51st Data Assimilation Seminar – Prof. Roland Potthast

We held the 51st Data Assimilation Seminar at RIKEN Center for Computational Science (R-CCS) on November 13. The seminar talk was given by Professor Roland Potthast from Deutscher Wetterdienst (DWD), the German National Meteorological Service, and University of Reading.

Prof. Potthast spoke about the ICON global and mesoscale model under development at DWD and alongside COSMO partners.

We would like to thank Prof. Potthast very much for visiting us at RIKEN and providing us with a good overview of all the interesting research happening at DWD. We look forward to his next visit to RIKEN.

For details on upcoming DA seminars, please see the following DA seminar page:

第51回データ同化セミナー (11月13日)のご案内

Prof. Roland Potthast (DWD/U of Reading)

da-seminar(please remove here)@riken.jp




Date: *Tuesday 13 November 2018, 10:30-12:00 *
Place: Room – R104-2 at R-CCS
Language: English
Speaker: Prof. Roland Potthast (DWD/U of Reading)

*Title: New Observations and Algorithmic Developments for Convective Scale
Ensemble Data Assimilation*

*Abstract: *
We first present the setup of the ensemble data assimilation (EDA) and
forecasting systems (EPS) which have been developed and are under
development at the German Weather Service DWD and its COSMO partners. This
is first the ICON global+mesoscale model (two-way nested), 13km/6.5km
resolution, with its hybrid ensemble variational data assimilation
(LETKF+EnVAR) run on a 3h cycle,and the ensemble prediction system ICON
EPS. Second, this system drives the high-resolution ensemble data
assimilation system COSMO-KENDA (Kilometer Scale Ensemble Data
Assimilation) with 2.2km operational resolution at DWD and up to 1km
resolution at further members of the COSMO consortium (Germany,
Switzerland, Italy, Russia, Poland, Romania, Greece and Israel) to provide
initial conditions for the high-resolution ensemble forecasting systems,
e.g. the operational COSMO-D2-EPS or experimental ICON-LAM EPS. The system
is also successfully run on GPU based supercomputers.

The core task of the talk is to discuss recent and current developments on
new observations and on new algorithmic developments on the convective
scale, but many of them relevant for global NWP as well. We discuss recent
insight into the importance of quality control, report on the large success
and positive impact of Mode-S data assimilation, discuss the assimilation
of RADAR radial winds and reflectivity with an ensemble Kalman filter and
finally report on some initial tests on the assimilation of visible
channels SEVIRI VIS on the convective scale.

Second, we will discuss new algorithmical developments, in particular
aspects of 4D-LETKF versus 3D-LETKF and initial tests on the ICON-LAM data
assimilation with the KENDA system. Then, we present the particle filter
for global or convective scale EDA as well as ultra-rapid data assimilation
(URDA) on a scale of minutes imbedded into an operational rapid update
cycle (RUC) of a convection resolving model.



We held the 50th Data Assimilation Seminar at RIKEN Center for Computational Science (R-CCS) on October 25. The seminar talk was given by Dr. Jing-Shan Hong from the Central Weather Bureau (CWB) in Taiwan.

In this seminar Dr. Hong's talked about his research applying a multi-scale blending scheme on continuous cycling radar data assimilation. He showed how using a multi-scale blending scheme he can take advantage of analysis from both a global and regional model to improve the prediction of accumulated rainfall and typhoon track forecasts. He also showed how the blending scheme can be used to remove accumulated bias from continuous cyclic data assimilation.

We would like to thank Dr. Hong very much for visiting us at RIKEN and we look forward to seeing him again in the near future!

For details on upcoming DA seminars, please see the following DA seminar page:

(917) 896-4046

Dr. Jing-Shan Hong  (Central Weather Bureau (CWB), Taiwan)

da-seminar(please remove here)@riken.jp




Date:   October 25, 15:30-16:30
Place:   Room – C107 at R-CCS
Language:  English
Speaker:  Dr. Jing-Shan Hong, Central Weather Bureau (CWB), Taipei, Taiwan

Title: Re-Center algorithm on the Continuous Cycling Radar Assimilation:
Multi-scale Blending Scheme

The torrential rains result from the short duration extreme rainfall system
is of most critical for the disaster prevention. However, the limited
predictability is the essence of the short duration extreme rainfall system
due to the multi-scale interaction, fast evolution and strong nonlinearity.
The assimilation of the radar observation with rapid, continuous update
cycle is a key to level up the predictability of such a system.

The continuous rapid update cycle is able to capture and keep
convective-scale structure and avoid the model spin-up problems. However,
many challenges were faced in the continuous update cycle data
assimilation. For example, the limited-area model systems in general suffer
a deficiency to effectively represent the large-scale features and are
unavoidable to experience the obvious large-scale forecast errors. In
particular, the domain size is restricted due to the compromise of
increasing model resolution and limited computer resources. Furthermore,
the model errors are ease to accumulate over the sparse observation area,
especially as the data assimilation system configured as a continuous cycle

In this study, a multi-scale blending scheme using a low-pass spatial
filter (Hsiao et al. 2015) was applied to a continuous cyclic radar data
assimilation system. The blending scheme combines the global model analysis
and the convective scale model forecast. It is expected the blended field
takes the advantage from the global large scale environment and the
convective scale perturbations. The scheme was applied to the hourly
updated 3DVAR based radar data assimilation system. In addition, it also
applied to re-center the ensemble mean of the cyclic LETKF radar data
assimilation system. Case studies show that the blending scheme is able to
correct the bias of the large scale monsoon flow from the global model and
keep the convective rainfall structure from the convective scale radar data
assimilation system. The results also show that the performance of
quantitative precipitation forecasts from both the 3DVAR and LETKF radar
data assimilation system improved significantly as applying the blending
scheme. The more detailed sensitivity on the blending scheme also discussed
in this study.


理研データ同化合宿2018 (基礎編) 開催のお知らせ (12月3-7日)


これまでの合宿では,Lorenz-96 モデルを題材に,多くの参加者がカルマンフィルタ―およびアンサンブルカルマンフィルタ―を自分の手で実装できるようになりました(さらに発展的な演習課題も用意しています).


第2回 生態系データ同化に関する研究会を行いました(9月18日)

9月18日に理研R-CCSにて第2回 生態系データ同化に関する研究会を行いました.


(980) 498-4345

Abstract submission now accepted for 7th International Symposium on Data Assimilation (ISDA 2019)




Dear Colleagues:

It is our pleasure to announce the 7th International Symposium on Data
Assimilation (ISDA 2019), which will be held on January 21 – 24, 2019 in
Kobe, Japan. We are now accepting abstract submissions. To apply, please
visit the symposium website:

Important Dates (Tentative):
* Abstract submission deadline: October 14, 2018
* Speakers confirmed and program made available: November 2018
* Registration deadline: December 16, 2018
* Symposium: January 21 – 24, 2019

Scientific Organizing Committee:

Chair: Takemasa Miyoshi (RIKEN / University of Maryland / JAMSTEC /
Kyoto University)
Henry Abarbanel (University of California, San Diego)
Serge Guillas (University College London)
Ibrahim Hoteit (KAUST)
Nancy Nichols (University of Reading)
Roland Potthast (Deutscher Wetterdienst / University of Reading)
Sebastian Reich (University of Potsdam / University of Reading)
Hiromu Seko (JMA,MRI / JAMSTEC)
Peter Jan Van Leeuwen (Colorado State University)
Martin Weissmann (Ludwig-Maximilians-Universität München)
Shu-Chih Yang (Taiwan National Central University)

We really look forward to seeing you in Kobe.

Sincerely yours,

Takemasa Miyoshi
Chair of the 7th International Symposium on Data Assimilation (ISDA 2019)


The 48th and 49th Data Assimilation Seminar 27 July

We held the 48th and 49th Data Assimilation Seminars at RIKEN Center for Computational Science (R-CCS) on July 27.

The first talk was given by Dr. Hironori Arai from the Institute of Industrial Science, University of Tokyo. He has been a visiting scientist with the Data Assimilation team here at RIKEN since May. He discussed his work in developing monitoring systems of greenhouse gas emissions in tropical rice cropping systems based on satellite remote sensing data.

The second talk was given by Professor Pierre Tandeo from IMT Atlantique in France. He presented his work on a review paper he is currently writing about the different methods in data assimilation to estimate the observation error covariance matrix (R) and model error (Q). He also discussed various applications on data-driven methods in geophysics, including a future project where he will be trying to predict the development of rogue ocean waves.

DA seminar group photo

For details, please see the following DA seminar page:


平成30年7月豪雨に関する緊急対応研究会 (8月17日) のご案内




(330) 444-7693


Dr. Hironori Arai (Institute of Industrial Science, The University of Tokyo)
Prof. Pierre Tandeo (IMT Atlantique)

da-seminar(please remove here)@riken.jp




Date:  July 27, 15:30-16:30
Place:   Room – C107 at R-CCS
Language:  English
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 –

Establishing an integrated MRV system of Greenhouse gas emission from wetlands
with Japanese earth-observation/modelling technologies and a data assimilation technique

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 –

Data-driven methods in geophysics

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.