Extreme precipitation in Northern Italy

Weather prediction is a fundamental scientific challenge and crucial to society. Predicting extreme weather events is one of the outstanding achievements of science. Despite the enormous progress made by modern meteorology, the precise prediction of certain critical phenomena, like extreme precipitation, can still be uncertain even at shorter time ranges. This research aims to identify the relevant atmospheric processes for the formation of extreme precipitation. We investigate the relationship between the predictable large-scale dynamics that create the right conditions for the genesis of extreme events, and fast small-scale processes, such as convection, which rapidly destroy predictability and pose a challenge for a correct forecast. In the aim to identify common dynamical states, we designed a systematic investigation on extreme precipitation events (EPEs), based on a very large number of episodes (> 800), which occurred between 1979 and 2015 in northern-central Italy, used as a test region. Through the optimal blending of ECMWF reanalysis of meteorological fields and high resolution gridded daily precipitation (ARCIS), we classify, with a machine learning approach, extreme precipitation events into three categories (Cat1, Cat2, Cat3). The categories do not only differ locally, successfully reflecting the precipitation processes on the region (frontal and orographic precipitation, frontal precipitation and embedded deep convection, diurnal or weakly forced convection), but also in the dynamical evolution of their precursor: the upper-level wave, and the associated wave packet. So far, this is the first attempt to classify EPEs on physical processes and make connections with predictability. We show that EPEs falling in Cat1 and Cat2 are associated with upper-level wave packets propagating from remote regions, while for EPEs in Cat3 local instability is dominating. The strongest EPEs, mostly populating Cat2, are characterised by a recurrent dynamic evolution consisting of a substantial upstream wave amplification in the N. Atlantic, arguably due to diabatic heating sources. Cat2 events are more predictable than moderate events falling into the other two categories in the region under investigation. This original result has important practical implications. It shows that not all extreme precipitation events have the same level of predictability. The uncertainty does not depend on the intensity of the phenomenon but on the particular dynamic evolution.Die Wettervorhersage ist eine grundlegende wissenschaftliche Herausforderung und für die Gesellschaft von großer Bedeutung. Die Vorhersage von Extremwetterereignissen ist eine der herausragenden Leistungen der Wissenschaft. Trotz des enormen Gesamtfortschritts der modernen Meteorologie kann die präzise Vorhersage bestimmter kritischer Phänomene, wie z. B. extremer Niederschläge, auch bei kürzeren Vorhersagezeiträumen noch unsicher sein. Diese Forschung zielt darauf ab, die relevanten atmosphärischen Prozesse für die Bildung von Extremniederschlägen zu identifizieren. Wir untersuchen die Beziehung zwischen vorhersagbarer großskaliger Dynamik, die die richtigen Bedingungen für die Bildung von Extremereignissen schafft, und schnellen kleinskaligen Prozessen, wie Konvektion, die die Vorhersagbarkeit schnell zerstören und eine Herausforderung für eine korrekte Vorhersage darstellen. Mit dem Ziel, gemeinsame dynamische Zustände zu identifizieren, haben wir eine systematische Untersuchung vieler (> 800) extremer Niederschlagsereignisse (EPEs) entworfen, die zwischen 1979 und 2015 in Nord- und Mittelitalien. Durch die optimale Kombination von ECMWF-Reanalysen meteorologischer Felder und hochaufgelöstem, gerastertem Tagesniederschlag (ARCIS) klassifizieren wir mit einem maschinellen Lernansatz extreme Niederschlagsereignisse in drei Kategorien (Cat1, Cat2, Cat3). Die Kategorien unterscheiden sich nicht nur lokal und spiegeln erfolgreich die Niederschlagsprozesse in der Region wider (frontaler und orographischer Niederschlag, frontaler Niederschlag und eingebettete tiefe Konvektion, tageszeitliche oder schwach erzwungene Konvektion), sondern auch in der dynamischen Entwicklung ihres Vorläufers: der atmosphärische Rossby-Welle und des zugehörigen Wellenpakets. Bislang ist dies der erste Versuch, EPEs nach physikalischen Prozessen zu klassifizieren und mit der Vorhersagbarkeit in Verbindung zu bringen. Wir zeigen, dass EPEs, die in Cat1 und Cat2 fallen, mit Wellenpaketen aus der oberen Atmosphäre, assoziiert sind, die sich aus entfernten Regionen ausbreiten, während bei EPEs in Cat3 lokale Instabilität dominiert. Die stärksten EPEs, die meist in Cat2 fallen, sind durch eine wiederkehrende dynamische Entwicklung gekennzeichnet, die aus einer erheblichen stromaufwärts gerichteten Wellenverstärkung im Nordatlantik besteht, die vermutlich auf diabatische Heizquellen zurückzuführen ist. Cat2-Ereignisse sind in der untersuchten Region besser vorhersagbar als gemäßigte Ereignisse, die in die beiden anderen Kategorien fallen. Dieses Ergebnis hat wichtige praktische Implikationen. Es zeigt, dass nicht alle extremen Niederschlagsereignisse den gleichen Grad an Vorhersagbarkeit haben. Die Unsicherheit hängt nicht von der Intensität des Phänomens ab, sondern von der jeweiligen dynamischen Entwicklung.La previsione del tempo è una sfida scientifica cruciale per la nostra società. La previsione di eventi meteorologici estremi è una delle conquiste più importanti della scienza. Nonostante gli enormi progressi fatti dalla meteorologia moderna, la previsione precisa di alcuni fenomeni critici, come le precipitazioni estreme, può essere incerta anche a brevi intervalli temporali. Questa ricerca mira a identificare i processi atmosferici rilevanti per la formazione di precipitazioni estreme. In particolare, lo studio approfondisce la relazione tra le dinamiche prevedibili su larga scala che creano le giuste condizioni per la genesi di eventi estremi, e i processi veloci su piccola scala, come la convezione, che distruggono rapidamente la prevedibilità e rappresentano una sfida per una corretta previsione. Al fine di identificare gli stati dinamici comuni, abbiamo realizzato un'indagine sistematica sugli eventi estremi di precipitazione (EPEs), basata su un numero molto elevato di episodi (> 800), avvenuti tra il 1979 e il 2015 nell'Italia centro-settentrionale, utilizzata come regione test. Attraverso la fusione ottimale delle rianalisi ECMWF dei campi meteorologici e delle precipitazioni giornaliere del dataset ARCIS, classifichiamo, con un approccio di machine learning, gli eventi di precipitazione estrema in tre categorie (Cat1, Cat2, Cat3). Le categorie non differiscono solo localmente, riflettendo con successo i diversi processi che danno origine alla precipitazione sulla regione (precipitazione frontale e orografica, sinergia fra precipitazione frontale e convezione profonda, convezione diurna o debolmente forzata), ma anche nell'evoluzione dinamica del loro precursore: l'onda di Rossby e il pacchetto d'onda associato. Questo risulta essere il primo tentativo di classificare gli EPE e mettere in relazione la loro prevedibilità con la particolare evoluzione dinamica. Si osserva che gli EPE che cadono in Cat1 e Cat2 sono associati a pacchetti d'onda di Rossby che si propagano da regioni remote, mentre negli EPE in Cat3 domina l'instabilità locale. Gli EPE più forti, che popolano principalmente la Cat2, sono caratterizzati da un'evoluzione dinamica ricorrente che consiste in una sostanziale amplificazione di un'onda di Rossby nell'Atlantico settentrionale, probabilmente dovuta a fonti di riscaldamento diabatico. Gli eventi Cat2 sono più prevedibili degli eventi moderati che rientrano nelle altre due categorie nella regione in esame. Questo risultato, originale in letteratura, ha importanti implicazioni pratiche. Mostra che non tutti gli eventi di precipitazione estrema hanno lo stesso livello di prevedibilità. L'incertezza non dipende dall'intensità del fenomeno ma dalla particolare evoluzione dinamica

