投稿者「ArakidaHazuki」のアーカイブ

第52回 データ同化セミナー(12/19)のご案内

12/19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、
気象庁気象研究所の中澤哲夫氏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:    *Wednesday 19 December 2018, 15:30-16:30 *
Place:   Seminar Room at R-CCS
Language:  English
Speaker:  Dr. Tetsuo Nakazawa (Meteorological Research Institute (MRI))

*Title: Is the Trend in Tropical Cyclone Formation Frequency due to Global
Warming?*

*Abstract: *

Under the warmer climate projection, several models simulate that the
annual number of tropical cyclone formation will decrease, but the intense
tropical cyclone number will increase globally. The former may be related
with the stabler condition of the atmosphere, and the latter may be related
with the possible rapid intensification under the more humid condition near
the surface.  To understand the former hypothesis, Arakawa and Schubert’s
cumulus parameterization method has been utilized to estimate all possible
cloud types at each grid, using the ECMWF reanalysis dataset (ERA20C) from
1900 to 2010. The result shows that the tropical atmosphere is getting
stabler and the cloud top height of the deepest cloud type is getting
lower. This may explain why the annual number the tropical cyclone
formation will decrease in future.

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第51回データ同化セミナー (11月13日)のご案内

11/13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、
Prof. Roland Potthast (DWD/U of Reading)
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: *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.

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The 50th Data Assimilation Seminar 25th October

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:
http://www.data-assimilation.riken.jp/en/events/da_seminar/index.html

第50回データ同化セミナー (10月25日)のご案内

10月25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. Jing-Shan Hong  (Central Weather Bureau (CWB), Taiwan)
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:   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

Abstract:
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
mode.

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.

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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.
講師の方々より、生態学分野におけるデータ同化研究の紹介を行っていただくとともに、新たな分野への応用可能性について議論を行いました.

主催:理化学研究所計算科学研究センター

詳細は以下のページをご覧ください.
http://www.data-assimilation.riken.jp/jp/events/ecology_ws_2018fall/index.html

第48回/49回データ同化セミナー(7月27日)のご案内

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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第47回 データ同化セミナーを行いました(6月28日)

6月28日に理研計算科学研究センターで第47回データ同化セミナーを行いました.

講演者は、立命館大学の John C. Wells 教授です.ウェルズ先生は、理研の客員研究員として5月から2か月間当チームに滞在され、琵琶湖の水流・水温のナウキャストを目指した研究を進めてこられました.

今回のセミナーでは、Coastal Acoustic Tomography (海洋音響トモグラフィー)という手法を用いた琵琶湖における水流測定や、水流を推定するための既往の理論研究の紹介をいただきました.

DA seminar, Lecture, 28th Jun. 2018

DA seminar, Group photo, 28th Jun. 2018

詳細は以下のページをご覧ください.
http://www.data-assimilation.riken.jp/en/events/da_seminar/archive/20180628_Wells.html

第47回データ同化セミナー(6月28日)のご案内

6月28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、
Prof. John C. Wells(立命館大学、理研R-CCS客員研究員) に、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-team-desk(please remove here)@ml.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:      June 28, 15:30-16:30
Place:     Room – C107 at R-CCS
Language:  English
Speakers:  Prof. John C. Wells (Department of Civil Engineering,
Ritsumeikan University)

Title:
Towards nowcasting in Lake Biwa: field tests of acoustic tomography, and
discussion of some theorems relating the flow at a water surface to that
below

Abstract:
I will discuss two topics that are motivated by my lab’s objective to
establish a nowcasting system that can track the current and temperature
fields in Lake Biwa, Japan.

First, I will present results from a test of Coastal Acoustic Tomography
(CAT) in Lake Biwa in November 2017. Three 5 kHz transducers were deployed
along a 10.2 km line from the West Shore of the lake to Takeshima. Acoustic
travel times between transducers are computed from correlograms of the
emitted “M11” quasi-random code with the received signal. Small but
consistent differences in travel times between reciprocal paths were
observed, whence we estimate path-averaged currents along the dominant
acoustic path on the order of 5 cm/s, which is not inconsistent with
expected magnitudes at this site. For the temperature profile in November,
ray paths pass almost entirely below the thermocline. To my knowledge this
is the first reported estimate of currents by Acoustic Tomography in a lake.

Second I will consider how to estimate subsurface flow from the fluctuating
velocities and height at the surface of a river or sea, supposed to be
accessible from high resolution, high-speed video recordings, perhaps by a
stereo pair of bank-mounted cameras. Restricting attention to
constant-density flow, a kinematic relation will first be derived that can
be considered to extend the classical Biot-Savart law between vorticity and
velocity. Next, the Navier-Stokes equations for constant-density liquid
lead to dynamical relations between quantities at the surface with the flow
field below. Empirical relations might also be used to estimate subsurface
flow. For example, Large Eddy Simulation (LES) permits statistical
correlations between the surface and subsurface flow to be estimated. Some
relevant results by my laboratory, based on the Proper Orthogonal
Decomposition, have been presented in Nguyen et al. (2011). A weakness of
such empirical estimation methods is that if the actual flow includes
events are not “spanned” by samples in the LES database, the predictions
will fail. Thus it is important to have other tools available, such as the
kinematic and dynamical relations derived here.

Reference:
TD Nguyen, TX Dinh, JC Wells, P Mokhasi, D Rempfer 2011 “POD-Based
Estimation of the Flow Field from Free-Surface Velocity in the
Backward-Facing Step” – TSFP DIGITAL LIBRARY ONLINE, 2011
http://www.tsfp-conference.org/proceedings/2011/6d2p.pdf

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