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Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Differential Leveling01:12

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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Automated method for lidar determination of cloud-base height and vertical extent.

S R Pal, W Steinbrecht, A I Carswell

    Applied Optics
    |August 20, 2010
    PubMed
    Summary

    An automated algorithm analyzes lidar cloud returns to determine cloud-base and cloud-top heights. This new method effectively processes complex cloud data, improving atmospheric measurements.

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    Area of Science:

    • Atmospheric Science
    • Remote Sensing
    • Cloud Physics

    Background:

    • Accurate cloud-base and cloud-top height determination is crucial for atmospheric research and weather forecasting.
    • Existing methods using rotating beam and laser ceilometers have limitations in handling complex cloud conditions.

    Purpose of the Study:

    • To develop and evaluate an automated algorithm for analyzing lidar cloud returns.
    • To determine cloud-base height, cloud-top height, and altitude of maximum signal return.
    • To assess the algorithm's performance across a wide range of complex cloud scenarios.

    Main Methods:

    • Development of an automated analysis algorithm for lidar data.
    • Utilizing data from the Experimental Cloud Lidar Pilot Study program.
    • Evaluation of the algorithm using cloud data obtained at 532 nm and 1064 nm with a Nd:YAG lidar.

    Main Results:

    • The developed algorithm successfully determines cloud-base and cloud-top heights.
    • The algorithm accurately identifies the altitude of the maximum lidar return signal.
    • The automated method demonstrated capability in handling diverse and complex cloud situations.

    Conclusions:

    • The automated lidar data analysis algorithm is effective for determining key cloud height parameters.
    • The algorithm shows promise for improving the accuracy and efficiency of cloud measurements.
    • Further refinement in defining cloud-base and cloud-top heights is discussed in the context of automated lidar analysis.