Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Mansfield, Laura A.; Gupta, Aman; Burnett, Adam C.; Green, Brian; Wilka, Catherine; Sheshadri, Aditi

doi:10.1029/2023ms003715

Cited by 3 publications

(1 citation statement)

References 137 publications

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“…Quantitative data was further supported by qualitative interviews, which aimed to measure the extent to which the individual sentences were satisfactory with the type of explanation produced by each algorithm [27]. The respondents were asked to give easy, useful, and dependable explanations scores on a Likert scale.…”

Section: Human Satisfaction Evaluationmentioning

confidence: 99%

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

2024

Journal of Informatics Education and Research

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Explainable AI (XAI) is one of the key game-changing features in machine learning models, which contribute to making them more transparent, regulated and usable in different applications. In (the) investigation of this paper, we consider the four rows of explanation methods-LIME, SHAP, Anchor, and Decision Tree-based Explanation-in disentangling the decision-making process of the black box models within different fields. In our experiments, we use datasets that cover different domains, for example, health, finance and image classification, and compare the accuracy, fidelity, coverage, precision and human satisfaction of each explanation method. Our work shows the rule trees approach called (Decision Tree-based explanation) is mostly superior in comparison to other non-model-specific methods of explanation performing higher accuracy, fidelity, coverage and precision regardless the classifier. In addition to this, the respondents who answered the qualitative evaluation indicated that they were very content with the decision tree-based explanations and that these types of explanations are very easy and understandable. Furthermore, most of the respondents famous that these sorts of clarifications are more instinctive and significant. The over discoveries stretch on the utilize of interpretable AI strategies for facilitating the hole between machine learning models and human understanding and thus advancing straightforwardness and responsibility in AI-driven decision-making.

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Section: Human Satisfaction Evaluationmentioning

confidence: 99%

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

2024

Journal of Informatics Education and Research

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Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System

Gupta,

Sheshadri,

Anantharaj

2024

Sci Data

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Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

Schneider,

Leung,

Wills

2024

Atmos. Chem. Phys.

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Abstract. Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10 km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.

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Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Cited by 3 publications

References 137 publications

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System

Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

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