Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 36 publications
Neural general circulation models for modeling precipitation
Stephan Hoyer
Dmitrii Kochkov
Janni Yuval
Ian Langmore
Science Advances (2026)
Preview abstract
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. While hybrid models combining machine learning and physics have emerged with the premise of improving precipitation simulations, none have proven sufficiently skillful or stable enough to outperform existing models in simulating precipitation.
Here, we present the first hybrid model that is trained directly on precipitation observations. The model runs at 2.8 degrees resolution and is built on the differentiable NeuralGCM framework. This model is stable for decadal simulations and demonstrates significant improvements over existing GCMs, ERA5 reanalysis, and a Global Cloud-Resolving Model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle.
Furthermore, it outperforms the ECMWF ensemble for mid-range weather forecasting.
This advance paves the way for more reliable simulations of current climate and for the ability to fully utilize the abundance of existing observations to further improve GCMs.
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Assessing Global Flood Risk from Atmospheric Rivers through Physically Guided Machine Learning
Assaf Shmuel
Oleg Zlydenko
Martin Gauch
Colin Price
Scientific Reports (2026)
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Atmospheric rivers (ARs) are narrow corridors of concentrated moisture transport that play a crucial role in the global water cycle, delivering both beneficial rainfall and severe floods. Here, we develop a physically guided, explainable machine learning framework to predict flood occurrence during AR conditions worldwide by integrating AR characteristics with meteorological and topographic variables. The best model achieves a receiver operating characteristic area under the curve (ROC AUC) of 0.94, outperforming a logistic regression baseline at 0.81. Despite their limited footprint, we find that one third of large midlatitude floods occur under AR conditions, reflecting their disproportionate role in global flood risk. SHAP analysis highlights integrated vapor transport, precipitation, and elevation as dominant predictors. We show the non-linear amplification of flood risk under combined conditions of high soil moisture and persistent AR activity, underscoring the importance of antecedent wetness in modulating flood risk. Using consistent reanalysis inputs, we find that model-estimated high-flood-risk AR conditions increased globally by over 10% from 1980 to 2020 and shifted poleward. Validation on an independent satellite-based flood database shows comparable skill. We estimate that roughly 90% of the global population lives in regions that experience at least one AR annually, underscoring the broad societal relevance of AR dynamics. These findings highlight the value of physically guided machine learning for mapping and monitoring AR-related flood risk globally, offering actionable insights for preparedness and climate adaptation.
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Preview abstract
The need for characterizing global variability of atmospheric carbon dioxide (CO2) is quickly increasing, with a growing urgency for tracking greenhouse gasses with sufficient resolution, precision and accuracy so as to support independent verification of CO2 fluxes at local to global scales. The current generation of space-based sensors, however, can only provide sparse observations in space and/or in time, by design. While upcoming missions may address some of these challenges, most are still years away from launch. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths at an approximately 2km2 spatial resolution. Here, we leverage this high temporal resolution by developing a single-pixel, fully connected neural network to estimate dry-air column CO2 mole fractions (XCO2). The model employs a time series of GOES-East's 16 spectral bands, ECMWF ERA5 lower tropospheric meteorology, MODIS surface reflectance, solar angles, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations. The model is able to capture realistic XCO2 variability when compared against held-out years of OCO-2 observations and against observations from the TCCON network. We also present case studies illustrating the use of the model to observe XCO2 enhancements over urban areas, drawdown over agricultural regions, and enhanced ability to observe regions with persistent cloud cover thanks to GOES-East’s full spatial coverage and high observational frequency. Overall, although GOES-East derived XCO2 precision cannot rival that of dedicated instruments, its unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric CO2 variability currently unseen from space.
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Regional climate risk assessment from climate models using probabilistic machine learning
Rob Carver
Tapio Schneider
John Anderson
Fei Sha
Nature Machine Intelligence (2026)
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Accurate, actionable climate information at kilometer scales is crucial for robust natural hazard risk assessment and infrastructure planning. Simulating the impact of climate at these resolutions remains intractable, forcing reliance on downscaling, either physics-based or statistical methods which transform climate simulations from coarse to impact-relevant resolutions. It is essential to comprehensively capture the interdependency among climate processes of interest. However, current approaches, either lack the desired scalability or are bespoke to specific types of hazards. We introduce GenFocal, a step change in paradigm. GenFocal is a computationally efficient, general-purpose, end-to-end probabilistic, generative framework that gives rise to full probabilistic characterizations of complex climate processes interacting at fine spatiotemporal scales. When employed to assess risk in the present climate, GenFocal more faithfully projects the risk of extremes than state-of-the-art approaches including one recommended by the US Fifth National Climate Assessment. GenFocal produces plausible tracks of tropical cyclones, providing accurate statistics of their genesis and evolution, even when they are absent from the corresponding climate simulations. GenFocal also shows compelling results that are consistent with the literature on projecting climate impact in an extended time frame (five decades to one century). In short, GenFocal transforms the numerical computation into statistical knowledge bases that can be efficiently queried, thus opening up new possibilities of assessing the impacts of future climates at the spatio-temporal scales relevant to local and regional communities. We believe this work, as a starting point, establishes generative AI technology as an effective and potent paradigm for modeling complex, high-dimensional multivariate statistical correlations that have deterred precise quantification of climate risks associated with hazards such as wildfires, extreme heat, tropical cyclones, and flooding; thereby enabling the evaluation of adaptation strategies for affected populations and the built environment.
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Identification of camera trap images by artificial intelligence and human experts produces similar multi-species occupancy models
Daniel Thornton
Travis King
Lucy Perera-Romero
Alissa Anderson
Rony Garcia-Anleu
Scott Fitkin
Carly Vynne
Journal of Applied Ecology (2026)
Preview abstract
The use of camera traps in ecology and conservation has expanded rapidly, but the time spent to accurately identify species in camera trap images remains a fundamental challenge that limits project scope and impact. Although artificial intelligence (AI) is often used to speed up image processing, a human review step is still standard practice to arrive at final species identifications. A potentially transformative next step is thus to remove humans entirely from the analysis chain and still produce accurate statistical models for subsequent inference across a wide diversity of species and regions.
Here, we compare the output of Bayesian multi-species occupancy models derived from a complete AI workflow (no human review of images) with a general species classifier (SpeciesNet) to those from an expert (human) workflow, using large-scale camera datasets from three study areas and two distinct and diverse mid-large mammal assemblages. We apply several pre- and post-processing steps to the AI workflow to improve model agreement. We perform a comprehensive model comparison, assessing agreement in identified species–environment relationships and rates of occupancy and detection, and similarity of spatial projections of occupancy.
We found that for most species of mammal, AI based models were remarkably similar to expert-based models, with some variability based on post-processing decisions. This agreement was robust, holding across multiple metrics of comparison (e.g. parameter estimates, precision, occupancy and detection rates), multiple study sites and at species and community levels. Similarity in model output occurred even in the presence of misclassification errors, suggesting our approach was resilient to some level of false negatives and positives. Substantial divergence in model output and subsequent inference, while rare, was most prevalent for rarely detected species.
Synthesis and applications: The use of a global AI classifier to identify species and reproducible pre- and post-processing decisions makes our approach broadly applicable and particularly beneficial for national and international monitoring programs that collect large amounts of photo data on threatened, at risk, or management sensitive species and wildlife communities. A fully automated workflow will allow such programs to progress more rapidly from photo collection to analysis, inference and decision-making.
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Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Michelangelo Conserva
Alex Wilson
Anna Michalak
Philip Brodrick
Andrew Thorpe
Proceedings of the National Academy of Sciences (PNAS) (2026)
Preview abstract
Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system inefficiencies. Global tracking with imaging spectroscopy is just becoming feasible, but still largely relies on manual tracking. Here we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that advances the state of the practice by directly utilizing the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach joins spectral information content - where the methane signature resides - with essential spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports quantification, plume delineation, and source localization, even for multiple overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Evaluation against synthetic observations confirms the model’s ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 79% of known hand-annotated NASA L2B plume complexes across a test set of 1084 EMIT granules, while identifying twice as many plausible plumes than identified by human analysts. Further validation against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the models efficacy at identifying previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can further limit false-positive rates. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring from labor-intensive workflows to a rapid, scalable paradigm for facility-level accountability.
