An Automated Method for Low Level Wind Shear Alert SyStem (LLWAS) Data Quality Analysis

Abstract

The Low Level Wind shear Alert System (LLWAS) is an anemometer-based surface network used for detection of hazardous wind shear and acquisition of operational wind information in the airport terminal area. The quality of wind data provided by the LLWAS anemometers is important for the proper performance of the LLWAS wind shear detection algorithms. This report describes the development of an automated method for anemometer data quality analysis (DQA). This method identifies potential data quality problems through comparison of wind data from each sensor within a network to the mean wind speed and direction of the entire network. The design approach and implementation are described, and results from testing using data from the demonstration Phase III LLWAS network in Orlando, FL are reported. Potential improvements to the automated DQA algorithm are presented based on experience gained during analysis of the Orlando data. These recommended improvements are provided to assist future development and refinement of the DQA methodology to he performed by the FAA technical Center. LLWAS, Wind shear detection, Data quality, Wind measurement, Mesonet, Wind sensor siting, Anemometer network.

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Document Details

Document Type
Technical Report
Publication Date
May 26, 1994
Accession Number
ADA280313

Entities

People

  • David A. Clark
  • F. W. Wilson

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Anemometers
  • Boundary Layer
  • Data Processing
  • Data Science
  • Databases
  • Detection
  • Detectors
  • Information Processing
  • Information Science
  • Maintenance Personnel
  • Statistical Data
  • Statistics
  • Test And Evaluation
  • Wind
  • Wind Shear

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Software Engineering