1Dept. of Applied Environmental Science, Stockholm University, Sweden
2The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, USA
3Earth and Environmental Sciences Division, Los Alamos National Laboratory, Mail Stop T003, Los Alamos, NM, 87545, USA
4Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
5Dept. of Meteorology, Stockholm University, Sweden
6Dept. of Chemical and Environmental Engineering, The University of Arizona, Tucson, USA
7Dept. of Atmospheric Sciences, The University of Arizona, Tucson, USA
Abstract. This paper explores the feasibility of inverse modeling to determine cloud-aerosol interactions using a pseudo-adiabatic cloud-parcel model. Two-dimensional plots of the objective function, containing the difference between the measured and model predicted droplet size distribution, are presented for selected pairs of cloud parcel model parameters. From these response surfaces it is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attractions, numerous local optima, and considerable parameter insensitivity. Sensitivity analysis is performed to help select an appropriate objective function that maximizes information retrieval from the measured droplet size distribution to help identify the unknown model parameters. The identifiability of the model parameters will be dependent on the choice of the objective function; including the interstitial aerosol will aid the calibration of parameters describing the smaller aerosol mode. Cloud parcel models that employ a moving-centre based calculation of the droplet size distribution require both the X and Y components of the dN/dlogdp size distribution function to be explicitly included in the objective function. Other possible improvements identified include an improved representation of the resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, and further development of fixed-sectional cloud models that minimize numerical diffusion. Despite these developments, powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.