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Welcome to the Group: Helena Soares Barros

Fri, 21 Aug 2026 00:00:00 +0000

We are excited to welcome Helena Soares Barros to the group as an incoming PhD student in the Department of Atmospheric and Oceanic Sciences at CU Boulder, starting Fall 2026!

Background

Helena joins us from Brown University, where she completed a Bachelor of Science in Applied Mathematics and Computer Science. While at Brown, she worked as a research assistant in the Department of Earth, Environmental and Planetary Sciences (DEEPS), contributing to research on precipitation.

Her combined background in applied math, computer science, and atmospheric research gives her a strong foundation for tackling problems at the intersection of data-driven methods and weather and climate prediction.

Looking Ahead

We’re looking forward to Helena bringing her quantitative and computational skills to the group’s research on weather and climate prediction. Welcome, Helena!

New Paper Published: Tim Higgins Leads Study Generating a 1,000-Member Diffusion Ensemble for Atmospheric River Forecasting

Thu, 20 Aug 2026 00:00:00 +0000

We are delighted to share that a new paper led by Dr. Timothy B. Higgins — a former PhD student in the group, now a postdoctoral research associate — has been published in the American Meteorological Society journal .

Generating a 1,000-Member Ensemble with Diffusion

The paper, “Generating a 1000-Member Ensemble of Integrated Water Vapor Transport Forecasts with Diffusion,” tackles a long-standing bottleneck in probabilistic weather prediction: traditional ensemble systems are so computationally expensive that operational centers can only afford to run a small number of perturbed simulations, limiting how well forecasters can characterize the risk of extreme events.

Tim and coauthors instead use diffusion, a form of generative AI, to expand a single deterministic forecast from the West Coast-focused West-WRF model into a full 1,000-member ensemble of integrated water vapor transport (IVT) over the North Pacific — at a fraction of the computational cost of a traditional dynamical ensemble.

Evaluated against binned rank histograms, continuous ranked probability score, spread-to-error ratio, reliability diagrams, and Brier skill scores, the diffusion ensemble outperforms operational ensembles from both the Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The authors also show that the ensemble produces realistic, high-quality representations of real extreme events — a promising sign for its use in operational risk assessment.

A Collaborative Effort

The study was coauthored by Dr. Timothy B. Higgins, William E. Chapman (NCAR), Dr. Aneesh Subramanian (University of Colorado Boulder), and Dr. Luca Delle Monache (CW3E / Scripps Institution of Oceanography), building on Tim’s dissertation work and his time in the Advanced Study Program at NCAR.

This publication formalizes the diffusion-based approach that already underpins the operational ensemble forecasting system now live at the Center for Western Weather and Water Extremes (CW3E) — see our about that launch.

View the Live Model Forecasts

Congratulations to Tim and the entire team on this well-earned publication!

Aneesh Subramanian Co-Directs FERS Summer School on AI and Machine Learning for Earth System Modeling

Mon, 15 Jun 2026 00:00:00 +0000

Prof. Aneesh Subramanian is serving as Co-Director — alongside Prof. Will Chapman (University of Colorado Boulder) — of the FERS Summer School on AI and Machine Learning for Earth System Modeling and Prediction, currently underway June 8–19, 2026 in Bertinoro, Italy. The school is organized by the CMCC Foundation’s Future Earth Research School (FERS).

About the School

The two-week intensive course brings together 25 early-career researchers — including PhD students, postdoctoral fellows, and early-career professionals — for 76 hours of instruction at the forefront of machine learning and AI applications in Earth system science.

Topics covered include:

Physics-informed and hybrid AI/climate models
Generative modeling for weather and climate
Uncertainty quantification and probabilistic forecasting
Causal inference in Earth science
Data management and high-performance computing workflows

The curriculum is designed to prepare the next generation of scientists to work across the boundary of traditional numerical modeling and modern machine learning — a skill set increasingly central to operational forecasting, climate projection, and scientific discovery.

Faculty

The school brings together an internationally diverse group of scientists and practitioners:

Faculty Member	Institution
Will Chapman	University of Colorado Boulder
Tom Beucler	University of Lausanne
Annalisa Bracco	CMCC Foundation
Donatello Elia & Marco De Carlo	CMCC Foundation
Sandro Fiore	University of Trento
Pierre Gentine	Columbia University
Donata Giglio	University of Colorado Boulder
David Hall	NVIDIA
David Lavers	ECMWF
Laure Berti-Equille	IRD
Mike Sierks	WindBorne Systems

The faculty roster spans academia, national laboratories, operational forecasting centers, and industry — reflecting the school’s emphasis on the full pipeline from foundational research to real-world deployment.

FERS Feature: Data-Driven Climate Modelling

In parallel with the summer school, FERS has published a companion feature article — — that provides accessible context for the scientific questions at the heart of the curriculum.

