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 (Nd) 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 Nd 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 Nd. Applied over the satellite record, NDnet produces a more gradual decrease of Nd with altitude than suggested by the reanalysis data and higher Nd at high latitudes. The resulting fields also show substantial vertical heterogeneity, particularly in tropical and subtropical regions and at high altitude. The three-dimensional Nd dataset can be compared directly with model output and provides a new constraint for evaluating aerosol–cloud interactions in atmospheric models.