+ Storm Tracking and Lightning Cell Clustering using GLM for Data Assimilation and Forecast Applications Principal Investigators: Kristin Kuhlman and Don.

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Presentation transcript:

+ Storm Tracking and Lightning Cell Clustering using GLM for Data Assimilation and Forecast Applications Principal Investigators: Kristin Kuhlman and Don MacGorman Collaborators: Valliappa Lakshmanan, Ted Mansell, Travis Smith, and Kiel Ortega

+ Segmotion/K-means Identifies segments (or storm cells/clusters) of image & estimates motion Extracts properties of each cell Size of cell (# of pixels) Pull data from all grids, including radar, lightning, and derived properties such as maximum expected size of hail (MESH) Moves grid based on motion field Tracking: Lightning vs. Reflectivity vs. Hybrid Want a relatively stable area, without drops or mismatches Past track given by centroid of area, if loses section or adds will change centroid location and can account for difference in track

+ Comparison between methods Evaluating Track Duration (length), Linearity, and cell size Better tracks are expected to be longer, more linear, and are consistent (less variability in cell properties) Both Lightning Density and -10 C provide consistent tracks for storm clusters / cells (and perform better than tracks on Composite Reflectivity ) At smallest scales: Lightning Density provides longer, more linear tracks than Ref. Reverses at larger scales. Regions lightning tend to not be as consistent across large storm complexes.

+ Questions: For assimilation: # of flash initiations, # of flashes traversing a grid cell, or some measure of the summed density/duration of flashes at a particular grid cell? Are the answers to these questions different for different types of storms or at different resolutions/spatial scales? How does this translate to pGLM and ultimately GLM?

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