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Testing Methods of Pattern Extraction for Climate Data Using Synthetic Modes

JOURNAL OF CLIMATE 2021年第18期34卷 7645-7660页

作者： Fulton, D. James Hegerl, Gabriele C. Univ Edinburgh Sch GeoSci Edinburgh Midlothian Scotland

In this paper we develop a method to quantify the accuracy of different pattern extraction techniques for the additive space-time modes often assumed to be present in climate data. It has previously been shown that the standard technique of principal component analysis (PCA;also known as empirical orthogonal functions) may extract patterns that are not physically meaningful. Here we analyze two modern pattern extraction methods, namely dynamical mode decomposition (DMD) and slow feature analysis (SFA), in comparison with PCA. We develop a Monte Carlo method to generate synthetic additive modes that mimic the properties of climate modes described in the literature. The datasets composed of these generated modes do not satisfy the assumptions of any pattern extraction method presented. We find that both alternative methods significantly outperform PCA in extracting local and global modes in the synthetic data. These techniques had a higher mean accuracy across modes in 60 out of 60 mixed synthetic climates, with SFA slightly outperforming DMD. We show that in the majority of simple cases PCA extracts modes that are not significantly better than a random guess. Finally, when applied to real climate data these alternative techniques extract a more coherent and less noisy global warming signal, as well as an El Nino signal with a clearer spectral peak in the time series, and more a physically plausible spatial pattern.

关键词： Algorithms Data mining Empirical orthogonal functions Pattern detection Principal components analysis Statistical techniques Climate variability Data science Dimensionality reduction model interpretation and visualization Pattern recognition

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Learning Forecasts of Rare Stratospheric Transitions from Short Simulations

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MONTHLY WEATHER REVIEW 2021年第11期149卷 3647-3669页

作者： Finkel, Justin Webber, Robert J. Gerber, Edwin P. Abbot, Dorian S. Weare, Jonathan Univ Chicago Comm Computat & Appl Math Chicago IL 60637 USA CALTECH Dept Comp & Math Sci Pasadena CA 91125 USA NYU Courant Inst Math Sci New York NY USA Univ Chicago Dept Geophys Sci 5734 S Ellis Ave Chicago IL 60637 USA

Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event's progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.

关键词： Stratospheric circulation Extreme events Stratosphere Classification Differential equations Regression analysis Risk assessment Statistical techniques Statistics Uncertainty Diagnostics Nonlinear models Parameterization Stochastic models Anomalies Internal variability Intraseasonal variability Clustering model interpretation and visualization Other artificial intelligence/machine learning

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A Machine Learning Approach for Classifying Bird and Insect Radar Echoes with S-Band Polarimetric Weather Radar

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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 2021年第10期38卷 1797-1812页

作者： Jatau, Precious Melnikov, Valery Yu, Tian-You Univ Oklahoma Adv Radar Res Ctr Norman OK 73019 USA Univ Oklahoma Cooperat Inst Mesoscale Meteorol Studies Norman OK 73019 USA Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA Natl Severe Storms Lab Norman OK 73069 USA Univ Oklahoma Sch Meteorol Norman OK 73019 USA

The S-band WSR-88D is sensitive enough to observe biological scatterers like birds and insects. However, their nonspherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (insect) features by coherently averaging dual-polarization measurements from multiple radar scans containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual-polarization inputs and then different combinations of the remaining features are added. The performance of all models for both methods are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration, and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual-polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the U.S. Next Generation Weather Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing hydrometeor classification algorithm (HCA), where the HCA would first separate biological from nonbiological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train amachine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual-polarization variables at each range gate.

关键词： Algorithms Data processing Data quality control Remote sensing Weather radar signal processing Artificial intelligence Classification Data science Decision trees Machine learning model interpretation and visualization Neural networks Pattern recognition

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Evaluating Foehn Occurrence in a Changing Climate Based on Reanalysis and Climate model Data Using Machine Learning

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WEATHER AND FORECASTING 2021年第6期36卷 2039-2055页

作者： Mony, Christoph Jansing, Lukas Sprenger, Michael Swiss Fed Inst Technol Inst Atmospher & Climate Sci Zurich Switzerland

