PeopleofSuperintelligence

Researcher

Griffin Mooers

Griffin Mooers applies machine learning to weather and climate models, with work on cloud emulation, convection, and precipitation. As a child, he watched New Hampshire snowstorms; he later studied atmospheric science at Cornell and presented rainfall research in 2018. In 2020, he coauthored papers on neural emulation and generative models for convection. He defended a dissertation on machine learning for tropical convection and precipitation in April 2023 and coauthored ClimSim, which received a NeurIPS 2023 Outstanding Datasets and Benchmark Track Paper award. As of October 2026, he is a postdoctoral scholar in Paul O’Gorman’s MIT group, where he uses machine learning to improve climate models.

Works
23
Citations
559
h-index
6
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2018

    Cornell and rainfall research

    As a child, he watches snowstorms at his home in New Hampshire and becomes fascinated by weather. He studies atmospheric science at Cornell and writes an undergraduate thesis on changes in extreme precipitation. In January 2018, he presents a coauthored study of rainfall frequency and spatial distribution in the northeastern United States at the American Meteorological Society’s annual meeting.

  2. 2020

    Neural climate emulation

    In 2020, he coauthors a paper that uses feed-forward deep neural networks to emulate cloud superparameterization in realistic geography. It reports that the network explains over 70 percent of temporal variance at a 15-minute sampling scale in the mid-to-upper troposphere.

  3. 2020

    Generative models for convection

    In 2020, he coauthors a generative model for atmospheric convection that uses a variational autoencoder to reconstruct high-resolution vertical-velocity fields. It clusters convective regimes and identifies anomalous storm activity.

  4. June 2022

    Google weather forecasting

    In June 2022, he takes leave from doctoral study in UC Irvine’s Earth System Science Department to join Google’s Accelerated Sciences team, where he builds neural networks for medium-range weather forecasting.

  5. April 2023

    A dissertation on convection

    In April 2023, he defends his dissertation, “Improving Modeling and Analysis of Tropical Convection and Precipitation through Machine Learning Methods,” at UC Irvine. It uses feed-forward neural networks to augment the Super-parameterized Community Atmospheric Model and deep generative models to analyze convection, storm anomalies, and changes in extreme precipitation.

  6. 2023

    ClimSim earns a NeurIPS award

    In 2023, ClimSim receives a NeurIPS Outstanding Datasets and Benchmark Track Paper award. He coauthors the multi-scale dataset, which provides climate simulations for developing hybrid physics-ML emulators.

  7. 2024

    Unsupervised rainfall analysis

    In 2024, he coauthors a study that applies unsupervised spatial machine learning to storm dynamics and heavy precipitation. The analysis finds that changes in event frequency largely explain changes in precipitation above the 80th percentile.

  8. As of October 2026

    Postdoctoral scholar at MIT

    As of October 2026, he is a postdoctoral scholar in Paul O’Gorman’s MIT group, where he works on machine learning to improve climate models. In October 2026, he coauthors a neural-network deep-convection scheme for the Community Atmosphere Model that reduces biases in light and extreme precipitation.

Career

  1. Since 2026Massachusetts Institute of TechnologyPostdoctoral scholar
  2. 2022–2024Google

Education

  1. Cornell University
  2. University of California, IrvinePhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Griffin Mooers. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Griffin Mooers. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  3. 16% of AI career moves in the 2020s went from a university to a companyIncludes Griffin Mooers. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Massachusetts Institute of Technology. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Griffin Mooers yet. Send us a free-licensed photo.

  1. About | Griffin Mooers griffinmooers.com. Checked 22 October 2026.
  2. Public Speaking and Teaching | Griffin Mooers griffinmooers.com. Checked 22 October 2026.
  3. [2010.12996] Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions arxiv.org. Checked 22 October 2026.
  4. [2007.01444] Generative Modeling for Atmospheric Convection arxiv.org. Checked 22 October 2026.
  5. Dissertation Defense: Griffin Mooers | ESS ess.uci.edu. Checked 22 October 2026.
  6. ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation papers.neurips.cc. Checked 22 October 2026.
  7. Announcing the NeurIPS 2023 Paper Awards – NeurIPS Blog blog.neurips.cc. Checked 22 October 2026.
  8. Understanding precipitation changes through unsupervised machine learning | Environmental Data Science | Cambridge Core cambridge.org. Checked 22 October 2026.
  9. Homepage of Paul O’Gorman pog.mit.edu. Checked 22 October 2026.
  10. [2610.20405] Improved Precipitation Variability in the Community Atmosphere Model through Implementation of a Neural-Network Deep Convection Scheme arxiv.org. Checked 22 October 2026.