A diffusion generative inverse model conditioned on temperature targets produces diverse, physically plausible urban vegetation patterns that achieve specified regional temperature shifts.
Weather prediction with diffusion guided by realistic forecast processes
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.
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Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns
A diffusion generative inverse model conditioned on temperature targets produces diverse, physically plausible urban vegetation patterns that achieve specified regional temperature shifts.
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RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.