Sensitivity Testing of a Single-Doppler Temperature Retrieval and Forecast System

Abstract

Results are presented from the second year of a three-year contract to explore the feasibility of local thunderstorm forecasting based on the assimilation of single-Doppler radar data. The method used can assimilate Doppler radial winds, derive a tangential component, infer temperature distributions from the wind, and assimilate the resultant information. The underlying analysis and assimilation methodology is explained in detail. This is then followed with a set of sensitivity experiments using simulated Doppler radar data generated from a numerical model forecast. A simple 2-D experiment is first presented which confirms the benefit of temperature assimilation to complement wind data assimilation. Second, a set of 3-D model experiments is presented to test the sensitivity of the model to observational data type and error characteristics. The results suggest that forecast accuracy is relatively insensitive to the observational error, though this result is in part due to the inherent smoothing built into the objective analysis and the assumption of no biases in the wind observations.... Numerical weather prediction, Mesoscale meteorology, Doppler radar data analysis, Thunderstorm forecasting.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1993
Accession Number
ADA267278

Entities

People

  • Thomas M. Hamill
  • Thomas Nehrkorn

Organizations

  • Atmospheric and Environmental Research, Inc

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Atmospheric Sciences
  • Atmospheric Temperature
  • Boundary Layer
  • Computational Science
  • Convection
  • Data Analysis
  • Data Sets
  • Doppler Radar
  • Equations Of Motion
  • High Resolution
  • Meteorology
  • Radar
  • Radial Velocity
  • Temperature Gradients
  • Three Dimensional
  • Weather Forecasting
  • Wind Velocity

Fields of Study

  • Environmental science

Readers

  • Artificial Intelligence
  • Atmospheric Science/Meteorology
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference