[p1] Multi-scale and multi-component treatments


Dual-Scale Neighboring Ensemble Variational Assimilation of Satellite Microwave Imager Brightness Temperatures for Typhoon ETAU

Kazumasa AONASHI(Meteorological Research Institute), Kozo OKAMOTO(Meteorological Research Institute), Tomoko TASHIMA (RESTEC)


The present study developed an Ensemble-based Variational Assimilation (EnVAR) scheme for a Cloud-Resolving Model (CRM) using a dual scale Neighboring Ensemble method (DuNE). In the EnVAR, the control variables included precipitation and the ratio of total water content to the saturation mixing ratio (RHW2) etc. We calculated the mixing ratios of water subsistence from precipitation and RHW2 using the minimum square method. We constructed an EnVAR forecast analysis (FA) cycle system to assimilate satellite microwave imager (MWI) brightness temperatures (TBs).

We incorporated MWI TBs to this system for Typhoon Etau (T1518) case. We used the average of 52-member CRM ensemble forecast started at 00 UTC 7th Sep. 2015 as the first guess of EnVAR. Then we assimilated TBs of GMI (14 UTC 7th), SSMIS (07 UTC 8th), and AMSR2 (17 UTC 8th) using the FA system. The assimilation made large-scale analysis increments for relative humidity, surface pressure, and horizontal wind speeds. The assimilation also changed meso-scale precipitation rate and vertical updraft patterns around the typhoon. The assimilation significantly improved a CRM precipitation forecast up to 30 hours, in particular, by strengthening and stagnating a rain band over the Kanto Plain.