Mesoscale Near-Surface Wind Speed Variability Mapping with Synthetic Aperture Radar

Area of Science:

• Oceanography
• Meteorology
• Remote Sensing

Background:

• Synthetic aperture radar-derived wind speed (SDWS) images frequently display operationally significant wind speed variability.
• Automated identification of meteorological phenomena causing this variability is crucial for operational applications.

Purpose of the Study:

• To develop and test feature extraction and pixel aggregation techniques for analyzing mesoscale variability in SDWS images.
• To lay the groundwork for automated distinguishing of meteorological phenomena impacting sea surface wind speeds.

Main Methods:

• Utilized a dataset of twenty-eight SDWS images with diverse near-surface wind speed variability.
• Applied Gaussian high- and low-pass filters, local entropy, and local standard deviation for feature extraction.
• Employed principle component analysis for pixel aggregation of filtered data.

Main Results:

• Gaussian filters, local entropy, and local standard deviation proved effective for feature extraction.
• Principle component analysis demonstrated strong performance in pixel aggregation.
• The tested techniques showed promise for analyzing mesoscale wind speed variability.

Conclusions:

• The findings support the effectiveness of the tested feature extraction and pixel aggregation methods.
• Recommendations are provided for advancing automated analysis of SDWS data for meteorological applications.
• This research serves as a foundational step towards automated weather phenomenon identification in radar wind speed data.