Validation of Special Sensor Microwave/Imager Ocean Surface Wind Retrievals in Equatorial Regions.

Abstract

The Fleet Numerical Meteorology and Oceanography Center (FNMOC) has the charter to provide Special Sensor Microwave/Imager(SSM/I) data to DOD and NOAA users. This tasking has led to new methods for processing SSM/I data being developed to improve NAVY SSM/I products, in particular the ability to remotely sense ocean surface winds. Currently, alternative SSM/I ocean surface wind speed algorithms include 'physical' or 'statistical' methods. Typically "physical" retrievals require additional data. e.g., cloud liquid water, along with SSM/I brightness temperatures while statistical methods are stand alone algorithms based on brightness temperature only. In this study four candidate wind retrieval methods proposed at the SSM/I Algorithm Symposium (June 1993) for implementation at FNMOC are examined. Limitations of the SSM/I calibration/validation data set to the mid-latitude region prompted the requirement to develop a tropical data set for evaluation of alternative algorithms. Comparison of SSM/I wind retrieval methods reveal neural networks display a high wind speed bias for winds above 11 m/s and a low wind speed bias for winds below 3 mi. The current FNMOC operational algorithm may overestimates wind speeds when water vapor is greater than 50 kg/m2. Partitioning of SSM/l retrieved wind speeds according to accuracy is by accomplished when using brightness temperature received at 37 GHZ. (MM)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1994
Accession Number
ADA293750

Entities

People

  • Elton G. Sayward

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Satellites
  • Atmospheric Attenuation
  • Calibration
  • Data Sets
  • Earth Sciences
  • Equatorial Regions
  • Geography
  • Latitude
  • Measurement
  • Meteorological Satellites
  • Neural Networks
  • Oceans
  • Radiation
  • Regions
  • Water Vapor

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference