NCAR Auto-Nowcast System

C. Mueller National Center for Atmospheric Research, * Boulder, Colorado

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T. Saxen National Center for Atmospheric Research, * Boulder, Colorado

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R. Roberts National Center for Atmospheric Research, * Boulder, Colorado

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J. Wilson National Center for Atmospheric Research, * Boulder, Colorado

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T. Betancourt National Center for Atmospheric Research, * Boulder, Colorado

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S. Dettling National Center for Atmospheric Research, * Boulder, Colorado

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N. Oien National Center for Atmospheric Research, * Boulder, Colorado

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J. Yee National Center for Atmospheric Research, * Boulder, Colorado

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Abstract

The Auto-Nowcast System (ANC), a software system that produces time- and space-specific, routine (every 5 min) short-term (0–1 h) nowcasts of storm location, is presented. A primary component of ANC is its ability to identify and characterize boundary layer convergence lines. Boundary layer information is used along with storm and cloud characteristics to augment extrapolation with nowcasts of storm initiation, growth, and dissipation. A fuzzy logic routine is used to combine predictor fields that are based on observations (radar, satellite, sounding, mesonet, and profiler), a numerical boundary layer model and its adjoint, forecaster input, and feature detection algorithms. The ANC methodology is illustrated using nowcasts of storm initiation, growth, and dissipation. Statistical verification shows that ANC is able to routinely improve over extrapolation and persistence.

Corresponding author address: Dr. Cynthia Mueller, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. Email: mueller@ucar.edu

Abstract

The Auto-Nowcast System (ANC), a software system that produces time- and space-specific, routine (every 5 min) short-term (0–1 h) nowcasts of storm location, is presented. A primary component of ANC is its ability to identify and characterize boundary layer convergence lines. Boundary layer information is used along with storm and cloud characteristics to augment extrapolation with nowcasts of storm initiation, growth, and dissipation. A fuzzy logic routine is used to combine predictor fields that are based on observations (radar, satellite, sounding, mesonet, and profiler), a numerical boundary layer model and its adjoint, forecaster input, and feature detection algorithms. The ANC methodology is illustrated using nowcasts of storm initiation, growth, and dissipation. Statistical verification shows that ANC is able to routinely improve over extrapolation and persistence.

Corresponding author address: Dr. Cynthia Mueller, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. Email: mueller@ucar.edu

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