scholarly journals An Alternative to PCA for Estimating Dominant Patterns of Climate Variability and Extremes, with Application to US Rainfall

2020 ◽  
Author(s):  
Stephen Jewson
Keyword(s):  
Climate Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Historical Data ◽  
Heat Waves ◽  
Component Analysis ◽  
Rainfall Anomaly ◽  
Identification Algorithm ◽  
Total Rainfall ◽  
Spatial Extremes

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

scholarly journals An Alternative to PCA for Estimating Dominant Patterns of Climate Variability and Extremes, with Application to U.S. and China Seasonal Rainfall

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 354
Author(s):  
Stephen Jewson
Keyword(s):  
Climate Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Historical Data ◽  
Component Analysis ◽  
Rainfall Anomaly ◽  
Rapid Identification ◽  
Total Rainfall ◽  
Spatial Extremes ◽  
Rainfall Anomalies

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the rapid identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for the rapid identification of patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive floods, droughts or heatwaves, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as “sum of all points in the spatial field”. We compare the first PCA and DCA spatial patterns for U.S. and China winter and summer rainfall anomalies, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain. In three out of four of the examples the first DCA spatial pattern is more uniform over a wide area than the first PCA spatial pattern and as a result is more obviously relevant to large-scale flooding or drought. Also, in all cases the definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for a given likelihood. The first DCA spatial pattern is arguably the best answer to the question: what single spatial pattern is most likely to drive large total rainfall anomalies in the future? It is also simpler to calculate than PCA. In combination PCA and DCA patterns yield more insight into rainfall variability and extremes than either pattern on its own.

scholarly journals An Alternative to PCA for Estimating Dominant Patterns of Climate Variability, with Application to US Rainfall

2020 ◽  
Author(s):  
Stephen Jewson
Keyword(s):  
Climate Variability ◽  
Heat Waves ◽  
Rainfall Variability ◽  
Extreme Temperature ◽  
Principal Component ◽  
Extreme Rainfall ◽  
Component Analysis ◽  
Identification Algorithm ◽  
Spatial Extremes ◽  
Preferred Direction

Floods and droughts are driven, in part, by patterns of extreme rainfall. Heat waves are driven, in part, by patterns of extreme temperature. The standard work-horse for understanding patterns of climate variability is Principal Component Analysis (PCA) and its variants. But PCA does not optimize for spatial extremes, and so there is no particular reason why the first PCA pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the field'. We compare the first PCA and DCA patterns for US rainfall on a 6 month timescale, using the sum metric for the definition of DCA, and find that they are somewhat different. The definitions of PCA and DCA mean that the first PCA pattern has the larger explained variance of the two, while the first DCA pattern, when scaled appropriately, is both more likely and captures more rainfall. In combination these two patterns yield more insight into rainfall variability than either pattern on its own.

scholarly journals Extremal dependence as given by the tail pairwise dependence matrix inprecipitation and temperature data

2021 ◽  
Author(s):  
Svenja Szemkus ◽  
Petra Friederichs
Keyword(s):  
Principal Component Analysis ◽  
Spatial Patterns ◽  
Heat Waves ◽  
Principal Component ◽  
Component Analysis ◽  
Extreme Weather ◽  
Temperature Data ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Pairwise Dependence

<p>A better understanding of the dynamics and impacts of extreme weather events and their changes due to climate change is the subject of the ClimXtreme project (climxtreme.net) funded by the German Federal Ministry of Education and Research. <br>The CoDEx project is investigating how data compression techniques can contribute to a better description and understanding of extremes. Various unsupervised learning approaches, such as clustering or principal component analysis, focusing on extremes have been developed recently and will be investigated and compared within the project. <br>We use principal component analysis to study the spatial (co-)occurrence during extreme weather events such as heavy precipitation, heat waves or droughts. The focus on extreme events is done by using the tail pairwise dependence matrix (TPDM), proposed by Cooley and Thibaud (2019) as an analogue to the covariance matrix for extremes. Since the simultaneous occurrence of precipitation deficits and high temperature played an important role, especially in heat waves, we explore how Cooley and Thibaud's concept can be used in this regard. We propose an estimation of the TPDM based on pairwise dependencies of two variables. A singular value decomposition gives us insight into the spatial co-occurrence of extreme spatial patterns, which contributes to the understanding of so-called compound events. <br>We use daily precipitation and temperature data, including observational stations and regional reanalyses in Germany and Europe. Using this method, we extract spatial patterns over Germany and Europe based on extreme dependencies. In addition, we identify historical events, and examine them in more detail in this context.</p>

scholarly journals Climate bones of contention: How climate variability influences territorial, maritime, and river interstate conflicts

2021 ◽  
Vol 58 (1) ◽  
pp. 132-150
Author(s):  
Cody J Schmidt ◽  
Bomi K Lee ◽  
Sara McLaughlin Mitchell
Keyword(s):  
Climate Variability ◽  
Climate Changes ◽  
Heat Waves ◽  
World Politics ◽  
Western Hemisphere ◽  
Intrastate Conflict ◽  
Short Term ◽  
Climate Conditions ◽  
Interstate Conflicts ◽  
The Relationship

