Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

Andrew Wilkins,Xuguang Wang,Aaron Johnson,Yongming Wang,Nicholas A Gasperoni

doi:10.3390/atmos12121630

Abstract

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.

Highlights

Numerical weather prediction (NWP) since the early 2000s has benefitted from advances in computational resources that allow the routine use of high-resolution, convectionallowing models (CAMs) [1,2,3,4,5]
The impacts of convection-allowing ensembles (CAEs) design on probabilistic forecast skill have primarily focused on spatial coverage of precipitation through neighborhood-based methods rather than explicit verification of storm morphology
Studies that have evaluated CAE storm morphology forecasts have relied on deterministic forecasts or subjective evaluation rather than objective evaluation of probabilistic storm morphology forecasts

Summary

Introduction

Numerical weather prediction (NWP) since the early 2000s has benefitted from advances in computational resources that allow the routine use of high-resolution, convectionallowing models (CAMs) [1,2,3,4,5]. Increasing emphasis has been placed on convection-allowing ensembles (CAEs) rather than deterministic CAM forecasts [6,7,8,9,10,11,12,13,14,15,16,17,18]. [7] demonstrate that simple, accessible post-processed products can improve both the qualitative interpretations and quantitative reliability of CAE high precipitation and severe weather forecasts. The authors of [14] showed that the underdispersion of CAE forecasts can be improved through the incorporation of land surface model (LSM). [17] found that multi-model and multi-physics CAE designs improve forecasts of mesoscale precipitation location, relative to single-model, single-physics designs

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