RT-IDA3D: Towards a Real-Time Computerized Ionospheric Tomography System Suitable as Input to Ionospheric Data Assimilation Models

2002 ◽  
Author(s):  
Gary S. Bust
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Ionospheric Tomography ◽  
Tomography System ◽  
Ionospheric Data Assimilation ◽  
Computerized Ionospheric Tomography

scholarly journals Complementing regional ground GNSS-STEC computerized ionospheric tomography (CIT) with ionosonde data assimilation

GPS Solutions ◽  
2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Nicholas Ssessanga ◽  
Mamoru Yamamoto ◽  
Susumu Saito ◽  
Akinori Saito ◽  
Michi Nishioka
Keyword(s):  
Data Assimilation ◽  
Region Of Interest ◽  
Satellite System ◽  
Peak Height ◽  
Ionospheric Tomography ◽  
Occultation Data ◽  
Ionospheric Imaging ◽  
Computerized Ionospheric Tomography ◽  
Log Normal ◽  
Global Navigation Satellite

AbstractA near-real-time computerized ionospheric tomography (CIT) technique was developed over the East Asian sector to specify the 3-D electron density field. The technique is based on a plethora of Global Navigation Satellite System observables within the region of interest which is bounded horizontally 110°–160°E and 10°–60°N and extending from 80 to 25,000 km in altitude. Prior to deployment, studies validated the CIT results using ionosonde, middle-upper atmosphere radar and occultation data and found the technique to adequately reconstruct the regional ionosphere vertical structure. However, with room for improvement in estimating the peak height and avoiding physically unrealistic negative densities in the final solution, we present preliminary results from a technique that addresses these issues by incorporating CIT results into a data assimilation (DA) technique. The DA technique adds ionosonde bottomside measurements into CIT results, thereby improving the accuracy of the reconstructed bottomside 3-D structure. More specifically, on average CIT NmF2 and hmF2 improve by more than 60%. Further, during analysis, ionospheric electron densities are assumed to be better described by probability log-normal distribution, which introduces the positivity constraint that is mandatory in ionospheric imaging.

Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean

2010 ◽  
Author(s):  
Constantinos Evangelinos ◽  
Pierre F. Lermusiaux ◽  
Jinshan Xu ◽  
Jr Haley ◽  
Hill Patrick J. ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Uncertainty Prediction ◽  
Time Uncertainty

scholarly journals Fast High-Precision Electrical Impedance Tomography System for Real-Time Perfusion Imaging

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61570-61580 ◽  
Author(s):  
Weichen Li ◽  
Junying Xia ◽  
Ge Zhang ◽  
Hang Ma ◽  
Benyuan Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Electrical Impedance Tomography ◽  
High Precision ◽  
Perfusion Imaging ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography System

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.

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