scholarly journals Ionospheric Vertical Correlation Distances: Estimation From ISR Data, Analysis and Implications For Ionospheric Data Assimilation

Radio Science ◽  
2020 ◽  
Author(s):  
Victoriya V. Forsythe ◽  
Irfan Azeem ◽  
Geoff Crowley ◽  
David R. Themens
Keyword(s):  
Data Analysis ◽  
Data Assimilation ◽  
Ionospheric Data Assimilation ◽  
Vertical Correlation

Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation

2016 ◽  
Vol 11 (2) ◽  
pp. 164-174 ◽  
Author(s):  
Shunichi Koshimura ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Assimilation ◽  
Real Time ◽  
Big Data Analysis ◽  
Disaster Mitigation ◽  
Observation Data ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Analysis Platform

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

Ionospheric data assimilation: recovery of strong mid-latitudinal density gradients

2003 ◽  
Vol 65 (10) ◽  
pp. 1087-1097 ◽  
Author(s):  
Jan J. Sojka ◽  
Donald C. Thompson ◽  
Robert W. Schunk ◽  
J.Vincent Eccles ◽  
Jonathan J. Makela ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradients ◽  
Ionospheric Data Assimilation

Joint use of monitoring and modelling

2007 ◽  
Vol 56 (8) ◽  
pp. 21-29 ◽  
Author(s):  
L.F. Jørgensen ◽  
J.C. Refsgaard ◽  
A.L. Højberg
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Data Assimilation ◽  
Conceptual Models ◽  
Data Availability ◽  
Policy Makers ◽  
Daily Work ◽  
European Water ◽  
Process Understanding ◽  
Potential Benefits

There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive. Nevertheless, it is rare to see forces combined in these two disciplines. To bring the monitoring and the modelling communities together, a number of workshops have been arranged with discussions on benefits and constraints in joint use of monitoring and modelling. The workshops have been attended by scientists, water managers, policy makers as well as stakeholders and consultants. Emphasis has been put on data availability and accessibility, remote sensing and data assimilation techniques, monitoring programmes and modelling support to the design or optimisation of these as well as potential benefits of using supporting modelling tools in the process of designing Programmes of Measures by impact assessment etc. The way models can support in extrapolation in time and space, in data analysis, in process understanding (conceptual models), in accessing correct interaction between pressures and impacts etc. have also been elaborated. Although practitioners have been open-minded to the presented ideas, they are somewhat reluctant towards how to implement this in their daily work. This paper presents some experiences from the workshops.

scholarly journals Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance

2014 ◽  
Vol 7 (3) ◽  
pp. 2631-2661 ◽  
Author(s):  
C. Y. Lin ◽  
T. Matsuo ◽  
J. Y. Liu ◽  
C. H. Lin ◽  
H. F. Tsai ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Electron Density ◽  
Radio Occultation ◽  
Model Error ◽  
Background Model ◽  
Ionospheric Electron Density ◽  
Error Covariance ◽  
Ionospheric Data Assimilation ◽  
Stationary Location ◽  
Assimilation Model

Abstract. Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations. We present a data assimilation model for the ionosphere, based on the Gauss–Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model, to assimilate two different types of total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. The objective of this study is to investigate impacts of stationary (location-independent) and non-stationary (location-dependent) classes of the background model error covariance on the quality of assimilation analyses. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing System Simulation Experiments suggest that assimilation of TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. Our new ionospheric data assimilation model that employs the location-dependent background model error covariance outperforms the earlier assimilation model with the location-independent background model error covariance, and can reconstruct the 3-D ionospheric electron density distribution satisfactorily from both ground- and space-based GPS observations.

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