Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid physics-ML simulations require domain-specific data and workflows that have been inaccessible to many ML experts. This paper is an extended version of our NeurIPS award-winning ClimSim dataset paper. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors spanning ten years at high temporal resolution, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. In this extended version, we introduce a significant new contribution in Section 5, which provides a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various baselines of ML models and hybrid simulators to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res, also in a low-resolution version at https://huggingface.co/datasets/LEAP/ClimSim_low-res and an aquaplanet version at https://huggingface.co/datasets/LEAP/ClimSim_low-res_aqua-planet) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid physics-ML and high-fidelity climate simulations.

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

Multi-attribute decision-making (MADM) is one of the most important parts in decision-making theory, and related research is becoming more and more popular over the past few years. Investigating that the information could be qualitative and quantitative, and the different measurement units cause difficulties in some MADM problems, the additive ratio assessment system (ARAS) method was proposed. The method tries to solve MADM problems through a simple way efficiently, and at the same time eliminates the influence of different measurement units. Till now, the method has received extensive attention and has been extended to different information environments and application fields. To know about the development of the method and improve the method efficiently, this paper reviews the studies on the ARAS method from the perspectives of basic information (including the bibliometrics analyses and the outline of ARAS method), the development on theory (including the development on MADM mechanism, different information environments, and combination with different methods), the development on the application and the future challenge. From the overview, the basic situations and the development of the ARAS method are presented clearly, and the analyses of the challenges can also provide useful and sufficient instructions for the future application and improvement of the method.

Considering that it is difficult for experts to provide precise preference values for the site selection of electric vehicle charging station in risky environment, this paper develops an approach for linear ordinal ranking aggregation to validly improve the efficiency and accuracy of electric vehicle charging station site selection. At first, the inverse value function of prospect theory is applied to reduce the impact of risk. Then, through combining with the concept of information energy, the experts' weights can be derived. Besides, a consistency constraint is added to the individual ranking-based alternatives' weights deriving model, which can guarantee the consistency degree at an acceptable level. Additionally, a consensus and standard deviation-based model is established to aggregate the alternatives' weights. Finally, a numerical case about the electric vehicle charging station site selection is presented to show the usage of the approach, meanwhile, comparative analysis and sensitivity analysis are also conducted which show the robustness and practicability of the approach.