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Summary Account


Our research aims to improve the accuracy of short-range rainfall forecasts and to prevent disasters caused by local heavy rainfall and other mesosclae severe weather.

We develop high-resolution data assimilation methods to better estimate the state of the atmosphere as the initial conditions of NWP (Numerical Weather Prediction). We also utilize ensemble prediction forecasting techniques to forecast the probabilities of different possible NWP reuslts and to provide informaiton about uncertainties in high-impact weather prediction.


Job titleNamee-mail xxxxx@domainLink to personal pages
xxxxx domain
HeadHiromu Sekohsekomri-jma.go.jpProfile and Paper Lists
Senior ResearcherTakuya Kawabatatkawabatmri-jma.go.jpProfile and Paper Lists
Senior ResearcherMichiko
Senior ResearcherDaisuke
Guest ResearcherLe
Guest ResearcherKosuke Group, University of the Ryukyus
Guest ResearcherTsutao
Guest ResearcherGenta
Guest ResearcherKazuo and Paper Lists


Research on improvement of mesoscale weather prediction and information for disaster prevention

High-resolution data assimilation methods and ensemble prediction forecasting techniques for the numerical prediction of mesoscale severe weather events

The post K computer development plan of the FLAGSHIP2020 Project : Development of a wide range of applications that will address social and scientific priority issues

Advancement of meteorological and global environmental predictions utilizing observational 'Big Data'(Theme 4)

  • Innovative numerical weather predictions and advanced weather disaster prevention based on damage-level estimation

CREST: Innovating "Big Data Assimilation" Technology for Revolutionizing Very-short-range Severe Weather Prediction

KAKENHI : Study on uncertainty of cumulonimbus initiation and development using particle filter

KAKENHI : Research on genesis and intensification mechanism of cumulonimbus clouds using next-generation data assimilation and ensemble simulation

KAKENHI : A hyper-resolution land data assimilation system

KAKENHI : Ensemble assimilation of remotely-sensed densed obsercations through direct model-space covariance localization

last update : Nov. 5 2014
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