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20242026
most citedDeep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies

1 citations · 2 across the 5 of their papers we have counts for

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physics.ao-ph2026

Coupled multiscale paleoclimate reconstruction with four-dimensional variational data assimilation

Zilu Meng, Gregory J. Hakim, Julien Emile-Geay +2

Paleoclimate archives extend climate knowledge beyond the instrumental era, registering different seasons, variables, time averages, and memory lengths. A longstanding problem is t…

physics.ao-ph2026

Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability

Zilu Meng, Gregory J. Hakim, Wenchang Yang +1

Atmospheric blocking events drive persistent weather extremes in midlatitudes, but isolating the influence of sea surface temperature (SST) from chaotic internal atmospheric variab…

physics.ao-ph2025

Extratropical Atmospheric Circulation Response to ENSO in Deep Learning Pacific Pacemaker Experiments

Zhanxiang Hua, Christina Karamperidou, Zilu Meng

Coupled atmosphere-ocean deep learning (DL) climate emulators are a new frontier but are known to exhibit weak ENSO variability, raising questions about their ability to simulate t…

physics.ao-ph2025★ 1 cited

Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies

Zilu Meng, Gregory J. Hakim, Wenchang Yang +1

Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unkno…

physics.ao-ph2025

Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium

Zilu Meng, Gregory J. Hakim, Eric J. Steig

"Online" data assimilation (DA) is used to generate a seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean--atmosphere--sea-ice coup…

physics.ao-ph2024★ 1 cited

Reconstructing the Tropical Pacific Upper Ocean using Online Data Assimilation with a Deep Learning model

Zilu Meng, Gregory J. Hakim

A deep learning (DL) model, based on a transformer architecture, is trained on a climate-model dataset and compared with a standard linear inverse model (LIM) in the tropical Pacif…