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) starting with the origin and moving clockwise. The exact formulation of the forecast precipitation field is where x and y are the grid indices of the 601 × 501 grid, a controls the width of the ellipse along the x axis, and b controls the width along the y axis; ( x 1 , y 1 ) is the center of the low-intensity ellipse, and ( x 2 , y 1 ) is the center of the high-intensity ellipse. The precipitation value, R , is either zero outside the low-intensity ellipse (i.e., in the background
) starting with the origin and moving clockwise. The exact formulation of the forecast precipitation field is where x and y are the grid indices of the 601 × 501 grid, a controls the width of the ellipse along the x axis, and b controls the width along the y axis; ( x 1 , y 1 ) is the center of the low-intensity ellipse, and ( x 2 , y 1 ) is the center of the high-intensity ellipse. The precipitation value, R , is either zero outside the low-intensity ellipse (i.e., in the background
for parameters of interest (e.g., spatial and intensity errors). The image-warping procedure described here is more closely related to the methods introduced independently by Alexander et al. (1999) , Hoffman et al. (1995) , Nehrkorn et al. (2003) , Reilly et al. (2004) , and Sampson and Guttorp (1999) ; the primary differences being the method for choosing the subset of points for fitting the image warp function (or control points), and specific choices of warping functions and their
for parameters of interest (e.g., spatial and intensity errors). The image-warping procedure described here is more closely related to the methods introduced independently by Alexander et al. (1999) , Hoffman et al. (1995) , Nehrkorn et al. (2003) , Reilly et al. (2004) , and Sampson and Guttorp (1999) ; the primary differences being the method for choosing the subset of points for fitting the image warp function (or control points), and specific choices of warping functions and their
functions for the warping function. They found that informative warps are determined using a relatively small number of regularly spaced grid points for control points, at least for the test cases analyzed so far in the ICP. Venugopal et al. (2005) introduce an image comparison metric called the forecast quality index (FQI), which combines both distance between two binary images (created by thresholding the observation and forecast fields) and intensity errors. The numerator of the index is a measure
functions for the warping function. They found that informative warps are determined using a relatively small number of regularly spaced grid points for control points, at least for the test cases analyzed so far in the ICP. Venugopal et al. (2005) introduce an image comparison metric called the forecast quality index (FQI), which combines both distance between two binary images (created by thresholding the observation and forecast fields) and intensity errors. The numerator of the index is a measure
