A technique for the verification of precipitation forecasts and its application to a problem of predictability

Abstract

A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2.