Calibrated Probabilistic Quantitative Precipitation Forecasts Based on the MRF Ensemble

Abstract

Probabilistic quantitative precipitation forecasts (PQPF) based on the medium range forecast (MRF) ensemble are currently in operational use below their full potential quality (i.e., accuracy and reliability). This unfulfilled potential is due to the MRF ensemble being adversely affected by systematic errors which arise from an imperfect model and less than ideal ensemble initial perturbations. This thesis sought to construct a calibration to account for these systematic errors and thus produce higher quality PQPF. Systematic errors were explored with the use of the verification rank histogram, which tracks the performance of the ensemble. The information in these histograms was then used in interpreting MRF ensemble forecasts to produce calibrated PQPF. While the calibration technique did noticeably improve the quality of PQPF, its usefulness was bounded by the natural predictability limits of cumulative precipitation. It was discovered that higher levels of cumulative precipitation cannot be reliably predicted in the medium range. Due to this limit of predictability, for significant levels of precipitation (high threshold), the calibration designed in this thesis was found to be useful only for short range PQPF. For low precipitation thresholds, the calibrated PQPF did prove to be of value in the medium range.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1998
Accession Number
ADA340919

Entities

People

  • Frederick Anthony Eckel

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Calibration
  • Data Sets
  • Differential Equations
  • Distribution Functions
  • Graphs
  • Grids
  • Measurement
  • Precipitation
  • Rain Gages
  • Random Variables
  • Reliability
  • Two Dimensional
  • United States
  • Weather Forecasting

Fields of Study

  • Environmental science

Readers

  • Approximation Theory.
  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation