Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

Photovoltaic (PV) energy grows rapidly and is crucial for the decarbonization of electric systems. However, centralized registries recording the technical characteristics of rooftop PV systems are often missing, making it difficult to monitor this growth accurately. The lack of monitoring could threaten the integration of PV energy into the grid. To avoid this situation, remote sensing of rooftop PV systems using deep learning has emerged as a promising solution. However, existing techniques are not reliable enough to be used by public authorities or transmission system operators (TSOs) to construct up-to-date statistics on the rooftop PV fleet. The lack of reliability comes from deep learning models being sensitive to distribution shifts. This work comprehensively evaluates distribution shifts’ effects on the classification accuracy of deep learning models trained to detect rooftop PV panels on overhead imagery. We construct a benchmark to isolate the sources of distribution shifts and introduce a novel methodology that leverages explainable artificial intelligence (XAI) and decomposition of the input image and model’s decision regarding scales to understand how distribution shifts affect deep learning models. Finally, based on our analysis, we introduce a data augmentation technique designed to improve the robustness of deep learning classifiers under varying acquisition conditions. Our proposed approach outperforms competing methods and can close the gap with more demanding unsupervised domain adaptation methods. We discuss practical recommendations for mapping PV systems using overhead imagery and deep learning models.

Sustainable agricultural practices have become increasingly important due to growing environmental concerns and the urgent need to mitigate the climate crisis. Digital agriculture, through advanced data analysis frameworks, holds promise for promoting these practices. Pesticides are a common tool in agricultural pest control, which are key in ensuring food security but also significantly contribute to the climate crisis. To combat this, Integrated Pest Management (IPM) stands as a climate-smart alternative. We propose a causal and explainable framework for enhancing digital agriculture, using pest management and its sustainable alternative, IPM, as a key example to highlight the contributions of causality and explainability. Despite its potential, IPM faces low adoption rates due to farmers’ skepticism about its effectiveness. To address this challenge, we introduce an advanced data analysis framework tailored to enhance IPM adoption. Our framework provides (i) robust pest population predictions across diverse environments with invariant and causal learning, (ii) explainable pest presence predictions using transparent models, (iii) actionable advice through counterfactual explanations for in-season IPM interventions, (iv) field-specific treatment effect estimations, and (v) assessments of the effectiveness of our advice using causal inference. By incorporating these features, our study illustrates the potential of causality and explainability concepts to enhance digital agriculture regarding promoting climate-smart and sustainable agricultural practices, focusing on the specific case of pest management. In this case, our framework aims to alleviate skepticism and encourage wider adoption of IPM practices among policymakers, agricultural consultants, and farmers.

Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for generation dispatch are computationally intractable when costs are nonconvex, and machine learning-based approaches lack worst-case guarantees on their performance. In this work, we propose a learning-augmented algorithm, RobustML, that exploits the good average-case performance of a machine-learned algorithm for minimizing dispatch and ramping costs of dispatchable generation resources while providing provable worst-case guarantees on cost. We evaluate the algorithm on a realistic model of a combined cycle cogeneration plant, where it exhibits robustness to distribution shift while enabling improved efficiency as renewables penetration increases.

Peat is formed by the accumulation of organic material in water-saturated soils. Drainage of peatlands and peat extraction contribute to carbon emissions and biodiversity loss. Most peat extracted for commercial purposes is used for energy production or as a growing substrate. Many countries aim to reduce peat usage but this requires tools to detect its presence in substrates. We propose a decision support system based on deep learning to detect peat-specific testate amoeba in microscopy images. We identified six taxa that are peat-specific and frequent in European peatlands. The shells of two taxa (Archerella sp. and Amphitrema sp.) were well preserved in commercial substrate and can serve as indicators of peat presence. Images from surface and commercial samples were combined into a training set. A separate test set exclusively from commercial substrates was also defined. Both datasets were annotated and YOLOv8 models were trained to detect the shells. An ensemble of eight models was included in the decision support system. Test set performance (average precision) reached values above 0.8 for Archerella sp. and above 0.7 for Amphitrema sp. The system processes thousands of images within minutes and returns a concise list of crops of the most relevant shells. This allows a human operator to quickly make a final decision regarding peat presence. Our method enables the monitoring of peat presence in commercial substrates. It could be extended by including more species for applications in restoration ecology and paleoecology.

When using machine learning to model environmental systems, it is often a model’s ability to predict extreme behaviors that yields the highest practical value to policy makers. However, most existing error metrics used to evaluate the performance of environmental machine learning models weigh error equally across test data. Thus, routine performance is prioritized over a model’s ability to robustly quantify extreme behaviors. In this work, we present a new error metric, termed Reflective Error, which quantifies the degree at which our model error is distributed around our extremes, in contrast to existing model evaluation methods that aggregate error over all events. The suitability of our proposed metric is demonstrated on a real-world hydrological modeling problem, where extreme values are of particular concern.