Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System

Abstract

This study seeks to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches using the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM). The newly developed NHM-LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance, and it permits a one-way nested analysis in which a finer-resolution LETKF is conducted by using the output of an outer model. These new features enhance the potential of the LETKF for convective-scale events. The NHM-LETKF was applied to a local severe rainfall event in Japan during 2012. Comparison of the root-meansquare errors between the model first guess and analysis showed that the system assimilated observations appropriately. Analysis ensemble spreads indicated a significant increase around the time torrential rainfall occurred, implying an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully captured intense rainfalls, suggesting that the system could work effectively for local severe weather events. Investigation of probabilistic forecasts by ensemble forecasting indicated that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme was also tested. The results demonstrated that assimilation with a finer-resolution model improved the precipitation forecasting of local severe weather conditions.

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.…”

The Impact of Surface Wind Data Assimilation on the Predictability of Near-Surface Plume Advection in the Case of the Fukushima Nuclear Accident

“…The meteorological data assimilation system of Sekiyama et al (2015) was developed by Kunii (2014) and is composed of the JMA's non-hydrostatic regional weather prediction model (JMANHM, cf., Saito et al 2006Saito et al , 2007 and the local ensemble transform Kalman filter (LETKF, cf., Miyoshi and Aranami 2006). In this study, this system was driven by 20 ensemble members and a 3-km horizontal resolution within the model domain of eastern Japan (215 × 259 grids, cf., Fig.…”

The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall

“…It is shown to be suitable for parallel computing because it performs analysis independently for each grid with a subset of observations (Miyoshi and Yamane 2007). We have developed the SCALE-LETKF by coupling the LETKF with the SCALE-RM; besides, we take advantage of the experiences gained from past implementations with other models (e.g., Miyoshi and Kunii 2012;Kunii 2014;Yashiro et al 2016) to make it a highly configurable (Supplement 1) and computationally scalable data assimilation package for regional NWP, aiming for the capability of NRT weather analyses and forecasts at high resolution. We have made the SCALE-LETKF code available at https://github.com/takemasa-miyoshi/ letkf, integrated with the LETKF applications to other models.…”

“…A regional NWP system can be used to increase the model resolution of an area of interest at a relatively low cost. Therefore, regional EnKF systems have also been widely developed and tested (Zhang et al 2006;Meng and Zhang 2007;Torn and Hakim 2008;Anderson et al 2009;Miyoshi and Aranami 2006;Miyoshi and Kunii 2012;Kunii 2014), and they have shown promising results in analyzing and predicting a variety of mesoscale phenomena, such as tropical cyclones (e.g., Zhang et al 2009;Zhang and Weng 2015;Torn 2010) and convective rainstorms (e.g., Yussouf et al 2013;Kunii 2014;Jones et al 2015).…”