Integrated Microwave and Infrared Precipitation Analysis

Abstract

GOES infrared (IR) data is intercompared with rain analyses from the SSM/I exponential rain algorithm for the purpose of determining thresholds and statistics from IR imagery which delineate oceanic rain area. Data from ERICA cyclogenesis cases were evaluated. Discriminant analysis was performed using IR mean cloud top temperature, standard deviation and kurtosis as discriminating variables. Resulting functions separated rain from no-rain areas with average Probability of Detection (POD) and Percentage Error (ERR) scores of 0.68 and 0.30 for development data (0.62 and 0.37 for validation data). The scheme demonstrated little skill in discriminating rain categories beyond rain/no-rain. An IR threshold scheme was used to delineate rain/no-rain areas by optimizing a set of evaluation statistics. Optimal thresholds attained a predetermined POD level of 0.60 while minimizing percent misclassification error and SSM/I-IR rain area difference. The scheme yielded average POD and ERR scores of 0.64 and 0.38 with IR thresholds from 229 to 232 K. Results for both the discriminant analysis and optimal threshold schemes compare favorably with previous studies. The use of the SSM/I rain analyses with geostationary imagery allows reliable, frequent, large scale analysis of oceanic precipitation. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA246070

Entities

People

  • Lisa E. Frailey

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Detection
  • Detectors
  • Discriminant Analysis
  • Geographic Regions
  • Geosynchronous Satellites
  • Grids
  • Meteorological Phenomena
  • Meteorological Satellites
  • Meteorology
  • Oceanography
  • Probability
  • Satellite Imaging
  • Standards
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Computer Vision.
  • Statistical inference.