A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states

2016 ◽  
Vol 84 ◽  
pp. 35-49 ◽  
Author(s):  
Mathieu Mure-Ravaud ◽  
Guillaume Binet ◽  
Michael Bracq ◽  
Jean-Jacques Perarnaud ◽  
Antonin Fradin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Flood Forecasting ◽  
Web Based ◽  
Using Data

scholarly journals Estimation of Lateral Inflows Using Data Assimilation in the Context of Real-Time Flood Forecasting for the Marne Catchment in France

2013 ◽  
pp. 93-105
Author(s):  
Johan Habert ◽  
Sophie Ricci ◽  
Andrea Piacentini ◽  
Gabriel Jonville ◽  
Etienne Le Pape ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Flood Forecasting ◽  
Using Data

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean

2019 ◽  
Vol 6 ◽  
Author(s):  
Andrew M. Moore ◽  
Matthew J. Martin ◽  
Santha Akella ◽  
Hernan G. Arango ◽  
Magdalena Balmaseda ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
The State ◽  
Complete Picture ◽  
Using Data ◽  
Ocean Observations

Real-time tracking of neuronal network structure using data assimilation

2013 ◽  
Vol 88 (5) ◽  
Author(s):  
Franz Hamilton ◽  
Tyrus Berry ◽  
Nathalia Peixoto ◽  
Timothy Sauer
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Neuronal Network ◽  
Network Structure ◽  
Real Time Tracking ◽  
Using Data

scholarly journals IRI the International Standard for the Ionosphere

2018 ◽  
Vol 16 ◽  
pp. 1-11 ◽  
Author(s):  
Dieter Bilitza
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Scientific Literature ◽  
Data Sources ◽  
International Union ◽  
Radio Science ◽  
European Cooperation ◽  
To Come ◽  
Using Data ◽  
Standardization Organization

Abstract. This paper gives a brief overview over the International Reference Ionosphere (IRI) project and model. IRI is recognized as the official standard for the ionosphere by the International Standardization Organization (ISO), the International Union of Radio Science (URSI), the Committee on Space Research (COSPAR), and the European Cooperation for Space Standardization (ECCS). Of great importance are the external drivers of the model that help IRI to represent ionospheric conditions as realistically as possible. The paper discusses the drivers currently used presents recent improvements and changes. Besides the standard solar, magnetic, and ionospheric indices the paper also reports on the adjustment of the model with data and equivalent indices and on the progress towards a Real-Time IRI using data assimilation. IRI has been widely validated with many different data sources and has fared very well in community wide assessment studies. We present some of these studies and document the wide usages of the model in the scientific literature. Finally, we present an outlook on things to come in IRI-2018 and thereafter.

An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting

2014 ◽  
Vol 519 ◽  
pp. 2722-2736 ◽  
Author(s):  
Yuan Li ◽  
Dongryeol Ryu ◽  
Andrew W. Western ◽  
Q.J. Wang ◽  
David E. Robertson ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Real Time ◽  
Flood Forecasting ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme ◽  
Error Parameter ◽  
Integrated Error

scholarly journals Realized ecological forecast through interactive Ecological Platform for Assimilating Data into model (EcoPAD)

2018 ◽  
Author(s):  
Yuanyuan Huang ◽  
Mark Stacy ◽  
Jiang Jiang ◽  
Nilutpal Sundi ◽  
Shuang Ma ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Methane Production ◽  
Data Transfer ◽  
Model Simulation ◽  
Meteorological Data ◽  
Sensor Data ◽  
Model Structure ◽  
Web Based ◽  
Ecological Forecasting

Abstract. Predicting future changes in ecosystem services is not only highly desirable but also becomes feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyberinfrastructure) are converging to transform ecological research to quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD is a web-based software system that automates data transfer and processes from sensor networks to ecological forecasting through data management, model simulation, data assimilation, and visualization. It facilitates interactive data-model integration from which model is recursively improved through updated data while data is systematically refined under the guidance of model. EcoPAD relies on data from observations, process-oriented models, DA techniques, and web-based workflow. We applied EcoPAD to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment at North Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into Terrestrial ECOsystem (TECO) model, and recursively forecast responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near real-time (weekly). The near real-time forecasting with EcoPAD-SPRUCE has revealed that uncertainties or mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH4:CO2 ratio and temperature sensitivity (Q10) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers so as to identify model components to be improved and additional measurements to be made. It becomes the first interactive model-experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. EcoPAD also has the potential to become an interactive tool for resource management, to stimulate citizen science in ecology, and transform environmental education with its easily accessible web interface.

Human Mobility Estimation Following Massive Disaster Using Filtering Approach

2016 ◽  
Vol 11 (2) ◽  
pp. 217-224 ◽  
Author(s):  
Akihito Sudo ◽  
◽  
Takehiro Kashiyama ◽  
Takahiro Yabe ◽  
Hiroshi Kanasugi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Mobile Phones ◽  
Large Scale ◽  
Population Distribution ◽  
Human Mobility ◽  
Time Estimation ◽  
Observation Data ◽  
Real Time Estimation ◽  
Using Data

Real-time estimation of people distribution immediately after a disaster is directly related to disaster reduction and is also highly beneficial in society. Recently, traffic estimation research has been actively performed using data assimilation techniques for observation data obtained from mobile phones. However, there has been no research on data assimilation technique using real-time gridded aggregated observation data obtained from mobile phones, which are available and can be used to estimate population flow and distribution in a metropolitan area during a large-scale disaster. In this research, population distribution in an urban area during a disaster was estimated using gridded aggregated observation data obtained from mobile phones, using particle filter. The experimental results indicated that the particle filters enabled high-precision real-time estimation in the Kanto district.

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