Estimating near-surface specific humidity over convective oceanic regions from cloud base height observations

The surface moisture flux is a large term in the surface energy balance and difficult to estimate remotely. The main difficulty for its remote estimation is a poor ability to measure near-surface humidity. Current methods to retrieve near-surface specific humidity approach the problem statistically and have errors of approximately 1 g kg −1 even in global, annual averages. Using extensive measurements from the EUREC 4 A field campaign (ElUcidating the RolE of Cloud-Circulation Coupling in Climate), we demonstrate that remote sensing measurements of cloud base height can provide useful estimates of near-surface humidity over convective oceanic regions where optically-thick clouds do not prevent lidar sampling. First applying the method to 171 coincident radiosonde and ceilometer pairings collected from a research vessel from 18 January to 14 February 2020 yields skillful predictions of near-surface specific humidity regarding the mean (mean bias 0.43 g kg −1 compared to observed) and its variability ( r =0.76). We then apply this method using an airborne lidar to estimate cloud base height from above. In two representative case studies, we find similar skill in the predicted humidity, with low mean biases (0.04 and −0.06 g kg −1 compared to observed) with substantial variability captured ( r =0.61 and r =0.57, respectively). Besides estimates of cloud base height, we highlight two main error sources: (i) the relative humidity lapse rate below cloud base and (ii) the temperature difference between the sea surface and near-surface air, which would need to be calibrated if using this method to develop an operational product to estimate the near-surface specific humidity from downward-looking spaceborne lidar. This proof of concept raises the potential for application over convective oceanic regions where lidar sampling of cloud base is possible. This method could provide a physics-based augmentation to existing, more empirical approaches and therefore provide an additional observational constraint on the surface energy budget.