Characteristics of Water Vapor Tracked Winds.

Abstract

Wind measurements were obtained by tracking water vapor features on METEOSAT and GOES/VAS 6.7 micron water vapor images. While pure water vapor features are fuzzy, there are discernible features which can be tracked. An investigation of preprocessing algorithms designed to bring out the features to be tracked showed that high pass filters tended to bring out the noise in the image, while low pass filters washed out the features. Equal population gray scale stretching enhanced both the water vapor features and the cloud features. Sliced linear gray scale stretching under operator control gave the best enhancement to the water vapor features. The height assignment of water vapor features is uncertain. The feature is in the middle troposphere, but can vary between 300 mb and 700 mb. Rather than assign all the winds at an arbitrary height, an attempt was made to infer the water vapor height from the cloud heights and motions as compared to the water vapor motions. Water vapor tracking was done interactively using the Man-computer Interactive Data Access System (McIDAS). Analysis of the derived wind fields showed that water vapor does provide additional meteorological information around jets and mid-troposphere motions not normally provided by cloud drift winds. Comparisons with radiosonde and cloud winds showed the water vapor winds to have a slightly lower quality then cloud winds. Errors tended to be about 2n/sec worse than cloud tracked wind errors.

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1981
Accession Number
ADA113651

Entities

People

  • Frederick R. Mosher
  • Tod Stewart

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computers
  • Filters
  • Gray Scale
  • High Pass Filters
  • Low Pass Filters
  • Measurement
  • Preprocessing
  • Radiosondes
  • Troposphere
  • Vapors
  • Water Vapor

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Atmospheric Science/Meteorology
  • Computer Vision.

Technology Areas

  • AI & ML