Global vertically resolved reconstruction of cloud droplet number concentration combining satellite retrievals, reanalysis data, and in situ observations

Abstract. This work develops a global estimate of vertically resolved cloud droplet number concentration (N_d) by combining reanalysis data, satellite retrievals, ground-based observations, and field campaign measurements. This is accomplished by developing a neural-network estimator, termed NDnet, that uses satellite-retrieved cloud properties to infer three-dimensional N_d fields. Training proceeds in two stages, with a prior network first emulating a reanalysis and then being refined using observations to impose a global observational constraint. Probabilistic objective functions account for observational and representativeness errors and provide an explicit estimate of uncertainty in N_d. Applied over the satellite record, NDnet produces a more gradual decrease of N_d with altitude than suggested by the reanalysis data and higher N_d at high latitudes. The resulting fields also show substantial vertical heterogeneity, particularly in tropical and subtropical regions and at high altitude. The three-dimensional N_d dataset can be compared directly with model output and provides a new constraint for evaluating aerosol–cloud interactions in atmospheric models.