| A WRF-Based Tool for Forecast Sensitivity to the Initial Perturbation: The Conditional Nonlinear Optimal Perturbations versus the First Singular Vector Method and Comparison to MM5 - :20597 | Office of Oceanic and Atmospheric Research (OAR)
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A WRF-Based Tool for Forecast Sensitivity to the Initial Perturbation: The Conditional Nonlinear Optimal Perturbations versus the First Singular Vector Method and Comparison to MM5
  • Published Date:
    2017
  • Source:
    Journal of Atmospheric and Oceanic Technology, 34(1), 187-206.
Filetype[PDF-3.88 MB]


Details:
  • Description:
    A forecast sensitivity to initial perturbation (FSIP) analysis tool for the WRF Model was developed. The tool includes two modules respectively based on the conditional nonlinear optimal perturbation (CNOP) method and the first singular vector (FSV) method. The FSIP tool can be used to identify regions of sensitivity for targeted observation research and important influential weather systems for a given forecast metric. This paper compares the performance of the FSIP tool to its MM5 counterpart, and demonstrates how CNOP, local CNOP (a kind of conditional nonlinear suboptimal perturbation), and FSV were detected using their evolutions of cost function. The column-integrated features of the perturbations were generally similar between the two models. More significant differences were apparent in the details of their vertical distribution. With Typhoon Matsa (2005) in the western North Pacific and a winter storm in the United States (2000) as validation cases, this work examined the tool's capability to identify sensitive regions for targeted observation and to investigate important influential weather systems. The location and pattern of the sensitive areas identified by CNOP, local CNOP, and FSV were quite similar for both the Typhoon Matsa case and the winter storm case. The main differences were mainly in their impact on the growth of forecast difference and the details of their vertical distributions. For both cases, the wind observations might be more important than temperature observations. The results also showed that local CNOP was more capable of capturing the influence of important weather systems on the forecast of total dry energy in the verification area.

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