NETwork of sun photometers (AERONET) measurements of the 500 nm coarse-mode AOD and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements of altitude at 38 AERONET sites (sea and land) within the tropical belt (30 • N-30 • S). The period covered extends from July 2007 to June 2013. The evaluation goes through the analysis of Taylor diagrams and box-and-whiskers plots, separating situations over oceanic regions and over land. For the AOD, such an evaluation raises the problem of the difference between the two spectral domains used: infrared for IASI and visible for AERONET. Consequently, the two measurements do not share the same metrics. For that reason, AERONET coarse-mode AOD is first "translated" into IASI-equivalent infrared AOD. This is done by the determination, site by site, of an infrared to visible AOD ratio. Because translating visible coarse-mode AOD into infrared AOD requires accurate knowledge of variables, such as the infrared refractive index or the particle size distribution, quantifying the bias between these two sources of AOD is not straightforward. This problem is detailed in this paper, in particular in Appendix A. For the sites over oceanic regions, the overall AOD temporal correlation comes to 0.86 for 786 items (IASI and AERONET monthly mean bins). The overall normalized standard deviation (i.e. ratio of the standard deviation of the test data (IASI) to that of the reference data (AERONET)) is 0.93, close to the desired value of 1. Over land, essentially desert, correlation is 0.74 for 619 items and the normalized standard deviation is 0.86. This slight but significant degradation over land most probably results from the greater complexity of the surface (heterogeneity, elevation) and, to a lesser extent, to the episodic presence of dust within the boundary layer (particularly for sites close to active sources) to which IASI, as any thermal infrared sounder, is poorly sensitive, unlike AERONET. Site by site, disparities appear that are principally due to either the insufficient number of AERONET observations throughout the period considered, to the complexity of the location leading to the mixing of several aerosol types (in the case of the Persian Gulf, for example), to surface heterogeneities (elevation, emissivity, etc.), or to the use of a single aerosol model (called "MITR"). Results using another aerosol model, with a different refractive index, are presented and discussed. Concerning altitude over oceanic regions, correlation is 0.70 for 853 items and the normalized standard deviation is 0.92. A systematic bias of −0.4 km (IASI-CALIOP) is observed, with a standard deviation of 0.48 km. This result is satisfactory, considering the important differences between the two instruments (space-time coverage, definition of the altitude). Altitude results over land, essentially over ...

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

“…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.…”

“…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 . Following the results of previous sensitivity studies (Appendix A, Capelle et al, 2014;Pierangelo et al, 2005), fixed values are taken for the effective radius (R eff = 2.3 µm) and for the standard deviation of the size distribution (σ g = 0.65).…”

“…Several aspects of the retrieval algorithm, e.g. robustness in the aerosol model (size distribution, shape and refractive indices), possible contamination by other aerosol species, radiative transfer model bias removal or cloud mask including discrimination between clouds and aerosols, were investigated and details may be found, for example, in Pierangelo et al (2004) and Capelle et al (2014). The surface emissivity spectrum is supposed to be known and is read from a 0.5 • monthly grid retrieved from IASI (Capelle et al, 2012).…”

“…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.…”

“…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.…”