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The 10th International Symposium on Data Assimilation


ISDA KOBE


OCTOBER 21-25, 2024

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INTRODUCTION

Data assimilation (DA) is a cross-disciplinary science to bring together
computer simulations and real-world data in a synergistic manner based on
statistical mathematics and dynamical systems theory. DA has long been playing a
crucial role in numerical weather prediction (NWP), and recently, DA has been
applied more widely to numerical model simulations beyond geophysical
applications, such as planetary and biological sciences as well as engineering
applications. Contemporary fundamental challenges include better treatment of
nonlinear and multi-scale system evolution, complex observation operators, and
variables with non-Gaussian properties. Developing efficient computational
algorithms has also been a major issue.

The 10th International Symposium on Data Assimilation (ISDA2024) is organized by
RIKEN and will be held at the Convention Hall of Integrated Research Center of
Kobe University, continuing a series of well-received events: ISDA2023 in
Bologna, ISDA2022 in Fort Collins, ISDA2019 in Kobe, ISDA2018 in Munich,
ISDA2016 in Reading, ISDA2015 in Kobe, ISDA2014 in Munich and the first two
symposia at DWD in Offenbach. The symposium is open for oral and poster
contributions in various fields of DA research.

The symposium will focus on the cross-cutting issues shared in broad
applications of DA from geoscience to various physical and biological sciences
and engineering. In particular, the symposium will enhance discussions among
researchers with various backgrounds on, for example, nonlinear and non-Gaussian
DA, Uncertainty Quantification (UQ), Artificial Intelligence (AI) and Machine
Learning (ML), observational issues, mathematical problems, and predictability
and controllability. DA is at the core of more general predictive sciences, and
the symposium celebrates the final year of the 5-year RIKEN Pioneering Project
"Prediction for Science" and the launch of "RIKEN Prediction Science", a theme
of "Transformative Research Innovation Platform of RIKEN platforms (TRIP)".


IMPORTANT NOTES

Abstract Submission: Closed

Registration deadline: 23:59 [UTC] on August 12, 2024

To attend ISDA2024, you need to register in advance.

Program : Available 

Event format: In-person only

Submission/Registration Fee: Free

Side event: IMT-Atlantique & Kyoto University & RIKEN joint Data Assimilation
workshop

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Schedule


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Program


DAY 1, OCTOBER 21

09:00-09:30Registration 09:30-10:15Opening
Takemasa Miyoshi (RIKEN) 10:15-11:15Keynote
High-Dimensional Covariance Estimation From a Small Number of Samples
 Matthias Morzfeld (Scripps Institution of Oceanography)
11:15-11:30Group Photo 11:30-13:00Lunch 13:00-14:20DA Theory and Mathematics 1
13:00-13:20TM1-1 Ensemble Data Assimilation Methods for Applications with Mixed
Probability Distributions
 Jeffrey Anderson (NSF National Center for Atmospheric Research)
13:20-13:40TM1-2 Sequential estimation of model error
 Sergey Frolov (NOAA) 13:40-14:00TM1-3 Including cross correlations between the
forecast and observation errors in the ensemble Kalman filter
 Yuki Kobayashi (Kyoto University) 14:00-14:20TM1-4 The Control-Pert method of
ensemble data assimilation and its implementation in the JEDI framework
 Tsz Yan Leung (Met Office) 14:20-15:00Break 15:00-16:00Poster Session 1
Broad Applications, Perspectives to Predictive Sciences 16:00-16:30Fugaku Tour
[Day1 group] 16:30-18:10Broad Applications, Perspectives to Predictive Sciences
1
16:30-16:50BA1-1 Assessment of short-range forecast atmosphere-ocean
cross-covariances from the Met Office coupled NWP system.
 Amos S. Lawless (University of Reading & National Centre for Earth Observation)
16:50-17:10BA1-2 Assessment of weakly and strongly coupled data assimilation in
ocean-biogeochemical modeling
 Lars Nerger (Alfred Wegener Institute, Bremerhaven) 17:10-17:30BA1-3 Towards
the Exclusive Use of the Global Ensemble Prediction System for Global
Forecasting in Canada
 Mark Buehner (ECCC) 17:30-17:50BA1-4 Pre-emptive Forecasting with the Ensemble
Transform Kalman Filter
 Jordan Brook (Bureau of Meteorology) 17:50-18:10BA1-5 The Joint Effort for Data
assimilation Integration (JEDI)
 Yannick Tremolet (JCSDA) 18:15-19:15Ice Breaker
Location: R-CCS seminar room (Ground floor)


