研究目的
To demonstrate the capability of an airglow imager for deriving cloud motion vectors (CMVs) by adopting advanced satellite retrieval algorithms and implementing them on airglow imager-derived cloud imageries.
研究成果
The study successfully demonstrates the use of an airglow imager for deriving CMVs by adapting satellite retrieval algorithms. The derived winds show good correspondence with MST radar-derived winds, with RMSEs < 2.4 m s?1. The technique offers high temporal and spatial resolution but has limitations related to weather conditions and cloud types.
研究不足
The technique is limited to non-rainy days due to the optical nature of the airglow imager. Accurate estimation of cloud height requires additional lidar measurements, which can be costly. The limited field of view of the imager restricts its applicability to certain clouds. Rapidly evolving clouds may not be suitable for tracking.
1:Experimental Design and Method Selection
The study adopts an advanced photogrammetric technique for the estimation of CMVs, modifying it to suit airglow imager observations. The methodology includes image processing for noise removal and enhancing image contrast, identification of target cloud, estimation of cloud height using lidar measurements, and employing 2-D cross-correlation to estimate the CMVs.
2:Sample Selection and Data Sources
Cloud imageries obtained from an airglow imager located at Gadanki, India, are used. The data sets are augmented with Rayleigh–Mie lidar (RML) and boundary layer lidar (BLL) for cloud height estimation, GPS radiosonde-derived winds for building wind climatology, and MST radar-derived winds for validation.
3:List of Experimental Equipment and Materials
Airglow imager with a circular medium format F/4 Mamiya fish eye lens,Rayleigh–Mie lidar (RML),Boundary layer lidar (BLL),GPS radiosonde,MST radar
4:Experimental Procedures and Operational Workflow
The process involves cropping and enhancing the original images, removing noisy structures, identifying the target cloud, estimating cloud height and pixel width, and tracking the target cloud using 2-D cross-correlation to estimate CMVs.
5:Data Analysis Methods
The derived CMVs are evaluated against MST radar-derived winds. The comparison includes calculating root mean square errors (RMSEs) for zonal and meridional wind velocities.
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