A5053117953- Meteorological Phenomena and Simulations
- Climate variability and models
- Reservoir Engineering and Simulation Methods
- Hydrological Forecasting Using AI
- Marine and coastal ecosystems
- Flood Risk Assessment and Management
- Model Reduction and Neural Networks
- Oceanographic and Atmospheric Processes
- Atmospheric and Environmental Gas Dynamics
- Statistical and numerical algorithms
- Air Quality Monitoring and Forecasting
- Geophysics and Gravity Measurements
- Scientific Research and Discoveries
- Remote Sensing and LiDAR Applications
- Anomaly Detection Techniques and Applications
- 3D Surveying and Cultural Heritage
- Constraint Satisfaction and Optimization
- Robotics and Sensor-Based Localization
- Target Tracking and Data Fusion in Sensor Networks
- Hydrology and Drought Analysis
- demographic modeling and climate adaptation
- Neural Networks and Applications
- Geochemistry and Geologic Mapping
- Environmental Monitoring and Data Management
- Underwater Acoustics Research
RIKEN Center for Computational Science
2025
NOAA Physical Sciences Laboratory
2022
Cooperative Institute for Research in Environmental Sciences
2021-2022
University of Colorado Boulder
2021-2022
National Oceanic and Atmospheric Administration
2022
Colorado State University
2019-2022
University of Colorado System
2021
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
2019
University of Reading
2014-2017
National Institute of Meteorology
2017
Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models replace key components of the DA cycle numerical weather prediction (NWP), including conventional forecast model, error covariance matrix, and tangent linear adjoint models. It shown how these RNNs can be initialized using methods directly update hidden/reservoir observations target...
Abstract The Diabatic Influences on Mesoscale Structures in Extratropical Storms (DIAMET) project aims to improve forecasts of high-impact weather extratropical cyclones through field measurements, high-resolution numerical modeling, and improved design ensemble forecasting data assimilation systems. This article introduces DIAMET presents some the first results. Four campaigns were conducted by project, one which, late 2011, coincided with an exceptionally stormy period marked unusually...
Abstract. Data-driven models (DDMs) are developed by analysing extensive datasets to detect patterns and make predictions, without relying on predefined rules or instructions from humans. In fields like numerical weather prediction (NWP), DDMs gaining popularity as potential replacements for traditional models, thanks their grounding in a multi-decadal, high-quality data assimilation (DA) analysis product. Recent studies, such Lang et al. (2024), have demonstrated that training using the...
At RIKEN, the Japan’s national flagship research institute for all sciences, we have been exploring several attempts to integrate data assimilation (DA) and AI/ML. DA integrates (usually process-driven) model data, while AI/ML is purely driven proven be very powerful in many applications. An example data-driven AI/ML-based precipitation nowcasting with process-driven numerical weather prediction (NWP). We developed a system based on convolutional long short term memory (LSTM) which...
Abstract For the majority of data assimilation (DA) applications, a Gaussian assumption is made to model behaviour errors associated with specific situation. This generally false in geoscience fields, especially for variables that are positive (semi‐)definite. The three‐dimensional variational (3DVar) data‐assimilation method was traditionally generated through Bayes' theorem multivariate probability density functions; however, over last 15 years this has been modified allow lognormal...
We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR ETKF) an ensemble transform Kalman smoother (ETKS) on Lorenz 1963 model. specifically investigated this increasing non-linearity using a quasi-static variational assimilation algorithm as comparison. Using analysis root mean square error (RMSE) metric, these have been compared considering (1) window length observation interval size (2) investigate influence hybrid...
4DEnsembleVar is a hybrid data assimilation method which purpose not only to use ensemble flow-dependent covariance information in variational setting, but altogether avoid the computation of tangent linear and adjoint models. This formulation has been explored context perfect In this all from observations be brought back start window using space-time covariances ensemble. large models, localisation these essential, standard time-independent leads serious problems when advection strong....
Abstract In most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for behavior of random variables/errors that are involved. At Cooperative Institute Research in Atmosphere theory has been developed different forms variational schemes enables to be relaxed. For certain variable types, a lognormally distributed can combined mixed Gaussian‐lognormal distribution better capture interactions errors distributions. However, assuming change time, then...
Abstract In this paper we present the derivation of two new forms Kalman filter equations; first is for a pure lognormally distributed random variable, while second set equations will be combination Gaussian and variables. We show that appearance similar to Gaussian-based equations, but analysis state multivariate median not mean. also results mixed distribution with Lorenz 1963 model lognormal errors background observations z component, compare them from traditional extended under certain...
In recent years, hybrid data-assimilation methods which avoid computation of tangent linear and adjoint models by using ensemble 4-dimensional cross-time covariances have become a popular topic in Numerical Weather Prediction. 4DEnsembleVar is one such method. spite its capabilities, application can sometimes problematic due to the not-trivial task localising covariances. this work we propose formulation that helps alleviate issues exploiting presence model error, i.e. weak-constraint...
Abstract An important assumption made in most variational, ensemble, and hybrid‐based data assimilation systems is that all minimized errors are Gaussian random variables. A theory developed at the Cooperative Institute for Research Atmosphere enables different types of to be relaxed a lognormally distributed variable. While this first step toward using more consistent distributions model involved numerical weather/ocean prediction, we still need able identify when assign lognormal...
Abstract Four-dimensional variational (4D-Var) data assimilation (DA) is developed for a coupled atmosphere–ocean quasigeostrophic application. Complications arise in (CDA) systems due to the presence of multiple spatiotemporal scales. Various formulations background error covariance matrix ( ), using different localization strategies, are explored evaluate their impact on 4D-Var performance CDA setting. requires access tangent linear and adjoint models (TLM/AM) propagate information about...
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Advances Modeling Systems (JAMES). Version RecordESSOAr is a venue early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Integrating recurrent neural networks with data assimilation scalable data-driven state estimationAuthorsStephen GregoryPennyiDTimothy ASmithiDTse-ChunChenJason...
Data-driven models (DDMs) are mathematical or computational built upon data, where patterns, relationships, predictions derived directly from the available information rather than through explicit instructions rules defined by humans. These constructed analysing large volumes of data to identify correlations, and trends. In areas such as numerical weather (NWP), these DDMs becoming increasingly popular with an aim replace (or components of) based on real observations. Data assimilation (DA)...
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Geophysical Research - Atmospheres. Version RecordESSOAr is a venue early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Non-Gaussian Detection using Machine Learning with Assimilation ApplicationsAuthorsMichaelGoodliffiDSteven JamesFletcheriDAntonKliewerAndrew S.JonesJohn...