This study explores the possibilities of employing machine learning algorithms to predict foehn occurrence in Switzerland at a north Alpine (Altdorf) and south Alpine (Lugano) station from its synoptic fingerprint in reanalysis data and climate simulations. This allows for an investigation on a potential future shift in monthly foehn frequencies. First, inputs from various atmospheric fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) were used to train an XGBoost model. Here, similar predictive performance to previous work was achieved, showing that foehn can accurately be diagnosed from the coarse synoptic situation. In the next step, the algorithm was generalized to predict foehn based on the Community Earth System model (CESM) ensemble simulations of a present-day and warming future climate. The best generalization between ERAI and CESM was obtained by including the present-day data in the training procedure and simultaneously optimizing two objective functions, namely, the negative log loss and squared mean loss, on both datasets, respectively. It is demonstrated that the same synoptic fingerprint can be identified in CESM climate simulation data. Finally, predictions for present-day and future simulations were verified and compared for statistical significance. Our model is shown to produce valid output for most months, revealing that south foehn in Altdorf is expected to become more common during spring, while north foehn in Lugano is expected to become more common during summer.

关键词： Downslope winds Climate prediction Ensembles Forecast verification/skill Forecasting techniques Numerical weather prediction/forecasting Probability forecasts/models/distribution Statistical forecasting Classification Data science Machine learning model interpretation and visualization

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Interactive 3D Visual Analysis of Weather Prediction Data Reveals Midlatitude Overshooting Convection during the CIRRUS-HL Field Experiment

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第8期104卷 E1426-E1434页

作者： Schaefler, Andreas Rautenhaus, Marc Deutsch Zentrum Luft & Raumfahrt Inst Phys Atmosphare Oberpfaffenhofen Germany Univ Hamburg Reg Comp Ctr Visual Data Anal Grp Hamburg Germany

In summer 2021, microphysical properties and climate impact of high-and midlati-tude ice clouds over Europe and the North Atlantic were studied during the Cirrus High Latitude (CIRRUS-HL) airborne field campaign. The related forecasting and flight planning tasks provided a testbed for interactive 3D visual analysis. Operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) were visualized with the open-source software "Met.3D." A combination of traditional 2D displays with innovative 3D views in the interactive visualization framework facilitated rapid and comprehensive exploration of the NWP data. By this means, the benefit of interactive 3D visual forecast products in the routine flight planning procedure was evaluated. Here, we describe the use of 3D tropopause and cloud visual-izations during a convective event over the Alps, which became one of the CIRRUS-HL observation targets. For the planning of the research flight on 8 July 2021, our analysis revealed that simulated strong convective updrafts locally disturb the tropopause and inject ice water across the dynamical tropopause into the lower stratosphere. The presented example provides a novel 3D perspective of convective overshooting in a global NWP model and its impact on the tropopause and lower stratosphere. The case study shall encourage the atmospheric science community to further evalu-ate the use of modern 3D visualization capabilities for NWP analysis.

关键词： Convective clouds Stratosphere Tropopause model interpretation and visualization Forecasting techniques Field experiments

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A Weather-Prediction Verification Tool for Effective Product Development

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第9期104卷 E1610-E1618页

作者： Nipen, Thomas N. Stull, Roland B. Lussana, Cristian Seierstad, Ivar A. Norwegian Meteorol Inst Oslo Norway Univ British Columbia Vancouver BC Canada

Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input files containing predictions and observations for multiple point-locations, forecast lead times, and forecast initialization times. It supports over 90 verification metrics and diagrams and can evaluate deterministic and probabilistic predictions. An extensive set of command-line flags control how the input data are aggregated, filtered, stratified, and visualized. The broad range of metrics and data manipulation options allows the user to gain insight from both summary scores and detailed time series of individual weather events. Verif is suitable for many applications, including assessing numerical weather prediction models, climate models, reanalyses, machine learning models, and even the fidelity of emerging observational sources. The tool has matured through long-term development at the Norwegian Meteorological Institute and the University of British Columbia. Verif comes with an extensive wiki page and example input files covering a wide range of prediction applications, allowing students and researchers interested in verification to get hands-on experience with real-life datasets. This article describes the functionality of Verif version 1.3 and shows how the tool can be used for effective product development.