Predicting extreme events from data using deep machine learning : when and where

ACKNOWLEDGMENTS The work at Arizona State University was supported by AFOSR under Grant No. FA9550-21-1-0438 and by ONR under Grant No. N00014-21-1-2323. The work at Xi’an Jiaotong University was supported by the National Key R&D Program of China (Grant No. 2021ZD0201300), National Natural Science Foundation of China (Grant No. 11975178), and K. C. Wong Education Foundation.Peer reviewedPublisher PD

Predicting extreme events in a data-driven model of turbulent shear flow using an atlas of charts

Dynamical systems with extreme events are difficult to capture with data-driven modeling, due to the relative scarcity of data within extreme events compared to the typical dynamics of the system, and the strong dependence of the long-time occurrence of extreme events on short-time conditions.A recently developed technique [Floryan, D. & Graham, M. D. Data-driven discovery of intrinsic dynamics. Nat Mach Intell $\textbf{4}$, 1113-1120 (2022)], here denoted as $\textit{Charts and Atlases for Nonlinear Data-Driven Dynamics on Manifolds}$, or CANDyMan, overcomes these difficulties by decomposing the time series into separate charts based on data similarity, learning dynamical models on each chart via individual time-mapping neural networks, then stitching the charts together to create a single atlas to yield a global dynamical model. We apply CANDyMan to a nine-dimensional model of turbulent shear flow between infinite parallel free-slip walls under a sinusoidal body force [Moehlis, J., Faisst, H. & Eckhardt, B. A low-dimensional model for turbulent shear flows. New J Phys $\textbf{6}$, 56 (2004)], which undergoes extreme events in the form of intermittent quasi-laminarization and long-time full laminarization. We demonstrate that the CANDyMan method allows the trained dynamical models to more accurately forecast the evolution of the model coefficients, reducing the error in the predictions as the model evolves forward in time. The technique exhibits more accurate predictions of extreme events, capturing the frequency of quasi-laminarization events and predicting the time until full laminarization more accurately than a single neural network.Comment: 9 pages, 7 figure

