XAI refers to techniques that allow humans to understand and “trust” the outputs of complex AI weather models. In a field where lives depend on the forecast, a “black box” that says “A tornado is coming” isn’t enough; XAI tools show the meteorologist which features (like wind shear or moisture levels) led the AI to that conclusion.
Saliency maps are the most common XAI tool in 2026. They “highlight” the parts of a satellite image or model grid that the AI is paying attention to. A professional searches for “XAI heatmaps for convective initiation” to see if the AI is looking at the correct atmospheric triggers; if the AI is focused on a mountain range, the forecaster can trust it more than if it is focused on random “noise.”
Professionals are searching for “Human-AI Teaming” (HAT) research. The goal isn’t just for the AI to be right, but for the human to know when the AI is likely to be wrong. XAI provides “uncertainty metrics” that tell the forecaster, “I am 90% sure about this storm, but my reasoning is based on weak data from the Pacific,” allowing the human to override the AI.
When an AI model misses a major snowstorm, developers use XAI to “reverse-engineer” the failure. Searching for “Layer-wise Relevance Propagation (LRP) in NWP” allows scientists to see which “layer” of the neural network failed to account for the freezing line, allowing them to retrain the model with better physics constraints for the next winter season.