climate science

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using hand-crafted topographic and surface descriptors. We ask instead whether Earth observation foundation models can provide transferable subgrid surface representations for probabilistic weather downscaling. We augment a convolutional conditional neural process (ConvCNP) that downscales coarse ERA5 reanalysis fields at ~25 km resolution with a learned local surface descriptor, obtained by compressing a patch of TESSERA embeddings at 10 m resolution. Although these embeddings summarize annual surface conditions, they improve downscaling by encoding persistent surface properties that capture a location's departure from the coarse-grid atmospheric state. Across five climatically diverse regions, the embedding improves point and probabilistic skill at stations held out in both space and time, overall improving CRPS skill by 11.5% for 2 m temperature and 6.2% for 10 m wind speed relative to a topography-only ConvCNP baseline. A hand-crafted descriptor incorporating richer surface information than topography alone captures comparable persistent subgrid signal but yields far smaller predictive gains than the learned embedding representation. These improvements persist when forecasts from the Aurora AI model replace ERA5 reanalysis fields and when predicting at newly deployed weather station networks. To our knowledge, this is the first evidence that long-timescale Earth observation embeddings can support short-timescale weather downscaling where subgrid departures are systematically structured by persistent surface properties.

Improving precipitation forecasts in an AI weather model using observational data

Artificial intelligence weather prediction (AIWP) systems now surpass state-of-the-art physical models for medium-range weather forecasting. Current global AIWP models are trained almost exclusively using one reanalysis dataset, ERA5, but it has known biases, particularly for precipitation. Here we fine-tune a graph-transformer architecture with IMERG precipitation data at 0.25{\deg} resolution. The resulting model improves medium-range continuous ranked probability scores by up to 19%, while also demonstrating superior skill for tropical storms and drizzle events. Our model exceeds the Brier skill score of state-of-the-art operational models on extreme rainfall prediction by 57% globally; however, a physics-based operational model remains more reliable for the heaviest precipitation events. Our results demonstrate that incorporating observations-based precipitation data directly into training can substantially improve precipitation forecasts.

From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting methods can preserve observed cloud placement during the first forecast hours, but their skill decreases when cloud fields evolve through formation, dissipation and deformation. Longer lead times require accounting for atmospheric evolution, but operational numerical weather prediction (NWP) forecasts may not accurately represent the satellite-observed cloud state at initialization. We develop CloudCast v2, a machine-learning model for 12-hour cloud-cover forecasting from observation-based initial conditions. The model is first trained on the Copernicus European Regional Reanalysis (Ridal2024) to learn cloud-evolution dynamics, and is then adapted to satellite-derived cloud fields using conditional flow matching (Lipman2023), a generative method that transforms noise into cloud-cover forecasts conditioned on the observed initial cloud fields and NWP inputs. CloudCast v2 reduces mean absolute error by 10% relative to its predecessor, CloudCast v1 (Partio2025), over the 1-12 h range. It also overtakes CloudCast v1 in fractions skill score, a neighborhood-based measure of spatial agreement, after approximately 3-6 h, depending on the cloudiness category. These results show that observation-initialized machine-learning forecasts can extend beyond the usual 1-3-hour nowcasting range while retaining spatial detail from satellite cloud fields.

Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features using window-based cross-correlation and estimate heights via infrared brightness temperatures paired with numerical weather prediction (NWP) background states, creating a circular dependency that yields inaccurate heights, high computational cost, and sparse retrievals. Stereo winds from GEO-GEO and GEO-LEO geometrically resolve heights from parallax shifts across different poses, eliminating NWP dependence and improving accuracy, but they remain computationally heavy with limited coverage. In this work, we replace window-based tracking in stereo matching with deep optical flow for efficient, improved retrieval. Fine-tuning balances a self-supervised geometric residual loss with supervised radiosonde reconstruction. To eliminate multi-satellite overlap requirements, we distill the stereo teacher into a single-satellite student model. Chi-square and height uncertainties from the teacher are emulated by the student for quality assurance. The student generates winds across full-disk GEO imagery globally. Validation compares stereo and student models against radiosondes, operational AMVs, ERA5 reanalysis, and EarthCARE cloud profiles. Results through triple collocation show that stereo winds improve performance beyond operational AMVs for water vapor bands (6.2, 6.9, and 7.3 {\mu}m), wit degradation in the long-wave infrared (11.2 {\mu}m) band.

GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting

High-resolution precipitation nowcasting is critical for reducing the impacts of severe weather but remains difficult because of rapid storm evolution. Deep learning models have shown great promise for this task, but their predictive skill often deteriorates over longer forecast horizons. This leads to increasingly blurry forecasts that fail to capture the complex, non-linear evolution of storm systems. In order to address these limitations, we introduce Spatio-Temporal U-DeepONet (GenONet), a novel architecture for long-range precipitation forecasting up to 3 hours, specifically designed to produce sharp and physically consistent results. GenONet's architecture pioneers the use of a Deep Operator Network (DeepONet) as a generator within a Generative Adversarial Network (GAN) framework for this task. The DeepONet learns the continuous-time dynamics of precipitation, ensuring stability over long forecast horizons. Adversial training against a spatio-temporal discriminator compels the model to produce sharp, coherent forecasts, while a physics-informed loss regularizer, derived from the Moisture Conservation Equation, improves physical plausibility in our ablation setting. Quantitative evaluations show that our model achieves consistently higher scores on most of the metrics, especially for highintensity events and at longer lead times. Qualitatively, GenONet produces structurally coherent forecasts that maintain their integrity, whereas baseline models degrade into indistinct patterns. Finally, an ablation study confirms the benefit of this physics-informed loss, highlighting the strength of combining operator learning with adversarial training.

A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth pausing to consider what our design decisions imply for the science and for the computational resources we consume. A growing body of literature addresses the ethical and sustainable development of ML/AI, yet translating these principles into day-to-day research practice remains a challenge as most of best practices are dispersed across multiple studies and commentaries. Here, we distill these discussions into a practical checklist that ML/AI and Earth system science practitioners can use to assess and reduce the environmental footprint of their own applications, organised around the successive stages of the model development pipeline. We complement the checklist with a selection of metrics drawn from the literature for estimating the energy consumption and carbon footprint of a project. For each question, we point to concrete examples and actionable suggestions from recent literature, aiming to bridge the gap between aspirational principles and the decisions researchers face at every stage of the development cycle.

CDEP Agent: Connecting Meteorologically Detected Temporal Compound Events to Real-World Documentary Evidence

Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recognition carries over into real-world early warning and post-event documentation is unknown, so a meteorologically real CDEP event may pass with neither advance warning nor any later record. Here we present CDEP Agent, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions. Using California as a case study, we identify 408 candidate CDEP events from ERA5 observations during 2021-2025 and evaluate each against the U.S. Drought Monitor, NOAA Storm Events, and public webpages along five dimensions: antecedent drought, extreme rainfall, local impact, hazard-impact attribution, and explicit drought-to-rainfall linkage. Only 34.3% of candidates are corroborated on both hazard components, and just 1.5% are ever explicitly linked to their antecedent drought, indicating that most meteorologically detected CDEP events go undocumented and their compound nature almost never enters the record at all. Our framework gives climate scientists a way to test physical event definitions against what actually gets documented, and gives social scientists, economists, and disaster-response agencies a provenance-linked evidence base for compound events that current warning and reporting systems largely fail to capture.

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction

Accurate prediction of the peptide-protein interaction (PepPI) is crucial for developing peptide-based therapeutics and vaccines. However, this computational task has traditionally faced significant challenges, such as the scarcity of structure data along with the corresponding label of the binding affinity for bound complexes. To address these challenges, we introduce PepBAN, a deep learning framework for modeling PepPI predictions. PepBAN incorporates two technical advancements: (1) adopting the protein language model ESM-2 to characterize proteins and ESM-2 or a graph-based foundation model for peptides without structure data and (2) leveraging the conditional domain adversarial learning to enhance generalization across a broad range of protein targets, especially when there are limited binding data. At the core of PepBAN is a bilinear attention network (BAN) that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepPIs via analyzing attention weights. Our numerical experiments demonstrated that PepBAN outperformed the previous state-of-the-art models across several well-established benchmark studies. Furthermore, we evaluated PepBAN's applicability in predicting cyclic peptide-protein interactions, a task that poses significant challenges due to the presence of noncanonical amino acids. These nonstandard residues require specialized handling, which most existing sequence-based PepPI prediction models did not adequately address, and we adopt an atom-resolved molecular graph approach to process cyclic peptides. Despite this complexity, PepBAN demonstrated a clear advantage by achieving a superior prediction performance and offering a distinct edge in tackling the emerging chemical space of cyclic peptides, which has great potential for novel therapeutic development. In summary, PepBAN serves as a valuable tool for advancing peptide-based drug and therapeutic development.

SynGFN: learning across chemical space with generative flow-based molecular discovery

Allosteric Cooperativity Mechanism Investigation of Orthosteric and Allosteric Ligands in Modulating AR Activity: A Molecular Dynamics Study

The androgen receptor (AR) represents a pivotal therapeutic target for prostate cancer. However, existing orthosteric ligand-binding pocket (LBP) antagonists [e.g., enzalutamide (ENZ)] encounter significant obstacles due to resistance-conferring mutations in the LBP. Allosteric antagonists targeting the BF3 site exhibit great potential in overcoming such resistance but have low inhibitory efficacy. In our study, we employed an integrated computational modeling strategy, including Gaussian-accelerated molecular dynamics (GaMD), MM/GBSA free-energy calculations, and elastic network model (ENM)-based signaling communication pathway analyses. This approach is used to probe the cooperativity of allosteric BF3 antagonists [e.g., VPC-13808 (VPC)] with diverse orthosteric LBP ligands [e.g., ENZ and testosterone (TES)] in suppressing AR activity. Herein, four types of AR systems were examined: AR bound to LBP agonist (AR·TES), LBP antagonists (e.g., AR·ENZ), and combinations of LBP agonist/antagonist with BF3 antagonist (e.g., AR·TES·VPC and AR·ENZ·VPC). Results indicate that BF3 antagonists can synergize with the LBP antagonist to amplify conformational flexibility in H12 and induce anticorrelated dynamics of H12 with H3 and H4. This induces the downward movement of H12 and its displacement away from H3/H4, triggering the wide opening of the AF2 binding cleft and substantially reducing the coactivator recruitment. Furthermore, the BF3 antagonist can interact with specific residues (e.g., F673, F826, L830, and Y834) and cooperate with the LBP agonist or antagonist to allosterically perturb the AF2 conformation. Multiple short- and/or long-range BF3→AF2 and LBP→AF2 signaling transition pathways are involved, such as F673→Y834→L722→L812→L744→V746→L873→ENZ→L880/V889/V891. These mechanistic insights establish the foundation for developing novel AR BF3 antagonist and LBP-BF3 combination therapies, suggesting a promising avenue for enhancing the efficacy and overcoming the resistance in castration-resistant prostate cancer treatment.

ChargeNet: E(3) Equivariant Graph Attention Network for Atomic Charge Prediction

Atomic charge is a fundamental quantum chemical property essential for advancing drug design and discovery. Although quantum mechanics (QM) methods offer the highest level of accuracy, their computational demands scale quadratically with the number of atoms, limiting their practicality for large-scale applications. In light of this, empirical and semiempirical methods have been introduced to improve computational efficiency, albeit often at the expense of accuracy. The advent of artificial intelligence has witnessed a growing application of machine learning (ML) techniques to accelerate atomic charge predictions. However, existing ML models often suffer from low accuracy and limited generalization capabilities. To address these challenges, we introduce an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision. This model introduces a sophisticated global graph attention mechanism, enabling it to capture charge contributions across multiple scales. By utilizing a combination of structural symmetry-preserving transformations and multiscale attention, our approach not only preserves the inherent symmetries of molecular structures but also substantially improves the model's accuracy, generalization, and robustness in complex scenarios. Our empirical analyses demonstrate that, compared to leading baseline models, the proposed model improves charge prediction accuracy by over 40% on average across various charge-calculation schemes. Remarkably, the model achieves superior performance on the external RESP (restrained electrostatic potential) test data sets, with a 54.6% improvement over the baseline. Additionally, we evaluated our charge model under the setting of virtual screening, where it outperforms both the OPLS3 charges and baseline deep learning models across all evaluation metrics, highlighting its extensive potential for scientific discovery.

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

Accurate modeling of protein-peptide interactions is essential for understanding fundamental biological processes and designing peptide-based drugs. However, predicting the complex structures of these interactions remains challenging, primarily due to the high conformational flexibility of peptides. To support a fair and systematic evaluation of recent deep learning (DL) approaches, we introduce PepPCBench, a benchmarking framework tailored to assess protein folding neural networks (PFNNs) in protein-peptide complex prediction. As part of this framework, we curated PepPCSet, a data set of 261 experimentally resolved complexes with peptides ranging from 5 to 30 residues. We benchmark five full-atom PFNNs, including AlphaFold3 (AF3), AlphaFold-Multimer (AFM), Chai-1, HelixFold3 (HF3), and RoseTTAFold-All-Atom (RFAA), using comprehensive evaluation metrics. Our benchmarking reveals meaningful performance differences among these methods and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy. While AF3 shows strong performance in structure prediction, further analysis indicates that confidence metrics correlate poorly with experimental binding affinities, underscoring the need for improved scoring strategies and generalizability. By providing a reproducible and extensible framework, PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction.