Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas

Abstract

A recently developed method of defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models is described. Precipitation objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. In an application of the new verification approach, the forecasts produced by the Weather Research and Forecasting (WRF) model are evaluated on a 22-km grid covering the continental United States during July-August 2001. Observed rainfall is derived from the stage-IV product from NCEP on a 4-km grid (averaged to a 22-km grid). It is found that the WRF produces too many large rain areas, and the spatial and temporal distribution of the rain areas reveals regional underestimates of the diurnal cycle in rain-area occurrence frequency. Objects in the two datasets are then matched according to the separation distance of their centroids. Overall, WRF rain errors exhibit no large biases in location, but do suffer from a positive size bias that maximizes during the later afternoon. This coincides with an excessive narrowing of the rainfall intensity range, consistent with the dominance of parameterized convection. Finally, matching ability has a strong dependence on object size and is interpreted as the influence of relatively predictable synoptic-scale systems on the larger areas.

Simulating North American mesoscale convective systems with a convection-permitting climate model

“…To identify MCSs in observations and our simulation we use a new variation of afeature-based verification method called the method for object-based diagnostic evaluation (MODE) (Davis et al 2006(Davis et al , 2009) that incorporates the time dimension [MODE time-domain or short MTD; Clark et al (2014)]. MTD is part of the Developmental Testbed Centers (DTC) Model Evaluation Tools (MET; the current version is available online at http://www.dtcenter.org/met/ users/downloads/).…”

“…MODE has been used successfully to detect biases in short-term weather forecasts (e.g., Davis et al 2006Davis et al , 2009Clark et al 2012;Mittermaier and Bullock 2013). It was reported that coarse-resolution forecast models produce too many large rain areas and underestimate rainfall intensities (e.g., Davis et al 2006;Wernli et al 2008).…”

“…It was reported that coarse-resolution forecast models produce too many large rain areas and underestimate rainfall intensities (e.g., Davis et al 2006;Wernli et al 2008). Convectionpermitting weather forecasts show substantial improvements over coarse-resolution forecasts and reduce the size and intensity biases (Wernli et al 2008;Davis et al 2009).…”

Fuzzy verification of high‐resolution gridded forecasts: a review and proposed framework

“…Examples are the techniques of Ebert and McBride (2000); Davis et al (2006); and Marzban and Sandgathe (2006). Objectoriented techniques are quite intuitive and effective when the features are well defined and can be associated with the forecasts and the observations.…”

Application of a Spatial Verification Method to GFS Precipitation Forecasts

“…This verification tool for the evaluation of precipitation fields is an "Object-based" approach, in which forecast and observed areas of precipitation are represented and compared as objects, and characterized by attributes such as location, size, and intensity of the systems (Davis et al, 2006a;Brown et al, 2007;Davis et al, 2009). Besides, MODE is publicly available at the Model Evaluation Tools website (MET -www.dtcenter.org/met/users).…”