データ同化研究チーム Data Assimilation Research Team 2018年の新着情報

7月27日のデータ同化セミナーについてのご案内です。

今回のセミナーでは、
Dr. Hironori Arai (Institute of Industrial Science, The University of Tokyo)
Prof. Pierre Tandeo (IMT Atlantique)
の2名の講演者よりご講演頂きます。

※どなたでもご参加いただけますが、入館に手続きが必要なため、 事前に下記までご連絡をお願い致します。
da-seminar(please remove here)@riken.jp

以下URLに随時情報を更新しています。 http://data-assimilation.riken.jp/en/events/da_seminar/

以下、詳細です。

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  Date：  July 27, 15:30-16:30
  Place：   Room - C107 at R-CCS
  Language:  English
  Speakers：
  15:30-16:00 Dr. Hironori Arai (Institute of Industrial Science, The University of Tokyo)
  16:00-16:30 Prof. Pierre Tandeo (IMT Atlantique)
  
  - Dr. Hironori Arai -
  
  Title：
  Establishing an integrated MRV system of Greenhouse gas emission from wetlands
  with Japanese earth-observation/modelling technologies and a data assimilation technique
  
  Abstract：
  Greenhouse gas (GHG) emission observation/reduction technologies are attracting
  greater deal of attention from policy makers to achieve Sustainable Development
  Goals. In terms of GHG accounting, Monitoring, Reporting and Verification (MRV)
  systems have become significantly important for the countries which ratified
  Paris Agreement by promising Intended Nationally Determined Contributions (INDC).
  Not only evaluation of the amount of GHG emitted from the countries, but also
  the mitigation’s effect and its dissemination status need to be monitored by
  the policy makers. In this regard, the societies require the MRV systems with
  transparency and high cost-performance. To address such concern, the authors
  are building an efficient/transparent MRV system in a tropical rice cropping
  system based on satellite remote sensing data. We are developing a long-term
  consistent bottom-up approaching method with high spatio-temporal resolution,
  based on the Japanese earth observation technology (e.g., ALOS-2, AMSR-E/2,
  GCOM-C). In order to validate the outputs from the bottom-up approaching method,
  Now we are also challenging to build an independent top-down approaching 
  method based on the other satellites data (GOSAT,SCIAMACHY,AIRS) using
  NICAM-LETKF with 1way-multivariate variable localization, which can estimate
  the surface fluxes without requiring any direct observation or a-priori information
  of the fluxes with K-computer. In this presentation, we would like
  to discuss the development plan and expected collaboration with further 
  cross-disciplinary collaboration.
  
  - Prof. Pierre Tandeo -
  
  Title：
  Data-driven methods in geophysics
  
  Abstract：
  This seminar will be divided in two parts. Firstly, I will present some
  recent results about a review paper I am preparing. It deals with the
  different methods we find in the data assimilation literature to jointly
  estimate Q and R. These error covariance matrices are crucial because
  they control the relative weights of the model forecasts and the
  observations in filtering methods. I will remind the different methods
  and present some numerical comparisons on toy-models.
  Secondly, I plan to present various applications of data-driven methods
  in geophysics, not especially for data assimilation. I will show some
  applications of the analog method and deep learning in environmental
  problems, e.g. the nowcasting of solar irradiance using geostationary
  satellites and the classification of oceanic and atmospheric phenomena
  using SAR images.
  
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