DAY 2, OCTOBER 22

09:00-09:30Registration 09:30-10:30Keynote
Future Plans for NCEP Global Data Assimilation
 Catherine Thomas (NOAA/NWS/NCEP/EMC)
10:30-11:30DA Theory and Mathematics 2
10:30-10:50TM2-1 An Ensemble Score Filter for Tracking High-Dimensional
Nonlinear Dynamical Systems
 Feng Bao (Florida State University) 10:50-11:10TM2-2 The Tensor Hybrid forecast
covariance model: unifying localization and hybridization
 Francesco Sardelli (University of Melbourne) 11:10-11:30TM2-3 Correlation-based
localization in iterative ensemble methods
 Geir Evensen (NORCE) 11:30-12:50Lunch 12:50-14:30Nonlinear and Non-Gaussian DA
1
12:50-13:10NL1-1 Flow- and Diffusion-based fully nonlinear Data Assimilation
 Peter Jan van Leeuwen (Colorado State University) 13:10-13:30NL1-2 Unbiased
fully nonlinear data assimilation: the Stochastic Particle Flow Smoother
 Hao-Lun Yeh (CSU) 13:30-13:50NL1-3 Particle filter combined with an ensemble
synchronization for data assimilation in a high-dimensional system
 Flavia Pinheiro (Brazilian Navy, in charge of Numerical Forecast Division)
13:50-14:10NL1-4 Local Variational Mapping Particle Filter
 Guerrieri, Juan Martin (UNNE) 14:10-14:30NL1-5 A New Localization Method for
Multi-Scale Non-Gaussian Data Assimilation.
 Diego S. Carrio (UIB) 14:30-15:00Break 15:00-16:00Poster Session 2
Nonlinear and Non-Gaussian DA / Predictability and Controllability
16:00-17:40Nonlinear and Non-Gaussian DA 2
16:00-16:20NL2-1 Manifold aspect of data assimilation
 Daisuke Hotta (MRI/JMA) 16:20-16:40NL2-2 A hybrid particle filter/ensemble
Kalman filter implementation with an intermediate AGCM
 Keiichi Kondo (Meteorological Research Institute) 16:40-17:00NL2-3 Assimilation
of Bounded Observations whose Errors are a Function of the True State:
Evaluation Using a New Tracer Model Based on Lorenz-96
 Jianyu Liang (RIKEN) 17:00-17:20NL2-4 Exploring non-Gaussian data assimilation
for precipitation variables:A case for global precipitation estimation from rain
gauge observation
 Yuka Muto (Chiba University) 17:20-17:40NL2-5 Deep Bayesian Filtering for
nonlinear data assimilation
 Yuta Tarumi (PFN)


DAY 3, OCTOBER 23

09:00-09:30Registration 09:30-10:30Keynote
Exploring weather control technology to steer the atmosphere towards favorable
directions based on ensemble data assimilation
 Shunji Kotsuki (Chiba U.)
10:30-11:30Predictability and Controllability 1
10:30-10:50PC1-1 Ensemble Approximation-based Model Predictive Control for
Disaster Mitigation in Extreme Weather Events
 Kenta Kurosawa (Chiba University) 10:50-11:10PC1-2 Assimilation of dual
multi-parameter phased array radar observations for high precision convective
forecasting
 James Taylor (RIKEN) 11:10-11:30PC1-3 A Variance Budget to quantify the Growth
and Interaction of Uncertainties on Convective Scales
 Takumi Matsunobu (LMU München) 11:30-12:50Lunch 12:50-13:50Keynote
Exploring the Possibility of Weakening Typhoons Through Human Intervention
 Lin Li (Sichuan University)
13:50-14:30Predictability and Controllability 2
13:50-14:10PC2-1 Predictability of weakly turbulent systems from spatially
sparse measurements
 Vikrant Gupta (GUANGDONG TECHNION) 14:10-14:30PC2-2 Predictability of climate
tipping: data assimilation approach
 Amane Kubo (The University of Tokyo) 14:30-15:00Break 15:00-16:00Poster Session
3
Observation and Diagnostics 16:00-16:30Fugaku Tour [Day3 group]
16:30-18:10Observation and Diagnostics 1
16:30-16:50OD1-1 Joint Data Assimilation Testbed for the Community
 Thomas Auligne (Joint Center for Satellite Data Assimilation) 16:50-17:10OD1-2
Multivariate Land Surface Reanalysis at CMCC using EaKF/SPREADS
 Luis Gustavo Gonçalves de Gonçalves (CMCC) 17:10-17:30OD1-3 A fast computation
algorithm for nonnegative KDP estimation based on the self-consistency principle
 Daichi Kitahara (Keio University) 17:30-17:50OD1-4 Assimilating of 3D radar
information at convective scales at Deutscher Wetterdienst (DWD)
 Kobra Khosravian (DWD) 17:50-18:10OD1-5 Inter-channel error correlations in
all-sky assimilation at ECMWF
 Liam Steele (ECMWF) 18:10-18:30Discussion Time 19:00-21:00Banquet
Location: Portopia Hotel


