Detecting atmospheric rivers in large climate datasets (original) (raw)
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Geoscientific Model Development
Identifying weather patterns that frequently lead to extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Here, we propose an automated method for recognizing atmospheric rivers (ARs) in climate data using topological data analysis and machine learning. The method provides useful information about topological features (shape characteristics) and statistics of ARs. We illustrate this method by applying it to outputs of version 5.1 of the Community Atmosphere Model version 5.1 (CAM5.1) and the reanalysis product of the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). An advantage of the proposed method is that it is threshold-free-there is no need to determine any threshold criteria for the detection method-when the spatial resolution of the climate model changes. Hence, this method may be useful in evaluating model biases in calculating AR statistics. Further, the method can be applied to different climate scenarios without tuning since it does not rely on threshold conditions. We show that the method is suitable for rapidly analyzing large amounts of climate model and reanalysis output data.
Journal of Geophysical Research: Atmospheres, 2020
Atmospheric rivers (ARs) are responsible for a majority of extreme precipitation and flood events along the U.S. West Coast. To better understand the present-day characteristics of AR-related precipitation extremes, a selection of nine most intense historical AR events during 1980-2017 is simulated using a dynamical downscaling modeling framework based on the Weather Research and Forecasting Model. We find that the chosen framework and Weather Research and Forecasting Model configuration reproduces both large-scale atmospheric features-including parent synoptic-scale cyclones-as well as the filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracy of simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations, improves notably with increasing model resolution, with improvements as large as 40-60% for fine scale (3 km) relative to coarse-scale (27 km) simulations. A separate set of simulations using smoothed topography suggests that much of these gains stem from the improved representation of complex terrain. Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstrate that only the highest-resolution simulations resolve important fine-scale features-such as localized orographically forced vertical motion and powerful near hurricane-force boundary layer winds. Given the demonstrated ability of a targeted dynamical downscaling framework to capture both local extreme precipitation and key fine-scale characteristics of the most intense ARs in the historical record, we argue that such a configuration may be highly conducive to understanding AR-related extremes and associated changes in a warming climate.
TECA: Petascale Pattern Recognition for Climate Science
Lecture Notes in Computer Science, 2015
We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these modes of parallelism and demonstrate a prototype implementation for detecting and tracking three classes of extreme events: tropical cyclones, extra-tropical cyclones and atmospheric rivers. We process a massive 10TB CAM5 simulation dataset with TECA, and demonstrate good runtime performance for the three case studies.
TECA: A Parallel Toolkit for Extreme Climate Analysis
2012
We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these modes of parallelism and demonstrate a prototype implementation for detecting and tracking three classes of extreme events: tropical cyclones, extra-tropical cyclones and atmospheric rivers. We process a modern TB-sized CAM5 simulation dataset with TECA, and demonstrate good runtime performance for the three case studies.
Focal Area(s) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics-or knowledge-guided AI Science Challenge Impacts of climate are usually felt through extreme events such as droughts, floods, thunderstorms, windstorms, wildfires, and so on, that are intimately tied to the water cycle. Predicting the frequency and severity of extreme events under climate change remains a significant challenge; meanwhile, the mechanisms and impacts of these extremes are far from well understood. There are several major science challenges: (1) Lack of labelled extreme events data and missing standards in defining extremes; (2) Computational demand of high-resolution ensemble climate modeling; (3) Modeling the multiscale multi-physics hierarchical structure of compound extremes; (4) Lack of understanding of mechanisms of extreme events; (5) Large uncertainty in extreme events impacts on infrastructure; (6) Subjective assessment of weatherrelated risk from seasonal to multi-decadal time scales and lack of metrics for risk assessment and mitigation control. We identify the following high-priority research needs that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may enable transformational breakthroughs, by integrating general purpose GPU, cloud, and edge computing as well as database management:
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
arXiv (Cornell University), 2022
Storm-resolving models (SRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how SRMs resolve complex atmospheric formations. This lack of appropriate tools for comparing model similarities is a problem in many disparate fields that involve simulation tools for complex data. To address this challenge we develop methods to estimate distributional distances based on both nonlinear dimensionality reduction and vector quantization. Our approach automatically learns appropriate notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine SRMs based on their high-dimensional simulation data and reveals that only six are similar in their representation of atmospheric dynamics. Furthermore, we uncover signatures of the convective response to global warming in a fully unsupervised way. Our study provides a path toward evaluating future high-resolution simulation data more objectively.
Computational methods for climate data
Wiley Interdisciplinary Reviews: Computational Statistics, 2012
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.
Assessing the climate‐scale variability of atmospheric rivers affecting western North America
Geophysical Research Letters, 2017
A new method for automatic detection of atmospheric rivers (ARs) is developed and applied to an atmospheric reanalysis, yielding an extensive catalog of ARs land-falling along the west coast of North America during 1948-2017. This catalog provides a large array of variables that can be used to examine AR cases and their climate-scale variability in exceptional detail. The new record of AR activity, as presented, validated and examined here, provides a perspective on the seasonal cycle and the interannual-interdecadal variability of AR activity affecting the hydroclimate of western North America. Importantly, AR intensity does not exactly follow the climatological pattern of AR frequency. Strong links to hydroclimate are demonstrated using a high-resolution precipitation data set. We describe the seasonal progression of AR activity and diagnose linkages with climate variability expressed in Pacific sea surface temperatures, revealing links to Pacific decadal variability, recent regional anomalies, as well as a generally rising trend in land-falling AR activity. The latter trend is consistent with a long-term increase in vapor transport from the warming North Pacific onto the North American continent. The new catalog provides unprecedented opportunities to study the climate-scale behavior and predictability of ARs affecting western North America. Plain Language Summary We have created a new seven-decade-long catalog of atmospheric river behavior land-falling upon the west coast of North America. The catalog has been validated against independent precipitation observations to ensure that the atmospheric rivers represented therein are associated with extreme orographic precipitation. Our results clearly delineate a prominent role for atmospheric rivers in California's hydroclimate. Atmospheric river variability has been particularly important in the recent California drought as well as its most recent lapse. We also detect a long-term increasing trend in water vapor transport impinging on the west coast of North America associated with atmospheric rivers and overall wintertime water vapor transport associated with climate warming. Our results, moreover, suggest that potential predictability of seasonal behavior of atmospheric rivers may hinge on sources of climatic variability somewhat different from that of total water vapor transport.
Understanding the Role of Atmospheric Rivers in Heavy Precipitation in the Southeast United States
Monthly Weather Review, 2016
An analysis of atmospheric rivers (ARs) as defined by an automated AR detection tool based on integrated water vapor transport (IVT) and the connection to heavy precipitation in the southeast United States (SEUS) is performed. Climatological water vapor and water vapor transport fields are compared between the U.S. West Coast (WCUS) and the SEUS, highlighting stronger seasonal variation in integrated water vapor in the SEUS and stronger seasonal variation in IVT in the WCUS. The climatological analysis suggests that IVT values above ~500 kg m−1 s−1 (as incorporated into an objective identification tool such as the AR detection tool used here) may serve as a sensible threshold for defining ARs in the SEUS. Atmospheric river impacts on heavy precipitation in the SEUS are shown to vary on an annual cycle, and a connection between ARs and heavy precipitation during the nonsummer months is demonstrated. When identified ARs are matched to heavy precipitation days (>100 mm day−1), an av...