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Observational constraints on net radiative forcing confirm aviation contrail warming
Aarón Sonabend
Joe Yue-Hei Ng
arXiv (2026)
Preview abstract
Contrail cirrus represents a critical component of aviation’s non-CO2 climate impact, but its net radiative forcing, the balance between longwave warming and shortwave cooling, remains poorly constrained by direct observations. As a result, current assessments rely almost exclusively on microphysical models such as CoCiP and global climate simulations. Existing empirical estimates are largely restricted to young, linear tracks, because satellite detection masks have a poor recall of contrails once they spread and merge with natural cirrus, leaving a structural gap in our understanding of long-lived, non-linear contrail cirrus. To address this we use a causal framework that isolates the net radiative contrail effect of flight traffic over the Americas. Building on recent progress that quantified the longwave warming contrail effect using advected flight paths as a proxy for contrails, we expand this continuous treatment approach to capture the highly skewed shortwave cooling impact, delivering a 12-hour lifespan net observational radiative forcing. Our analysis reveals a statistically significant net warming energy forcing of 33.7 (95% CI: 20.8, 47.8) GJ/km flown from April 2019 to April 2020, providing a large-scale empirical quantification of long contrail lifespan impact of the same order as, though somewhat larger than, previous bottom-up simulation estimates. This observational benchmark offers an independent line of evidence on the sign and magnitude of the climate impact of contrails.
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Improving reanalysis weather for contrail validation by incorporating satellite observations
Susanne Rohs
Roger Teoh
Journal of Environmentally Compatible Air Transport System (JECATS) (2026)
Preview abstract
Aviation-induced condensation trails (contrails) contribute significantly to anthropogenic radiative forcing. While navigational contrail avoidance has been proposed as a strategy to mitigate this climate impact, the operational viability of such maneuvers relies on the ability to verify their efficacy. Current verification methodologies often employ contrail models driven by reanalysis weather data; however, these assessments are limited by the variable fidelity of the underlying meteorological datasets. In this work, we address this uncertainty by leveraging satellite observations to refine reanalysis estimates for specific contrail events. We demonstrate that this approach significantly improves the agreement between reanalysis data and in-situ measurements obtained from the IAGOS program, thereby offering a more robust framework for evaluating avoidance strategies.
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Estimating high-resolution albedo for urban applications
Elizabeth Wesley
Salil Banerjee
Aniruddh Chennapragada
Kevin Crossan
Bryce Cronkite-Ratcliff
Ellie Delich
Tristan Goulden
Mansi Kansal
Eric Mackres
Yael Mayer
Rebecca Milman
Shravya Shetty
Charlotte Stanton
Wayne Sun
Lucy Hutyra
Nature Communications (2026)
Preview abstract
Extreme urban heat poses a growing threat to global health, yet a critical cooling intervention-reflective cool roofs-lacks the high-resolution data needed for building-level planning. While satellite-based albedo estimates are globally available, their coarse resolution cannot resolve individual rooftops, limiting targeted climate action. Here we show that fusing freely available 10-m Sentinel-2 imagery with high-resolution satellite data enables urban albedo mapping at 30-cm resolution. This approach combines the radiometric accuracy and global coverage of Sentinel-2 with the spatial detail of commercial imagery to produce actionable urban datasets. Validated against airborne hyperspectral measurements (root mean square error = 0.04), the method resolves albedo at individual building footprints across diverse urban environments. Applied to 12 global cities, our analysis shows that prioritizing large-footprint buildings for cool roof retrofits can reduce citywide temperatures by up to 0.5 °C. This work validates Sentinel-2 for city-scale albedo estimation and enables globally deployable, building-scale rooftop albedo mapping to support targeted cool roof interventions.
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Heterogeneous graph neural networks for species distribution modeling
Christine Kaeser-Chen
Keith Anderson
Michelangelo Conserva
Elise Kleeman
Maxim Neumann
Matt Overlan
Millie Chapman
Drew Purves
arxiv (2025)
Preview abstract
Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.
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Day-of-the-week Awareness in Time of Day Breakpoints for Traffic Light Plans
Ori Rottenstreich
Eliav Buchnik
Shai Ferster
Tom Kalvari
Ron Tsibulsky
Danny Veikherman
Jack Haddad
2025
Preview abstract
Time-of-day breakpoints (TODs) refer to the times over the day in which the plan of a traffic light is changed. Traditionally, TODs are selected jointly for all weekdays (Monday-Friday), typically with additional TODs dedicated to weekends. In this paper, we present an alternative approach motivated by traffic characteristics that can differ among the weekdays Monday-Friday and consider TODs which are day-of-the-week aware. The traffic-aware approach studies similarities among days and computes TODs that can be shared among days with similar characteristics but can also have other forms for weekdays with unique characteristics. Based on traffic properties derived from anonymized trajectories, we apply the new methodology to compute time-of-day breakpoints that are day-of-the-week aware in the city of Rio de Janeiro, Brazil and estimate the impact of the new methodology.
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Study of Arterials in the City of Rio de Janeiro for Traffic Coordination
Ori Rottenstreich
Eliav Buchnik
Danny Veikherman
Dan Karliner
Tom Kalvari
Shai Ferster
Ron Tsibulsky
Jack Haddad
2025
Preview abstract
Urban traffic congestion is a growing challenge, and optimizing signal timing strategies is crucial for improving traffic flow and reducing emissions. The coordination of signalized intersections improves both traffic operations and environmental aspects. Coordination is particularly important along arterials, sequences of signalized intersections that serve as the primary routes and carry a high volume of traffic. In this paper we analyze real data from the city of Rio de Janeiro to study properties of arterials. We refer to their length, the distance between intersections and to the properties of the traffic light plans such as cycle time. We then study their in practice level of coordination in terms of number of stops and their common locations along the arterials. We dive into particular arterials and provide insights that can be useful for efficient design of arterials in additional cities. Based on the analysis, we show how simple traffic properties can indicate the potential upon coordinating two adjacent intersections as part of an arterial in improving traffic performance.
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Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model
Gan Zhang
Megha Rao
Janni Yuval
Ming Zhao
Environmental Research Letters (2025)
Preview abstract
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 (r = ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks (p < 0.1) and basin-wide accumulated cyclone energy (ACE) (p < 0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions.
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Preview abstract
The need for characterizing global variability of atmospheric carbon dioxide (CO2) is quickly increasing, with a growing urgency for tracking greenhouse gasses with sufficient resolution, precision and accuracy so as to support independent verification of CO2 fluxes at local to global scales. The current generation of space-based sensors, however, can only provide sparse observations in space and/or in time, by design. While upcoming missions may address some of these challenges, most are still years away from launch. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability.
The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths. We leverage this high temporal resolution by developing a single-pixel, fully-connected neural network to estimate dry-air column CO2 mole fractions (XCO2). The model employs a time series of GOES-East's 16 spectral bands, which aids in disentangling atmospheric CO2 from surface reflectance, alongside ECMWF ERA5 lower tropospheric meteorology, solar angles, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations (2017-2020, within 5 km and 10 minutes), with validation and testing performed on 2021 data.
The model successfully captures monthly latitudinal XCO2 gradients and shows reasonable agreement with ground-based TCCON measurements. Furthermore, we demonstrate the model's ability to detect elevated XCO2 signals from high-emitting power plants, particularly over low-reflectance surfaces. We also confirm that removing bands 5 (1.6 µm) and 16 (13.3 µm) substantially decreases performance, indicating that the model is able to extract useful information from these bands.
Although GOES-East derived XCO2 precision may not rival dedicated instruments, its unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric CO2 variability currently unseen from space, with further potential through spatio-temporal aggregation.
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Toward Foundation Models for Mobility Enriched Geospatially Embedded Objects
Shang-Ling Hsu
Shushman Choudhury
Cyrus Shahabi
Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, ACM (2025)
Preview abstract
Recent advances in large foundation models (FMs) have enabled
learning general-purpose representations in natural language, vi-
sion, and audio. Yet geospatial artificial intelligence (GeoAI) still
lacks widely adopted foundation models that generalize across
tasks. We argue that a key bottleneck is the absence of unified,
general-purpose, and transferable representations for geospatially
embedded objects (GEOs). Such objects include points, polylines,
and polygons in geographic space, enriched with semantic context
and critical for geospatial reasoning. Much current GeoAI research
compares GEOs to tokens in language models, where patterns of
human movement and spatiotemporal interactions yield contextual
meaning similar to patterns of words in text. However, modeling
GEOs introduces challenges fundamentally different from language,
including spatial continuity, variable scale and resolution, temporal
dynamics, and data sparsity. Moreover, privacy constraints and
global variation in mobility further complicates modeling and gen-
eralization. This paper formalizes these challenges, identifies key
representational gaps, and outlines research directions for build-
ing foundation models that learn behavior-informed, transferable
representations of GEOs from large-scale human mobility data.
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