This thesis described a morphing-based precipitation verification strategy inspired by Keil and Craig. This strategy is based on an optical flow algorithm to morph the image (field) of the forecast precipitation into an image that resembles the image (field) of the observed (analyzed) precipitation. This method treats the precipitation as a passive scalar and carries out the morphing by computing a vector field, called the optical flow, which is then used to advect the original forecast precipitation field. The information provided by the optical flow and the morphed image of the forecast precipitation field is used to define the measures of the displacement error and residual error. There are two novel aspects of our strategy. First, it imposes a constrain on the morphing process in order to prevent the over-convergence of pixels during morphing to a few locations of large errors. Second, it uses a new definition of the displacement error and provides a new interpretation of the other error terms. By applying the new morphing-based precipitation strategy to a schematic idealized example and a real hurricane example, we demonstrate that the constrain imposed largely reduces the risk of over-convergence and the error measures we derive from the morphing process accurately measure the corresponding error components.
