Events / Media

Data Assimilation Seminar Series

Next Seminar

Date & Time 15:30-16:30, Jun. 28 2018 (2018-06(47th))
Place & Contact Room - C107
RIKEN Center for Computational Science (R-CCS)
7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
Click here for directions.
To enter the R-CCS building, please contact the following address in advance.
Language English
Speaker 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.

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

Upcoming Seminars

Date & Time 15:30-16:15, Jul. 27 2018 (2018-07(48th))
Place & Contact Room - C107
RIKEN Center for Computational Science (R-CCS)
7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
Click here for directions.
To enter the R-CCS building, please contact the following address in advance.
Language English
Speaker Prof. Pierre Tandeo
(IMT Atlantique)
Title TBA
Abstract TBA
Date & Time 16:15-17:00, Jul. 27 2018 (2018-08(49th))
Place & Contact Room - C107
RIKEN Center for Computational Science (R-CCS)
7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan
Click here for directions.
To enter the R-CCS building, please contact the following address in advance.
Language English
Speaker Dr. Hironori Arai
(Institute of Industrial Science, The University of Tokyo)
Title TBA
Abstract TBA


Number Date Speaker Title (crick the title to see abstract & PDF)
2018-05(46th) Apr 17, 2018 Dr. Kohei Takatama
Regional atmospheric data assimilation coupled with an ocean mixed layer model: a case of typhoon Soudelor (2015)
2018-04(45th) Feb 16, 2018 Prof. Pierre Tandeo
(IMT Atlantique)
The analog data assimilation: method, applications and implementation
2018-03(44th) Feb 9, 2018 Prof. Roland Potthast
(DWD/U of Reading)
Data Assimilation From Minutes to Days
2018-02(43rd) Jan 29, 2018 Dr. Shinsuke Satoh
Three-dimensional precipitation data measured by phased array weather radar every 30 seconds
2018-01(42nd) Jan 18, 2018 Prof. David J. Stensrud
(Penn State U)
2017-16(41st) Dec 11, 2017 Dr. Tsuyoshi Thomas Sekiyama
(Meteorological Research Institute, Japan Meteorological Agency)
Data assimilation of atmospheric chemistry: past, present, and future
2017-15(40th) Nov 17, 2017 Prof. Yusuke Uchiyama
(Kobe U)
Challenges and issues in forward regional ocean modeling: Eddies, terrestrial influences, and surface gravity waves
2017-14(39th) Oct 12, 2017 Mr. Krishnamoorthy Chandramouli
(IIT Madras)
Dr. Koji Terasaki
Impact of assimilating humidity sounder radiances with the NICAM-LETKF system
2017-13(38th) Sep 26, 2017 Mr. Cheng Da
(U of Maryland)
Dr. Guo-Yuan Lien
Assimilation of the GSMaP Precipitation Data with the SCALE-LETKF System
2017-12(37th) Aug 16, 2017 Ms. Paula Maldonado
(CONICET-U of Buenos Aires)
Radar Data Assimilation in a Case of Deep Convection in Argentina
2017-11(36th) Aug 16, 2017 Mr. Sam Hatfield
(Oxford U)
How low can you go? Reducing the precision of data assimilation to improve forecast skill
2017-10(35th) Aug 4, 2017 Dr. Chih-Chien Tsai
(TTFRI, Taiwan)
Preliminary Experimental Results of Polarimetric Radar Data Assimilation in the Case of Typhoon Soudelor (2015)
2017-09(34th) Jun 26, 2017 Dr. Toshio Iguchi
Radar Measurement of Precipitation from Space: TRMM/PR and GPM/DPR rain retrieval algorithms data
2017-08(33rd) May 18, 2017 Dr. Guo-Yuan Lien
30-second-cycle convection-resolving data assimilation of dense phased array weather radar data
2017-07(32nd) Mar 7, 2017 Dr. Alison Fowler
(U of Reading)
On the interaction of observation and a-priori error correlations in data assimilation
2017-06(31st) Mar 7, 2017 Dr. Joanne A. Waller
(U of Reading)
Diagnosing observation error statistics for numerical weather prediction
2017-05(30th) Mar 7, 2017 Prof. Nancy K. Nichols
(U of Reading)
New applications and challenges in data assimilation
2017-04(29th) Mar 6, 2017 Prof. Steven J. Greybush
(Penn State U)
Ensembles, Data Assimilation, and Predictability for Winter Storms
