Enhancement of the Daytime MODIS Based Icing Potential Using NOGAPS and COAMPS Model Data

Abstract

In this thesis, NOGAPS and COAMPS model data are fused with Alexander (2005) algorithm to determine its usefulness in enhancing satellite-based aircraft icing analysis. This is a follow on to Cooper (2006) research where MM5 and ETA were used. Using historical NOGAPS and COAMPS data (T, Td and RH) accessed from the GODAE server, several storms from 2004 were fused with available MODIS imagery from the same storms to produce an enhanced icing product. Pilot reports (PIREPS) were used as a validation tool to determine where icing was taking place during the storms analyzed. A comparison was made between the MODIS-based icing potential and the model-based icing potential. The two icing potentials were fused together to produce an enhanced icing product. Statistical analysis using ROC curves was performed on the various combinations to determine which product combination gave the best results. Two different available Tmap (Alexander and CIP) were used and had mixed results. Contrary to what Cooper (2006) found where weighting RH and the Alexander Tmap produced the best results; this study found that equal weighting of T and RH and the CIP Tmap produced the same or better results than weighting RH. This study also found that NOGAPS combined with the MODIS algorithm provide the best icing potential results.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2007
Accession Number
ADA473914

Entities

People

  • Richard L. Davidson

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Aircrafts
  • Algorithms
  • California
  • Detection
  • Fuzzy Logic
  • Humidity
  • Meteorological Data
  • Meteorology
  • Military Operations
  • North America
  • Refractive Index
  • Square Roots
  • Statistical Analysis
  • United States
  • Unmanned Aerial Vehicles

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Mathematics or Statistics
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • Space