4.7 Article Data Paper

Trade-wind clouds and aerosols characterized by airborne horizontal lidar measurements during the EUREC4A field campaign

期刊

EARTH SYSTEM SCIENCE DATA
卷 12, 期 4, 页码 2919-2936

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COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/essd-12-2919-2020

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  1. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [694768]
  2. French Space Agency CNES through the EECLAT proposal

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From 23 January to 13 February 2020, 20 manned research flights were conducted over the tropical Atlantic, off the coast of Barbados (13 degrees 30' N, 58 degrees 30' W), to characterize the trade-wind clouds generated by shallow convection. These flights were conducted as part of the international EUREC(4)A (Elucidating the role of cloud-circulation coupling in climate) field campaign. One of the objectives of these flights was to characterize the trade-wind cumuli at their base for a range of meteorological conditions, convective mesoscale organizations and times of the day, with the help of sidewards-staring remote sensing instruments (lidar and radar). This paper presents the datasets associated with horizontal lidar measurements. The lidar sampled clouds from a lateral window of the aircraft over a range of about 8 km, with a horizontal resolution of 15 m, over a rectangle pattern of 20 km by 130 km. The measurements made possible the characterization of the size distribution of clouds near their base and the presence of dust-like aerosols within and above the marine boundary layer. This paper presents the measurements and the different levels of data processing, ranging from the raw Level 1 data (https://doi.org/10.25326/57; Chazette et al., 2020c) to the Level 2 and Level 3 processed data that include a horizontal cloud mask (https://doi.org/10.25326/58; Chazette et al., 2020b) and aerosol extinction coefficients (https://doi.org/10.25326/59; Chazette et al., 2020a). An intermediate level, companion to Level 1 data (Level 1.5), is also available for calibrated and geolocalized data (https://doi.org/10.25326/57; Chazette et al., 2020c).

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