Meteorological Error Budget Using Open Source Data

Abstract

The meteorological (MET) error budget tables for artillery list MET errors that are based on radiosonde observations (RAOBs) at various staleness increments. Staleness increments are in terms of time (e.g., 30-min MET) or equivalent distance from the RAOB launch site. The values in the tables represent levels of error extracted from extensive sets of RAOB data generated several decades ago. The US Army Armament Research, Development, and Engineering Center asked for assistance to produce artillery MET error budget tables that account for expected errors when using MET model-based systems. Representatives of the US and other nations within the North Atlantic Treaty Organization expressed a need for shareable model-based MET error budgets. Use of an openly available civilian version of a MET model to generate the appropriate values will allow distribution without restrictions that could arise from extracting data from an operational military system. This investigation provides those model-based MET error budget values using an open-source version of the Weather Research and Forecasting (WRF) model. The MET error budget tables are formatted similarly to traditional RAOB-based tables. Consequently, the transition to the model-based tables should not require any significant effort on the part of the user.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2016
Accession Number
AD1017805

Entities

People

  • B Reen
  • J. Cogan
  • J. Cole Smith
  • P. Haines

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Artillery
  • Basic Programming Language
  • Databases
  • Engineering
  • Information Science
  • Meteorology
  • Military Research
  • Nato
  • Observation
  • Radiosondes
  • Sea Level
  • Spreadsheet Software
  • Standards
  • Statistics
  • Trajectories
  • Wind Direction

Readers

  • Atmospheric Science/Meteorology
  • Database Systems and Applications
  • Regression Analysis.