Artificial Intelligence Methods in the Environmental Sciences

Haupt, Sue Ellen; Pasini, Antonello; Marzban, Caren

doi:10.1007/978-1-4020-9119-3

Cited by 66 publications

(3 citation statements)

References 275 publications

(399 reference statements)

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“…The null hypothesis under test here is: "neither sub-grid variability nor bias, as represented by the FER distributions, depend on the governing variables". In principal there are many ways to test using the tabulated data, from fully automated machine learning 51 through to a wholly subjective approach. We adopt a straightforward, transparent and semi-subjective methodology, examining candidate variables individually.…”

Section: Test the Utility Of Governing Variablesmentioning

confidence: 99%

“…mapping function, being defined for all possible governing variable range combinations. Whilst acknowledging that there are many possible approaches 51 , we elected to construct a straightforward and easy-to-comprehend single decision tree, in which each of the 5 branch levels corresponded to one variable. Steered by the results from (d) above, by the original ecPoint concept, and by analysis of multiple case studies, we selected from the outset the following level hierarchy (top to bottom, most ϯ Also known as "cutting values" in decision-tree literature.…”

Section: Create the Decision Treementioning

confidence: 99%

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A low-cost post-processing technique improves weather forecasts around the world

Hewson

Pillosu

2021

Commun Earth Environ

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Computer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

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Section: Test the Utility Of Governing Variablesmentioning

confidence: 99%

Section: Create the Decision Treementioning

confidence: 99%

A low-cost post-processing technique improves weather forecasts around the world

Hewson

Pillosu

2021

Commun Earth Environ

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“…The GA finds the global optimal solution in the complex multidimensional search space [Ata and Myo, 2005]. The GA as an optimization tool has shown great success in solving problems not amenable to easy solutions via more traditional means [Haupt et al, 2009]. The GA has been extensively and successfully used in many researches in several disciplines as a powerful tool for optimization, parameter estimation, and curve-fitting [see, e.g., Goldberg, 1989;Kropp and Scheffran, 2007, and references therein].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network

Dwivedi

Pandey

2011

Geophysical Research Letters

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[1] The correct and timely forecast of the Indian summer monsoon Intraseasonal Oscillations (ISOs) is very important. It has great impact on the agriculture and economy of the Indian subcontinent region. The applicability of Genetic Algorithm (GA) is demonstrated for nonlinear curve fitting of the inherently chaotic and noisy Lorenz time series and the ISO data. A robust method is developed for the very long-range prediction of the ISO using a feed-forward time delay backpropagation Artificial Neural Network (ANN). Using an iterative one-step-ahead prediction strategy, five years (120 pentads) of advanced prediction is made for the ISO data with good forecast skill. It is shown that a hybrid GA-ANN model may be used as an early forecast model followed by ANN only model as a more reliable model.Citation: Dwivedi, S., and A. C. Pandey (2011), Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network, Geophys. Res. Lett., 38, L15801,

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Climatic attribution at the regional scale: a case study on the role of circulation patterns and external forcings

Pasini

Modugno

2013

Atmospheric Science Letters

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At the regional scale, enhanced climatic variability masks the role of external forcings. It has been shown that a consistent attribution of regional temperature behaviour can be achieved just by considering circulation patterns as driving elements. Here we address this question: is the role of external forcings completely hidden in the changes of circulation patterns (eventually induced by them), or is there evidence of a more direct role for these forcings? Performing a fully nonlinear analysis shows that a direct role for anthropogenic forcings can be detected also at this regional scale, while natural forcings do not seem to influence temperature behaviour.

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Artificial Intelligence Methods in the Environmental Sciences

Cited by 66 publications

References 275 publications

A low-cost post-processing technique improves weather forecasts around the world

A low-cost post-processing technique improves weather forecasts around the world

Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network

Climatic attribution at the regional scale: a case study on the role of circulation patterns and external forcings

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