Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics

Abstract

A recent study examined the predictability of an idealized baroclinic wave amplifying in a conditionally unstable atmosphere through numerical simulations with parameterized moist convection. It was demonstrated that with the effect of moisture included, the error starting from small random noise is characterized by upscale growth in the short-term (0-36 h) forecast of a growing synoptic-scale disturbance. The current study seeks to explore further the mesoscale error-growth dynamics in idealized moist baroclinic waves through convection-permitting experiments with model grid increments down to 3.3 km. These experiments suggest the following three-stage error-growth model: in the initial stage, the errors grow from small-scale convective instability and then quickly [O(1 h)] saturate at the convective scales. In the second stage, the character of the errors changes from that of convective-scale unbalanced motions to one more closely related to large-scale balanced motions. That is, some of the error from convective scales is retained in the balanced motions, while the rest is radiated away in the form of gravity waves. In the final stage, the large-scale (balanced) components of the errors grow with the background baroclinic instability. Through examination of the error-energy budget, it is found that buoyancy production due mostly to moist convection is comparable to shear production (nonlinear velocity advection). It is found that turning off latent heating not only dramatically decreases buoyancy production, but also reduces shear production to less than 20% of its original amplitude.

30-second-Update 100-m-Mesh Data Assimilation Experiments: A Sudden Local Rain Case in Kobe on 11 September 2014

“…Among others, much attention has been paid to skillful NWP for severe weather (e.g., Kain et al 2006, Hohenegger andSchär 2007a, b;Kawabata et al 2007;Roberts and Lean 2008). Recently, the ensemble Kalman filter (EnKF; Evensen 1994Evensen , 2003 has become a major method in data assimilation (DA), and has contributed to investigate convection-permitting regional NWP (e.g., Zhang et al 2007;Stensrud et al 2009Stensrud et al , 2013Clark et al 2010;Schwartz et al 2010;Baldauf et al 2011;Melhauser and Zhang 2012;Yussolf et al 2013, Kunii 2014a, Weng and Zhang 2016.…”

“…Among others, much attention has been paid to skillful NWP for severe weather (e.g., Kain et al 2006, Hohenegger and Schär 2007a, b;Kawabata et al 2007;Roberts and Lean 2008). Recently, the ensemble Kalman filter (EnKF;Evensen 1994Evensen , 2003 has become a major method in data assimilation (DA), and has contributed to investigate convection-permitting regional NWP (e.g., Zhang et al 2007;Stensrud et al 2009Stensrud et al , 2013Clark et al 2010;Schwartz et al 2010; Baldauf et al 2011;Melhauser and Zhang 2012; Yussolf et al 2013, Kunii 2014a, Weng and Zhang 2016.Recently, Miyoshi et al (2016aMiyoshi et al ( , 2016b reported an innovation of the "Big Data Assimilation" (BDA) technology, implementing a 30-second-update, 100-m-mesh local ensemble transform Kalman filter (LETKF;Hunt et al 2007) to assimilate data from a Phased Array Weather Radar (PAWR) at Osaka University (Ushio et al 2014) into regional NWP models known as the Japan Meteorological Agency non-hydrostatic model (JMA-NHM, Saito et al 2006Saito et al , 2007 and the Scalable Computing for Advanced Library and Environment-Regional Model (SCALE-RM, Nishizawa et al 2015). The PAWR captures the rapid development of convective activities every 30 seconds at approximately 100-m resolution.…”

Ensemble forecasts of a flood‐producing storm: comparison of the influence of model‐state perturbations and parameter modifications

“…However, the appropriate use and interpretation of convective-scale forecasts is a difficult issue, not least because there is currently only a limited understanding of predictability at such scales. The baroclinic wave simulations by Zhang et al (2003Zhang et al ( , 2007 provide a good example of the different (faster) character of error growth with explicitly modelled as opposed to parametrized deep convection. Stronger nonlinearities and faster error growth at convective scales imply that ensemble strategies may be particularly useful at such scales, as argued by various authors (e.g.…”

Two alternative implementations of the ensemble‐transform (ET) analysis‐perturbation scheme: The ET with extended cycling intervals, and the ET without cycling

“…It is possible that the overall strong performance of F nc is owed to an ability of random perturbations to tap into important moist convective dynamics. This notion originates from the work of Zhang (2005) and Zhang et al (2007), who found that random perturbations can give rise to considerable ensemble variance through a multistage process that is governed initially by small-scale moist convection and later by large-scale baroclinic dynamics. Reinforcing this notion is the fact that F nc performed best in the tropics, a region where moist convective processes predominate.…”