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Observable Surface Anomalies Preceding Simulated Isolated Convective Initiation



Details

  • Journal Title:
    Monthly Weather Review
  • Personal Author:
  • NOAA Program & Office:
    NWS (National Weather Service)
  • Description:
    Idealized ensemble simulations of isolated convective initiation (CI) are analyzed to identify storm-scale features in surface weather fields that precede initiation in a variety of background environments and the observations that would be needed to resolve these features. Precipitating storms are identified with an object-based method and composites of surface anomalies are generated for the variables of interest surrounding times and locations of initiation. Correlation length scales and anomaly magnitudes throughout the CI process are examined in detail with the latter comparing favorably to anomaly estimates obtained from previous observational and modeling studies. Negative temperature anomalies due to cloud shadowing are found to be the most prominent storm-scale feature prior to initiation. Significant spatial correlations are shown to extend from the surface throughout the boundary layer and even into the cloud-bearing layer once deep convective clouds become established. The findings are discussed in the context of data assimilation, particularly with respect to current assumptions about surface observation error. It is shown that, to resolve the storm-scale anomalies in these simulations, the minimum necessary temperature and wind observation densities would likely be limited by spatial correlation length scale while moisture and pressure observations are more limited by observation error.
  • Source:
    Monthly Weather Review, 144(6), 2265-2284
  • DOI:
  • ISSN:
    0027-0644 ; 1520-0493
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA13NWS4680006
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:5aadd3dedb1644b441c13b7f72e87c890782fecafc586430979464c3a9317be324d8b373faeaeaf1708bb78b60fd6a4c6d724bcc80599d0c8e225624919788c4
  • Download URL:
  • File Type:
    Filetype[PDF - 2.01 MB ]
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