scholarly journals Real-Time Thermospheric Density Estimation Via Radar And GPS Tracking Data Assimilation

2020 ◽  
Author(s):  
David Jonathan Gondelach ◽  
Richard Linares
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Density Estimation ◽  
Gps Tracking ◽  
Tracking Data

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

Global ionospheric electron density estimation based on multisource TEC data assimilation

GPS Solutions ◽  
2017 ◽  
Vol 21 (3) ◽  
pp. 1125-1137 ◽  
Author(s):  
Chengli She ◽  
Weixing Wan ◽  
Xinan Yue ◽  
Bo Xiong ◽  
You Yu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Electron Density ◽  
Density Estimation ◽  
Ionospheric Electron Density

Data assimilation and adaptive real-time forecasting of water levels in the river Eden catchment, UK

2008 ◽  
pp. 1281-1285 ◽  
Author(s):  
David Leedal ◽  
Keith Beven ◽  
Peter Young ◽  
Renata Romanowicz
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Water Levels

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 Population-level inference for home-range areas

2021 ◽  
Author(s):  
Christen Herbert Fleming ◽  
Iman Deznabi ◽  
Shauhin Alavi ◽  
Margaret C. Crofoot ◽  
Ben T. Hirsch ◽  
...  
Keyword(s):  
Home Range ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Population Level ◽  
Home Ranges ◽  
Tracking Data ◽  
Temporal Autocorrelation ◽  
Time Space ◽  
Minimal Convex

· Home-range estimates are a common product of animal tracking data, as each range informs on the area needed by a given individual. Population-level inference on home-range areas—where multiple individual home-ranges are considered to be sampled from a population—is also important to evaluate changes over time, space, or covariates, such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous. · Here, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. · We apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white‐nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes. · Our approach allows researchers to more accurately compare different populations with different movement behaviors or sampling schedules, while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

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