The modelling of small-scale processes is a major source of error in climate
models, hindering the accuracy of low-cost models which must approximate such
processes through parameterization. Red noise is essential to many operational
parameterization schemes, helping model temporal correlations. We show how to
build on the successes of red noise by combining the known benefits of
stochasticity with machine learning. This is done using a physically-informed
recurrent neural network within a probabilistic framework. Our model is
competitive and often superior to both a bespoke baseline and an existing
probabilistic machine learning approach (GAN) when applied to the Lorenz 96
atmospheric simulation. This is due to its superior ability to model temporal
patterns compared to standard first-order autoregressive schemes. It also
generalises to unseen scenarios. We evaluate across a number of metrics from
the literature, and also discuss the benefits of using the probabilistic metric
of hold-out likelihood.

研究了如何通过机器学习，将红噪声与强化学习相结合，提高气候模型对小尺度过程的近似准确性。

应用概率机器学习更好地建模参数化中的时间模式：以 Lorenz 96 模型为例

Using Probabilistic Machine Learning to Better Model Temporal Patterns  in Parameterizations: a case study with the Lorenz 96 model

Transits of habitable planets around solar-like stars are expected to be
shallow, and to have long periods, which means low information content. The
current bottleneck in the detection of such transits is caused in large part by
the presence of red (correlated) noise in the light curves obtained from the
dedicated space telescopes. Based on the groundbreaking results deep learning
achieves in many signal and image processing applications, we propose to use
deep neural networks to solve this problem. We present a feasibility study, in
which we applied a convolutional neural network on a simulated training set.
The training set comprised light curves received from a hypothetical
high-cadence space-based telescope. We simulated the red noise by using
Gaussian Processes with a wide variety of hyperparameters. We then tested the
network on a completely different test set simulated in the same way. Our study
proves that very difficult cases can indeed be detected. Furthermore, we show
how detection trends can be studied, and detection biases be quantified. We
have also checked the robustness of the neural-network performance against
practical artifacts such as outliers and discontinuities, which are known to
affect space-based high-cadence light curves. Future work will allow us to use
the neural networks to characterize the transit model and identify individual
transits. This new approach will certainly be an indispensable tool for the
detection of habitable planets in the future planet-detection space missions
such as PLATO.

使用深度神经网络解决红色（相关）噪声问题，以便更好地侦测未来行星探测空间任务（如 PLATO）中的可居住星球。