PROJECT TITLE :

Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors

ABSTRACT:

Geostationary satellite knowledge from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) at the side of A-Train knowledge are used to develop an algorithm for detecting biomass-burning smoke aerosols above closed-cell stratocumulus (Sc) clouds. The detection depends on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI information. A-Train knowledge as well as the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference information for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the aptitude for identifying smoke on top of closed-cell Sc with an OMI aerosol index price exceeding zero.five and a cloud optical thickness greater than vi at 0.eighty one $mumboxm$. The user accuracy of this algorithm is ∼49% when using solely spectral signature and textural tests. When incorporating the “temporal consistency” tests into the algorithm, the user accuracy will increase to ∼sixty five%. The producer accuracy is over ∼77%, implying that the SEVIRI algorithm usually identifies smoke on top of clouds when CALIOP also identifies the identical feature at the collocated pixel. But, CALIOP has the tendency to underestimate the presence of skinny smoke aerosols above liquid clouds during daytime. This algorithm will be used to detect and study the daytime variation of smoke above liquid clouds.