Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements

Abstract

Abstract. Liquid water path (LWP) is an important quantity to characterize clouds. Passive microwave satellite sensors provide the most direct estimate on a global scale but suffer from high uncertainties due to large footprints and the superposition of cloud and precipitation signals. Here, we use high spatial resolution airborne microwave radiometer (MWR) measurements together with cloud radar and lidar observations to better understand the LWP of warm clouds over the tropical North Atlantic. The nadir measurements were taken by the German High Altitude and LOng range research aircraft (HALO) in December 2013 (dry season) and August 2016 (wet season) during two Next-generation Advanced Remote sensing for VALidation (NARVAL) campaigns. Microwave retrievals of integrated water vapor (IWV), LWP, and rainwater path (RWP) are developed using artificial neural network techniques. A retrieval database is created using unique cloud-resolving simulations with 1.25 km grid spacing. The IWV and LWP retrievals share the same eight MWR frequency channels in the range from 22 to 31 GHz and at 90 GHz as their sole input. The RWP retrieval combines active and passive microwave observations and is able to detect drizzle and light precipitation. The comparison of retrieved IWV with coincident dropsondes and water vapor lidar measurements shows root-mean-square deviations below 1.4 kg m−2 over the range from 20 to 60 kg m−2. This comparison raises the confidence in LWP retrievals which can only be assessed theoretically. The theoretical analysis shows that the LWP error is constant with 20 g m−2 for LWP below 100 g m−2. While the absolute LWP error increases with increasing LWP, the relative one decreases from 20 % at 100 g m−2 to 10 % at 500 g m−2. The identification of clear-sky scenes by ancillary measurements, here backscatter lidar, is crucial for thin clouds (LWP < 12 g m−2) as the microwave retrieved LWP uncertainty is higher than 100 %. The analysis of both campaigns reveals that clouds were more frequent (47 % vs. 30 % of the time) in the dry than in the wet season. Their average LWP (63 vs. 40 g m−2) and RWP (6.7 vs. 2.7 g m−2) were higher as well. Microwave scattering of ice, however, was observed less frequently in the dry season (0.5 % vs. 1.6 % of the time). We hypothesize that a higher degree of cloud organization on larger scales in the wet season reduces the overall cloud cover and observed LWP. As to be expected, the observed IWV clearly shows that the dry season is on average less humid than the wet season (28 vs. 41 kg m−2). The results reveal that the observed frequency distributions of IWV are substantially affected by the choice of the flight pattern. This should be kept in mind when using the airborne observations to carefully mediate between long-term ground-based and spaceborne measurements to draw statistically sound conclusions.

Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds

“…The LWP can be inaccurate from traditional (satellite and ground-based) algorithms that neglect the scattering due to drizzle drops for clouds with LWP greater than 500 g m −2 . This can lead to inaccurate quantification of adiabaticity (e.g., Kim et al, 2003Kim et al, , 2008, precipitation susceptibility (e.g., Sorooshian et al, 2009), and aerosol-cloud interactions (e.g., McComiskey et al, 2009). LWP is also one of the primary metrics for evaluating single-column model simulations and large-eddy simulation (LES) models in stratocumulus cloud conditions (e.g., Remillard et al, 2017;McGibbon and Bretherton, 2017).…”

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

“…Finally, it has to be noted that the available datasets have a great spatiotemporal overlap but do not match perfectly. The consequences of this are probably less severe than they would be for example in the mid-latitudes, a region that is heavily influenced by synoptic systems, because the study area and period is characterized as mostly undisturbed (Vial et al, 2019) and 440 the variation from flight to flight in the winter season is limited (Jacob et al, 2019b). Nevertheless, the methods presented in this study show high potential to benchmark realistically driven large eddy simulations.…”

“…The radar is part of the HAMP (HALO microwave package, Mech et al, 2014) together with a microwaveradiometer. The latter provides the vertically integrated LWP (Jacob et al, 2019b), which helps to approach the liquid water 60 content which is a key quantity to describe clouds in models like the ICON. The direct observation of the liquid water content profile is difficult (Crewell et al, 2009), but the LWP can be used to estimate the water content when combined with estimates of cloud vertical extend by lidar and radar either in a simple average approach or more sophisticated as a profile (Frisch et al, 1998;Küchler et al, 2018).…”

“…This section briefly describes the measurement principles of the radar and lidar and the respectively used thresholds for cloud detection. The LWP retrieval form the microwave radiometer has a high accuracy, which is better than 20 g m −2 for LWP < 100 g m −2 and better than 20 and 10 % for LWP greater than 100 g m −2 and 500 g m −2 , respectively, as described by Jacob et al (2019b). The LWP is defined as the integral of all liquid in the column comprising cloud liquid and rain water.…”

Synergy of Active- and Passive Remote Sensing: An Approach to Reconstruct Three-Dimensional Cloud Macro- and Microphysics

“…The cloud top height from the WALES lidar (Wirth et al, 2009) and the cloud bottom height calculated from dropsonde data create the possibility to reconstruct single-layer cumulus clouds in all three dimensions. Using the liquid water path from the HAMP radiometer (Mech et al, 2014) derived by Jacob et al (2019a) and the cloud droplet effective radii retrieved from specMACS as constraints for a simple microphysical model allows us to resolve the three-dimensional cloud microphysics. In addition, the radar is used to determine multiple cloud layers.…”