关键词： Forecast verification/skill Forecasting Statistical forecasting Software Data science model interpretation and visualization

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An Iterative Approach toward Development of Ensemble visualization Techniques for High-Impact Winter Weather Hazards

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第9期104卷 E1649-E1669页

作者： Radford, Jacob T. Lackmann, Gary M. Goodwin, Jean Correia Jr, James Harnos, Kirstin North Carolina State Univ Dept Marine Earth & Atmospher Sci Raleigh NC 27695 USA Colorado State Univ Cooperat Inst Res Atmosphere Ft Collins CO 80523 USA North Carolina State Univ Dept Commun Raleigh NC USA NOAA Natl Weather Serv Natl Ctr Environm Predict Weather Predict Ctr College Pk MD USA Univ Colorado Cooperat Inst Res Environm Sci Boulder CO USA

We developed five prototype convection-allowing model ensemble visualization products with the goal of improving depictions of the timing of winter weather hazards. These products are interactive, web-based plots visualizing probabilistic onset times and durations of intense snowfall rates, probabilities of heavy snow at rush hour, periods of heightened impacts, and mesoscale snowband probabilities. Prototypes were evaluated in three experimental groups coordinated by the Weather Prediction Center (WPC) Hydrometeorological Testbed (HMT), with a total of 53 National Weather Service (NWS) forecasters. Forecasters were asked to complete a simple forecast exercise for a snowfall event, with a control group using the Storm Prediction Center's (SPC) High-Resolution Ensemble Forecast (HREF) system viewer, and an experimental group using both the HREF viewer and the five experimental graphics. Forecast accuracy was similar between the groups, but the experimental group exhibited smaller mean absolute error for snowfall duration forecasts. A series of Likert-scale questions saw participants respond favorably to all of the products and indicated that they would use them in operational forecasts and in communicating information to core partners. Forecasters also felt that the new products improved their comprehension of ensemble spread and reduced the time required to complete the forecasting exercise. Follow-up plenary discussions reiterated that there is a high demand for ensemble products of this type, though a number of potential improvements, such as greater customizability, were suggested. Ultimately, we demonstrated that social science methods can be effectively employed in the atmospheric sciences to yield improved visualization products.

关键词： Uncertainty Ensembles Forecasting techniques Probability forecasts/models/ distribution Risk assessment model interpretation and visualization

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Users' Cognitive Load A Key Aspect to Successfully Communicate Visual Climate Information

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2022年第1期103卷 E1-E16页

作者： Calvo, Luz Christel, Isadora Terrado, Marta Cucchietti, Fernando Perez-Montoro, Mario Barcelona Supercomp Ctr Barcelona Spain Univ Barcelona Fac Informat & Audiovisual Media Barcelona Spain

The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction, and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users' experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort, and comments, among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users' response time by half, significantly improved success ratios, and eased decision-making by filtering nonrelevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and nontechnical audiences.

关键词： Forecasting Seasonal forecasting Decision support Software model interpretation and visualization

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Edge Detection Reveals Abrupt and Extreme Climate Events

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JOURNAL OF CLIMATE 2020年第15期33卷 6399-6421页

作者： Bathiany, Sebastian Hidding, Johan Scheffer, Marten Wageningen Univ & Res Wageningen Netherlands Netherlands eSci Ctr Amsterdam Netherlands Helmholtz Zentrum Geesthacht Climate Serv Ctr Germany GERICS Hamburg Germany

The most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions ("extreme events'') or abrupt shifts to a new persistent climate state ("tipping points''). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edges in 2D images to many dimensions (including time). Our edge detector identifies abrupt or extreme climate events in spatiotemporal data, quantifies their abruptness (or extremeness), and provides diagnostics that help one to understand the causes of these shifts. We also publish a comprehensive toolset of code that is documented and free to use. We document the performance of the new edge detector by analyzing several datasets of observations and models. In particular, we apply it to all monthly 2D variables of the RCP8.5 scenario of the Coupled model Intercomparison Project (CMIP5). More than half of all simulations show abrupt shifts of more than 4 standard deviations on a time scale of 10 years. These shifts are mostly related to the loss of sea ice and permafrost in the Arctic. Our results demonstrate that the edge detector is particularly useful to scan large datasets in an efficient way, for example multimodel or perturbed-physics ensembles. It can thus help to reveal hidden "climate surprises'' and to assess the uncertainties of dangerous climate events.

关键词： Data mining Changepoint analysis Pattern detection Data science model interpretation and visualization

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Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 2020年第10期59卷 1671-1689页

作者： McNeely, Trey Lee, Ann B. Wood, Kimberly M. Hammerling, Dorit Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA Mississippi State Univ Dept Geosci Mississippi State MS 39762 USA Colorado Sch Mines Dept Appl Math & Stat Golden CO 80401 USA

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine-learning algorithms have limited applicability on this front because of their "black box" structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for overocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed feature suite targets the global organization, radial structure, and bulk morphology (ORB) of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine-learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (vs absence) of such intensity-change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine-learning methods did not perform better than the linear logistic lasso model for current data.

关键词： Tropical cyclones Satellite observations Classification Statistics model interpretation and visualization

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