739 30-s Cycle LETKF Assimilation of Dual-Phased Array Weather Radar Observations to Short-Range Convective Forecasts

Tuesday, 8 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
James Taylor, RIKEN Center for Computational Science, Kobe, Japan; and G. Y. Lien, Y. Maejima, S. Satoh, and T. Miyoshi

The assimilation of Doppler velocity and reflectivity observations from phased array weather radar (PAWR) have been widely studied for the use of short-range numerical weather prediction (NWP) and have been found to have positive impact to analyses and forecasts. However, these studies only assimilated observations from a single PAWR and the use of multiple PAWR observations for NWP has not yet been explored. Yet the recent development of PAWR located at sites in Osaka and Kobe, Japan mean that a common observation region now exists where we are able to obtain dual PAWR coverage over the Kobe region, an area where convective storms can develop suddenly bringing intense rainfall.
This study represents the first attempt at assimilating dual PAWR observations for the purpose of improved short-range weather forecasts of a sudden convective rainfall event. We focus on a case that occurred in Kobe city on 11 September 2014, which brought intense rainfall to the region and was well observed by both Kobe and Osaka PAWR. Simulations are performed with 30-second-cycling of PAWR observations within a high-resolution 100-m mesh. We employ the SCALE-LETKF system which couples the Local Ensemble Transform Kalman Filter (LETKF) with the Scalable Computing for Advanced Library and Environment (SCALE)-RM model. We aim to develop an effective data assimilation method which fully exploits the availability of two PAWR systems to observe a single convective rainfall event and show how the data can be optimally combined to improved analyses and short-range forecasts compared to assimilating observations from a single PAWR.
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