DAY 4, OCTOBER 24

09:00-09:30Registration 09:30-10:30Keynote
Data-driven identification and reconstruction of partially observed dynamical
systems
 Pierre Tandeo (IMT Atlantique & RIKEN)
10:30-11:30Data Assimilation & Machine Learning 1
10:30-10:50ML1-1 AI-based data assimilation: Learning the functional of analysis
estimation
 Jan D Keller (Deutscher Wetterdiensty) 10:50-11:10ML1-2 Using Data Assimilation
to Improve Data Driven Surrogate Models
 Michael Goodliff (RIKEN) 11:10-11:30ML1-3 A Multi-Fidelity Ensemble Kalman
Filter with a machine learned surrogate model
 Jeffrey van der Voort (TU Delft) 11:30-12:50Lunch 12:50-14:30Data Assimilation
& Machine Learning 2
12:50-13:10ML2-1 Assimilating real Earth system observations with machine
learning models
 Laura C. Slivinski (NOAA/OAR/Physical Sciences Laboratory) 13:10-13:30ML2-2 A
Neural network based MPAS Shallow Water Model and its 4DVar Data Assimilation
System
 Xiaoxu Tian (NOAA/NCEP/EMC) 13:30-13:50ML2-3 Sparse identification of nonlinear
dynamics and its use in the estimation of model errors
 Le Duc (the University of Tokyo) 13:50-14:10ML2-4 Assimilation of cloud
profiling radar using machine-learning-generated background errors and local
ensemble tangent linear models
 Yasutaka Ikuta (MRI/JMA) 14:10-14:30ML2-5 Machine Learning methodology for
generating ensemble members in Data Assimilation of Earth Observations
 Alessandro D'Ausilio (Arianet/SUEZ) 14:30-15:00Break 15:00-16:00Poster Session
4
Data Assimilation & Machine Learning 16:00-16:30Fugaku Tour [Day4 group]
16:30-17:30Data Assimilation & Machine Learning 3
16:30-16:50ML3-1 Learning Optimal Filters Using Variational Inference
 Enoch Luk (Caltech) 16:50-17:10ML3-2 Generating Cost-Saving Surrogate
Background Ensemble in Ensemble-Based Data Assimilation
 Yongming Wang (OU/MAP) 17:10-17:30ML3-3 Investigation of Machine Learning on
Satellite Radiance Bias Correction in the NCEP DA system
 Xin Jin (SAIC(NOAA/NWS/NCEP/EMC))


DAY 5, OCTOBER 25

09:00-09:30Registration 09:30-10:30Keynote
Localisation in iterative ensemble smoothers for coupled nonlinear multiscale
models
 Femke C. Vossepoel (TU Delft)
10:30-11:30Uncertainty Quantification 1
10:30-10:50UQ1-1 Ensemble of Data Assimilations Spread Optimisation
 Elias Holm (ECMWF) 10:50-11:10UQ1-2 Incorporating Measurement Error
Distribution into Tsunami Data Assimilation using High-Frequency Radar
 Muhammad Irham Sahana (Ehime University) 11:10-11:30UQ1-3 Comparison of
uncertainty quantification methods for cloud simulation
 Tijana Janjic (KUEI) 11:30-12:50Lunch 12:50-14:30Satellite DA 1
12:50-13:10SA1-1 Global assimilation of all-sky radiance from infrared imagers
and sounders
 Kozo Okamoto (JMA/MRI) 13:10-13:30SA1-2 On the Potential Impact of Visible and
Infrared Radiance Assimilation and the Effect of Nonlinear Observation Operators
 Lukas Kugler (University of Vienna) 13:30-13:50SA1-3 Advancing EnKF-based
Infrared Radiance DA through Understanding and Mitigating Non-Gaussian Artefacts
 Man-Yau "Joseph" Chan (Department of Geography, The Ohio State University)
13:50-14:10SA1-4 Examinations of nonlinearities and non-Gaussianities in the
assimilation of all-sky microwave observations
 Chih-Chi Hu (Princeton University) 14:10-14:30SA1-5 Recent Development in
Assimilating Satellite Data for Improved Tropical Cyclone Prediction
 Zhaoxia Pu (University of Utah) 14:30-15:00Break 15:00-16:00Poster Session 5
Satellite DA 16:00-16:30Fugaku Tour [Day5 group] 16:30-17:30Satellite DA 2
16:30-16:50SA2-1 Towards the assimilation of near-infrared satellite images
 Leonhard Scheck (DWD/LMU Munich) 16:50-17:10SA2-2 Use of altimeter data in a
coupled data assimilation system
 Noureddine Semane (ECMWF) 17:10-17:30SA2-3 Simultaneous Assimilation of
Dual-Polarization Radar and All-Sky Satellite Observations to Improve Convection
Forecasts
 Keenan Eure (CIRA/NOAA) 17:30-17:45Closing


