From Event: SPIE Remote Sensing, 2021
Influence of aerosol particles in the cloud formation and microphysical properties, commonly known as aerosolcloud interaction (ACI), has emerged as a research field of special interest since the low-level knowledge on this topic causes the largest uncertainties in climate projections. Active remote sensing already provided promising results combining backscatter lidars and cloud radars to characterize atmospheric aerosol and cloud droplets. The retrieval of aerosol microphysical properties is of high interest for ACI studies. However, complex inversion techniques are usually required. Mamouri and Ansmann (2016) proposed to derive the so-called extinctionto- number-concentration factors to retrieve aerosol number concentration from aerosol extinction in a more straightforward way. These factors can be easily obtained from the relationship between the aerosol number concentration and the aerosol optical depth from AERONET measurements. However, experimental data are not always available. This study demonstrates that it is also possible to obtain the extinction-to-number-concentration factors using pre-calculated optical properties. To do so, MOPSMAP, open-source software based on a data set of pre-calculated single-particle optical properties, is used. Using a study case (9 July 2021), it is shown that number concentration of aerosol-particle with radius larger than 250 μm (n250) derived with MOPSMAP is within the uncertainty range of the one derived with AERONET.
© (12 September 2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
C. M. Fajardo-Zambrano, J. A. Bravo-Aranda, M. J. Granados-Muñoz, D. Bermejo-Pantaleón, and L. Alados-Arboledas, "Retrieval of the relation between aerosol number concentration and aerosol optical depth using MOPSMAP," Proc. SPIE 11859, Remote Sensing of Clouds and the Atmosphere XXVI, 118590K (Presented at SPIE Remote Sensing: 12 September 2021; Published: 12 September 2021); https://doi.org/10.1117/12.2599930.
