Meteorological Sensor Array (MSA)-Phase I. Volume 3 (Pre-Field Campaign Sensor Calibration)

Abstract

Army decisions are strengthened by accurate input. The US Army Research Laboratory (ARL) is developing a Meteorological Sensor Array (MSA) to provide reliable and persistent atmospheric data resources, which allow modelers and sensor developers to validate model and sensor performance with atmospheric observations. Such models and sensor data become a foundation for future, environmentally dependent Army decision aids. As an MSA feasibility study, ARL conducted an MSA-Phase I ("Proof of Concept") field campaign in 2014, which consisted of 5 meteorological towers, more than 25 sensors, and a 5.5-week dataset of 24 h/day - 7 days/week (24/7) atmospheric measurements. Before this field campaign could be executed, 2 side-by-side relative calibration exercises were conducted. The first exercise examined the MSA-Phase I dynamic sensors (ultrasonic anemometers); the second assessed the MSA-Phase I thermodynamic sensors (barometers, thermometers, hygrometers, and pyranometers). This report documents the results of a detailed calibration assessment. In short, the results showed that most sensors were within the manufacturer's specifications, confirming the qualitative assessments executed just prior to the MSA-Phase I field campaign execution.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2015
Accession Number
ADA622325

Entities

People

  • Gail Tirrell Vaucher
  • Robert Edmonds

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Acoustic Propagation
  • Acquisition
  • Anemometers
  • Barometers
  • Calibration
  • Data Acquisition
  • Data Analysis
  • High Resolution
  • Hygrometers
  • Information Science
  • Measurement
  • Meteorological Instruments
  • Military Research
  • Observation
  • Pressure Measurement
  • Standards

Fields of Study

  • Environmental science

Readers

  • Astronomy/Astrophysics
  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation