scholarly journals Spatio-Temporal Inversion Using the Selection Kalman Model

2021 ◽  
Vol 7 ◽  
Author(s):  
Maxime Conjard ◽  
Henning Omre
Keyword(s):  
Gaussian Distribution ◽  
Recursive Algorithm ◽  
Initial Distribution ◽  
Pollution Monitoring ◽  
Initial State ◽  
Gaussian Distributions ◽  
Initial States ◽  
Spatio Temporal ◽  
Spatial Histogram

Data assimilation in models representing spatio-temporal phenomena poses a challenge, particularly if the spatial histogram of the variable appears with multiple modes. The traditional Kalman model is based on a Gaussian initial distribution and Gauss-linear forward and observation models. This model is contained in the class of Gaussian distribution and is therefore analytically tractable. It is however unsuitable for representing multimodality. We define the selection Kalman model that is based on a selection-Gaussian initial distribution and Gauss-linear forward and observation models. The selection-Gaussian distribution can be seen as a generalization of the Gaussian distribution and may represent multimodality, skewness and peakedness. This selection Kalman model is contained in the class of selection-Gaussian distributions and therefore it is analytically tractable. An efficient recursive algorithm for assessing the selection Kalman model is specified. The synthetic case study of spatio-temporal inversion of an initial state, inspired by pollution monitoring, suggests that the use of the selection Kalman model offers significant improvements compared to the traditional Kalman model when reconstructing discontinuous initial states.

scholarly journals Spatio-temporal Inversion using the Selection Kalman Model

2020 ◽  
Author(s):  
Maxime Conjard ◽  
Henning Omre
Keyword(s):  
Gaussian Distribution ◽  
Extreme Event ◽  
Recursive Algorithm ◽  
Initial Distribution ◽  
Pollution Source ◽  
Pollution Monitoring ◽  
Initial State ◽  
Linear Dynamic ◽  
Spatio Temporal ◽  
Spatial Histogram

<p>The challenge in data assimilation for models representing spatio-temporal phenomena is made harder when the spatial histogram of the variable of interest appears with multiple modes. Pollution source identification constitutes one example where the pollution release represents an extreme event in a fairly homogeneous background. Consequently, our prior belief is that the spatial histogram is bimodal. The traditional Kalman model is based on a Gaussian initial distribution and Gauss-linear dynamic and observation models. This model is contained in the class of Gaussian distribution and is therefore analytically tractable. These properties that make its strenght also render it unsuitable for representing multimodality. To address the issue, we define the selection Kalman model. It is based on a selection-Gaussian initial distribution and Gauss-linear dynamic and observation models. The selection-Gaussian distribution may represent multimodality, skewness and peakedness. It can be seen as a generalization of the Gaussian distribution. The proposed selection Kalman model is contained in the class of selection-Gaussian distributions and therefore analytically tractable. The recursive algorithm used for assessing the selection Kalman model is specified. We present a synthetic case study of spatio-temporal inversion of an initial state containing an extreme event. The study is inspired by pollution monitoring. The results suggest that the use of the selection Kalman model offers significant improvements compared to the traditional Kalman model when reconstructing discontinuous initial states. </p>

scholarly journals Data Assimilation in Spatio-Temporal Models with Non-Gaussian Initial States—The Selection Ensemble Kalman Model

2020 ◽  
Vol 10 (17) ◽  
pp. 5742
Author(s):  
Maxime Conjard ◽  
Henning Omre
Keyword(s):  
Posterior Distribution ◽  
Prior Distribution ◽  
Temporal Data ◽  
Initial State ◽  
Gaussian Distributions ◽  
Temporal Models ◽  
Parameter Field ◽  
Spatio Temporal ◽  
Non Gaussian

Assimilation of spatio-temporal data poses a challenge when allowing non-Gaussian features in the prior distribution. It becomes even more complex with nonlinear forward and likelihood models. The ensemble Kalman model and its many variants have proven resilient when handling nonlinearity. However, owing to the linearized updates, conserving the non-Gaussian features in the posterior distribution remains an issue. When the prior model is chosen in the class of selection-Gaussian distributions, the selection Ensemble Kalman model provides an approach that conserves non-Gaussianity in the posterior distribution. The synthetic case study features the prediction of a parameter field and the inversion of an initial state for the diffusion equation. By using the selection Kalman model, it is possible to represent multimodality in the posterior model while offering a 20 to 30% reduction in root mean square error relative to the traditional ensemble Kalman model.

