Airborne Lidar Observations of Water Vapor Variability in Tropical Shallow Convective Environment

Abstract

An airborne downward-pointing water vapor lidar provides two-dimensional, simultaneous curtains of atmospheric backscatter and humidity along the flight track with high accuracy and spatial resolution. In order to improve the knowledge on the coupling between clouds, circulation and climate in the trade wind region, the DLR (Deutsches Zentrum fu¨ r Luft- und Raumfahrt) water vapor lidar was operated on board the German research aircraft HALO during the NARVAL (Next Generation Aircraft Remote Sensing for Validation Studies) field experiment in December 2013. Out of the wealth of about 30 flight hours or 25,000 km of data over the Tropical Atlantic Ocean east of Barbados, three ~ 2-h-long, representative segments from different flights were selected. Analyses of Meteosat Second Generation images and dropsondes complement this case study. All observations indicate a high heterogeneity of the humidity in the lowest 4 km of the tropical troposphere, as well as of the depth of the cloud (1–2 km thick) and sub-cloud layer (~ 1 km thick). At the winter trade inversion with its strong humidity jump of up to 9 g/kg in water vapor mixing ratio, the mixing ratio variance can attain 9 (g/kg)2, while below it typically ranges between 1 and 3 (g/kg)2. Layer depths and partial water vapor columns within the layers vary by up to a factor of 2. This affects the total tropospheric water vapor column, amounting on average to 28 kg/m2, by up to 10 kg/m2 or 36%. The dominant scale of the variability is given by the extent of regions with higher-than-average humidity and lies between 300 and 600 km. The variability mainly stems from the alternation between dry regions and moisture lifted by convection. Occasionally, up to 100-km large dry regions are observed. In between, convection pushes the trade inversion upward, sharpening the vertical moisture gradient that is colocated with the trade inversion. In most of the water vapor profiles, this gradient is stronger than the one located at the top of the sub-cloud layer. Lidar observations in concert with models accurately reproducing the observed variability are expected to help evaluate the role these findings play for climate.