Identifying Representative Weather-Impact Scenarios for Flow Contingency Management

Tien, Shin-Lai; Taylor, Christine; Wan, Yan

doi:10.2514/6.2013-4216

Cited by 8 publications

(15 citation statements)

References 6 publications

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“…Also related, multiple metrics of NAS performance have been used to cluster similar days [6]. 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.…”

Section: B Review Of Related Work In the Atm Domainmentioning

confidence: 99%

“…These two-element vectors are then used to compute the pairwise scenario distances based on their Euclidean distances. It was noted that summing weather-impact intensities along one dimension does not retain the spatial topology or temporal sequence, and thus may cause problems such as the inability to differentiate time sequences with opposite ordering ("metric symmetry") [4].…”

Section: B Review Of Related Work In the Atm Domainmentioning

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%

“…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. In particular, a two-element vector is calculated for each scenario.…”

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.…”

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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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