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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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理研データ同化合宿2018(基礎編)を開催しました

理研データ同化合宿2018(基礎編) 修了式の様子

12/3~12/7に理研データ同化合宿2018(基礎編)を開催しました.今年度は21名の方が受講され,定員満員の盛況となりました.

合宿はLorenz-96 モデルを題材としたデータ同化について,午前中には座学で,午後には各自がプログラミングの実習を行うという形式で進んでいきました.講義の冒頭では毎日,受講者の皆さま全員から進捗報告をしていただき,それをもとに実装における「罠」なども議論しています.

最終日までに多くの方がカルマンフィルタと3次元変分法の実装を完了し,一部の方はアンサンブルカルマンフィルタや4次元変分法についても結果を示すところまで進みました.合宿の最後には皆さまに修了書をお渡しすることができました.

データ同化合宿2018(基礎編)の講義の様子 データ同化合宿2018(基礎編)の実習の様子

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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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)

一昨年・昨年に行われ好評を博した「理研データ同化合宿(基礎編)」を今年も開催します!

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).

参加申し込みの締め切り日は11月22日(木)となっています.
詳細はチームWEBサイト内に特設した以下のページをご覧ください.

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

先日の豪雨災害について,気象学者による最初の緊急対応として研究会を開催します.

本研究会は専門家間の情報交換・議論を目的としておりますが,ご興味のある方はどなたでも参加いただくことができます(ただし会場の都合により,ご参加のためには事前の登録が必要です).

詳細は下記,特設ページをご覧ください.
研究会ウェブページ

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9回理研・京大データ同化研究会(7月17日)のご案内

7月17日の第9回理研・京大データ同化研究会のご案内です。

今回のセミナーでは、以下の方々にご講演いただきます。
森下喜弘 理化学研究所 生命システム研究センター
鎌谷研吾 大阪大学 大学院基礎工学研究科
小槻峻司 理化学研究所 計算科学研究センター

詳細は こちら です。
参加ご希望の方は、7月13日(金)までに da-ws-staff(please remove here)@riken.jp へご連絡ください。

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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第2回 理研・気象庁データ同化研究会を行いました(4月25-26日)

4月25-26日に気象庁東京管区気象台で第2回 理研・気象庁データ同化研究会を行いました.

主催:気象庁気象研究所
共催:理化学研究所計算科学研究センター

第2回 理研・気象庁データ同化研究会 集合写真

詳細は以下のページをご覧ください.
http://www.data-assimilation.riken.jp/jp/events/riken_jma_ws_2018spring/index.html