Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

• We demonstrate a method of providing a mean precipitation area nowcast from a number of ensemble nowcast realizations.
• Using Bayesian Procrustes shape analysis provides credible sets that represent the ensemble spread.
• Bayesian Procrustes shape analysis is used to verify the quality of the ensemble mean and the individual member forecasts
• Method accounts for differences in orientation, shape, size and location of contiguous rain areas in ensemble forecast members.

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System — Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme.