Classical approaches for flood prediction apply numerical methods for the solution of partial differential equations that capture the physics of inundation processes (e.g., the 2D Shallow Water equations). However, traditional inundation models are still unable to satisfy the requirements of many relevant applications, including early-warning systems, high-resolution (or large spatial domain) simulations, and robust inference over distributions of inputs (e.g., rainfall events). Machine learning (ML) approaches are a promising alternative to physics-based models due to their ability to efficiently capture correlations between relevant inputs and outputs in a data-driven fashion. In particular, once trained, ML models can be tested/deployed much more efficiently than classical approaches. Yet, few ML-based solutions for spatio-temporal flood prediction have been developed, and their reliability/accuracy is poorly understood. In this paper, we propose FloodGNN-GRU, a spatio-temporal flood prediction model that combines a graph neural network (GNN) and a gated recurrent unit (GRU) architecture. Compared to existing approaches, FloodGNN-GRU (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. We evaluate FloodGNN-GRU using a LISFLOOD-FP simulation of Hurricane Harvey (2017) in Houston, Texas. Our results, based on several metrics, show that FloodGNN-GRU outperforms several data-driven alternatives in terms of accuracy. Moreover, our approach can be trained 100x faster and tested 1000x faster than the time required to run a comparable simulation. These findings illustrate the potential of ML-based methods to efficiently emulate physics-based inundation models, especially for short-term predictions.

Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots, and carry out what-if analysis to reduce the carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hotspots, explainability, and what-if analysis to recommend measures to reduce the carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle-to-gate extent of the palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hotspot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield.

Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1) we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data, (2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension of one of the key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change.

In the context of the ongoing biodiversity crisis, understanding forest ecosystems, their tree species composition, and especially the successional stages of their development is crucial. They collectively shape the biodiversity within forests and thereby influence the ecosystem services that forests provide, yet this information is not readily available on a large scale. Remote sensing techniques offer promising solutions for obtaining area-wide information on tree species composition and their successional stages. While optical data are often freely available in appropriate quality over large scales, obtaining light detection and ranging (LiDAR) data, which provide valuable information about forest structure, is more challenging. LiDAR data are mostly acquired by public authorities across several years and therefore heterogeneous in quality. This study aims to assess if heterogeneous LiDAR data can support area-wide modeling of forest successional stages at the tree species group level. Different combinations of spectral satellite data (Sentinel-2) and heterogeneous airborne LiDAR data, collected by the federal government of Rhineland-Palatinate, Germany, were utilized to model up to three different successional stages of seven tree species groups. When incorporating heterogeneous LiDAR data into random forest models with spatial variable selection and spatial cross-validation, significant accuracy improvements of up to 0.23 were observed. This study shows the potential of not dismissing initially seemingly unusable heterogeneous LiDAR data for ecological studies. We advocate for a thorough examination to determine its usefulness for model enhancement. A practical application of this approach is demonstrated, in the context of mapping successional stages of tree species groups at a regional level.

Large-scale atmospheric circulation patterns, so-called weather regimes, modulate the occurrence of extreme events such as heatwaves or extreme precipitation. In their role as mediators between long-range teleconnections and local impacts, weather regimes have demonstrated potential in improving long-term climate projections as well as sub-seasonal to seasonal forecasts. However, existing methods for identifying weather regimes are not specifically designed to capture the relevant physical processes responsible for variations in the impact variable in question. This paper introduces a novel probabilistic machine learning method, RMM-VAE, for identifying weather regimes targeted to a local-scale impact variable. Based on a variational autoencoder architecture, the method combines non-linear dimensionality reduction with a prediction task and probabilistic clustering in one coherent architecture. The new method is applied to identify circulation patterns over the Mediterranean region targeted to precipitation over Morocco and compared to three existing approaches: two established linear methods and another machine-learning approach. The RMM-VAE method identifies regimes that are more predictive of the target variable compared to the two linear methods, both in terms of terciles and extremes in precipitation, while also improving the reconstruction of the input space. Further, the regimes identified by the RMM-VAE method are also more robust and persistent compared to the alternative machine learning method. The results demonstrate the potential benefit of the new method for use in various climate applications such as sub-seasonal forecasting, and illustrate the trade-offs involved in targeted clustering.