POSTER SESSIONS

Poster Session 1 (October 21, 2024)Broad Applications, Perspectives to
Predictive Sciences P1-01Variable-dependent and selective multivariate
localization for EnVar in the Tropics
 Joshua Chun Kwang Lee (National Environment Agency, Singapore: University of
Reading) P1-02Meso-scale Ensemble Data Assimilation Systems based on ASUCA-Var
Developed at MRI
 Takuya Kawabata (Meteorological Research Institute / Japan Meteorological
Agency) P1-03Exploring Quantitative Observation Impact in Partial and Continuous
Cycling Ensemble Kalman Filter Data Assimilation Systems
 Gimena Casaretto (UBA) P1-04Data Assimilation System Development and Testing
for Rapid Refresh Forecast System
 Shun Liu (NOAA) P1-05To the ocean and beyond: Extending DWD's atmospheric
data-assimilation system
 M. Ghanbarpour (DWD) P1-06Development of LETKF-based systems assimilating radar
and ground-based observations for precipitation forecast in urban areas in
Argentina
 Arata Amemiya (RIKEN) P1-07LETKF-based Ocean Research Analysis (LORA): A new
ensemble ocean analysis
 Shun Ohishi (RIKEN) P1-08Open-source developments for community data
assimilation software with PDAF
 Lars Nerger (Alfred Wegener Institute, Bremerhaven) P1-09Impact of
flow-dependent background error covariances on Meso-scale simulations of a
band-shaped heavy rainfall event in Japan
 Akane Saya (Meteorologiacal Research institute (MRI)) P1-10Ensemble Kalman
Control
 Yohei Sawada (University of Tokyo) P1-11Averaging Sequential Data with Path
Signatures: Applications in Geosciences
 Nozomi Sugiura (JAMSTEC) P1-12Implementation of EnVAR/LETKF for the ocean at
DWD
 Nora Schenk (DWD) P1-13Data assimilation in a 1.5km shelf seas model with
wetting and drying.
 James While (Met Office) P1-14Recent developments in global ocean data
assimilation at the Met Office
 Jennifer Waters (Met Office) P1-15Hybrid 4DVar with Mesoscale Ensemble
Prediction System for JMA's Mesoscale Analysis
 Sho Yokota (JMA) P1-16Towards a convection-permitting reanalysis for Australia
 Chun-Hsu Su (Bureau of Meteorology) P1-17Data Assimilation of ground-based
remote sensing instruments in KENDA
 Jens Pruschke (Deutscher Wetterdienst (DWD)) P1-18Four-dimensional ensemble
sensitivity analysis with 1000 members for typhoons and frontal heavy rainfall
 Pin-Ying Wu (Japan Society for the Promotion of Science) P1-19Kalman force
inference for epithelial deformation: a force inference method for time-lapse
movies
 Goshi Ogita (RIKEN BDR) P1-20From past to present: Understanding Japan's rice
yield dynamics over 120 years through data assimilation
 Tatsuki Nakagawa (Hokkaido University) P1-21Quantifying performances of
multiple parameter estimation with different identifiability using ETKF-based
data-assimilation method
 Kaman Kong (RIKEN) P1-22Hurricane Dynamics and Predictability: Coupling
boundary-layer tocloud observations in a Nonlinear Data Assimilation Framework
 Yu-An Chen (Colorado State University) P1-23Variational assimilation of
spectral wave buoy measurements by warping directional spectra
 Arthur Filoche (University of Western Australia) P1-24Impact of covariance
localization on multiscale EnKF assimilation
 Shu-Chih Yang (National Central University) P1-25Developing fully coupled data
assimilation at Environment and Climate Change Canada
 Sergey Skachko (ECCC) P1-26Hybrid Gain Data Assimilation in the Taiwan's Global
Weather Prediction System (TGFS)
 Chih-Chien Chang (NCU) P1-27Analog offline ensemble data assimilation for
estimating precipitation patterns from gauge observations
 Daiya Shiojiri (Chiba U.) P1-28TEMPERED LOCAL ENSEMBLE TRANSFORM KALMAN FILTER:
SIMPLE MODEL EXPERIMENTS
 Jorge Gacitua Gutierrez (UBA) P1-29A new research infrastructure for exploiting
future Earth observations in weather models: insights from the Earth, Moon, Mars
project
 A. Ortolani (CNR-IBE, LaMMA)

