Creating accurate hyper-local climate Artificial Intelligence (AI) models requires neighborhood-level weather measurements and community partnerships. In this paper, we describe a three year case study of using a participatory approach to support the creation of hyper-local climate AI models, or what we term “precision weather.” Using participatory design to involve stakeholders in the climate AI pipeline design process i.e., “participatory AI,” we collaborated with a national laboratory and a community organization in a major metropolitan area in the United States, working with community members and scientists. We held interviews, co-design workshops (“Community Cafes”), and created an app for the community to collect flood reports in their neighborhood for advocacy and to contribute data to the AI model pipeline. We discuss our findings, lessons learned, and implications for future participatory projects to support hyper-local climate AI.

We propose an approach QRRS (Query Relaxative Ranking SVM) that divides a ranking function into different relaxation steps, so that only cheap features are used in Ranking SVM of early steps for query efficiency. We show search quality in the approach is improved compared to conventional Ranking SVM.