Many scholars examine the relationship between climate variability and intrastate conflict onset. While empirical findings in this literature are mixed, we know less about how climate changes increase the risks for conflicts between countries. This article studies climate variability using the issue approach to world politics. We examine whether climate variability influences the onset and militarization of interstate diplomatic conflicts and whether these effects are similar across issues that involve sovereignty claims for land (territory) or water (maritime, river). We focus on two theoretical mechanisms: scarcity ( abundance) and uncertainty. We measure these concepts empirically through climate deviation (e.g. droughts/floods, heat waves/cold spells) and climate volatility (greater short-term variance in precipitation/temperature). Analyses of issue claims in the Western Hemisphere and Europe (1901–2001) show that greater deviations and volatility in climate conditions increase risks for new diplomatic conflicts and militarization of ongoing issues and that climate change acts as a trigger for revisionist states.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

Spatial-Pattern-Induced Evolution of a Self-Replicating Loop Network

2006 ◽  
Vol 12 (4) ◽  
pp. 461-485 ◽  
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Pattern Formation ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spiral Structure ◽  
Scale Interactions ◽  
Micro Scale ◽  
Macro Scale ◽  
Loop Network ◽  
Spatial Pattern Formation

We study a system of self-replicating loops in which interaction rules between individuals allow competition that leads to the formation of a hypercycle-like network. The main feature of the model is the multiple layers of interaction between loops, which lead to both global spatial patterns and local replication. The network of loops manifests itself as a spiral structure from which new kinds of self-replicating loops emerge at the boundaries between different species. In these regions, larger and more complex self-replicating loops live for longer periods of time, managing to self-replicate in spite of their slower replication. Of particular interest is how micro-scale interactions between replicators lead to macro-scale spatial pattern formation, and how these macro-scale patterns in turn perturb the micro-scale replication dynamics.

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

scholarly journals The spatial pattern of methacholine bronchoconstriction recurs when supine independently of posture during provocation but does not recur between postures

2018 ◽  
Vol 125 (6) ◽  
pp. 1720-1730 ◽  
Author(s):  
Eric T. Geier ◽  
Kent Kubo ◽  
Rebecca J. Theilmann ◽  
Gordon K. Prisk ◽  
Rui C. Sá
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Odds Ratio ◽  
Dependent Lung ◽  
Lung Inflation ◽  
Imaging Condition ◽  
Prone Imaging ◽  
Ventilation Imaging ◽  
Supine Posture ◽  
Gravitational Influence

The location of lung regions with compromised ventilation (often called ventilation defects) during a bronchoconstriction event may be influenced by posture. We aimed to determine the effect of prone versus supine posture on the spatial pattern of methacholine-induced bronchoconstriction in six healthy adults (ages 21–41, 3 women) using specific ventilation imaging. Three postural conditions were chosen to assign the effect of posture to the drug administration and/or imaging phase of the experiment: supine methacholine administration followed by supine imaging, prone methacholine administration followed by supine imaging, and prone methacholine administration followed by prone imaging. The two conditions in which imaging was performed supine had similar spatial patterns of bronchoconstriction despite a change in posture during methacholine administration; the odds ratio for recurrent constriction was mean (SD) = 7.4 (3.9). Conversely, dissimilar spatial patterns of bronchoconstriction emerged when posture during imaging was changed; the odds ratio for recurrent constriction between the prone methacholine/supine imaging condition and the prone methacholine/prone imaging condition was 1.2 (0.9). Logistic regression showed that height above the dependent lung border was a significant negative predictor of constriction in the two supine imaging conditions ( P < 0.001 for each) but not in the prone imaging condition ( P = 0.20). These results show that the spatial pattern of methacholine bronchoconstriction is recurrent in the supine posture, regardless of whether methacholine is given prone or supine but that prone posture during imaging eliminates that recurrent pattern and reduces its dependence on gravitational height. NEW & NOTEWORTHY The spatial pattern of methacholine bronchoconstriction in the supine posture is recurrent and skewed toward the dependent lung, regardless of whether inhaled methacholine is administered while supine or while prone. However, both the recurrent pattern and the gravitational skew are eliminated if imaging is performed prone. These results suggest that gravitational influence on regional lung inflation and airway topography at the time of measurement play a role in determining regional bronchoconstriction in the healthy lung.

scholarly journals Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

scholarly journals Mining multiple sources of historical data: The example of a standardized dataset of medieval monasteries and convents in France

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Adam Mertel ◽  
David Zbíral
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Data Model ◽  
Computational Analysis ◽  
Historical Data ◽  
Model Data ◽  
Multiple Sources ◽  
The Public ◽  
Comprehensive Collection ◽  
Online Databases

<p><strong>Abstract.</strong> In this paper, we present a dataset of medieval monasteries and convents on the territory of today’s France and discuss the workflow of its integration. Spatial historical data are usually dispersed and stored in various forms &amp;ndash; encyclopedias and catalogues, websites, online databases, and printed maps. In order to cope with this heterogeneity and proceed to computational analysis, we have devised a method that includes the creation of a data model, data mining from sources, data transformation, geocoding, editing, and conflicts solving.</p><p> The resulting dataset is probably the most comprehensive collection of records on medieval monasteries within the borders of today’s France. It can be used for understanding the spatial patterns of medieval Christian monasticism and the implantation of the official Church infrastructure, as well as the relation between this official infrastructure and phenomena covered in other datasets. We open this dataset, as well as scripts for mining, to the public (https://github.com/adammertel/dissinet.monasteries) and provide a map tool to visualize, filter, and download the records (http://hde.geogr.muni.cz/monasteries).</p>

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