Open-source climate and weather AI models — typified by NVIDIA’s Earth-2 suite and a growing set of community rivals — combine high‑resolution spatiotemporal modeling, satellite and reanalysis assimilation, and machine‑learning components to accelerate forecasting, climate scenario generation, and risk analytics. As of 2026, the field is moving from research prototypes toward operational pipelines that require scalable data platforms, production-grade analytics, and reproducible research tools. Key trends driving relevance today include tighter integration of physics and ML (hybrid models), wider availability of high‑frequency Earth observation data, model distillation for edge and low‑latency deployment, and stronger requirements for provenance, observability, and open reproducibility. These shifts make AI Data Platforms, Data Analytics Tools, and AI Research Tools central to turning open models into usable forecasts and decision systems. Typical toolchain roles: LangChain provides an open SDK and orchestration framework for building LLM‑based agents and retrieval pipelines; Pinecone is used as a serverless vector database for semantic search and retrieval‑augmented generation against documents, sensor logs, and feature stores; DeeperMind.ai offers semantic document search and content management for research artifacts and operational SOPs; Stable Code supplies compact, edge‑ready code LLMs to accelerate model development and automation of data pipelines; Replit supports cloud IDE workflows and rapid prototyping, testing, and hosting; MindStudio enables no‑/low‑code composition and deployment of agents and monitoring dashboards. This topic focuses on evaluating open-model architectures and the surrounding stack needed for production use—data ingestion, RAG, model tuning and distillation, deployment, and observability—so practitioners can compare NVIDIA Earth‑2 and alternative open projects in terms of accuracy, latency, reproducibility, and operational costs.