A Stochastic Spatiotemporal Weather-Impact Simulator: Representative Scenario Selection

Xue, Mengran; Roy, Sandip; Zobell, Stephen; Wan, Yan; Taylor, Christine; Wan, Yan

doi:10.2514/6.2011-6812

Cited by 22 publications

(18 citation statements)

References 9 publications

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“…Clustering spatiotemporal weather-impact scenarios to facilitate the weather-impact-scenario-driven strategic ATM decision support framework has also been recently studied [3,4,12]. Three distance measures were used to cluster spatiotemporal weather-impact scenarios [3]: 1) total capacity-based measure, which is calculated by summing capacities over all time points and sectors for each scenario and using the total capability of each scenario to calculate pairwise scenario distances; 2) timeaggregated sector capacity-based measure, which is calculated by summing capacities over all time points for each sector and then using the 2-norm difference of the sector vector as pairwise scenario distances; and 3) single sector capacity-based measure, which is calculated by selecting an important sector to represent each scenario and using the 2-norm difference of its time vector as pairwise scenario distances. In the following works [4,12], an adjacency weighted measure is introduced to incorporate neighboring information.…”

Section: B Review Of Related Work In the Atm Domainmentioning

confidence: 99%

“…The simplest and currently most widely used approach to process spatiotemporal data in the strategic ATM application is to separate the spatial and temporal dimensions by projecting spatiotemporal intensities to each dimension [3,4,12,30]. Figure 2 shows a set of three spatiotemporal scenarios (each constituting six snapshots of four spatial cells with different weather-impact spread patterns).…”

Section: B Problem For Separating the Spatial And Temporal Dimensionsmentioning

confidence: 99%

“…In the ATM domain, clustering spatiotemporal weather-impact scenarios to support strategic ATM are also new [3][4][5][6]. Daily WITI scores obtained through summing WITI scores over the whole NAS in one day span were used to cluster similar weather-impact days [5].…”

Section: B Review Of Related Work In the Atm Domainmentioning

confidence: 99%

“…Automating strategic ATM is expected to significantly improve the efficiency and safety of the NAS. The main challenge of this automation is the significant uncertainty in predicting convective weather events [3][4][5][6][7][8]. Convective weather events are considered the driving force for strategic ATM planning, as they reduce the capacities of the NAS, cause traffic demand and capacity imbalance at regions (e.g., sectors and centers † † [9,10]), and thus necessitate the redistribution of flows and resources.…”

mentioning

confidence: 99%

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Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Xie¹,

Wan²,

Zhou³

et al. 2015

Journal of Aerospace Information Systems

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Convective weather, recognized as the leading contributor to delays in the National Airspace System, causes demand-capacity imbalance in the airspace. Strategic air traffic management aims to resolve the imbalance for offnominal conditions (for example, convective weather events) through redistributing flows and resources at the strategic timeframe (2-15 h in advance). As a component of scenario-driven strategic air traffic management, this paper develops a multiresolution spatiotemporal distance measure that, combined with standard distance-based clustering algorithms, can be used to group a wide range of possible weather-impact scenarios into a few representative clusters to facilitate corresponding management strategy design. Motivations of this new distance measure, its generation algorithm, and parameter impact analysis are described in detail to facilitate practical implementation and subsequent use in decision support. This multiresolution spatiotemporal distance measure not only addresses the needs of scenario-driven strategic air traffic management but also generally applies to broad data-driven decision support applications that involve large-scale physical processes of spatiotemporal spread dynamics, as the measure captures the similarity between scenarios of spatiotemporal spread patterns of varying shape, size, location, and intensity; corrects the "boundary effects"; and is flexible to a variety of data features in temporal and spatial dimensions. Performance evaluation is conducted through comparative studies and sensitivity analysis, using real weather-impact datasets. Nomenclature b i;j = number of hops between spatial cells g i and g j D i;j = pairwise scenario distance between scenarios s i and s j D i;j = pairwise scenario distance between scenarios s i and s j after normalization d i;j;w;h = distance between scenarios s i and s j for spatial resolution w and temporal resolution h (subscript of w or h is eliminated for convenience if data do not have spatial or temporal dimension) F = similarity of two scenarios f w = similarity of two scenarios at spatial resolution w G = set of all spatial cells g k = two-dimensional (regularly or irregularly shaped) spatial cell of index k h = temporal resolution h max = upper bound of temporal resolution I i;k;l = intensity at spatial cell g k and time point t l for scenario s i (subscript of k or l is eliminated for convenience if data do not have spatial or temporal dimension) I i;k;l = intensity at spatial cell g k and time point t l for scenario s i after weighting with β k;l I i;k;l;w;h = intensity at spatial cell g k and time point t l for scenario s i after correcting boundary effects at spatial resolution w and temporal resolution h (subscripts of k, w or l, h are eliminated for convenience if data do not have spatial or temporal dimension) M = adjacency matrix of spatial cells M i;j = (i, j) entry of adjacency matrix M S = set of all scenarios s i = scenario of index i T = set of all time points t l = time point of index l w = spatial resolution w...

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Section: B Review Of Related Work In the Atm Domainmentioning

confidence: 99%

Section: B Problem For Separating the Spatial And Temporal Dimensionsmentioning

confidence: 99%

Section: B Review Of Related Work In the Atm Domainmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Xie¹,

Wan²,

Zhou³

et al. 2015

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sumaili et al (2011) proposed a technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast, which will allow the reduction of the computational burden in stochastic models that require scenario reduction. Xue et al (2011) studied an approach for extracting representative spatio-temporal weather-impact scenarios and the corresponding probabilities of occurrence, from a large stochastically generated ensemble of possible scenarios. Costa et al (2006) studied the stochastic hydrothermal scheduling optimization problem, and used principal component analysis to reduce the effective dimensionality of the scenario specification problem so that a discretization technique can be used in a smaller dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: II. Sequential reduction

Floudas

2016

Computers & Chemical Engineering

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a b s t r a c tIn this paper, a novel sequential scenario reduction framework for general optimization problem is proposed. The proposed method extends the previous work (Li and Floudas, 2014) and aims to tackle optimization problems with a large number of uncertain parameters and a huge number of scenarios generated from the factorial combination. The proposed method first ranks the uncertain parameters based on their effects on the optimal objective using global sensitivity analysis. Then, the parameters are sequentially considered in generating uncertainty scenarios. This method can essentially reduce the computational efforts needed for evaluating the objective values of all scenarios, which is often impractical for a huge number of scenarios. Criteria for quantifying the quality of scenario reduction are also proposed based on robust optimization and scenario optimization. Case studies are presented to illustrate the sequential scenario reduction framework and the results verify the efficiency of the proposed approach.

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Decision-Support Tools for Renewables-Rich Power Systems: A Stochastic Futures

Jiang

Roy

Liu

et al. 2015

Cyber Physical Systems Approach to Smart Electric Power Grid

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A Stochastic Spatiotemporal Weather-Impact Simulator: Representative Scenario Selection

Cited by 22 publications

References 9 publications

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: II. Sequential reduction

Decision-Support Tools for Renewables-Rich Power Systems: A Stochastic Futures

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