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1. The current state of numerical weather prediction (NWP) in the United States involves a broad array of models serving various end users, with recent advances in observations and data assimilation contributing to model improvement.

2. Model dynamics, parameterizations, and ensemble prediction are furthering model accuracy and value, extending model range, and providing users with more reliable probabilistic information to aid in decision-making for a wide variety of activities.

3. Artificial intelligence and machine learning (AI/ML) are now key tools applied in the postprocessing of model output for downscaling and calibration, with social science integral to merging AI/ML into the forecast process. Efforts are being made to democratize access to NWP output for all users regardless of their location, knowledge, abilities, or resources.

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