Project Extreme events in Climate Science based on papers in
BDA 2022 ICPR 2021 JGRA 2019 GMD 2019 GMD 2018 CI 2018
This project develops parameter-free methods to detect extreme weather events such as Atmospheric Rivers abd Blocks.
Pattern Recognition in Climate Data
 |
|
@article{muszynski2022topological, title={Topological Methods for Pattern Detection in Climate Data}, author={Muszynski, Grzegorz and Kurlin, Vitaliy and Morozov, Dmitriy and Wehner, Michael and Kashinath, Karthik and Ram, Prabhat}, journal={Big Data Analytics in Earth, Atmospheric, and Ocean Sciences}, pages={221--235}, year={2022}, publisher={Wiley Online Library} }- DOI : 10.1002/9781119467557
- Abstract.
Back to Top of this page | Back to Research & papers | Back to Home page
Atmospheric Blocking Pattern Recognition
 |
|
@inproceedings{muszynski2021, title={Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data}, author={Grzegorz Muszynski and Prabhat Ram and Jan Balewski and Karthik Kashinath and Michael Wehner and Vitaliy Kurlin}, booktitle={Proceedings of the International Conference on Pattern Recognition}, year={2021} }- DOI : 10.1109/ICPR48806.2021.9412736
- Abstract. We address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a crucial problem to society and natural infrastructure, as they often lead to weather extremes, such as heat waves, heavy precipitation, and the unusually poor air condition. Moreover, it is very challenging to detect these events as there is no physics-based model of blocking dynamic development that could account for their spatiotemporal characteristics. Here, we propose a new twostage hierarchical pattern recognition method for detection and localisation of atmospheric blocking events in different regions over the globe. For both the detection stage and localisation stage, we train five different architectures of a convolutional neural network (CNN) based classifier and regressor. The results show the general pattern of the atmospheric blocking detection performance increasing significantly for the deep CNN architectures. In contrast, we see the estimation error of event location decreasing significantly in the localisation problem for the shallow CNN architectures. We demonstrate that CNN architectures tend to achieve the highest accuracy for blocking event detection and the lowest estimation error of event localisation in regions of the Northern Hemisphere than in regions of the Southern Hemisphere.
Back to Top of this page | Back to Research & papers | Back to Home page
Quantifying Uncertainties in Atmospheric River Climatology
 |
|
@article{rutz2019atmospheric, title={Quantifying Uncertainties in Atmospheric River Climatology}, author={Rutz, Jonathan et al.}, journal={Journal of Geophysical Research: Atmospheres}, volume = {124}, issue = {24}, pages= {13777-13802}, year={2019} }- Abstract. Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions, and hence use different criteria (e.g., geometry, threshold values of key variables, time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to MERRA v2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.
Back to Top of this page | Back to Research & papers | Back to Home page
Recognizing Atmospheric River Patterns
 |
|
@article{muszynski2019topological, title={Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets}, author={Grzegorz Muszynski and Karthik Kashinath and Vitaliy Kurlin and Michael Wehner and Prabhat}, journal={Geoscientific Model Development}, volume={12}, pages = {613-628}, year={2019} }- Abstract. 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 5 version 5.1 of the Community Atmosphere Model (CAM5.1) and 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. 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.
Back to Top of this page | Back to Research & papers | Back to Home page
ARTMIP : project goals and experimental design
 |
|
@article{shields2018atmospheric, title={Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design}, author={Shields, Christine and Rutz, Jonathan and Leung, Lai-Yung and Ralph Martin and Wehner, Michael and others}, journal={Geoscientific Model Development}, volume={11}, number={6}, pages={2455-2474}, year={2018} }- Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project
Back to Top of this page | Back to Research & papers | Back to Home page
A pattern detection in fluid and climate data
 |
|
@inproceedings{muszynski2018towards, title={Towards a topological pattern detection in fluid and climate simulation data}, author={Grzegorz Muszynski and Karthik Kashinath and Vitaliy Kurlin and Michael Wehner and Prabhat}, booktitle={Climate Informatics workshop}, year={2018} }- Abstract. Increasingly massive amounts of high resolution climate datasets are being generated by observations as well as complex climate models. As the unprecedented growth of data continues, a massive challenge is to design automated and efficient data analysis techniques that can extract meaningful insights from vast datasets. In particular, a key challenge is the detection and characterization of weather and climate patterns. Machine learning, including deep learning, are currently popularly used for these tasks. These techniques, however, do not incorporate geometric features of data and temporal persistence information. In this paper, we develop a novel approach to pattern detection and characterization based on dynamical systems, manifold learning and topological data analysis (i.e., persistent homology) that utilize important geometric and topological properties of underlying patterns in datasets.
Back to Top of this page | Back to Research & papers | Back to Home page