Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Abstract

Meaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach-a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]-was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold used to define objects [8 km and 0.10 in. (1 in. 5 2.54 cm), respectively], the most common object duration was 3 h in both models and observations. With an increased smoothing radius and rainfall threshold, the most common duration became shorter. The simulations depicted the diurnal cycle of object frequencies well, but overpredicted object frequencies uniformly across all forecast hours. The simulations had spurious maxima in initiating objects at the beginning of the forecast and a corresponding spurious maximum in dissipating objects slightly later. Examining average object velocities, a slow bias was found in the simulations, which was most pronounced in the Thompson member. These findings should aid users and developers of convection-allowing models and motivate future work utilizing time-domain methods for verifying high-resolution forecasts.

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

“…The realistic simulation of MCS characteristics is a major advantage compared to coarser resolution climate simulations that have to parameterize deep convection (e.g., Chang et al 2016). These results agree well with results from convection-permitting weather forecast evaluation study (Clark et al 2014;Davis et al 2009). The largest biases are found in the model's ability to reproduce the frequency of MCSs.…”

“…In the Southeast and the MidAtlantic region, MCS frequency is up to 70 % overestimated during JJA. Similar overestimations are found in convectionpermitting weather forecasting models in all US regions (Johnson and Wang 2012;Clark et al 2014). Different from forecasting biases are the 50 % underestimation of MCSs in the central US during late summer.…”

“…MCSs can be identified in multiple atmospheric fields such as mid-tropospheric vorticity (Wang et al 2011), cloud-related fields such as cloud top temperatures (Feng et al 2016), or precipitation (Clark et al 2014). Here we use hourly precipitation because of its high socioeconomic relevance and the availability of the stage-IV dataset that allows a sound model evaluation.…”

“…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/).…”

“…Using the MTD tracking algorithm, Clark et al (2014) showed that convectionpermitting weather forecasts are able to capture the spatial distribution, lifetime, and the diurnal cycle of MCSs but underestimate their translation speed.…”