scholarly journals Visual Analytics for Climate Change Detection in Meteorological Time-Series

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 276-289
Author(s):  
Milena Vuckovic ◽  
Johanna Schmidt
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Change Detection ◽  
Visual Analytics ◽  
Structural Changes ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Climate Change Signal ◽  
Climate Change Detection

The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations.

A daily burned area mapping method using AVHRR time-series data

2021 ◽  
Author(s):  
Shuhan Lou ◽  
Yuanhong Liao ◽  
Yufu Liu ◽  
Yuqi Bai
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Time Series Data ◽  
Mapping Method ◽  
Series Data ◽  
Burned Area ◽  
Burnt Area ◽  
Area Data

<p>The study of complex interactions between fire and atmospheric dynamics of the earth system is drawing increasing attention in recent years, especially when fire seasons are extended due to global warming, where the historical daily burnt area data played a pivotal role in analyzing wildfire regimes change. Existing products could not fully meet the temporal requirements: daily burnt area data in global fire emissions database (GFED4) starts from mid-2000 using MODIS while ESA Fire Climate Change Initiative (FireCCILT10) Dataset from 1982 to 2017 is provided on a monthly grid.</p><p>Advanced Very High Resolution Radiometer (AVHRR) series of sensors are widely used to develop pre‐MODIS daily historical records. However, compared to MODIS, the AVHRR sensor has a lower radiometric and geometric quality and is missing Short Wave Infrared (SWIR) band. To address the data quality problem, this research study presents a time-series mapping method for daily burned area using AVHRR composite. Daily fire-sensitive indices are calculated to develop a time-series data composite which is masked by the burnable surface of GLASS_GLC land cover product. Then, Continuous Change Detection and Classification (CCDC) time-series model, which originally implemented on Landsat data monitoring land cover change, is revised to detect an abrupt change in the time-series data composite and remove noise, ensuring temporal consistency. The image of a time-series breakpoint is further classified using a spatial contextual method to distinguish biomass burning from other forest degradation change like a landslide and is used to generate burned area probability map.</p><p>The methodology is verified in California, US, where fuel aridity increased during 1984–2015 driven by anthropogenic climate change. The samples are collected based on the National Monitoring Trends in Burn Severity（MTBS）Burned Areas Boundaries Dataset from 1984 – 2018 and California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters from since 1950. Primary results show that the proposed method can effectively detect burned area on daily basis with CCDC algorithm reducing the complexity of change detection.</p>

scholarly journals Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method

2011 ◽  
Vol 24 (20) ◽  
pp. 5275-5291 ◽  
Author(s):  
Bettina C. Lackner ◽  
Andrea K. Steiner ◽  
Gabriele C. Hegerl ◽  
Gottfried Kirchengast
Keyword(s):  
Climate Change ◽  
Change Detection ◽  
Confidence Level ◽  
Geopotential Height ◽  
Radio Occultation ◽  
Lower Stratosphere ◽  
Quality Data ◽  
Climate Change Signal ◽  
High Quality ◽  
Climate Change Detection

Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

scholarly journals Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis

2013 ◽  
Vol 10 (80) ◽  
pp. 20120935 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Brian Ingalls ◽  
Eduardo Sontag
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Rhodobacter Sphaeroides ◽  
Time Series Data ◽  
Series Data ◽  
Transient Dynamic ◽  
Experimental Time ◽  
Theoretical Assumptions ◽  
Experimental Time Series ◽  
Fold Change Detection

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides shares many similarities with that of Escherichia coli . It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have correctly predicted that the E. coli output behaviour is unchanged under scaling of its ligand input signal; this property is known as fold-change detection (FCD). In the light of recent experimental results suggesting that R. sphaeroides may also show FCD, we present theoretical assumptions on the R. sphaeroides chemosensory dynamics that can be shown to yield FCD behaviour. Furthermore, it is shown that these assumptions make FCD a property of this system that is robust to structural and parametric variations in the chemotaxis pathway, in agreement with experimental results. We construct and examine models of the full chemotaxis pathway that satisfy these assumptions and reproduce experimental time-series data from earlier studies. We then propose experiments in which models satisfying our theoretical assumptions predict robust FCD behaviour where earlier models do not. In this way, we illustrate how transient dynamic phenotypes such as FCD can be used for the purposes of discriminating between models that reproduce the same experimental time-series data.

scholarly journals Climate Change Made Major Contributions to Soil Water Storage Decline in the Southwestern US during 2003–2014

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1947
Author(s):  
Jianzhao Liu ◽  
Liping Gao ◽  
Fenghui Yuan ◽  
Yuedong Guo ◽  
Xiaofeng Xu
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Water Resource Management ◽  
Time Series Data ◽  
Meteorological Data ◽  
Water Storage ◽  
Climate Change Impacts ◽  
Soil Water Storage ◽  
Series Data ◽  
Atmospheric Water

Soil water shortage is a critical issue for the Southwest US (SWUS), the typical arid region that has experienced severe droughts over the past decades, primarily caused by climate change. However, it is still not quantitatively understood how soil water storage in the SWUS is affected by climate change. We integrated the time-series data of water storage and evapotranspiration derived from satellite data, societal water consumption, and meteorological data to quantify soil water storage changes and their climate change impacts across the SWUS from 2003 to 2014. The water storage decline was found across the entire SWUS, with a significant reduction in 98.5% of the study area during the study period. The largest water storage decline occurred in the southeastern portion, while only a slight decline occurred in the western and southwestern portions of the SWUS. Net atmospheric water input could explain 38% of the interannual variation of water storage variation. The climate-change-induced decreases in net atmospheric water input predominately controlled the water storage decline in 60% of the SWUS (primarily in Texas, Eastern New Mexico, Eastern Arizona, and Oklahoma) and made a partial contribution in approximately 17% of the region (Central and Western SWUS). Climate change, primarily as precipitation reduction, made major contributions to the soil water storage decline in the SWUS. This study infers that water resource management must consider the climate change impacts over time and across space in the SWUS.

Visual analytics of phosphorylation time-series data on insulin response

2013 ◽  
Author(s):  
David K. G. Ma ◽  
Christian Stolte ◽  
Sandeep Kaur ◽  
Michael Bain ◽  
Seán I. O'Donoghue
Keyword(s):  
Time Series ◽  
Visual Analytics ◽  
Time Series Data ◽  
Insulin Response ◽  
Series Data

scholarly journals Splendide Mendax: False Label Claims About High and Rising Alcohol Content of Wine

2015 ◽  
Vol 10 (3) ◽  
pp. 275-313 ◽  
Author(s):  
Julian M. Alston ◽  
Kate B. Fuller ◽  
James T. Lapsley ◽  
George Soleas ◽  
Kabir P. Tumber
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Consumer Preferences ◽  
Series Data ◽  
Alcohol Content ◽  
Demand Shifts ◽  
Expert Ratings ◽  
Jel Classifications

AbstractAre wine alcohol labels accurate? If not, why? We explore the high and rising alcohol content of wine and examine incentives for false labeling, including the roles of climate, evolving consumer preferences, and expert ratings. We draw on international time-series data from a large number of countries that experienced different patterns of climate change and influences of policy and demand shifts. We find systematic patterns that suggest that rising wine alcohol content may be a nuisance by-product of producer responses to perceived market preferences for wines having more-intense flavours, possibly in conjunction with evolving climate. (JEL Classifications: D22, L15, L66, Q18, Q54).

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