Ensemble prediction with OpenIFS

2020 ◽  
Author(s):  
Pirkka Ollinaho
Keyword(s):  
Ensemble Prediction ◽  
Research Needs ◽  
Initial State ◽  
Data Set ◽  
Probabilistic Forecasts ◽  
Initial States ◽  
Prediction Systems ◽  
One Year ◽  
Typhoon Damrey

<div>Probabilistic forecasts provide information on how predictions of the atmospheric evolution may differ from the best guess solution provided by a deterministic forecast. Ensemble prediction systems generate this information through assessing uncertainties in both the model initial state and the model itself. In order to open up ensemble prediction research for a wider research community, we have recreated all 50+1 operational ECMWF ensemble initial states for OpenIFS. The data set covers one year (December 2016 to November 2017) twice a day. A range of model resolutions are provided to cover different research needs (TL159, TL399 and TL639). The probabilistic skill of OpenIFS ensembles using these initial states is showcased. A case study of typhoon Damrey, which severely affected Vietnam in 2017, will also be presented.</div>

Spatio-temporal approach for identification of critical conservation areas: a case study with two pine species from a threatened temperate forest in Mexico

2019 ◽  
Vol 28 (7) ◽  
pp. 1863-1883 ◽  
Author(s):  
Agustín Molina Sánchez ◽  
Patricia Delgado ◽  
Antonio González-Rodríguez ◽  
Clementina González ◽  
A. Francisco Gómez-Tagle Rojas ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Conservation Areas ◽  
Spatio Temporal ◽  
Pine Species

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Spatio-temporal analysis of COVID-19 incidence rate using GIS: a case study—Tehran metropolitan, Iran

GeoJournal ◽  
2021 ◽  
Author(s):  
R. Nasiri ◽  
S. Akbarpour ◽  
AR. Zali ◽  
N. Khodakarami ◽  
MH. Boochani ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Temporal Analysis ◽  
Spatio Temporal

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Seabird bycatch vulnerability in pelagic longline fisheries based on modelling of a long-term dataset

2021 ◽  
pp. 1-16
Author(s):  
CAN ZHOU ◽  
NIGEL BROTHERS
Keyword(s):  
Capture Event ◽  
Retrieval Rate ◽  
Competitive Environments ◽  
Global Concern ◽  
Competitive Species ◽  
Spatio Temporal ◽  
Longline Fisheries ◽  
Pelagic Longline

Summary The incidental mortality of seabirds in fisheries remains a serious global concern. Obtaining unbiased and accurate estimates of bycatch rates is a priority for seabird bycatch mitigation and demographic research. For measuring the capture risk of seabird interactions in fisheries, the rate of carcass retrieval from hauled gear is commonly used. However, reliability can be limited by a lack of direct capture observations and the substantial pre-haul bycatch losses known to occur, meaning incidence of seabird bycatch is underestimated. To solve this problem, a new measure (bycatch vulnerability) that links an observed interaction directly to the underlying capture event is proposed to represent the capture risk of fishery interactions by seabirds. The new measure is not affected by subsequent bycatch loss. To illustrate how to estimate and analyse bycatch vulnerability, a case study based on a long-term dataset of seabird interactions and capture confirmation is provided. Bayesian modelling and hypothesis testing were conducted to identify important bycatch risk factors. Competition was found to play a central role in determining seabird bycatch vulnerability. More competitive environments were riskier for seabirds, and larger and thus more competitive species were more at risk than smaller sized and less competitive species. Species foraging behaviour also played a role. On the other hand, no additional effect of physical oceanic condition and spatio-temporal factors on bycatch vulnerability was detected. Bycatch vulnerability is recommended as a replacement for the commonly used bycatch rate or carcass retrieval rate to measure the capture risk of an interaction. Combined with a normalized contact rate, bycatch vulnerability offers an unbiased estimate of seabird bycatch rate in pelagic longline fisheries.

scholarly journals A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

2021 ◽  
Vol 13 (15) ◽  
pp. 8263
Author(s):  
Marius Bodor
Keyword(s):  
Particulate Matter ◽  
Meteorological Data ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Indoor Location ◽  
Particle Matter ◽  
Indoor Particulate Matter

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

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