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Computational methods for climate data

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Abstract As climate changes, regional responses may become more apparent; impacts often can become natural hazards, adversely affecting millions of people, on all continents and most nations. The coupling of hazards to climate change scenarios is a great challenge of climate change science. Nevertheless, it is extremely important to observe, simulate and ultimately understand this coupling, for the benefit of society and sustainability of the Earth's environment. Different applied mathematical techniques have been used to discern real effects of these changes and study long term trends. Moreover, those techniques can be applied in addressing the intensity and frequency of extreme events associated with climate change at regional scales and would be an important step in facing future extreme events associated with climate change. Computational methods include applying statistical data analysis, mesoscale and climate simulations while assisting modeling efforts with satellite based observations. Predictive analytics platforms would be very useful for assessing impacts of climate variability and change on the frequency and intensity of extreme events and how these extreme events can affect water and air quality issues globally. These tools are an innovative technology that applies data mining methods, predictive models, analysis and reporting to data, without the inherent limitations of current On Line Analytical Processing tools that suffer from cube rigidity, database explosion and dimensional constriction. Climate‐induced changes are complex and vary across a wide range of dimensions. An important part of predictive analytics platforms are their unlimited dimensionality and the segmentation of data by ‘physical’ or ‘performance’ characteristics. For example, a physical dimension might be the climate divisions in the state of California. A performance dimension could be the arithmetical, mathematical, or statistical segmentation of data based on its performance with regard to time. WIREs Comput Stat 2012 doi: 10.1002/wics.1213 This article is categorized under: Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting Applications of Computational Statistics > Signal and Image Processing and Coding Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

The seven climate divisions of California viz. (1) North Coast Drainage, (2) Sacramento Drainage, (3) Northeast Interior Basins, (4) Central Coast Drainage, (5) San Joaquin Drainage, (6) South Coast Drainage, (7) Southeast Desert Basin. The background image is topography of California where green color represents plains (low elevation) and yellow–brown–white implies increasing higher elevation.

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The Southern Oscillation Index 1895–2009.

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Wavelet power spectrum plot for climate division 1.

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Index of stationarity for climate division 1.

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Residual precipitation values and model‐based 95% confidence intervals.

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Model assessment‐residual diagnostic for California, Division 1.

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Time series decomposition for California, Division 1.

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Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Applications of Computational Statistics > Signal and Image Processing and Coding
Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting

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