Spread and Skill in Mixed- and Single-Physics Convection-Allowing Ensembles

Abstract

Spread and skill of mixed- and single-physics convection-allowing ensemble forecasts that share the same set of perturbed initial and lateral boundary conditions are investigated at a variety of spatial scales. Forecast spread is assessed for 2-m temperature, 2-m dewpoint, 500-hPa geopotential height, and hourly accumulated precipitation both before and after a bias-correction procedure is applied. Time series indicate that the mixed-physics ensemble forecasts generally have greater variance than comparable single-physics forecasts. While the differences tend to be small, they are greatest at the smallest spatial scales and when the ensembles are not calibrated for bias. Although differences between the mixed- and single-physics ensemble variances are smaller for the larger spatial scales, variance ratios suggest that the mixed-physics ensemble generates more spread relative to the single-physics ensemble at larger spatial scales. Forecast skill is evaluated for 2-m temperature, dewpoint temperature, and bias-corrected 6-h accumulated precipitation. The mixed-physics ensemble generally has lower 2-m temperature and dewpoint root-mean-square error (RMSE) compared to the single-physics ensemble. However, little difference in skill or reliability is found between the mixed- and single-physics bias-corrected precipitation forecasts. Overall, given that mixed- and single-physics ensembles have similar spread and skill, developers may prefer to implement single- as opposed to mixed-physics convection-allowing ensembles in future operational systems, while accounting for model error using stochastic methods.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

“…10), which can hamper interpretation of verification metrics designed to quantify spatial errors (e.g., Baldwin and Kain 2006;Roberts and Lean 2008). Thus, forecasts were bias corrected before assessing measures of probabilistic forecast quality with a ''probability-matching'' approach that forced each ensemble member's distribution to the ST4 distribution by replacing the model grid point containing the most precipitation within the verification region with the highest ST4 amount within the verification region, and so on, thus eliminating bias (e.g., Ebert 2001;Clark et al 2009Clark et al , 2010aSchwartz et al 2015a;Loken et al 2019;Pyle and Brill 2019). Despite replacing model values with observations, this method preserves forecast spatial patterns.…”

“…Therefore, although NCAR's experimental CAE forecasts were credible and widely adopted by both researchers and forecasters (Schwartz et al 2019), it remains unclear whether large-domain, limited-area, continuously cycling EnKFs are optimal for producing CAE ICPs. Furthermore, objective assessments of systematic, controlled experiments designed to isolate impacts of ICPs on real-data CAE forecasts over the CONUS have yet to be reported, although subjective evaluations of two CAEs differing solely by ICPs performed during NOAA's 2019 Hazardous Weather Testbed Spring Forecasting Experiment suggested ICPs had little impact on severe weather forecasts (Clark et al 2019).…”

A New View of Heat Wave Dynamics and Predictability over the Eastern Mediterranean

“…However, it also reflects some characteristics of the atmospheric dynamics (e.g., Ferranti et al, 2015;Matsueda and Palmer 2018). An often-used method for quantifying the practical predictability is the spread or skill of ensemble forecasts (e.g., Loken et al, 2019).…”

Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

“…An example of a commonly used simulated severe storm ''surrogate'' (Sobash et al 2011(Sobash et al , 2016(Sobash et al , 2019, or proxy, is hourly maximum 2-5 km above ground level updraft helicity (hereafter, UH; e.g., Kain et al 2008Kain et al , 2010Guyer and Jirak 2014;Loken et al 2017;Sobash et al 2011Sobash et al , 2016Sobash et al , 2019. Large values of UH identify not only rotating updrafts associated with supercells, but also the sheared updrafts associated with severe mesoscale convective systems (MCSs; Sobash et al 2011).…”