Data Assimilation and Machine Learning

LEVERAGING EARTH OBSERVATIONS WITH MACHINE-LEARNING APPROACHES FOR WATER CYCLE MONITORING

2026年1月13日(火) 10:30 - 12:00

Victor Pellet (Professor, Laboratoire de Météorologie dynamique (LMD-X), Ecole Polytechnique, France)

Earth observation satellites provide unprecedented information to monitor the different components of the water cycle, from soil moisture to river dynamics. However, fully exploiting these observations remains challenging due to sensor limitations, data heterogeneity, complex physical processes, and spatio-temporal resolution constraints. This seminar provides an overview of machine-learning approaches that accompany and enhance remote sensing for water cycle analysis. It illustrates how statistical and machine-learning techniques can improve the exploitation of Earth observation (EO) data at different processing levels, from Level 1 to Level 4. Four examples are presented: (i) compressing hyperspectral information to reduce observation dimensionality, (ii) improving the retrieval of soil moisture from space by exploiting spatial patterns and handling missing data, (iii) harmonizing multi-source EO at the global scale for consistent water cycle monitoring, and (iv) modeling river dynamics using data-driven approaches. Together, these examples highlight the potential of machine-learning techniques to better integrate observations and improve our understanding of hydrological processes.

会場: 計算科学研究棟

イベント公式言語: 英語

DA Seminar: Machine learning for precipitation estimation and forecasting / Analysis of a Long-Lived Supercell: Life Cycle and Severe Weather Patterns in Northern Buenos Aires Province

2025年11月13日(木) 10:30 - 12:00

フアン・ルイス (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 予測科学研究チーム 客員研究員)
ルチアーノ・ヴィダル (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 予測科学研究チーム 客員研究員)

Title: Machine learning for precipitation estimation and forecasting Speaker: Dr. Juan Ruiz (University of Buenos Aires – CONICET) Abstract: Estimating and forecasting precipitation is essential for a wide range of human activities as well as for disaster prevention. In this talk we will discuss the application of deep neural networks to the estimation of precipitation with high time and spatial resolution, combining remote sensors and numerical weather predictions. The proposed models show that these information sources can be effectively combined to improve the accuracy of real-time precipitation estimates. Additionally, we will present the application of deep neural networks as a postprocessing tool for short-range deterministic and ensemble-based numerical weather predictions and for the quantification of their uncertainty. The performance of the machine-learning models in the quantification of the uncertainty is close to that achieved by the dynamical ensembles and can be even better in the presence of a model. Title: Analysis of a Long-Lived Supercell: Life Cycle and Severe Weather Patterns in Northern Buenos Aires Province Speaker: Dr. Luciano Vidal (National Meteorological Service, Argentina) Abstract: This work presents a detailed analysis of a long-lived convective supercell that affected the northern Buenos Aires province, Argentina, on March 19, 2024. The primary objective is to characterize its life cycle and associated severe weather patterns using an integrated multi-sensor approach. This methodology combines data from satellite imagery with documentation of surface damage caused by large hail and intense winds. The storm exhibited a remarkable longevity, traveling approximately 400 km over 5.5 hours and impacting a total of 11 municipalities before its dissipation. Throughout its trajectory, the supercell generated significant damage due to large hail and severe wind gusts that, in some areas, exceeded 150 km/h. Furthermore, the storm ultimately affected the Sarandí-Santo Domingo basin (the pilot basin of the Argentine-Japanese SATREPS/PREVENIR project) by generating flash floods. The results of this analysis provide crucial information for the improvement of forecasting and early warning systems for severe weather events in the region.

会場: Hybrid Format (RIKEN R-CCS room 107 and Zoom)

イベント公式言語: 英語

Temporal Evolution of Crustal Stress at Volcanoes During Periods of Unrest

2025年10月14日(火) 10:30 - 12:00

Eric Newland (Research Fellow, Faculty of Mathematical & Physical Sciences, University College London, UK)

Eruptions that occur at volcanoes after periods of quiescence are difficult to forecast. Pathways that connect the source to the surface may have become sealed. The pressurisation of the source leads to the deformation of the crust. Initially the crust deforms elastically, strain is accommodated via ground movement and elastic strain energy is stored to the crust. Then, the deformation transitions to inelastic where strain is accommodated via brittle failure (volcano-tectonic event), and elastic strain energy is transferred from the crust. We present a novel method to estimate the temporal evolution of elastic strain energy and bulk stress during periods of unrest. We consider the transfer of energy using measurements of surface deformation and seismic activity. We evaluate the temporal evolution of crustal bulk stress and investigate the progression of deformation in the crust. We apply our method to the unrest at the Campi Flegrei caldera, Italy from 2011-2024, and the eruption of Sierra Negra, Galapagos, 2018. Our calculations reveal that the bulk stress follows a characteristic progression, in which the stress initially increases linearly with time prior to the onset of significant seismicity, consistent with elastic deformation. We then observe a transition to inelastic deformation, when rate of elastic strain energy lost via fracturing increases and eventually exceeds the rate of elastic strain energy transferred to the crust. This results in a decrease in the bulk stress stored in the crust with time, indicating a progressive weakening of the crustal material due to seismicity-induced damage. Comparison with laboratory experiments show the behaviour is consistent with bulk failure in extension and the potential formation of new pathways in the crust. Finally, we demonstrate how our method, along with the understanding of eruption precursors gained from the results, can be used to constrain deformation regimes at reawakening volcanoes after extended repose and to evaluate the hazard posed during periods of unrest.

会場: Hybrid Format (RIKEN R-CCS room 107 and Zoom)

イベント公式言語: 英語

Data Assimilation for the Vicsek model

2025年9月25日(木) 13:00 - 14:00

Tomoharu Takaki (東京大学 大学院情報理工学系研究科 修士課程)

会場: 計算科学研究棟 R311 (メイン会場) / via Zoom

イベント公式言語: 英語

Steps between the Lorenz96 models and the real world (TBD)

2025年9月19日(金) 13:00 - 14:00

雨宮 新 (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 予測科学研究チーム 研究員)

会場: 計算科学研究棟 R511 (メイン会場) / via Zoom

イベント公式言語: 英語

Ensemble transform Kalman filter (ETKF) extensions for near-bound variables: Results from simple aerosol data assimilation experiments

2025年9月16日(火) 9:00 - 10:30

Jiang Richard Liang (慶応義塾大学 博士研究員)

Traditional data assimilation (DA) methods approximate the error distributions using Gaussian probability density functions (PDFs). However, the error distributions of some variables, such as clouds, precipitation, and aerosols, could be better approximated by gamma and inverse-gamma PDFs. For such bounded variables, the error standard deviation will likely increase with the distance of the unknown true value from its bound. To properly include these error distributions, a previous study by C. Bishop invented a method called the GIG filter, which is based on gamma and inverse-gamma distributions. We compared the performance of this new method and the traditional DA method with cycled DA experiments using a new tracer model based on the Lorenz-96 model. The GIG filter's performance is better for assimilating near-bound variables in our experiments.

会場: Hybrid Format (RIKEN R-CCS room 107 and Zoom)

イベント公式言語: 英語

Covariance Localization Local ensemble transform Kalman filter (LETKF)

2025年9月10日(水) 13:00 - 14:00

ウナシシュ・モンダル (理化学研究所 数理創造研究センター (iTHEMS) 数理展開部門 予測科学研究チーム 特別研究員)

会場: 計算科学研究棟 R511 (メイン会場) / via Zoom

イベント公式言語: 英語

Ensemble transform Kalman filter (ETKF)

2025年9月3日(水) 13:00 - 14:00

岩中 達郎 (理化学研究所 計算科学研究センター (R-CCS) データ同化研究チーム リサーチアソシエイト)

会場: 計算科学研究棟 R511 (メイン会場) / via Zoom

イベント公式言語: 英語

Perturbed Observation

2025年8月20日(水) 13:00 - 14:00

会場: 計算科学研究棟 R511 (メイン会場) / via Zoom

イベント公式言語: 英語

Extended Kalman filter: Lecture 2

2025年8月13日(水) 13:00 - 14:00

会場: 計算科学研究棟 (メイン会場) / via Zoom

イベント公式言語: 英語

DA Seminar: Prof. Dai Yamazaki and Hannah Cloke

2025年8月6日(水) 15:00 - 16:30

ダイ・ヤマザキ (東京大学 生産技術研究所)
Hannah Cloke (Professor, Department of Meteorology, University of Reading, UK)

The seminar will be jointly given by Associate Professor Dai Yamazaki (The university of Tokyo) and Professor Hannah Cloke (University of Reading). Speaker 1: Associate Professor Dai Yamazaki (Institute of Industrial Science, The University of Tokyo) Title: How can we achieve fast and realistic simulation of river and flood dynamics on the global scale? Abstract: Modeling river hydrodynamics across continental-scale basins is challenging due to their inherently multiscale nature. On one hand, we must account for the water budget along river systems that extend over 1,000 km. On the other hand, water movement within channels and floodplains is governed by topographic features smaller than 100 meters. The global river model CaMa-Flood addresses this complexity by employing the Catchment-based Macro-scale Floodplain modeling approach (CMF approach). This method approximates the relationship between water volume, flood extent, and water depth through sub-grid scale parameterizations. These parameters, derived from high-resolution satellite-based digital elevation models (DEMs) and hydrography datasets, enable realistic simulation of river discharge and flood stages—without explicitly resolving small-scale floodplain dynamics. To further accelerate simulations, recent developments in CaMa-Flood have introduced several performance optimizations, including MPI/OpenMP parallelization, SIMD vectorization, sparse matrix implementation, and a GPU-enabled Python version. These enhancements make the model more suitable for large-scale and near-real-time applications such as global flood monitoring and climate impact assessment. Speaker 2: Professor Hannah Cloke (Department of Meteorology, University of Reading) Title: Preparing for floods in an uncertain future

会場: Hybrid Format (RIKEN R-CCS room 107 and Zoom) (メイン会場) / via Zoom

イベント公式言語: 英語