Research questionHow can AI weather models produce high-resolution global forecasts without relying solely on analysis data?Many AI weather models are initialized and trained from analysis data, so their forecasts can inherit analysis biases and remain coarser than leading physics-based models. The central challenge is using observations directly without losing forecast skill at global scale. Latest papersRecent research connected to this question, newest first.WeatherNext 3: Increasing resolution and performance of global weather models with raw observationsThe source describes WeatherNext 3 using low-latency geostationary satellite data, satellite-derived precipitation estimates, tropical cyclone observations, and station data. It reports hourly forecasts, 0.1-degree resolution for single-level variables, and local 2-m temperature and dewpoint predictions; the evidence concerns probabilistic medium-range forecasting.research paper · Sep 3, 2026Improving precipitation forecasts in an AI weather model using observational dataApplies to a graph-transformer AI weather model using observational precipitation data at 0.25° resolution. The evidence covers continuous ranked probability and Brier skill for tropical storms, drizzle, and extreme rainfall, while noting that a physics-based operational model remains more reliable for the heaviest precipitation.research paper · Sep 2, 2026