A Method for Extrapolation of Atmospheric Soundings

Abstract

Meteorological models use terrain databases with resolutions on the order of model grid intervals (e.g., a terrain grid spacing of 4 km for a model with 4-km spacing). Generally, model software smoothes the terrain to a coarser resolution for computational stability (e.g., a 4-km grid model may have terrain smoothed to about 10 km). In more complex terrain, the difference between the smoothed terrain and actual elevation could reach hundreds of meters, perhaps >1000 m for mountainous regions. To obtain meteorological information between the actual height and the model height or a nearby rawinsonde, current methods often use the temperature at the lowest level (nominal surface) and assume a standard lapse rate down to the level of interest, which is normally the elevation of the actual surface or of a finer terrain database. Pressure is computed from temperature and height. Many methods keep the surface wind speed and direction constant throughout the extrapolated section of the profile. These methods are often used to extrapolate an observed sounding (e.g., radiosonde) to a nearby lower elevation. Our method extracts information from the model-generated atmospheric profile or measured sounding to help generate the extrapolated profile. It takes into account surface-based inversions, although, at present, does not explicitly account for elevated inversions.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2014
Accession Number
ADA604460

Entities

People

  • Brian Reen
  • James Cogan

Organizations

  • United States Army Research Laboratory

Tags

DTIC Thesaurus Topics

  • Atmospheric Temperature
  • Boundary Layer
  • Databases
  • Elevation
  • Extrapolation
  • Global Positioning Systems
  • Grids
  • Inversion
  • Lapse Rate
  • Measurement
  • Meteorology
  • Radiosondes
  • Remote Sensing
  • Sea Surface Temperature
  • Standards
  • Surface Temperature
  • Wind Direction

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Computer Vision.
  • Geodesy

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Space