Dataset Information

Cloud System Evolution in the Trades-CSET: Following the Evolution of Boundary Layer Cloud Systems with the NSF/NCAR GV.

ABSTRACT: The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the north-Pacific trade-winds. The study centered on 7 round-trips of the NSF NCAR Gulfstream V (GV) between Sacramento, CA and Kona, Hawaii between 1 July and 15 August 2015. The CSET observing strategy was to sample aerosol, cloud, and boundary layer properties upwind from the transition zone over the North Pacific and to resample these areas two days later. GFS forecast trajectories were used to plan the outbound flight to Hawaii with updated forecast trajectories setting the return flight plan two days later. Two key elements of the CSET observing system were the newly developed HIAPER Cloud Radar (HCR) and the High Spectral Resolution Lidar (HSRL). Together they provided unprecedented characterizations of aerosol, cloud and precipitation structures that were combined with in situ measurements of aerosol, cloud, precipitation, and turbulence properties. The cloud systems sampled included solid stratocumulus infused with smoke from Canadian wildfires, mesoscale cloud-precipitation complexes, and patches of shallow cumuli in very clean environments. Ultra-clean layers observed frequently near the top of the boundary layer were often associated with shallow, optically thin, layered veil clouds. The extensive aerosol, cloud, drizzle and boundary layer sampling made over open areas of the Northeast Pacific along 2-day trajectories during CSET is unprecedented and will enable modeling studies of boundary layer cloud system evolution and the role of different processes in that evolution.

SUBMITTER: Albrecht B

PROVIDER: S-EPMC7008952 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Cloud System Evolution in the Trades-CSET: Following the Evolution of Boundary Layer Cloud Systems with the NSF/NCAR GV.

Albrecht Bruce B Ghate Virendra V Mohrmann Johannes J Wood Robert R Zuidema Paquita P Bretherton Christopher C Schwartz Christian C Eloranta Edwin E Glienke Susanne S Donaher Shaunna S Sarkar Mampi M McGibbon Jeremy J Nugent Alison A Shaw Raymond A RA Fugal Jacob J Minnis Patrick P Paliknoda Robindra R Lussier Louis L Jensen Jorgen J Vivekanandan J J Ellis Scott S Tsai Peisang P Rilling Robert R Haggerty Julie J Campos Teresa T Stell Meghan M Reeves Michael M Beaton Stuart S Allison John J Stossmeister Gregory G Hall Samuel S Schmidt Sebastian S

Bulletin of the American Meteorological Society 20190201 1

The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the north-Pacific trade-winds. The study centered on 7 round-trips of the NSF NCAR Gulfstream V (GV) between Sacramento, CA and Kona, Hawaii between 1 July and 15 August 2015. The CSET observing strategy was to sample aerosol, cloud, and boundary layer properties upwind from the transition zone over t ...[more]

PMID: 32042201

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Project description:ReasonPedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing.MethodologyThe article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid.ResultsThe Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data.ConclusionThe proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.

Dataset Information

Cloud System Evolution in the Trades-CSET: Following the Evolution of Boundary Layer Cloud Systems with the NSF/NCAR GV.

Publications

Cloud System Evolution in the Trades-CSET: Following the Evolution of Boundary Layer Cloud Systems with the NSF/NCAR GV.

Similar Datasets

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