Progress and challenges with Warn-on-Forecast

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.…”

Climatologically based warning system against meteorological hazards and weather extremes: the example for Poland

“…Gaztelumendi et al 2012;Tang et al 2012), systems devoted to a particular phenomenon (e.g. Lakshmanan et al 2007;Stensrud et al 2013) or issues (e.g. Ebi and Schmier 2005) as well as those systems operating within the range of water-related hazards in some European countries (Alfieri et al 2012).…”

A four-dimensional variational system for skillful operational prediction of convective storms

“…Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead times, as operational forecasters begin to incorporate convective-scale forecasts into severe weather forecast operations (Stensrud et al, 2009(Stensrud et al, , 2013. This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards.…”

“…3DVAR, 4DVAR, and EnKF DA systems have been applied successfully to a wide range of cases and observational datasets, and are a key component of efforts within the United States (and around the world) to develop a warn-on-forecast weather warning system (Stensrud et al, 2013). Assimilation of radar data has been found to be * Corresponding author: Nathan SNOOK Email: nsnook@ou.edu vital for accurate storm prediction (e.g., Dawson et al, 2012;Snook et al, 2015;Yussouf et al, 2015), and the assimilation of surface observations has also been shown to be beneficial (e.g., Schenkman et al, 2011).…”