Non-parametric Estimation of Stochastic Differential Equations with Sparse Gaussian Processes

The application of Stochastic Differential Equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a non-parametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudo-samples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behaviour of complex systems

Model-assisted deep learning of rare extreme events from partial observations

To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data is obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead we only use a small subset of observable quantities which can be measured in practice. We investigate the feasibility of this model-assisted framework on three different dynamical systems (Rossler attractor, FitzHugh-Nagumo model, and a turbulent fluid flow) and three different deep neural network architectures (feedforward, long short-term memory, and reservoir computing). In each case, we study the prediction accuracy, robustness to noise, reproducibility under repeated training, and sensitivity to the type of input data. In particular, we find long short-term memory networks to be most robust to noise and to yield relatively accurate predictions, while requiring minimal fine-tuning of the hyperparameters.Comment: Accepted for publication in Chaos: An Interdisciplinary Journal of Nonlinear Scienc

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

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Climate change and impacts in the urban systems

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsUrban systems are not only major drivers of climate change, but also impact hotspots. The processes of global warming and urban population growth make our urban agglomerations vulnerable to chain reactions triggered by climate related hazards. Hence, the reliable and cost-effective assessment of future climate impact is of high importance. Two major approaches emerge from the literature: i) detailed spatially explicit assessments, and ii) more holistic approaches consistently assessing multiple cities. In this multidisciplinary thesis both approaches were addressed. Firstly, we discuss the underlying reasons and main challenges of the applicability of downscaling procedures of climate projections in the process of urban planning. While the climate community has invested significant effort to provide downscaling techniques yielding localised information on future climate extreme events, these methods are not widely exploited in the process of urban planning. The first part of this research attempts to help bridge the gap between the communities of urban planners and climatologists. First, we summarize the rationale for such cooperation, supporting the argument that the spatial scale represents an important linkage between urban and climate science in the process of designing an urban space. Secondly, we introduce the main families of downscaling techniques and their application on climate projections, also providing the references to profound studies in the field. Thirdly, special attention is given to previous works focused on the utilization of downscaled ensembles of climate simulations in urban agglomerations. Finally, we identify three major challenges of the wider utilization of climate projections and downscaling techniques, namely: (i) the scale mismatch between data needs and data availability, (ii) the terminology, and (iii) the IT bottleneck. The practical implications of these issues are discussed in the context of urban studies. The second part of this work is devoted to the assessment of impacts of extreme temperatures across the European capital cities. In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more vulnerable to extreme heat waves. In the period 1950-2015 the occurrence of extreme heat waves increased across European capitals. As an example, Moscow was hit by the strongest heat wave of the present era, killing more than ten thousand people. Here we focus on larger metropolitan areas of European capitals. By using an ensemble of eight EURO-CORDEX models under the RCP8.5 scenario, we calculate a suite of temperature based climate indices. We introduce a ranking procedure based on ensemble predictions using the mean of metropolitan grid cells for each capital, and socio-economic variables as a proxy to quantify the future impact. Results show that all the investigated European metropolitan areas will be more vulnerable to extreme heat in the coming decades. Based on the impact ranking, the results reveal that in near, but mainly in distant future, the extreme heat events in European capitals will be not exclusive to traditionally exposed areas such as the Mediterranean and the Iberian Peninsula. Cold waves will represent some threat in mid of the century, but they are projected to completely vanish by the end of this century. The ranking of European capitals based on their vulnerability to the extreme heat could be of paramount importance to the decision makers in order to mitigate the heat related mortality. Such a simplistic but descriptive multi-risk urban indicator has two major uses. Firstly, it communicates the risk associated with climate change locally and in a simple way. By allowing to illustratively relate to situations of other capitals, it may help to engage not only scientists, but also the decision makers and general public, in efforts to combat climate change. Secondly, such an indicator can serve as a basis to decision making on European level, assisting with prioritizing the investments and other efforts in the adaptation strategy. Finally, this study transparently communicates the magnitude of future heat, and as such contributes to raise awareness about heat waves, since they are still often not perceived as a serious risk. Another contribution of this work to communication of consequences of changing climate is represented by the MetroHeat web tool, which provides an open data climate service for visualising and interacting with extreme temperature indices and heat wave indicators for European capitals. The target audience comprises climate impact researchers, intermediate organisations, societal-end users, and the general public