Pattern Recognition for Weather Phenomena in Climate Data

Weather phenomena have long been objects of studies in atmospheric and climate science research. Studies on weather phenomena incorporate meteorological data, climate model simulations, and knowledge of physical processes of the Earth’s atmosphere. Understanding of the developing mechanisms, life cycles, and spatiotemporal dependencies of these phenomena requires accurately identifying them in space and time. Moreover, identifying weather phenomena in large-scale climate model simulations is critical for advancing our understanding of the Earth’s climate and risks of future extreme weather events. The main goal of this thesis is to design and develop pattern recognition methods that directly learn from examples of weather phenomena in climate data, rather than following heuristic algorithms containing threshold requirements on physical variables. In particular, we aim to classify and localise atmospheric river and blocking phenomena in global climate simulations and reanalysis data. In this thesis, we propose a novel pattern recognition method for identifying atmospheric river phenomena in climate datasets. This method consists of topological data analysis and machine learning methods. We demonstrate that the proposed method is reliable, robust, and achieves high accuracy. Also, we test the method on a wide range of spatial and temporal resolutions of global climate model outputs. We find that the method achieves the highest classification accuracy for low-resolution climate model outputs. Moreover, we propose a hierarchical pattern recognition method for identifying atmospheric blocking phenomena in climate reanalysis products. This pattern recognition method is based on deep convolutional neural networks. We demonstrate that the proposed method accurately detects and localises atmospheric blocks in climate reanalysis data. We also find that the method achieves higher accuracy for classification and lower estimation error for localisation of blocking phenomena in regions of the Northern Hemisphere than in regions of the Southern Hemisphere. Research outcomes presented in this thesis show that the proposed pattern recognition methods can be complementary tools to the existing identification methods of atmospheric rivers and blocks in climate data. In addition to that, the proposed methods offer automatic post-processing, quantitative assessment of climate datasets, and can facilitate analysis of the local impacts of weather phenomena on specific geographical areas

Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets

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 &amp;amp; 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. </jats:p

Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets

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 &amp;amp; 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. </jats:p

Concept and Design of Martian Far-IR ORE Spectrometer (MIRORES)

Sulfide ores are a major source of noble (Au, Ag, and Pt) and base (Cu, Pb, Zn, Sn, Co, Ni, etc.) metals and will, therefore, be vital for the self-sustainment of future Mars colonies. Martian meteorites are rich in sulfides, which is reflected in recent findings for surface Martian rocks analyzed by the Spirit and Curiosity rovers. However, the only high-resolution (18 m/pixel) infrared (IR) spectrometer orbiting Mars, the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), onboard the Mars Reconnaissance Orbiter (MRO), is not well-suited for detecting sulfides on the Martian surface. Spectral interference with silicates impedes sulfide detection in the 0.4–3.9 μm CRISM range. In contrast, at least three common hydrothermal sulfides on Earth and Mars (pyrite, chalcopyrite, marcasite) have prominent absorption peaks in a narrow far-IR (FIR) wavelength range of 23–28 μm. Identifying the global distribution and chemical composition of sulfide ore deposits would help in choosing useful targets for future Mars exploration missions. Therefore, we have designed a new instrument suitable for measuring sulfides in the FIR range called the Martian far-IR Ore Spectrometer (MIRORES). MIRORES will measure radiation in six narrow bands (~0.3 µm in width), including three bands centered on the sulfide absorption bands (23.2, 24.3 and 27.6 µm), two reference bands (21.5 and 26.1) and one band for clinopyroxene interference (29.0 µm). Focusing on sulfides only will make it possible to adapt the instrument size (32 × 32 × 42 cm) and mass (<10 kg) to common microsatellite requirements. The biggest challenges related to this design are: (1) the small field of view conditioned by the high resolution required for such a study (<20 m/pixel), which, in limited space, can only be achieved by the use of the Cassegrain optical system; and (2) a relatively stable measurement temperature to maintain radiometric accuracy and enable precise calibration

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

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, and 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 Modern‐Era Retrospective Analysis for Research and Applications Version 2 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.Fil: Rutz, Jonathan J.. National Ocean And Atmospheric Administration; Estados UnidosFil: Shields, Christine A.. National Center for Atmospheric Research; Estados UnidosFil: Lora, Juan M.. University of Yale; Estados UnidosFil: Payne, Ashley E.. University of Michigan; Estados UnidosFil: Guan, Bin. California Institute of Technology; Estados UnidosFil: Ullrich, Paul. University of California at Davis; Estados UnidosFil: O'Brien, Travis. Lawrence Berkeley National Laboratory; Estados UnidosFil: Leung, Ruby. Pacific Northwest National Laboratory; Estados UnidosFil: Ralph, F. Martin. Center For Western Weather And Water Extremes; Estados UnidosFil: Wehner, Michael. Lawrence Berkeley National Laboratory; Estados UnidosFil: Brands, Swen. Meteogalicia; EspañaFil: Collow, Allison. Universities Space Research Association; Estados UnidosFil: Goldenson, Naomi. University of California at Los Angeles; Estados UnidosFil: Gorodetskaya, Irina. Universidade de Aveiro; PortugalFil: Griffith, Helen. University of Reading; Reino UnidoFil: Kashinath, Karthik. Lawrence Bekeley National Laboratory; Estados UnidosFil: Kawzenuk, Brian. Center For Western Weather And Water Extremes; Reino UnidoFil: Krishnan, Harinarayan. Lawrence Berkeley National Laboratory; Estados UnidosFil: Kurlin, Vitaliy. University of Liverpool; Reino UnidoFil: Lavers, David. European Centre For Medium-range Weather Forecasts; Estados UnidosFil: Magnusdottir, Gudrun. University of California at Irvine; Estados UnidosFil: Mahoney, Kelly. Universidad de Lisboa; PortugalFil: Mc Clenny, Elizabeth. University of California at Davis; Estados UnidosFil: Muszynski, Grzegorz. University of Liverpool; Reino Unido. Lawrence Bekeley National Laboratory; Estados UnidosFil: Nguyen, Phu Dinh. University of California at Irvine; Estados UnidosFil: Prabhat, Mr.. Lawrence Bekeley National Laboratory; Estados UnidosFil: Qian, Yun. Pacific Northwest National Laboratory; Estados UnidosFil: Ramos, Alexandre M.. Universidade Nova de Lisboa; PortugalFil: Sarangi, Chandan. Pacific Northwest National Laboratory; Estados UnidosFil: Viale, Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin

Towards a topological pattern detection in fluid and climate simulation data

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

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project Goals and Experimental Design

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 MERRA-2 reanalysis from January 1980 to June of 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