Toward Improved Convection-Allowing Ensembles: Model Physics Sensitivities and Optimizing Probabilistic Guidance with Small Ensemble Membership

Abstract

During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced a daily 10-member 4-km horizontal resolution ensemble forecast covering approximately three-fourths of the continental United States. Each member used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model core, which was initialized at 2100 UTC, ran for 33 h, and resolved convection explicitly. Different initial condition (IC), lateral boundary condition (LBC), and physics perturbations were introduced in 4 of the 10 ensemble members, while the remaining 6 members used identical ICs and LBCs, differing only in terms of microphysics (MP) and planetary boundary layer (PBL) parameterizations. This study focuses on precipitation forecasts from the ensemble.The ensemble forecasts reveal WRF-ARW sensitivity to MP and PBL schemes. For example, over the 7-week experiment, the Mellor-Yamada-Janjić PBL and Ferrier MP parameterizations were associated with relatively high precipitation totals, while members configured with the Thompson MP or Yonsei University PBL scheme produced comparatively less precipitation. Additionally, different approaches for generating probabilistic ensemble guidance are explored. Specifically, a ''neighborhood'' approach is described and shown to considerably enhance the skill of probabilistic forecasts for precipitation when combined with a traditional technique of producing ensemble probability fields.

A Comparison of Ensemble Strategies for Flash Flood Forecasting: The 12 October 2007 Case Study in Valencia, Spain

“…First, the skill of each ensemble member is quantified by means of Taylor diagrams (Taylor 2001). Then, the skill of the probabilistic forecasts is assessed by means of relative operating characteristic (ROC) curves (Stanski et al 1989;Schwartz et al 2010).…”

“…ROC curves indicate the true hit rate of a probabilistic forecast at varying false alarm rates (Schwartz et al 2010), and the area under the curve (AUC) measures the ability of the system to discriminate between the occurrence or nonoccurrence of the event.…”

On the forecast skill of a convection-permitting ensemble

“…In order to increase ensemble size and to improve the representation of the ensemble distribution, some systems also apply the neighborhood method and/or lagged ensemble concepts (Ben Bouallègue et al, 2013). While the neighborhood method is based on ensemble probabilities derived from grid points of a defined environment (Theis et al, 2005;Schwartz et al, 2010), the lagged ensemble approach uses forecasts of successive ensemble runs (Ben Bouallègue et al, 2013).…”

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.…”