Understanding seasonal climatic conditions is critical for better management
of resources such as water, energy and agriculture. Recently, there has been a
great interest in utilizing the power of artificial intelligence methods in
climate studies. This paper presents a cutting-edge deep learning model
(UNet++) trained by state-of-the-art global CMIP6 models to forecast global
temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was
also used for finetuning as well performance analysis in the validation
dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation +
ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in
six different models. For each model 14 different sequential and non-sequential
temporal settings were used. The Mean Absolute Error (MAE) analysis revealed
that UNet++ with CMIP6 with elevation and ERA5 finetuning model with "Year 3
Month 2" temporal case provided the best outcome with an MAE of 0.7. Regression
analysis over the validation dataset between the ERA5 data values and the
corresponding AI model predictions revealed slope and $R^2$ values close to 1
suggesting a very good agreement. The AI model predicts significantly better
than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the
summer months more accurately than the winter months.

本文介绍了一种基于深度学习模型（UNet++），利用 ERA5 重分析数据集和全球 CMIP6 模型进行训练和验证，以预测未来一个月的全球温度，发现该模型可以显著地提高预测准确度。

基于深度学习的气候模型驱动季节预测方法

Climate Model Driven Seasonal Forecasting Approach with Deep Learning

Autonomous driving models should ideally be evaluated by deploying them on a
fleet of physical vehicles in the real world. Unfortunately, this approach is
not practical for the vast majority of researchers. An attractive alternative
is to evaluate models offline, on a pre-collected validation dataset with
ground truth annotation. In this paper, we investigate the relation between
various online and offline metrics for evaluation of autonomous driving models.
We find that offline prediction error is not necessarily correlated with
driving quality, and two models with identical prediction error can differ
dramatically in their driving performance. We show that the correlation of
offline evaluation with driving quality can be significantly improved by
selecting an appropriate validation dataset and suitable offline metrics. The
supplementary video can be viewed at
this https URL

本文探讨了自动驾驶模型评估的方法，发现离线预测误差并不一定与驾驶质量相关，而适当选择验证数据集和适当的离线指标可以显著提高离线评估与驾驶质量的相关性。