“Big Data Assimilation” Revolutionizing Severe Weather Prediction

Abstract

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

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

“…In addition, the 100-m-mesh forecasts also showed improvements in surface precipitation compared to the 1-km-mesh forecasts. It indicated that rapid-update, high-resolution DA cycles contributed to create a preferable initial condition.Simulating an isolated convective system is one of the challenging issues in the contemporary NWP, and this study suggests that the BDA system be a promising approach to sudden local severe weather prediction, following Miyoshi et al (2016aMiyoshi et al ( , 2016b. This study shows more details of the second case shown by Miyoshi et al (2016b).…”

“…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.…”

“…We apply the BDA system described by Miyoshi et al (2016aMiyoshi et al ( , 2016b for a sudden local severe rainstorm that occurred in the morning of 11 September 2014 local time in Kobe. In this event, an isolated convective system suddenly initiated and rapidly developed near the center of Kobe city, and reached rainfall intensity over 50 mm h −1 within 10 minutes.…”

“…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.…”

Observational operators for dual polarimetric radars in variational data assimilation systems

“…Most previous studies (e.g., Seko et al, 2004;Wattrelot et al, 2014), except Zeng et al (2016), consider only vertical beam broadening, because numerical models have horizontal grid spacings of several kilometres, whereas they have vertical grid spacings in the lower troposphere of less than one kilometre. However, data assimilation 20 systems must have sub-kilometre horizontal grid spacings as well (e.g., Kawabata et al 2014a, Miyoshi et al, 2016 so that the space interpolators can take account of horizontal beam broadening. In addition, several phased array radars recently deployed in Japan have different beam widths in the vertical and horizontal directions.…”