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  • An important but not always evident aspect of the EWA algorithm is a further adjustment of the weighting factors to implement a gaussian filter to the projection processing. The gaussian filter is important to minimizing the possible effects of aliasing and moiré effects when down-sampling a larger array of source data to a smaller set of target data … .

The algorithm itself




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For each point in swath (per row, per column)
  Pre-Calculate ll2rc – lat/lon-to-row-column
  (giving floating-point row/col in target space, not integer row/col) 
For each row-set in swath (rows-per-swath)
  Compute_ewa_parameters (ellipse parameters per column)
    For each column in swath
      <compute ellipse parameters>
  Compute_ewa (output values per target grid cell)
    For each row in row-sets
      For each column in swath
        <assign values for recurring factors >
        <get ewa_parameters for row & column (ewa_parameters, ellipse)
        <compute perimeter box for ellipse >
        For each target row in perimeter box
          For each target column in perimeter box
          If target point within ellipse
            Compute/Lookup weighting factor
            Numerator_array(target_cell)    += weighted-distance # grid_accums
            Denomitnator_array(target_cell) += weights           # grid_weights
Target_Values = Numerator_array / Denominator_array              # output_grid


“Off-label” application of EWA

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