Dataset Information

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

ABSTRACT: The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specific to each circulation pattern. To supplement this analysis, we have developed an interactive website with detailed information for every flood of record. We identify four primary categories of circulation patterns: tropical moisture exports, tropical cyclones, atmospheric lows or troughs, and melting snow. We find that large flood events are generally caused by tropical moisture exports (tropical cyclones) in the western and central (eastern) United States. We identify regions where extreme floods regularly occur outside the normal flood season (e.g., the Sierra Nevada Mountains due to tropical moisture exports) and regions where multiple extreme flood events can occur within a single year (e.g., the Atlantic seaboard due to tropical cyclones and atmospheric lows or troughs). These results provide the first machine-learning based near-continental scale identification of atmospheric circulation patterns associated with extreme floods with valuable insights for flood risk management.

SUBMITTER: Schlef KE

PROVIDER: S-EPMC6509142 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

Schlef Katherine E KE Moradkhani Hamid H Lall Upmanu U

Scientific reports 20190509 1

The massive socioeconomic impacts engendered by extreme floods provides a clear motivation for improved understanding of flood drivers. We use self-organizing maps, a type of artificial neural network, to perform unsupervised clustering of climate reanalysis data to identify synoptic-scale atmospheric circulation patterns associated with extreme floods across the United States. We subsequently assess the flood characteristics (e.g., frequency, spatial domain, event size, and seasonality) specifi ...[more]

PMID: 31073192

Similar Datasets

Project description:BackgroundAn increasing severity of extreme storms and more intense seasonal flooding are projected consequences of climate change in the United States. In addition to the immediate destruction caused by storm surges and catastrophic flooding, these events may also increase the risk of infectious disease transmission. We aimed to determine the association between extreme and seasonal floods and hospitalizations for Legionnaires' disease in 25 US states during 2000-2011.MethodsWe used a nonparametric bootstrap approach to examine the association between Legionnaires' disease hospitalizations and extreme floods, defined by multiple hydrometeorological variables. We also assessed the effect of extreme flooding associated with named cyclonic storms on hospitalizations in a generalized linear mixed model (GLMM) framework. To quantify the effect of seasonal floods, we used multi-model inference to identify the most highly weighted flood-indicator variables and evaluated their effects on hospitalizations in a GLMM.ResultsWe found a 32% increase in monthly hospitalizations at sites that experienced cyclonic storms, compared to sites in months without storms. Hospitalizations in months with extreme precipitation were in the 89th percentile of the bootstrapped distribution of monthly hospitalizations. Soil moisture and precipitation were the most highly weighted variables identified by multi-model inference and were included in the final model. A 1-standard deviation (SD) increase in average monthly soil moisture was associated with a 49% increase in hospitalizations; in the same model, a 1-SD increase in precipitation was associated with a 26% increase in hospitalizations.ConclusionsThis analysis is the first to examine the effects of flooding on hospitalizations for Legionnaires' disease in the United States using a range of flood-indicator variables and flood definitions. We found evidence that extreme and seasonal flooding is associated with increased hospitalizations; further research is required to mechanistically establish whether floodwaters contaminated with Legionella bacteria drive transmission.

Project description:Atmospheric circulation patterns derived from multi-spectral remote sensing can serve as a guide for choosing a suitable entry location for a future in situ probe mission to the Ice Giants. Since the Voyager-2 flybys in the 1980s, three decades of observations from ground- and space-based observatories have generated a picture of Ice Giant circulation that is complex, perplexing, and altogether unlike that seen on the Gas Giants. This review seeks to reconcile the various competing circulation patterns from an observational perspective, accounting for spatially-resolved measurements of: zonal albedo contrasts and banded appearances; cloud-tracked zonal winds; temperature and para-H2 measurements above the condensate clouds; and equator-to-pole contrasts in condensable volatiles (methane, ammonia, and hydrogen sulphide) in the deeper troposphere. These observations identify three distinct latitude domains: an equatorial domain of deep upwelling and upper-tropospheric subsidence, potentially bounded by peaks in the retrograde zonal jet and analogous to Jovian cyclonic belts; a mid-latitude transitional domain of upper-tropospheric upwelling, vigorous cloud activity, analogous to Jovian anticyclonic zones; and a polar domain of strong subsidence, volatile depletion, and small-scale (and potentially seasonally-variable) convective activity. Taken together, the multi-wavelength observations suggest a tiered structure of stacked circulation cells (at least two in the troposphere and one in the stratosphere), potentially separated in the vertical by (i) strong molecular weight gradients associated with cloud condensation, and by (ii) transitions from a thermally-direct circulation regime at depth to a wave- and radiative-driven circulation regime at high altitude. The inferred circulation can be tested in the coming decade by 3D numerical simulations of the atmosphere, and by observations from future world-class facilities. The carrier spacecraft for any probe entry mission must ultimately carry a suite of remote-sensing instruments capable of fully constraining the atmospheric motions at the probe descent location.

Project description:BackgroundHypoglossal nerve stimulator (HGNS) is a therapeutic option for moderate to severe obstructive sleep apnea (OSA). Improved patient selection criteria are needed to target those most likely to benefit. We hypothesized that the pattern of negative effort dependence (NED) on inspiratory flow limited waveforms recorded during sleep, which has been correlated with the site of upper airway collapse, would contribute to the prediction of HGNS outcome. We developed a machine learning (ML) algorithm to identify NED patterns in pre-treatment sleep studies. We hypothesized that the predominant NED pattern would differ between HGNS responders and non-responders.MethodsAn ML algorithm to identify NED patterns on the inspiratory portion of the nasal pressure waveform was derived from 5 development set polysomnograms. The algorithm was applied to pre-treatment sleep studies of subjects who underwent HGNS implantation to determine the percentage of each NED pattern. HGNS response was defined by STAR trial criteria for success (apnea-hypopnea index (AHI) reduced by > 50% and < 20/h) as well as by a change in AHI and oxygenation metrics. The predominant NED pattern in HGNS responders and non-responders was determined. Other variables including demographics and oxygenation metrics were also assessed between responders and non-responders.ResultsOf 45 subjects, 4 were excluded due to technically inadequate polysomnograms. In the remaining 41 subjects, ML accurately distinguished three NED patterns (minimal, non-discontinuous, and discontinuous). The percentage of NED minimal breaths was significantly greater in responders compared with non-responders (p = 0.01) when the response was defined based on STAR trial criteria, change in AHI, and oxygenation metrics.ConclusionML can accurately identify NED patterns in pre-treatment sleep studies. There was a statistically significant difference in the predominant NED pattern between HGNS responders and non-responders with a greater NED minimal pattern in responders. Prospective studies incorporating NED patterns into predictive modeling of factors determining HGNS outcomes are needed.

Dataset Information

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

Publications

Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets