Applying Machine Learning to Improve Simulations of a Chaotic Dynamical System Using Empirical Error Correction

Applications of Deep Learning to Ocean Data Inference and Sub-Grid Parameterisation

(2019) Thomas Bolton et al.   Journal of Advances in Modeling Earth Systems

Deep learning and process understanding for data-driven Earth system science

(2019) Markus Reichstein et al.   NATURE

Finding plausible and diverse variants of a climate model. Part 1: establishing the relationship between errors at weather and climate time scales

(2019) D. M. H. Sexton et al.   CLIMATE DYNAMICS

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

(2018) Jaideep Pathak et al.   CHAOS

Could Machine Learning Break the Convection Parameterization Deadlock?

(2018) P. Gentine et al.   GEOPHYSICAL RESEARCH LETTERS

Prognostic Validation of a Neural Network Unified Physics Parameterization

(2018) N. D. Brenowitz et al.   GEOPHYSICAL RESEARCH LETTERS

Deep learning to represent subgrid processes in climate models

(2018) Stephan Rasp et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Predicting weather forecast uncertainty with machine learning

(2018) Sebastian Scher et al.   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

(2017) Tapio Schneider et al.   GEOPHYSICAL RESEARCH LETTERS

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Calibrating Climate Change Time-Slice Projections with Estimates of Seasonal Forecast Reliability

(2016) M. Matsueda et al.   JOURNAL OF CLIMATE

Data-driven methods to improve baseflow prediction of a regional groundwater model

(2015) Tianfang Xu et al.   COMPUTERS & GEOSCIENCES

Does the ECMWF IFS Convection Parameterization with Stochastic Physics Correctly Reproduce Relationships between Convection and the Large-Scale State?

(2015) Peter A. G. Watson et al.   JOURNAL OF THE ATMOSPHERIC SCIENCES

Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

(2015) D. J. Lea et al.   MONTHLY WEATHER REVIEW

Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies

(2015) Fenwick C. Cooper et al.   OCEAN MODELLING

On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

(2013) H.-Y. Ma et al.   JOURNAL OF CLIMATE

Stochastic parametrizations and model uncertainty in the Lorenz '96 system

(2013) H. M. Arnold et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Winter 2010 in Europe: A cold extreme in a warming climate

(2010) J. Cattiaux et al.   GEOPHYSICAL RESEARCH LETTERS

Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Climate Simulations and Seasonal Predictions*

(2009) V. M. Krasnopolsky et al.   MONTHLY WEATHER REVIEW

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts

(2008) T. N. Palmer et al.   BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY

A Reduced Radiation Grid for the ECMWF Integrated Forecasting System

(2008) Jean-Jacques Morcrette et al.   MONTHLY WEATHER REVIEW