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Precipitation life cycle in trade wind cumuli

Subject Area Atmospheric Science
Term from 2020 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 437320342
 
Final Report Year 2024

Final Report Abstract

Precipitation development is crucial in cumulus clouds in the trade wind regions. It alters the marine boundary layer (MBL) thermodynamic conditions and drives the formation of cloud mesoscale organization, which impacts the radiation budget and climate sensitivity. The EUREC4A (Elucidating the Role of Cloud-Circulation Coupling in Climate) measurement campaign took place in the trade wind region in January and February 2020 to provide useful observations to understand how clouds and precipitation interact with MBL and mesoscale organization patterns. This project actively participated in the effort to monitor clouds and precipitation and how oceanic mesoscale patterns can impact the maritime boundary layer structure. It coordinated the operations of the ARM stable table, the W-band cloud radar, and the Micro rain radar onboard the research vessel Maria S. Merian (RV MSM). It also contributed to collecting precipitation samples and planning the radiosonde launch. We conducted continuous unprecedented high temporal and spatial resolution observations of clouds and precipitation. Our ship sampled a broad ocean region where evident SST mesoscale structures were present and could provide insights into the variability of cloud and MBL regimes with latitude. After the campaign, we cured the data post-processing, ensuring that all the data collected with our instruments were published and open-access. We fully document the ship-motion correction algorithms implemented and the solutions to the interference problems encountered on the ship in a related data publication. The scientific analysis revealed signatures in the dynamic and thermodynamic properties of the MBL that agree with a weaker mixing occurring on cold seawater patches. At the same time, clouds grow deeper, holding higher cloud water contents and eventually precipitating over warm seawater patches. We also quantify virga and precipitation-induced anomalies in the MBL thermodynamic and characterize cold pool intensity statistically. Finally, we also tried to link local processes with cloud mesoscale organization patterns derived with deep learning selfsupervised approaches.

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