2017-03(28th) Mar 6, 2017 Prof. Eugenia Kalnay
(U of Maryland)
Modeling Sustainability: Coupling Earth and Human System Models
2017-02(27th) Feb 7, 2017 Prof. Hiromichi Nagao
(U of Tokyo)
Promises and Challenges in Assimilation of Infrared and Microwave All-sky Satellite Radiances for Convection-Permitting Analysis and Prediction
2017-01(26th) Jan 13, 2017 Prof. Fuqing Zhang
(Penn State U)
Data assimilation for massive autonomous systems based on a second-order adjoint method
2016-09(25th) Dec 22, 2016 Dr. Takahiro Nishimichi
(Kavli IPMU, U of Tokyo)
Dark Emulator: cosmic large-scale structures and parameter estimate
2016-08(24th) Dec 21, 2016 Dr. Takumi Honda
Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: Preliminary Case Studies
2016-07(23rd) Nov 24, 2016 Mr. Yasumitsu Maejima
Impacts of dense and frequent surface observations on a sudden severe rainstorm forecast: A case of an isolated convective system
2016-06(22nd) June 13, 2016 Mr. Yasutaka Ikuta
Assimilation of GPM/DPR at JMA
2016-05(21st) Apr 14, 2016 Dr. Yohei Sawada
Advancing land data assimilation science to monitor terrestrial water and vegetation dynamics
2016-04(20th) Mar 16, 2016 Dr. Juan Ruiz
(U Buenos Aires (CIMA)/RIKEN AICS)
Implementation and evaluation of a regional data assimilation system based on WRF-LETKF
2016-03(19th) Mar 7, 2016 Prof. Roland Potthast
(DWD/U of Reading)
On Ensemble and Particle Filters for Large-Scale Data Assimilation
2016-02(18th) Mar 2, 2016 Ms. Chang Yaping
Dr. Shunji Kotsuki
Ensemble data assimilation of MODIS surface temperature into land surface model
2016-01(17th) Feb 17, 2016 Mr. Sho Yokota
Comparison between LETKF and EnVAR with observation localization
2015-10(16th) Dec 25, 2015 Dr. Shunji Kotsuki
Ensemble Data Assimilation of GSMaP precipitation into the nonhydrostatic global atmospheric model NICAM
2015-09(15th) Dec 15, 2015 Dr. Kozo Okamoto
Assimilation of cloud-affected infrared radiances
2015-08(14th) Oct 21, 2015 Prof. Kosuke Ito
(U of the Ryukyus)
Advanced data assimilation techniques for predicting tropical cyclone intensities
2015-07(13th) Sep 16, 2015 Mr. Shumpei Terauchi
(U of Tsukuba)
Dr. Guo-Yuan Lien
Verification of the near-real-time weather forecasts and study on 2015 typhoon Nangka with the SCALE-LETKF system
2015-06(12th) Sep 2, 2015 Dr. Daisuke Hotta
Diagnostic methods for ensemble data assimilation
2015-05(11th) Jun 25, 2015 Dr. Koji Terasaki
Applying the Local Transform Ensemble Kalman Filter to the non-hydrostatic atmospheric model NICAM
2015-04(10th) Jun 5, 2015 Prof. S. Lakshmivarahan
(U of Oklahoma)
Nonlinear dynamics and Predicitability
2015-03(9th) Apr 17, 2015 Prof. Takeshi Enomoto
Assimilation and forecast experiments using bright band heights
2015-02(8th) Mar 24, 2015 Dr. Shigenori Otsuka
Towards 100-m resolution prediction of local convective storms: predictability and nowcasting
2015-01(7th) Jan 20, 2015 Dr. Nobumasa Komori
Development of an ensemble-based data assimilation system with a coupled atmosphere-ocean GCM
2014-06(6th) Dec 26, 2014 Dr. Keiichi Kondo
The 10,240-member ensemble Kalman filtering with an intermediate AGCM without localization
2014-05(5th) Nov 26, 2014 Prof. Shin-ichiro Shima
(U of Hyogo/RIKEN AICS)
Data assimilation experiments of the dynamic global vegetation model SEIB-DGVM with simulated GPP observations
2014-04(4th) Nov 26, 2014 Prof. Takeshi Ise
(Kyoto U)
Simulating terrestrial ecosystems: current progress and future perspectives
2014-03(3rd) Oct 31, 2014 Prof. Masayuki Yokozawa
(Shizuoka U)
Evaluating the productivities of major crops at the global scale using process-based crop model
2014-02(2nd) Sep 10, 2014 Dr. Guo-Yuan Lien
Ensemble Assimilation of Global Large-scale Precipitation
2014-01(1st) July 23, 2014 Dr. Juan Ruiz
(U of Buenos Aires (CIMA)/RIKEN AICS)
Efficient parameter estimation for numerical weather prediction models using data assimilation