Error Growth Dynamics within Convection-Allowing Ensemble Forecasts over Central U.S. Regions for Days of Active Convection

Abstract

Error growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

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“…In recent years, convection‐allowing ensemble prediction systems (CAEPSs) with high resolutions of 2–4 km have emerged as a hot topic of current research at a variety of numerical weather prediction (NWP) centers worldwide (e.g., Clark et al ., 2010; Baldauf et al ., 2011; Baker et al ., 2014; Golding et al ., 2016; Nuissier et al ., 2016; Hagelin et al ., 2017; Müller et al ., 2017; Zhuang et al ., 2021). Compared with traditional global medium‐range ensemble systems and regional short‐range ensemble systems, CAEPSs with their higher spatial and temporal resolution have shown great advantages in many aspects (e.g., Clark et al ., 2009, 2016; Schwartz, 2009; Gowan et al ., 2018; Klasa et al ., 2018).…”

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