2023
Enrico Scoccimarro, Oreste Cattaneo, Silvio Gualdi, Francesco Mattion, Alexandre Bizeul, Arnau Martin Risquez & Roberta Quadrelli (2023) Communications Earth & Environment
Abstract
Cooling degree days provide a simple indicator to represent how temperature drives energy demand for cooling. We investigate, at country level, the changes in cooling degree days worldwide in a recent twenty-one-year period starting in 2000. A new database, jointly generated by CMCC and IEA based on ERA5 reanalysis’ global gridded data, is used for the analysis. In contrast to the existent literature, the factors of population-weighting and humidity are considered, which affect the magnitude and the spatial distribution of these changes. Annual tendencies show a general increase of cooling degree days over the different countries, fostering more energy consumption for cooling demand, as confirmed by some regional studies. We also focus on the temporal clustering, to measure if peaks occur evenly random or tend to cluster in shorter periods. We stress that including humidity is important both for general tendencies and clustering. India, Cambodia, Thailand and Vietnam represent the emerging countries where this effect is stronger.
Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dušan Fister, David Barriopedro, Ricardo García-Herrera, Matteo Giuliani & Andrea Castelletti
Abstract
Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn.
2022
Sušan Fister, Jorge Pérez-Aracil, César Peláez-Rodríguez, Javier Del Ser, Sancho Salcedo-Sanz (2022) Journal of Advances in Modelling Earth Systems
Abstract
In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and Córdoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and Córdoba regions.
Sušan Fister, Jorge Pérez-Aracil, César Peláez-Rodríguez, Javier Del Ser, Sancho Salcedo-Sanz (2022) Journal of Advances in Modelling Earth Systems
Abstract
In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and Córdoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and Córdoba regions.
C. Peláez-Rodríguez, J. Pérez-Aracil, D. Fister, L. Prieto-Godino, R.C. Deo, S. Salcedo-Sanz (2022) Renewable Energy
Abstract
A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors.
Antara Dasgupta, Louise Arnal, Rebecca Emerton, Shaun Harrigan, Gwyneth Matthews, Ameer Muhammad, Karen O’Regan, Teresa Pérez-Ciria, Emixi Valdez, Bart van Osnabrugge, Micha Werner, Carlo Buontempo, Hannah Cloke, Florian Pappenberger, Ilias G. Pechlivanidis, Christel Prudhomme, Maria-Helena Ramos, Peter Salamon, (2022) Journal of flood risk management
Abstract
The unprecedented progress in ensemble hydro-meteorological modelling and forecasting on a range of temporal and spatial scales, raises a variety of new challenges which formed the theme of the Joint Virtual Workshop, ‘Connecting global to local hydrological modelling and forecasting: challenges and scientific advances’. Held from 29 June to 1 July 2021, this workshop was co-organised by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Emergency Management (CEMS) and Climate Change (C3S) Services, the Hydrological Ensemble Prediction EXperiment (HEPEX), and the Global Flood Partnership (GFP). This article aims to summarise the state-of-the-art presented at the workshop and provide an early career perspective. Recent advances in hydrological modelling and forecasting, reflections on the use of forecasts for decision-making across scales, and means to minimise new barriers to communication in the virtual format are also discussed. Thematic foci of the workshop included hydrological model development and skill assessment, uncertainty communication, forecasts for early action, co-production of services and incorporation of local knowledge, Earth observation, and data assimilation. Connecting hydrological services to societal needs and local decision-making through effective communication, capacity-building and co-production was identified as critical. Multidisciplinary collaborations emerged as crucial to effectively bring newly developed tools to practice.
Antonio Manuel Gómez-Orellana, David Guijo-Rubio, Jorge Pérez-Aracil, Pedro Antonio Gutiérrez, Sancho Salcedo-Sanz, César Hervás-Martínez, (2022) Atmospheric research
Abstract
In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model.
N. Hempelmann, C. Ehbrecht, E. Plesiat, G. Hobona, J. Simoes, D. Huard, T. J. Smith, U. S. McKnight, I. G. Pechlivanidis, and C. Alvarez-Castro (2022) Journal of ISPRS Journal of Photogrammetry and Remote Sensing
Abstract
Recent advances in modelling capabilities and data processing combined with vastly improved observation tools and networks have resulted in the expansion of available weather and climate information, from historical observations to seasonal climate forecasts, as well as decadal climate predictions and multi-decadal climate change projections. However, it remains a key challenge to ensure this information reaches the intended climate-sensitive sectors (e.g. water, energy, agriculture, health), and is fit-for-purpose to guarantee the usability of climate information for these downstream users. Climate information can be produced on demand via climate resilience information systems which are existing in various forms. To optimise the efficiency and establish better information exchange between these systems, standardisation is necessary. Here, standards and deployment options are described for how scientific methods can be be deployed in climate resilience information systems, respecting the principles of being findable, accessible, interoperable and reusable. Besides the general description of OGC-API Standards and OGC-API Processes based on existing building blocks, ongoing developments in AI-enhanced services for climate services are described.
D1.1 Project Management Plan
D2.1 – Review of ML algorithms for climate science
D3.1 – Extreme events detections
D5.1 – Extreme events attribution
D6.1 – EU climate services on the impacts of extreme events
D7.1 – Local climate services
Extreme Events in Climate Resilience Information Systems architecture with AI
This is the first draft of the architecture of the prototype for Climate Resilience Information Systems, providing technical services to detect extreme events. The deployed application packages are running with AI components. The workflow on setting up a climate application package and deploying it into the architecture.
The entire architecture follows the FAIR principles to ensure interoperability with, for example, the COPERNICUS Climate Data Store (CDS) providing technical services for the Copernicus Climate Change Services (C3S). The sections are instructions, guidelines and backgrounds around OGC API based software which can be used to set up Climate Resilience Information Systems (CRIS).
The areas use the climate application package with the ‘inpainting process’, named Duck, as an example to guide through the different steps necessary to set up application packages for technical climate services.
The backend is deployed at the Climate Computing Center (DKRZ) running as a DEMO portal.