Floods of December 1978 in Kentucky

Sullavan, J.N.; Quinones, Ferdinand; Flint, Richard Foster

doi:10.3133/ofr79977

Cited by 2 publications

(1 citation statement)

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“…The scatterplot presents only validation points from the most recent 20% of samples. in a line of intense thunderstorms that persisted for greater than 24 h (NOAA 2008;Sullavan et al 1979;Jackson and Vivian 1997).…”

Section: ) Interpretation Of Convolutional Nns: Archetype P-w2mentioning

confidence: 99%

Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern Continental United States

Knighton

Pleiss

Carter

et al. 2019

Journal of Hydrometeorology

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Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes.

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Section: ) Interpretation Of Convolutional Nns: Archetype P-w2mentioning

confidence: 99%