The piece traces the evolution from classical numerical weather prediction, rooted in Edward Lorenz’s insights on chaos and predictability, to today’s AI-driven breakthroughs: ECMWF’s operational deployment of its AI Forecasting System (AIFS) in July 2025, DeepMind’s GenCast outperforming ensemble baselines at 15-day lead times, and the promise of hybrid approaches like NeuralGCM that preserve physical consistency while leveraging data-driven representations of unresolved processes.

The article also examines persistent challenges — including conservation law violations in pure ML models, limited generalization to novel climate states, and data equity disparities that leave the Global South underserved by current observational networks. Aneesh’s lecture content at the summer school addresses several of these open problems directly, drawing on his research in coupled ocean-atmosphere prediction, data assimilation, and machine learning for subseasonal-to-seasonal forecasting.

Course page:
FERS feature article:

Congratulations Dr. Ziqi Yin! Successful PhD Defense on Greenland Ice Sheet Surface Melt

Sat, 16 May 2026 00:00:00 +0000

We are incredibly proud to announce that Ziqi Yin has successfully defended his PhD dissertation today in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado Boulder.

Dissertation title: Advancing Understanding of Greenland Ice Sheet Surface Melt Using Physics-Based and Machine Learning Models

Defense committee: Dr. Aneesh Subramanian (advisor, CU Boulder), Dr. Alexandra Jahn (CU Boulder), Dr. Rajashree Tri Datta (Delft University of Technology), Dr. Adam Herrington (National Center for Atmospheric Research), and Dr. Jianwu Wang (University of Maryland, Baltimore County).

Research Overview

Ziqi’s doctoral work marks a major step forward in polar climate research, bridging advanced physically-coupled Earth system modeling with cutting-edge data science and deep learning. His research focuses on improving our understanding of Greenland Ice Sheet (GrIS) mass loss — specifically surface melt, which has emerged as the dominant contributor to Greenland’s ice sheet degradation and subsequent global sea level rise in recent decades.

His dissertation consists of three core research thrusts:

1. Topographic Resolution Matters in Long-Term Projections (Published in the Journal of Advances in Modeling Earth Systems, JAMES)

Ziqi investigated how atmospheric grid resolution impacts century-scale ice sheet simulations. Using a variable-resolution (VR) grid featuring a ¼° regional refinement over the Arctic within the fully coupled CESM2.2–CISM2.1 framework, he demonstrated that conventional 1° coarse models flatten Greenland’s steep coastal topography. This flattening artifact artificially accelerates the positive melt–albedo feedback. By accurately resolving the terrain, his refined grid projected a multi-century sea level rise contribution (831 mm by year 350) that is 20–40% smaller than traditional models, indicating that coarse-resolution models may be overestimating future sea level rise.

2. Disentangling Melt Drivers via Causal Discovery (Under review at Geophysical Research Letters)

Moving beyond traditional correlation metrics, Ziqi applied the PCMCI+ causal discovery algorithm to isolate direct physical causes of summer melt anomalies in the ablation zone. His work successfully identified net shortwave radiation (the melt–albedo feedback) and turbulent heat fluxes (sensible and latent heat) as the dominant contemporaneous drivers of monthly summer melt anomalies. Under late-century high-warming scenarios (SSP3-7.0), he found that turbulent heat links become undirected, indicating a transition toward a more tightly synchronous and intensely coupled surface–atmosphere regime.

3. Accelerating Climate Projections with Graph Transformers (In preparation for The Cryosphere)

Ziqi developed a novel machine learning spatial emulator using a hybrid Graph Transformer architecture that combines local message passing with global self-attention. Trained on the 100-member CESM2 Large Ensemble (LENS2), the emulator faithfully replicates complex annual spatial melt fields under various climate conditions with an R² score above 0.99 and a root-mean-square error below 10%. When deployed across available CMIP6 models, it projects a surface melt increase of 89% (under low-emission SSP1-2.6) to 267% (under high-emission SSP5-8.5) by end of century. Under high-emission scenarios, Greenland’s northern basins are projected to experience the strongest acceleration in melting and eventually become the largest regional contributors to mass loss.

Impact

By combining the predictive power of variable-resolution climate physics, causal graphs, and spatial machine learning emulators, Ziqi’s work provides the scientific community with highly efficient, robust tools to track ice sheet–atmosphere interactions. These frameworks open new pathways for fast uncertainty quantification, risk assessments, and the identification of potential climate tipping points.

Please join us in extending our warmest congratulations to Dr. Ziqi Yin on this stellar milestone and wishing him all the best in his future scientific career!

Dr. Timothy Higgins Launches Operational AI-Based Ensemble Forecasting System for West Coast Atmospheric Rivers

Sat, 16 May 2026 00:00:00 +0000

Accurate probabilistic forecasting of extreme weather events is critical for water management and disaster preparedness along the U.S. West Coast. We are excited to share that postdoctoral research associate and climate fellow Dr. Timothy B. Higgins has successfully developed and operationalized a cutting-edge, diffusion-based ensemble forecasting system for Integrated Vapor Transport (IVT).

The model — the culmination of the final chapter of Dr. Higgins’ dissertation — is now officially live and operational at the Center for Western Weather and Water Extremes (CW3E).

Bridging the Gap in Probabilistic Forecasting

Traditional ensemble forecasting systems are computationally expensive, often limiting the number of scenarios meteorologists can simulate in real time. Dr. Higgins’ diffusion model fundamentally shifts this paradigm, demonstrating an ability to generate realistic 1,000-member ensembles within a highly manageable timeframe.

The model exhibits exceptional probabilistic forecasting skill, comparing favorably against premier global standards including the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF IFS) and the Global Ensemble Forecast System (GEFS).

A Collaborative Effort

This work began during Dr. Higgins’ time in the Advanced Study Program (ASP) Graduate Visitor Program at the National Center for Atmospheric Research (NCAR), where he worked closely with William Chapman. The project was co-authored alongside Dr. Aneesh Subramanian (University of Colorado Boulder) and Dr. Luca Delle Monache (CW3E / Scripps Institution of Oceanography).

View the Live Model

The formal manuscript describing this system has since been published — see our — and the real-time model outputs are publicly accessible:

Congratulations to Tim and the entire team on moving this impactful AI-driven climate research from theory into active operations!

Applications Open: FERS Summer School on AI and Machine Learning for Earth System Modeling and Prediction

Wed, 22 Apr 2026 00:00:00 +0000

Applications are now open for the FERS Summer School on AI and Machine Learning for Earth System Modeling and Prediction, co-directed by Prof. Aneesh Subramanian and Prof. Will Chapman (University of Colorado Boulder).

This intensive summer school, organized by the CMCC Foundation’s Future Earth Research School (FERS), brings together early-career researchers to explore the latest advances in machine learning and artificial intelligence for climate science and Earth system modeling.

Dates: 8 – 19 June 2026

Location: Bertinoro (FC), Italy

Application deadline: May 3, 2026

For full details on the curriculum, dates, location, eligibility, and how to apply, visit the course page:

Luke Howard Defends His PhD Thesis

Mon, 06 Apr 2026 00:00:00 +0000

Congratulations to Lucas (Luke) Howard on defending his PhD dissertation today!

Title: Advancing Earth System Data Assimilation and Prediction with Probabilistic Machine Learning

Date: Monday, April 6, 2026, 10:00 AM MT

Location: SEEC N128

Abstract

Probabilistic machine learning methods offer significant potential for advancing earth system prediction, but existing applications have largely been deterministic, limiting their utility in contexts where uncertainty quantification is essential. This dissertation advances probabilistic machine learning for earth system prediction across three distinct but complementary applications, each targeting a persistent bottleneck in operational forecasting or data assimilation.

In the first application, a probabilistic U-Net-based neural network is developed for subseasonal marine heatwave forecasting in the northern Indian Ocean and Arabian Sea. The model produces skillful probabilistic forecasts at lead times of up to 10 weeks, outperforming persistence and climatology benchmarks across a range of deterministic and probabilistic skill metrics and performing comparably to or better than the ECMWF S2S dynamical forecast. The results suggest that planetary waves and low-frequency ocean dynamics provide windows of predictability that a probabilistic machine learning approach can exploit.

In the second application, a convolutional neural network is used to augment an ensemble Kalman filter for the assimilation of high-resolution observations that would otherwise be discarded due to computational constraints. Demonstrated as a proof-of-concept on the Lorenz-96 system, the augmented method reduces analysis error by 37% compared to an ensemble Kalman filter operating on spatially thinned observations alone, and produces more accurate and reliable ensemble forecasts at lead times of up to 10 days.

In the third application, a probabilistic neural network emulator of the Community Radiative Transfer Model is developed for the GOES Advanced Baseline Imager. The emulator matches the accuracy of the full physics-based model at a fraction of the computational cost, while generating reliable uncertainty estimates that could improve observation error characterization in data assimilation systems. Explainable AI methods applied across the second and third applications confirm that the trained networks extract physically meaningful information, increasing confidence in their reliability on out-of-sample data.

We are proud of Luke’s contributions to the group and wish him all the best in his next chapter!

Aneesh Subramanian Recognized as an Exceptional Graduate Faculty Mentor

Sat, 28 Mar 2026 00:00:00 +0000

Prof. Aneesh Subramanian has been honored by the University of Colorado Boulder Graduate School with the Exceptional Graduate Faculty Mentor Award. He is among twenty-two faculty members across the university recognized for going above and beyond in supporting their graduate students.

Recipients are nominated by students, administrators, faculty, or staff for their outstanding mentoring contributions and commitment to improving graduate education. Graduate School Dean Scott Adler noted that “the support and enhancement of mentoring is a priority initiative of the Graduate School,” and praised recipients for serving as trusted mentors for their students.

Prof. Subramanian is affiliated with the Department of Atmospheric and Oceanic Sciences and leads the CHAOS and Predictability group at CU Boulder, where he advises PhD students and postdoctoral researchers working at the intersection of ocean-atmosphere dynamics, data assimilation, and machine learning for weather and climate prediction.

Full announcement: