21 papers · ranked by Valyu relevance
Hamid Moradkhani
Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control…
Andrew Gettelman, Alan J. Geer, Richard M. Forbes, Greg R. Carmichael + 6 more
Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model…
Carsten Montzka, Valentijn R. N. Pauwels, Harrie-Jan Hendricks Franssen, Xujun Han + 1 more
'Harrie-Jan Hendricks Franssen' 'Xujun Han' 'Harry Vereecken'] More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often…
Konstantinos M. Andreadis, Narendra Das, Dimitrios Stampoulis, Amor Ines + 6 more
'Amor Ines' 'Joshua B. Fisher' 'Stephanie Granger' 'Jessie Kawata' 'Eunjin Han' 'Ali Behrangi' 'Guy J-P. Schumann'] The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and…
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson + 4 more
'Benjamin K. Johnson' 'Nancy Collins' 'Jeffrey L. Anderson' 'Jeff Steward' 'Mick Coady'] An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million…
Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang + 1 more
The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We…
Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter Dueben + 1 more
'Torsten Hoefler'] The generation of initial conditions via accurate data assimilation is crucial for weather forecasting and climate modeling. We propose DiffDA as a denoising diffusion model capable of assimilating atmospheric variables using predicted states and sparse observations. Exploiting the similarity between…
Andreas Svedin, Milena C. Cuéllar, Axel Brandenburg
We show how the 3DVAR data assimilation methodology can be used in the astrophysical context of a two-dimensional convection flow. We study the way this variational approach finds best estimates of the current state of the flow from a weighted average of model states and observations. We use numerical simulations to…
Mario J. Mendez, Matthew J. Hoffman, Elizabeth M. Cherry, Christopher A. Lemmon + 1 more
Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a major and potent inducer of this cellular transition, which is comprised of transitions from an epithelial…
Matthieu Blanke, Ronan Fablet, Marc Lelarge
Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the…
Alberto Carrassi, Marc Bocquet, Laurent Bertino, Geir Evensen
We commonly refer to state-estimation theory in geosciences as data assimilation. This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state.…
Yanfei Xiang, Weixin Jin, Haiyu Dong, Mingliang Bai + 6 more
Forecasting Authors: ['Yanfei Xiang' 'Weixin Jin' 'Haiyu Dong' 'Mingliang Bai' 'Zuliang Fang' 'Pengcheng Zhao' 'Hongyu Sun' 'Kit Thambiratnam' 'Qi Zhang' 'Xiaomeng Huang'] - An AI-based data assimilation framework (ADAF) for real-world observations from unfixed locations and multiple sources. - ADAF offers a simple…
Jing-An Sun, Hang Fan, Junchao Gong, Ben Fei + 7 more
'Fenghua Ling' 'Wenlong Zhang' 'Wanghan Xu' 'Li Yan' 'Pierre Gentine' 'Lei Bai'] Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the background. In atmospheric applications, this problem is…
Sibo Cheng, Jinyang Min, Che Liu, Rossella Arcucci
learning forward and transformation functions Authors: ['Sibo Cheng' 'Jinyang Min' 'Che Liu' 'Rossella Arcucci'] Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is…
Glenda M. Yenni, Erica M. Christensen, Ellen K. Bledsoe, Sarah R. Supp + 3 more
Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. “Evolving data” present unique challenges in quality assurance and control, data…
Nicolas Le Guillarme, Wilfried Thuiller
With the rapid accumulation of biodiversity data, data integration has emerged as a hot topic in soil ecology. Data integration has indeed the potential to advance our knowledge of global patterns in soil biodiversity by facilitating large-scale meta-analytical studies of soil ecosystems. However, ecologists are still…
Marieke Wesselkamp, Niklas Moser, Maria Kalweit, Joschka Boedecker + 1 more
Despite deep-learning being state-of-the-art for data-driven model predictions, it has not yet found frequent application in ecology. Given the low sample size typical in many environmental research fields, the default choice for the modelling of ecosystems and its functions remain process-based models. The process…
Matthias Scheffler, Stefan Bauer, Peter Benner, Tristan Bereau + 57 more
Matthias Scheffler 1 , Stefan Bauer 2 , Peter Benner 3 , Tristan Bereau 4 , Volker Blum 5 , Mario Boley 6 , Christian Carbogno 7 , C. Richard A. Catlow 8 , Gerhard Dehm 9 , Sebastian Eibl 10 , Ralph Ernstorfer 11 , Ádám Fekete 12 , Lucas Foppa 1 , Peter Fratzl 13 , Christoph Freysoldt 9 , Baptiste Gault 9 , Luca M.…
Matthias Scheffler
Matthias Scheffler 1 , Stefan Bauer 2 , Peter Benner 3 , Tristan Bereau 4 , Volker Blum 5 , Mario Boley 6 , Christian Carbogno 7 , C. Richard A. Catlow 8 , Gerhard Dehm 9 , Sebastian Eibl 10 , Ralph Ernstorfer 11 , Ádám Fekete 12 , Lucas Foppa 1 , Peter Fratzl 13 , Christoph Freysoldt 9 , Baptiste Gault 9 , Luca M.…
Authors not listed
The precision of thermodynamic modeling for ionic liquid (IL)–solute systems is fundamentally reliant on the quality of experimental data. However, prevalent databases such as ILThermo frequently exhibit conflicting measurements for the same systems under identical temperature and pressure conditions. These disparities…
Authors not listed
The nanosafety domain has seen significant advancements in data generation and sharing, yet challenges remain in ensuring data interoperability and reuse. This article focuses on developing a semantic interoperability framework for nanosafety data to maximize the FAIRness (Findability, Accessibility, Interoperability…