An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive. The accurate detection and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes that rely on subjective thresholds. Machine Learning (ML) models can complement deterministic approaches due to their ability to capture the mapping between the input climatic drivers and the geographical position of the TC center from the available data. This study presents a ML ensemble approach for locating TC center coordinates, embedding both TC classification and localization in a single end-to-end learning task. The ensemble combines TC center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis were used for model training and testing jointly with the International Best Track Archive for Climate Stewardship records. Results showed that the ML approach is well-suited for TC detection providing good generalization capabilities on out of sample data. In particular, it was able to accurately detect lower TC categories than those used for training the models. On top of this, the ensemble approach was able to further improve TC localization performance with respect to single model TC center estimates, demonstrating the good capabilities of the proposed approach.Comment: 27 pages, 8 figures, 1 table, submitted to Journal of Advances in Modeling Earth System

Tropical cyclone intensity estimation through convolutional neural network transfer learning using two geostationary satellite datasets

Accurate prediction and monitoring of tropical cyclone (TC) intensity are crucial for saving lives, mitigating damages, and improving disaster response measures. In this study, we used a convolutional neural network (CNN) model to estimate TC intensity in the western North Pacific using Geo-KOMPSAT-2A (GK2A) satellite data. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from 6 h earlier, transfer learning reduced the RMSE by 5.5%. These results suggest that transfer learning may represent a new breakthrough in geostationary satellite image–based TC intensity estimation, for which continuous long-term data are not always available

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models, while computing forecasts in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that combining different data sources and distinct machine learning methodologies can lead to superior tropical cyclone forecasting. We hope that this work opens the door for further use of machine learning in meteorological forecasting.Comment: Under revision by the AMS' Weather and Forecasting journa

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques

Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible

Modelling tools to support the management of crown-of-thorns starfish (Acanthaster cf. solaris) on Australia's Great Barrier Reef

Samuel Matthews studied outbreaks of the crown-of-thorns starfish (COTS) on the Great Barrier Reef. He developed a number of modelling and simulation tools to help predict when and where COTS outbreaks occur. Government agencies are using his results and tools to improve how outbreaks of COTS are managed and controlled on the GBR

Factors Affecting Short-term Oxygen Variability in the Northern Gulf of Mexico Hypoxic Zone

Open-water continuous monitoring of DO concentrations at a single station (C6) in the Gulf of Mexico from 1989 to 2008 afforded an excellent opportunity to characterize short-term oxygen variability and to estimate the relative importance of key physical and biological factors controlling the development, persistence, and dissipation of hypoxia. I investigated temporal trends in three aspects of short-term DO variability: respiration rates (i.e., how quickly bottom waters become hypoxic), persistence of hypoxia, and the dissipation of hypoxia (i.e., re-aeration events). I identified the range of respiration rates present at the study site, and showed how these rates vary throughout the year and from year to year. Although a strong relationship between the persistence of hypoxia at station C6 and the areal extent of hypoxia in the NGOM was not present, both were statistically related to the monthly Mississippi River nitrogen flux. Using time-series analysis, I found no consistent periodicities in DO across the three depths (near-surface, mid-water-column, and near-bottom) or related to water levels (tides). I did find a diel signal in the DO that could be related to the diel pattern in available light for photosynthesis reaching the near-bottom. Percent of days exhibiting a diel pattern was 20 at the surface, 12 at the mid-water column, and 7 for the near-bottom measurements. Using regression trees and matching of the timing of events, I found that the density gradient was a good predictor of severe hypoxia (DO -1) and that 65% of the re-aeration events could be associated with wind stress events, cold fronts or tropical cyclones. These results suggest that continuous DO monitoring at a single location provides valuable information on short-term variability that will help in assessing the exposure and the resulting biological responses to hypoxia, in interpreting the possible variability around the annual hypoxia maps generated from the single, shelf-wide cruises, and as a basis for improving predictive models of hypoxia

The impact of ocean eddies on tropical cyclone intensity on a global scale and the role of boundary layer dynamics

