Advances in Convection-Permitting Tropical Cyclone Analysis and Prediction through EnKF Assimilation of Reconnaissance Aircraft Observations

On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

“…Within the community of scientists working on tropical cyclones, efforts have been directed toward improving dedicated tropical cyclone models (e.g., Gopalakrishnan et al 2011), real-time in situ and remote sensing observations of storms (e.g., Ruf et al 2016), assimilation of those observations into models (Zhang and Weng 2015;Weng and Zhang 2016;Zhang et al 2016), and statistical forecast models, which are still competitive with deterministic models (Kaplan et al 2015). Numerical modeling of tropical cyclones is especially challenging owing to the very high resolution required to resolve the critical eyewall region (Rotunno et al 2009), to the complex physics of boundary layers and air-sea interaction at high winds speeds (Nolan et al 2009;Green and Zhang 2014;Andreas et al 2015;Green and Zhang 2015), and to the importance of correctly modeling the response of the upper ocean to the storms (Moon et al 2007;Yablonsky and Ginis 2009).…”

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 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.…”

“…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.…”

On the Role of Surface Fluxes and WISHE in Tropical Cyclone Intensification

“…4 except using the real-world event of Hurricane Edouard (2014). The control simulation uses WRF, version 3.5.1, the same model configuration as The Pennsylvania State University (PSU) experimental realtime hurricane analysis and prediction system based on WRF and an ensemble Kalman filter (EnKF), described in Weng and Zhang (2016), except that the enthalpy exchange coefficient is fixed at 0.001 for all wind speed ranges. The control simulation is initialized with the PSU real-time WRF-EnKF mean analysis starting at 1200 UTC 11 September 2014 integrated for 126 h. The innermost domain grid spacing is 3 km with 298 3 298 horizontal grid points movable centered on the tropical cyclone center.…”