Due to the F2 ionospheric layer’s ability to reflect radio waves, the foF2 critical frequency is essential since sudden irregularities can disrupt communication and navigation systems, affecting the weather forecast’s accuracy. This paper aims to develop accurate foF2 critical frequency prediction up to 24 hours ahead, focusing on mid and high latitudes, using the long short-term memory (LSTM) model covering the 24th solar cycle from 2008 to 2019. To evaluate the effectiveness of the proposed model, a comparative analysis is conducted with commonly referenced machine learning techniques, including linear regression, decision tree algorithms, and multilayer perceptron (MLP) using the Taylor diagram and error plots. The study involved five monitoring stations, different years with minimum and maximum solar activity, and prediction timeframes. Through extensive experimentation, a comprehensive set of outcomes is evaluated across diverse metrics. The findings conclusively established that the LSTM model has demonstrated superior performance compared to the other models across all stations and years. On average, LSTM is 1.2 times better than the second-best model (DT), 1.6 times as effective as the multilayer perceptron MLP, and three times more accurate than linear regression. The results of this research hold promise for increasing the precision of foF2-prediction, with potential implications for enhancing communication systems and weather forecasting capabilities.

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

Snow is a crucial element of the sea ice system, affecting the sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have a very idealized representation of snow, often including just one-layer thermodynamics, omitting several processes that shape its properties. Even though sophisticated snow process models exist, they tend to be excluded in climate modeling due to their prohibitive computational costs. For example, SnowModel is a numerical snow process model developed to simulate the evolution of snow depth and density, blowing snow redistribution and sublimation, snow grain size, and thermal conductivity in a spatially distributed, multilayer snowpack framework. SnowModel can simulate snow distributions on sea ice floes in high spatial (1-m horizontal grid) and temporal (1-hour time step) resolution. However, for simulations spanning over large regions, such as the Arctic Ocean, high-resolution runs face challenges of slow processing speeds and the need for large computational resources. To address these common issues in high-resolution numerical modeling, data-driven emulators are often used. However, these emulators have their caveats, primarily a lack of generalizability and inconsistency with physical laws. In our study, we address these challenges by using a physics-guided approach in developing our emulator. By integrating physical laws that govern changes in snow density due to compaction, we aim to create an emulator that is efficient while also adhering to essential physical principles. We evaluated this approach by comparing three machine learning models: long short-term memory (LSTM), physics-guided LSTM, and Random Forest, across five distinct Arctic regions. Our evaluations indicate that all models achieved high accuracy, with the physics-guided LSTM model demonstrating the most promising results in terms of accuracy and generalizability. Our approach offers a computationally faster way to emulate the SnowModel with high fidelity and a speedup of over 9000 times.

Comprehensive housing stock information is crucial for informing the development of climate resilience strategies aiming to reduce the adverse impacts of extreme climate hazards in high-risk regions like the Caribbean. In this study, we propose an end-to-end workflow for rapidly generating critical baseline exposure data using very high-resolution drone imagery and deep learning techniques. Specifically, our work leverages the segment anything model (SAM) and convolutional neural networks (CNNs) to automate the generation of building footprints and roof classification maps. We evaluate the cross-country generalizability of the CNN models to determine how well models trained in one geographical context can be adapted to another. Finally, we discuss our initiatives for training and upskilling government staff, community mappers, and disaster responders in the use of geospatial technologies. Our work emphasizes the importance of local capacity building in the adoption of AI and Earth Observation for climate resilience in the Caribbean.

Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide. It catalyzes many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts, it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here, we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and the pollutant substance. We analyze a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($ NO $), nitrogen dioxide ($ {NO}_2 $), and particulate matter ($ {PM}_{10} $ and $ {PM}_{2.5} $) concentrations generically exhibit heavy tails. These are asymptotically well approximated by $ q $-exponential distributions with a given entropic index $ q $ and width parameter $ \lambda $. We observe that the power-law parameter $ q $ and the width parameter $ \lambda $ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters $ q $ and $ \lambda $ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to the properties of the geographical region. We also present results on typical time scales associated with the dynamical behavior.

Risk-based surveillance is now a well-established paradigm in epidemiology, involving the distribution of sampling efforts differentially in time, space, and within populations, based on multiple risk factors. To assess and map the risk of the presence of the bacterium Xylella fastidiosa, we have compiled a dataset that includes factors influencing plant development and thus the spread of such harmful organism. To this end, we have collected, preprocessed, and gathered information and data related to land types, soil compositions, and climatic conditions to predict and assess the probability of risk associated with X. fastidiosa in relation to environmental features. This resource can be of interest to researchers conducting analyses on X. fastidiosa and, more generally, to researchers working on geospatial modeling of risk related to plant infectious diseases.

The growing demand for global wind power production, driven by the critical need for sustainable energy sources, requires reliable estimation of wind speed vertical profiles for accurate wind power prediction and comprehensive wind turbine performance assessment. Traditional methods relying on empirical equations or similarity theory face challenges due to their restricted applicability beyond the surface layer. Although recent studies have utilized various machine learning techniques to vertically extrapolate wind speeds, they often focus on single levels and lack a holistic approach to predicting entire wind profiles. As an alternative, this study introduces a proof-of-concept methodology utilizing TabNet, an attention-based sequential deep learning model, to estimate wind speed vertical profiles from coarse-resolution meteorological features extracted from a reanalysis dataset. To ensure that the methodology is applicable across diverse datasets, Chebyshev polynomial approximation is employed to model the wind profiles. Trained on the meteorological features as inputs and the Chebyshev coefficients as targets, the TabNet more-or-less accurately predicts unseen wind profiles for different wind conditions, such as high shear, low shear/well-mixed, low-level jet, and high wind. Additionally, this methodology quantifies the correlation of wind profiles with prevailing atmospheric conditions through a systematic feature importance assessment.

Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, regions, and time periods, to generate 2 m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.

Edge AI is the fusion of edge computing and artificial intelligence (AI). It promises responsiveness, privacy preservation, and fault tolerance by moving parts of the AI workflow from centralized cloud data centers to geographically dispersed edge servers, which are located at the source of the data. The scale of edge AI can vary from simple data preprocessing tasks to the whole machine learning stack. However, most edge AI implementations so far are limited to urban areas, where the infrastructure is highly dependable. This work instead focuses on a class of applications involved in environmental monitoring in remote, rural areas such as forests and rivers. Such applications have additional challenges, including failure proneness and access to the electricity grid and communication networks. We propose neuromorphic computing as a promising solution to the energy, communication, and computation constraints in such scenarios and identify directions for future research in neuromorphic edge AI for rural environmental monitoring. Proposed directions are distributed model synchronization, edge-only learning, aerial networks, spiking neural networks, and sensor integration.

This article addresses the challenges of assessing pedestrian-level wind conditions in urban environments using a deep learning approach. The influence of large buildings on urban wind patterns has significant implications for thermal comfort, pollutant transport, pedestrian safety, and energy usage. Traditional methods, such as wind tunnel testing, are time-consuming and costly, leading to a growing interest in computational methods like computational fluid dynamics (CFD) simulations. However, CFD still requires a significant time investment for such studies, limiting the available time for design modification prior to lockdown. This study proposes a deep learning surrogate model based on a MLP-mixer architecture to predict mean flow conditions for complex arrays of buildings. The model is trained on a diverse dataset of synthetic geometries and corresponding CFD simulations, demonstrating its effectiveness in capturing intricate wind dynamics. The article discusses the model architecture and data preparation and evaluates its performance qualitatively and quantitatively. Results show promising capabilities in replicating key wind features with a mean error of 0.3 m/s and rarely exceeding 0.75 m/s, making the proposed model a valuable tool for early-stage urban wind modelling.

Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly disregard the dependence between consecutive time points. As a result, climate statistics with long-range temporal properties, such as the number of heatwaves and their frequency, cannot be corrected accurately. This makes it more difficult to produce reliable impact studies on such climate statistics. This article offers a novel BC methodology to correct temporal biases. This is made possible by rethinking the philosophy behind BC. We will introduce BC as a time-indexed regression task with stochastic outputs. Rethinking BC enables us to adapt state-of-the-art machine learning (ML) attention models and thereby learn different types of biases, including temporal asynchronicities. With a case study of number of heatwaves in Abuja, Nigeria and Tokyo, Japan, we show more accurate results than current climate model outputs and alternative BC methods.

Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.

Airborne radar sensors capture the profile of snow layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate their thicknesses, which are required to investigate the contribution of polar ice cap melt to sea-level rise. However, automatically processing the radar echograms to detect the underlying snow layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve snow layer detection. These architectures estimate the layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision, achieving higher generalizability as compared to state-of-the-art snow layer detection networks. These depth estimates also agree well with physically drilled stake measurements. Such robust architectures can be used on echograms from future missions to efficiently trace snow layers, estimate their individual thicknesses, and thus support sea-level rise projection models.

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits.