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

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Avances en la regionalización estadística de escenarios de cambio climático para precipitación basados en técnicas de aprendizaje automático

A pesar de ser la principal herramienta para estudiar el cambio climático, los modelos globales de clima (GCM) siguen teniendo una resolución espacial limitada y presentan errores sistemáticos considerables con respecto al clima observado. La regionalización estadística pretende resolver este problema aprendiendo relaciones empíricas entre variables de larga escala, bien reproducidas por los GCM (por ejemplo, los vientos sinópticos o el geopotencial), y observaciones locales de la variable en superficie de interés, como la precipitación, objeto de esta tesis. Proponemos una serie de desarrollos novedosos que permiten mejorar la consistencia de los campos regionalizados y producir escenarios regionales plausibles de cambio climático. Los resultados de esta tesis tienen importantes implicaciones para los diferentes sectores que necesitan información fiable de precipitación para llevar a cabo sus evaluaciones de impactos.Even though they are the main tool to study climate change, global climate models (GCMs) still have a limited spatial resolution and exhibit considerable systematic errors with respect to the observed climate. Statistical downscaling aims to solve this issue by learning empirical relationships between large-scale variables, well reproduced by GCMs (such as synoptic winds or geopotential), and local observations of the target surface variable, such as precipitation, the focus of this thesis. We propose a series of novel developments which allow for improving the consistency of the downscaled fields and producing plausible local-to-regional climate change scenarios. The results of this thesis have important implications for the different sectors in need of reliable precipitation information to undertake their impact assessments

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

Climate change 2013: the physical science basis

This report argues that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. This is an an unedited version of the Intergovernmental Panel on Climate Change\u27s Working Group I contribution to the Fifth Assessment Report following the release of its Summary for Policymakers on 27 September 2013.  The full Report is posted in the version distributed to governments on 7 June 2013 and accepted by Working Group I and the Panel on 27 September 2013. It includes the Technical Summary, 14 chapters and an Atlas of Global and Regional Climate Projections. Following copy-editing, layout, final checks for errors and adjustments for changes in the Summary for Policymakers, the full Report will be published online in January 2014 and in book form by Cambridge University Press a few months later

Indirect impact of landslide hazards on transportation infrastructure

This thesis examines the indirect impact of natural hazards on infrastructure networks. It addresses several key themes and issues for hazard assessment, network modelling and risk assessment using the case study of landslides impacting the national road network in Scotland, United Kingdom. The research follows four distinct stages. First, a landslide susceptibility model is developed using a database of landslide occurrences, spatial data sets and logistic regression. The model outputs indicate the terrain characteristics that are associated with increased landslide potential, including critical slope angles and south westerly aspects associated with increased rates of solar irradiance and precipitation. The results identify the hillslopes and road segments that are most prone to disruption by landslides and these indicate that 40 % (1,700 / 4,300 km) of Scotland s motorways and arterial roads (i.e. strategic road network) are susceptible to landslides and this is above previous assessments. Second, a novel user-equilibrium traffic model is developed using UK Census origin-destination tables. The traffic model calculates the additional travel time and cost (i.e. indirect impacts) caused by network disruptions due to landslide events. The model is applied to calculate the impact of historic scenarios and for sets of plausible landslide events generated using the landslide susceptibility model. Impact assessments for historic scenarios are 29 to 83 % greater than previous, including £1.2 million of indirect impacts over 15 days of disruption at the A83 Rest and Be Thankful landslide October 2007. The model results indicate that the average impact of landslides is £64 k per day of disruption, and up to £130 k per day on the most critical road segments in Scotland. In addition to identifying critical road segments with both high impact and high susceptibility to landslides, the study indicates that the impact of landslides is concentrated away from urban centres to the central and north-west regions of Scotland that are heavily reliant on road and haulage-based industries such as seasonal tourism, agriculture and craft distilling. The third research element is the development of landslide initiation thresholds using weather radar data. The thresholds classify the rainfall conditions that are most commonly associated with landslide occurrence in Scotland, improving knowledge of the physical initiation processes and their likelihood. The thresholds are developed using a novel optimal-point threshold selection technique, high resolution radar and new rain variables that provide spatio-temporally normalised thresholds. The thresholds highlight the role of the 12-day antecedent hydrological condition of soils as a precursory factor in controlling the rain conditions that trigger landslides. The new results also support the observation that landslides occur more frequently in the UK during the early autumn and winter seasons when sequences or clustering of multiple cyclonic-storm systems is common in periods lasting 5 to 15 days. Fourth, the three previous elements are combined to evaluate the landslide hazard of the strategic road segments and a prototype risk assessment model is produced - a catastrophe model. The catastrophe model calculates the annual average loss and aggregated exceedance probability of losses due to the indirect impact of landslides in Scotland. Beyond application to cost-benefit analyses for landslide mitigation efforts, the catastrophe model framework is applicable to the study of other natural hazards (e.g. flooding), combinations of hazards, and other infrastructure networks