Warm-core ocean eddies have been linked to the rapid intensification of tropical cyclones across the world’s oceans, from Hurricane Katrina in the Atlantic, to Supertyphoon Maemi in the Pacific and Cyclone Nargis in the Indian Ocean. Normally, sea-surface cooling induced by tropical cyclones acts as a brake on tropical cyclone intensity. By suppressing sea-surface cooling, warm-core eddies enable tropical cyclones to intensify further. Although coupled atmosphere-ocean forecasting models simulate sea-surface cooling, rapid intensification remains difficult to predict. A better understanding of the effect of warm-core eddies on tropical cyclone intensity is key to improving forecasts. In my thesis, I focus on the dynamics of the tropical cyclone boundary layer. Boundary layer dynamics influence the structure of the tropical cyclone circulation, which is inextricably linked to tropical cyclone intensity. While boundary layer dynamics are important for tropical cyclone intensity in atmosphere-only models, they have not been explored in coupled models. I address this gap by analysing a simulation of a tropical cyclone that decays due to sea-surface cooling and subsequently reintensifies when the sea-surface cooling is suppressed by a warm-core eddy. I find boundary layer dynamics play an important role modulating the decay and reintensification through changes in the structure of the tropical cyclone. Moreover, I find the differing rates of decay cannot be explained without boundary layer dynamics. These results underscore the importance of accurately representing boundary layer dynamics in coupled forecasting models and guide the choice of boundary layer parameterisations, which affect the ability of forecasting models to predict rapid intensification. In addition, I investigate the impact of warm-core and cold-core eddies on tropical cyclone intensity on a global scale with the first analysis of a global coupled simulation that resolves tropical cyclones and ocean eddies. I find encounters between tropical cyclones and ocean eddies are common. My analysis reveals that tropical cyclones that encounter warm-core eddies reach on average a higher peak intensity, whereas cold-core eddies do not have an effect on the average peak intensity. If these results prove to be robust, they point to a potential bias in future projections of tropical cyclone intensity, which are based on climate simulations that do not resolve ocean eddies. Disentangling the impact of ocean eddies in future eddy-resolving climate simulations will hinge on the sensitivity of tropical cyclone intensity to boundary layer schemes and our understanding of boundary layer dynamics

Deep learning for Digital Typhoon: Exploring a typhoon satellite image dataset using deep learning

Exploring Deep Learning techniques to provide Emergency response using Deep Learning TechniquesEfficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several different approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset "Digital Typhoon". We focus on intensity measurement and categorisation of different natural phenomena. Firstly, we build a classifier to differentiate natural tropical cyclones and extratropical cyclones and, secondly, we implement a regression model to estimate the centre pressure value of a typhoon. In addition, we also explore cleaning methodologies to ensure that the data used is reliable. The results obtained show that deep learning techniques can be effective under certain circumstances, providing reliable classification/regression models and feature extractors. More research to draw more conclusions and validate the obtained results is expected in the future.Els sistemes d'alerta ràpida poden ajudar en la gestió dels esdeveniments de desastres naturals, permetent una evacuació i administració dels recursos adequada. En aquest sentit s'han utilitzat diferentes tècniques per implementar sistemes d'alerta, com ara simulacions o models estadístics, tots ells basats en la recollida de dades meteorològiques. S'ha demostrat que les tècniques basades en dades són eficaces a l'hora de construir models estadístics, podent generalitzar-se a a noves dades. Motivat per això, en aquest treball, explorem l'ús de tècniques d'apre-nentatge profund (o \emph{deep learning}) aplicades a les imatges meteorològi-ques per satèl·lit de tifons del projecte "Digital Typhoon". Ens centrem en la mesura i la categorització de la intensitat de diferents fenòmens naturals. En primer lloc, construïm un classificador per diferenciar ciclons tropicals naturals i ciclons extratropicals i, en segon lloc, implementem un model de regressió per estimar el valor de pressió central d'un tifó. A més, també explorem metodologies de neteja per garantir que les dades utilitzades siguin fiables.   Els resultats obtinguts mostren que les tècniques d'aprenentatge profundes poden ser efectives en determinades circumstàncies, proporcionant models fiables de classificació/regressió i extractors de característiques. Es preveu que hi hagi més recerques per obtenir més conclusions i validar els resultats obtinguts en el futur