Correcting Coarse-Resolution Weather and Climate Models by Machine Learning from Global Storm-Resolving Simulations

The recent boom in machine learning and data science has led to a number of new opportunities in the environmental sciences. In particular, climate models represent the best tools we have to predict, understand and potentially mitigate climate change, however these process-based models are incredibly complex and require huge amounts of high-performance computing resources. Machine learning offers opportunities to greatly improve the computational efficiency of these models.Here we discuss our recent efforts to reduce the computational cost associated with running a process-based model of the physical ocean by developing an analogous data-driven model. We train statistical and machine learning algorithms using the outputs from a highly idealised sector configuration of general circulation model (MITgcm). Our aim is to develop an algorithm which is able to predict the future state of the general circulation model to a similar level of accuracy in a more computationally efficient manner.We first develop a linear regression model to investigate the sensitivity of data-driven approaches to various inputs, e.g. temperature on different spatial and temporal scales, and meta-variables such as location information. Following this, we develop a neural network model to replicate the general circulation model, as in the work of Dueben and Bauer 2018, and Scher 2018.We present a discussion on the sensitivity of data-driven models and preliminary results from the neural network based model.&#160;Dueben, P. D., & Bauer, P. (2018). Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11(10), 3999-4009.Scher, S. (2018). Toward Data&#8208;Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning. Geophysical Research Letters, 45(22), 12-616.

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Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations

10.1002/essoar.10507879.1 ◽

2021 ◽

Author(s):

Christopher S. Bretherton ◽

Brian Henn ◽

Anna Kwa ◽

Noah D Brenowitz ◽

Oliver Watt-Meyer ◽

...

Keyword(s):

Machine Learning ◽

Climate Models ◽

Coarse Grid ◽

Weather And Climate

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Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2020.0091 ◽

2021 ◽

Vol 379 (2194) ◽

pp. 20200091 ◽

Cited By ~ 1

Author(s):

Sue Ellen Haupt ◽

William Chapman ◽

Samantha V. Adams ◽

Charlie Kirkwood ◽

J. Scott Hosking ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Climate Models ◽

Climate Modelling ◽

Post Processing ◽

Atmospheric Sciences ◽

Physical Knowledge ◽

Data Practices ◽

Weather And Climate ◽

State Of The Science

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Towards synthetic data generation for machine learning models in weather and climate

10.5194/egusphere-egu2020-20132 ◽

2020 ◽

Author(s):

David Meyer

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Climate Models ◽

Synthetic Data ◽

Real Data ◽

Data Generation ◽

Learning Models ◽

Synthetic Data Generation ◽

Weather And Climate ◽

Machine Learning Models

The use of real data for training machine learning (ML) models are often a cause of major limitations. For example, real data may be (a) representative of a subset of situations and domains, (b) expensive to produce, (c) limited to specific individuals due to licensing restrictions. Although the use of synthetic data are becoming increasingly popular in computer vision, ML models used in weather and climate models still rely on the use of large real data datasets. Here we present some recent work towards the generation of synthetic data for weather and climate applications and outline some of the major challenges and limitations encountered.

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Machine learning for weather and climate are worlds apart

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2020.0098 ◽

2021 ◽

Vol 379 (2194) ◽

pp. 20200098

Author(s):

D. Watson-Parris

Keyword(s):

Machine Learning ◽

Climate Models ◽

Climate Model ◽

Dynamical Evolution ◽

Weather Forecast ◽

Training Data ◽

Climate Modelling ◽

Current State ◽

Weather And Climate ◽

Condition Problem

Modern weather and climate models share a common heritage and often even components; however, they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour, there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem, which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. To emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make climate a domain which is rich in interesting machine learning challenges. Here, I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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