Poster Session 2 (October 22, 2024)Nonlinear and Non-Gaussian DA /
Predictability and Controllability P2-01A 4DEnvar scheme for the Météo-France
operational convective scale model Arome-France
 Pierre Brousseau, (Météo-France) P2-02Efficient dynamical downscaling of ocean
models using continuous data assimilation algorithm
 Peng Zhan (SUSTech) P2-03On the role of data assimilation in the prediction of
tropical rainfall
 Y. Ruckstuhl (KUEI) P2-04Deep Gaussian Process Emulation for Uncertainty
Quantification in Model Networks
 Deyu Ming (University College London) P2-05Investigating importance of
resampling frequency of the local particle filter with Gaussian mixture
 Akira Takeshima (Center for Environmental Remote Sensing, Chiba University)
P2-06Recent updates on observation error correlation modelling techniques and
computational aspects
 Oliver Guillet (Meteo France) P2-07Assimilation of ground-based radar
reflectivity in AROME-France: impact of 4DEnVar assimilation method and
scale-dependent localization
 Maud Martet (Météo-France, CNRM) P2-08Multiscale DA Method of Radar and
Conventional data for the Typhoon Landfalling Prediction
 Haiqin, Chen. (Nanjing University) P2-09Snow depth variational data
assimilation with JEDI
 Anna Shlyaeva (JCSDA) P2-10Predictability of moist convection through ensemble
convective-scale data assimilation
 Masashi Minamide (UTokyo) P2-11The introduction of Meteorological
Object-oriented Tools and Operators Repository-Data Assimilation (MOTOR-DA)
System
 Zilong QIN (GBA-MWF) P2-12To which degree do the details of stochastic
perturbation schemes matter for convective scale and mesoscale perturbation
growth?
 Christian Keil (University of Munich LMU) P2-13Underlying Dynamics between
Mixing in South China Sea and Abyssal Water Overflow at Luzon Strait Inferred
from Adjoint Sensitivity Analysis
 Yongsu Na (HKUST) P2-14Hybrid-3DEnVar in a convective scale NWP model
AROME-Austria
 Kaushambi Jyoti (University of Vienna) P2-15Introducing non-Gaussian
observation errors into Ensemble Kalman Filters
 Chih-Chi Hu (Princeton University) P2-16ParticleDA.jl v.1.0: a distributed
particle filtering data assimilation package
 Daniel Giles (UCL) P2-17Ensemble Sensitivity Analysis in the Operational Met
Office in the UK Ensemble System
 Brian Ancell (Texas Tech University) P2-18A kernel extension of the Ensemble
Transform Kalman Filter: a localization strategy and application to a
quasi-geostrophic model
 Ehouarn Simon (Univ. Toulouse / IRIT) P2-19Leading the dynamical system toward
the prescribed regime by model predictive control coupled with data assimilation
 Fumitoshi Kawasaki (Chiba University) P2-20Accommodation of near-bound
variables in 4DVars and EnKFs
 Craig H Bishop (University of Melbourne, Bureau of Meteorology, ARC Centre of
Excellence for Climate Extremes) P2-21Benefits of initializing equatorial waves
on accuracy of medium-range extratropical forecasts
 Chen Wang (University of Hamburg) P2-22TEMPERED ENSEMBLE KALMAN SMOOTHER FOR
NONLINEAR DATA ASSIMILATION
 Jorge Gacitua Gutierrez (UBA) P2-23Toward optimal state and time-varying
parameter estimation using the implicit equal-weights particle filter
 Mineto Satoh (Graduate Institute for Advanced Studies) P2-24Reduced
non-Gaussianity and improved analysis by assimilating every-30-second radar
observation: a case of idealized deep convection
 Arata Amemiya (RIKEN) P2-25Enhanced Methods for Evaluating Ensemble Consistency
in NWP
 Arlan Dirkson (ECCC) P2-26Using machine learning, data assimilation and their
combination to improve a new generation of Arctic sea-ice models
 Alberto Carrassi (University of Bologn)

Poster Session 3 (October 23, 2024)Observation and Diagnostics P3-01Ensemble
Forecast Sensitivity to Observations Impact (EFSOI) of a high impact weather
event using a convection permitting data assimilation
 Gimena Casaretto (UBA) P3-02Evaluating the operational km-scale ensemble data
assimilation system of MeteoSwiss following the transition to the ICON model
 Claire Merker (MeteoSwiss) P3-03Spatiotemporal estimation of analysis errors in
the operational global data assimilation system at the CMA using a modified SAFE
method
 Jie Feng (Department of Atmospheric and Oceanic Sciences, Fudan University)
P3-04Toward assimilation of large-scale currents measured by differential
acoustic travel times in a deep stratified lake.
 John C. Wells (Ritsumeikan University and RIKEN) P3-05A global atmospheric
climatology of observation impacts
 Akira Yamazaki (JAMSTEC) P3-06Benchmark study of the correlation between input
observation density and emission factor reproduction in a 4D-var data
assimilation model.
 Alexander Hermanns (FZJ) P3-07Sensitivity of 3D-Var assimilation in weather
forecasts to using seasonal background errors and different radar reflectivity
data
 Samantha Melani (CNR-IBE/LaMMA) P3-08A geometric interpretation of analysis
 Richard Menard (ECCC) P3-09Impact of ERA5 Virtual Sonde Data on KIM Forecast
Skill in East Asia based on OSSE
 Hyerim Kim(KIAPS) P3-10A Multi-University Consortium for Advanced Data
Assimilation Research and Education (CADRE)
 Xuguang Wang (University of Oklahoma, US) P3-11An Ensemble Approach for
Estimating Observation Errors and a Moments-Based Assessment of Ensemble
Consistency
 Arlan Dirkson (ECCC) P3-124DVAR Global Ocean Data Assimilation System for
Coupled Predictions in JMA: Evaluation and Observing System Experiments
 Yosuke Fujii (JMA/MRI) P3-13Verification of a 3DVAR ocean DA system in a
coupled framework
 R. Williams (DWD) P3-14Ensemble based Forecast Sensitivity Observation Impact
in the Hybrid 4DEnsemble Variational Data Assimilation System of the KIM Global
model
 Youngsoon Jo (KMA) P3-15Evaluating data assimilation techniques for
paleoclimate applications.
 Jarrah Harrison-Lofthouse (University of Melbourne) P3-16Biases in the Ensemble
Forecast Sensitivity to Observations Impact (EFSOI)
 P Griewank (Uni of Vienna (UoV)) P3-17Improvements to the observation operator
formulation in the KIM hybrid-4DEnVar system
 Adam Clayton (KIAPS) P3-18Updating the Height Error Profiles of AMV in the
KIM-Global Model
 Jiyoung Son (KMA) P3-19Regional and seasonal variations in the impact of ocean
buoy observations on global atmospheric model forecasts evaluated by EFSO
 Miki Hattori (JAMSTEC) P3-20On the assimilation of novel atmospheric boundary
layer observations over Germany
 Christoph Schraff (DWD) P3-21The impacts of background error covariance on
particulate matter assimilation and forecast
 Jiongming Pang (Shenzhen Institute of Meteorological Innovation)

Poster Session 4 (October 24, 2024)Data Assimilation & Machine Learning
P4-01Enhancing Forecast Accuracy in Chaotic Systems: Evaluating the Impact of
Observation Bias and Machine Learning Techniques
 Namal Rathnayake (University of Tokyo) P4-02A hybrid data driven and data
assimilation operational model for long term spatiotemporal forecasting: Global
and regional PM2.5 forecasting
 Fangxin Fang (ICL,UK) P4-03Dynamic Generative AI for Real-Time Data
Assimilation on High-Performance Computing Platforms
 Guannan Zhang (Oak Ridge National Laboratory) P4-04SphereDA: Convolutional
Spherical Neural Network for Global Data Assimilation
 Otavio M. Feitosa (INPE) P4-05Impact of RTPS and Random Additive Noise
Covariance Inflation in an Operational Convective-Scale Data Assimilation System
over Taiwan
 Chin-Cheng Tsai (CWA / NTU) P4-06The 3D Real-Time Mesoscale Analysis (3D-RTMA)
for Severe Weather, Aviation, Operational Forecasting, and Other Nowcast
Applications
 Guoqing Ge (NOAA) P4-07A Hybrid Tangent Linear Model in the Joint Effort for
Data Assimilation Integration (JEDI) system
 Christian Sampson (JCSDA) P4-08Using Machine learning for SMAP Soil moisture
retrieval
 Azadeh Gholoubi (NOAA) P4-09AIBased Ensemble Generation of Chemical Species for
Use in Data Assimilation and Inversion
 Michael Sitwell (Environment and Climate Change Canada) P4-10An open-access
large ensemble dataset and potential applications for improving data
assimilation
 Tobias Necker (ECMWF, University of Vienna) P4-11Development of Improved Static
Background Error Covariances for the KIM Hybrid-4DEnVar System
 Hanbyul Jang (Korea Institute of Atmospheric Prediction Systems, KIAPS)
P4-12Quantum Data Assimilation: Solving Data Assimilation On Quantum Annealers
 Shunji Kotsuki (Chiba U.) P4-13Global precipitation nowcasting using a ConvLSTM
with adversarial training
 Shigenori Otsuka (RIKEN R-CCS) P4-14Development of LETKF system based on the
JMA operational ASUCA-Var
 Koji Terasaki (MRI) P4-15Towards the Assimilation of Dual-Polarization Radar
Data
 Tatsiana Bardachova (MIDS KU) P4-16A clustering domain-based localisation
strategy for ensemble Kalman filters
 Ehouarn Simon (Toulouse Univ.,IRIT) P4-17Correcting air-sea heat fluxes in
ocean general circulation models with artificial neural networks
 Andrea Storto (CNR ISMAR) P4-18Reconstructing Rankine vortices from Doppler
wind data using deep-learning-based generative models.
 Keitaro Inuki (Chiba Univ) P4-19Ionospheric data assimilation into an emulator
of global MHD model of the magnetosphere-ionosphere system
 Shin'ya Nakano (ISM) P4-20Background error covariances in the JEDI system
 Nate Crossette (JCSDA) P4-21Enhancing Tropical Weather Forecasts with
Constrained Data Assimilation
 Maryam Ramezani Ziarani (Katholische Universität Eichstätt-Ingolstadt,
Mathematical Institute for Machine Learning and DataScience) P4-22Application of
sequential data assimilation method to trajectory analysis
 Kazue Suzuki (Meiji Univ.) P4-23Evaluation of MPAS-JEDI for Rapid Refresh
Forecast System Data Assimilation System
 Ming Hu (NOAA GSL) P4-24Robust parameter estimation using variational inference
and generative neural networks
 Exaucé Ngarti (Inria)

Poster Session 5 (October 25, 2024)Satellite DA P5-01Optimal vertical
localization of visible and infrared cloud-affected satellite channels
 P Griewank (Uni of Vienna (UoV)) P5-02Introducing horizontal correlations of
satellite observation errors into the data assimilation system of the AROME
model
 Thomas Buey (Météo-France) P5-03An Adaptive Channel Selection Method for
Assimilating the Hyperspectral Infrared Radiances
 Lili Lei (Nanjing University) P5-04Sea-ice concentration assimilation from
microwave imagers in a coupled ocean-atmosphere system
 Sebastien Massart (ECMWF) P5-05Lightning data assimilation in the Arome France
numerical weather prediction system
 Pauline Combarnous (CNRM/Meteo France) P5-06Improving FY-4A/AGRI assimilation
over land with the consideration of surface temperature constraint and sub-pixel
terrain radiation effect
 Xin Li (Nanjing Joint Institute for Atmospheric Sciences) P5-07Improving
Small-scale Tropical Precipitation Forecast by Assimilating Frequent and Dense
Satellite Microwave Observations
 Konduru Rakesh Teja (R-CCS) P5-08Assessing the impact of future FORUM satellite
measurements on weather forecasts: first steps towards data assimilation
 Cristina Sgattoni (CNR-IBE) P5-09Impact of all-sky radiance from INSAT-3D/3DR
satellite over South Asia region
 Prashant Kumar (University of Tokyo) P5-10Atmospheric composition data
assimilation: Tropospheric Chemistry Reanalysis version 3 (TCR3)
 Kazuyuki Miyazaki (NASA JPL) P5-11Assimilation of the temperature derived by
Akatsuki Longwave Infrared Camera (LIR) in the Venus atmosphere
 Yukiko Fujisawa (Keio Univ.) P5-12Probabilistic Evaluation of Real-Time Flood
Prediction by Integrating Remote Sensing Soil Moisture Data into the ParFlow-CLM
Model
 Samira Sadat Soltani (IBG3, FZJ) P5-13Rossby wave and its impact on the Venus
atmosphere evaluated by observing system simulation experiment
 Nobumasa Komori (Keio Univ.) P5-14Impact of Observation Errors in Assimilating
GK-2A All-Sky Radiance on Regional Summertime Precipitation Forecast
 Seo-Youn Jo (Department of Atmospheric Sciences, Kyungpook National University
(KNU)) P5-15Advancements in Far InfraRed data assimilation in the MC-FORUM
project for the meteorological exploitation of the future FORUM satellite
 A. Ortolani (CNR-IBE) P5-16Advances and applications of satellite data
assimilation of clouds, precipitation, and the ocean
 Takemasa Miyoshi (RIKEN) P5-17Joint Aerosol & Wind Data Assimilation of AEOLUS
and impact on Numerical Weather Prediction
 Thanasis Georgiou (NOA, AUTH) P5-18Enhanced Ensemble Data Assimilation
Techniques in Geosciences
 Simone Spada (OGS)

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Venue/Accommodations


ACCESS MAP

100 m

地理院地図Vector（仮称）
Convention Hall of Kobe University
RIKEN R-CCS, Fugaku

PLACE

Convention Hall of Integrated Research Center of Kobe University

ADDRESS

7-1-48, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan

In-person only


TRANSPORTATION

FROM KOBE AIRPORT

Take the Port Liner train "Kobe Airport" [P09] and get off at the next station,
"Keisan Kagaku Center" [P08].

FROM OSAKA INTERNATIONAL (ITAMI) AIRPORT

Take the Airport Limousine bus (Time Table) to "Sannomiya" (Kobe) [P01], and
then take the Port Liner train to the "Keisan Kagaku Center" station [P08].

FROM KANSAI INTERNATIONAL AIRPORT

 1. Take the Airport Limousine bus (Time Table) to "Sannomiya" (Kobe) [P01] and
    then take the Port Liner train to the "Keisan Kagaku Center" station [P08].
 2. Take a free bus to the high-speed Bay Shuttle ferry going to Kobe Airport
    (Note that you need to purchase a ferry ticket before boarding the shuttle
    bus). At Kobe Airport, walk or take the free shuttle bus to the terminal
    building. Then take the Port Liner train to the "Keisan Kagaku Center"
    station [P08].


ACCOMMODATIONS

Accommodations must be arranged by participants at their own expense. Here are
the ones close to the symposium venue.

 * Portopia Hotel
 * Hotel Pearl City
 * Ariston Hotel Kobe

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Organizer


SCIENTIFIC ORGANIZING COMMITTEE

 * Chair: Takemasa Miyoshi (RIKEN, Japan)
 * Javier Amezcua (Tecnológico de Monterrey, Mexico)
 * Alberto Carrassi (Università di Bologna, Italy)
 * Takuya Kawabata (Meteorological Research Institute, Japan)
 * Amos Lawless (University of Reading, UK)
 * Peter Jan van Leeuwen (Colorado State University, USA)
 * Lili Lei (Nanjing University, China)
 * Roland Potthast (Deutscher Wetterdienst, Germany)
 * Sebastian Reich (Universität Potsdam, Germany)
 * Juan Ruiz (CIMA/CONICET-UBA, Argentina)
 * Martin Weissmann (Universität Wien, Austria)
 * Shu-Chih Yang (National Central University, located in Taiwan)


LOCAL ORGANIZING COMMITTEE

 * Chair: Takemasa Miyoshi
 * Co-Chair: Shun Ohishi
 * Shigenori Otsuka
 * James Taylor
 * Michael Goodliff
 * Arata Amemiya
 * Jianyu (Richard) Liang
 * Rakesh Teja Konduru
 * Hideyuki Sakamoto
 * Takahisa Ishimizu
 * Kota Takeda
 * Yukie Komori
 * Saeko Imano
 * Aki Mukunoki

ORGANIZED BY

 * RIKEN Cluster for Pioneering Research (CPR)

CO-ORGANIZED BY

 * RIKEN Center for Computational Science (R-CCS)
 * RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program
   (iTHEMS)
 * RIKEN Information R&D and Strategy Headquarters (R-IH)
 * RIKEN Center for Advanced Intelligence Project (AIP)
 * RIKEN Center for Biosystems Dynamics Research (BDR)
 * RIKEN Center for Sustainable Resource Science (CSRS)

CONTACT

 * RIKEN CPR Prediction Science Laboratory / R-CCS Data Assimilation Research
   Team
 * isda2024@ml.riken.jp

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This Symposium is a part of RIKEN Symposium Series.

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Copyright © 2024 RIKEN CPR Prediction Science Laboratory / R-CCS Data
Assimilation Research Team. All rights reserved.