Remote Sensing (Mar 2022)
Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index
Abstract
Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.
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