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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-18
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-18
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Feb 2019

Research article | 20 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

Marek Jacob1, Felix Ament2, Manuel Gutleben3, Heike Konow2, Mario Mech1, Martin Wirth3, and Susanne Crewell1 Marek Jacob et al.
  • 1Institute for Geophysics and Meteorology, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
  • 2Meteorological Institute, University of Hamburg, Bundesstrasse 55, 20146 Hamburg, Germany
  • 3German Aerospace Center, Münchener Str. 20, 82234 Oberpfaffenhofen-Wessling, Germany

Abstract. Clouds are a strongly variable component of the climate system and several studies have identified especially marine low level clouds to play a critical role for the climate. Liquid water path (LWP) is an important quantity to characterize clouds. Passive microwave satellite sensors provide the most direct estimate on 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 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 campaigns (NARVAL).

Microwave retrievals of integrated water vapor (IWV), LWP and rain water path (RWP) are developed using artificial neural network techniques and a unique database based on cloud-resolving model simulations with 1.25 km grid spacing. The IWV and LWP retrievals share the same eight MWR frequency channels as their sole input. 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 the dependency of the uncertainty on LWP itself as the error is below 20 g m−2 for LWP below 100 g m−2 and below 20 % above. 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 RWP retrieval combines active and passive microwave observations and is able to detect drizzle and light precipitation.

The analysis of both campaigns reveals that clouds were more frequent in the dry than in the wet season and their LWP and RWP were higher, but microwave scattering of ice was observed more frequently in the wet season (1.6 % vs. 0.5 % of the time). As to be expected, the observed IWV clearly shows that the wet season (mean IWV = 41 kg m−2) is more humid than the dry season (mean IWV = 28 kg m−2). The results reveal that the observed frequency distributions of IWV are strongly affected by the choice of the flight pattern. Therefore, the airborne observations need to be used carefully to mediate between long-term ground-based and spaceborne measurements to draw statistically sound conclusions.

Marek Jacob et al.
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Short summary
Tropical clouds are a key climate component, but are still not fully understood. Therefore, we analyze airborne remote sensing measurements that were taken in the dry and wet seasons over the Atlantic east of Barbados. From these we derive sub-kilometer resolution data of vertically integrated atmospheric water vapor and liquid water. Results show, that although the humidity is lower in the dry season, clouds are more frequent, contain more water and produce more rain than in the wet season.
Tropical clouds are a key climate component, but are still not fully understood. Therefore, we...
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