Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure

Schnase, John L.; Lee, Tsengdar J.; Mattmann, Chris A.; Lynnes, Christopher; Cinquini, L.; Ramírez, Paul; Hart, Andrew F.; Williams, D. N.; Waliser, Duane E.; Rinsland, P. L.; Webster, William P.; Duffy, Daniel Q.; McInerney, M.; Tamkin, G.; Potter, Gerald L.; Carriere, L.

doi:10.1109/mgrs.2015.2514192

Cited by 33 publications

(11 citation statements)

References 15 publications

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“…Software tools for feature tracking, colloquially referred to as "trackers", are valuable for evaluating model performance (Davini and D'Andrea, 2016;Stansfield et al, 2020), understanding upstream process drivers, such as large-scale meteorological patterns (e.g., Grotjahn et al, 2016), and projecting future changes in feature characteristics and climatology (Roberts et al, 2020a). When wellengineered, these automated tools provide a means for analyzing the multiple petabytes of climate data now available and anticipated in the next decade (Schnase et al, 2016;Hassani et al, 2019). Since its introduction, TempestExtremes (TE; has been continuously augmented with new kernels -that is, basic data operators that can act as building-blocks for more complicated tracking algorithms -designed to streamline data analysis and generalize capabilities present in other trackers.…”

Section: Introductionmentioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

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Section: Introductionmentioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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“…Moreover, the resolution and complexity of numerical models -and hence the amount of data they produce -has increased vastly in recent years. This poses new challenges to the palaeoclimate community in how to efficiently process and interpret model output -indeed an issue that is faced by the broader climate community (Schnase et al, 2016).…”

Section: Motivationmentioning

confidence: 99%

Technical note: Characterising and comparing different palaeoclimates with dynamical systems theory

Messori

Faranda

2021

Clim. Past

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Abstract. Numerical climate simulations produce vast amounts of high-resolution data. This poses new challenges to the palaeoclimate community – and indeed to the broader climate community – in how to efficiently process and interpret model output. The palaeoclimate community also faces the additional challenge of having to characterise and compare a much broader range of climates than encountered in other subfields of climate science. Here we propose an analysis framework, grounded in dynamical systems theory, which may contribute to overcoming these challenges. The framework enables the characterisation of the dynamics of a given climate through a small number of metrics. These may be applied to individual climate variables or to several variables at once, and they can diagnose properties such as persistence, active number of degrees of freedom and coupling. Crucially, the metrics provide information on instantaneous states of the chosen variable(s). To illustrate the framework's applicability, we analyse three numerical simulations of mid-Holocene climates over North Africa under different boundary conditions. We find that the three simulations produce climate systems with different dynamical properties, such as persistence of the spatial precipitation patterns and coupling between precipitation and large-scale sea level pressure patterns, which are reflected in the dynamical systems metrics. We conclude that the dynamical systems framework holds significant potential for analysing palaeoclimate simulations. At the same time, an appraisal of the framework's limitations suggests that it should be viewed as a complement to more conventional analyses, rather than as a wholesale substitute.

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“…As a trending and emerging topic, big data researchers who are also interested in climate sciences have been exposed to abundant established resources, for instance, the Global Climate Observing System (GCOS), Earth System Grid Federation (esgf.llnl.gov), the National Center for Atmospheric Research (ncar.ucar.edu), United Nations Global Pulse (unglobalpulse.org), the Climate Data Guide (climatedataguide.ucar.edu), NASA Global Climate Change (climate.nasa.gov), the NASA Center for Climate Simulation (nccs.nasa.gov) and many other international and national climate monitoring and analysing institutions over the world. A detailed report that introduces the core of global scale climate research and cyber-infrastructure can be found in [11].…”

Section: Understanding Predicting and Optimizingmentioning

confidence: 99%

Big Data and Climate Change

Hassani

Huang

Silva

2019

BDCC

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Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

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Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure

Cited by 33 publications

References 15 publications

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

Technical note: Characterising and comparing different palaeoclimates with dynamical systems theory

Big Data and Climate Change

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