215 research outputs found
Using Machine Learning for Model Physics: an Overview
In the overview, a generic mathematical object (mapping) is introduced, and
its relation to model physics parameterization is explained. Machine learning
(ML) tools that can be used to emulate and/or approximate mappings are
introduced. Applications of ML to emulate existing parameterizations, to
develop new parameterizations, to ensure physical constraints, and control the
accuracy of developed applications are described. Some ML approaches that allow
developers to go beyond the standard parameterization paradigm are discussed.Comment: 50 pages, 3 figures, 1 tabl
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.Comment: 32 pages, 3 figure
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
We explore the potential of feed-forward deep neural networks (DNNs) for
emulating cloud superparameterization in realistic geography, using offline
fits to data from the Super Parameterized Community Atmospheric Model. To
identify the network architecture of greatest skill, we formally optimize
hyperparameters using ~250 trials. Our DNN explains over 70 percent of the
temporal variance at the 15-minute sampling scale throughout the mid-to-upper
troposphere. Autocorrelation timescale analysis compared against DNN skill
suggests the less good fit in the tropical, marine boundary layer is driven by
neural network difficulty emulating fast, stochastic signals in convection.
However, spectral analysis in the temporal domain indicates skillful emulation
of signals on diurnal to synoptic scales. A close look at the diurnal cycle
reveals correct emulation of land-sea contrasts and vertical structure in the
heating and moistening fields, but some distortion of precipitation.
Sensitivity tests targeting precipitation skill reveal complementary effects of
adding positive constraints vs. hyperparameter tuning, motivating the use of
both in the future. A first attempt to force an offline land model with DNN
emulated atmospheric fields produces reassuring results further supporting
neural network emulation viability in real-geography settings. Overall, the fit
skill is competitive with recent attempts by sophisticated Residual and
Convolutional Neural Network architectures trained on added information,
including memory of past states. Our results confirm the parameterizability of
superparameterized convection with continents through machine learning and we
highlight advantages of casting this problem locally in space and time for
accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of
Advances in Modeling Earth Systems April 202
Efficient Climate Simulation via Machine Learning Method
Hybrid modeling combining data-driven techniques and numerical methods is an
emerging and promising research direction for efficient climate simulation.
However, previous works lack practical platforms, making developing hybrid
modeling a challenging programming problem. Furthermore, the lack of standard
data sets and evaluation metrics may hamper researchers from comprehensively
comparing various algorithms under a uniform condition. To address these
problems, we propose a framework called NeuroClim for hybrid modeling under the
real-world scenario, a basic setting to simulate the real climate that we live
in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly
platform NeuroGCM for efficiently developing hybrid modeling in climate
simulation. (2) Dataset. We provide an open-source dataset for data-driven
methods in hybrid modeling. We investigate the characteristics of the data,
i.e., heterogeneity and stiffness, which reveals the difficulty of regressing
climate simulation data; (3) Metrics. We propose a methodology for
quantitatively evaluating hybrid modeling, including the approximation ability
of machine learning models and the stability during simulation. We believe that
NeuroClim allows researchers to work without high level of climate-related
expertise and focus only on machine learning algorithm design, which will
accelerate hybrid modeling research in the AI-Climate intersection. The codes
and data are released at https://github.com/x-w19/NeuroClim.Comment: Work in progres
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
Stochastic parameterizations account for uncertainty in the representation of
unresolved sub-grid processes by sampling from the distribution of possible
sub-grid forcings. Some existing stochastic parameterizations utilize
data-driven approaches to characterize uncertainty, but these approaches
require significant structural assumptions that can limit their scalability.
Machine learning models, including neural networks, are able to represent a
wide range of distributions and build optimized mappings between a large number
of inputs and sub-grid forcings. Recent research on machine learning
parameterizations has focused only on deterministic parameterizations. In this
study, we develop a stochastic parameterization using the generative
adversarial network (GAN) machine learning framework. The GAN stochastic
parameterization is trained and evaluated on output from the Lorenz '96 model,
which is a common baseline model for evaluating both parameterization and data
assimilation techniques. We evaluate different ways of characterizing the input
noise for the model and perform model runs with the GAN parameterization at
weather and climate timescales. Some of the GAN configurations perform better
than a baseline bespoke parameterization at both timescales, and the networks
closely reproduce the spatio-temporal correlations and regimes of the Lorenz
'96 system. We also find that in general those models which produce skillful
forecasts are also associated with the best climate simulations.Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES
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