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Abstract Numerous artificial intelligence-based weather prediction (AIWP) models have emerged over the past 2 years, mostly in the private sector. There is an urgent need to evaluate these models from a meteorological perspective, but access to the output of these models is limited. We detail two new resources to facilitate access to AIWP model output data in the hope of accelerating the investigation of AIWP models by the meteorological community. First, a 3-yr (and growing) reforecast archive beginning in October 2020 containing twice daily 10-day forecasts forFourCastNet v2-small,Pangu-Weather, andGraphCast Operationalis now available via an Amazon Simple Storage Service (S3) bucket through NOAA’s Open Data Dissemination (NODD) program (https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html). This reforecast archive was initialized with both the NOAA’s Global Forecast System (GFS) and ECMWF’s Integrated Forecasting System (IFS) initial conditions in the hope that users can begin to perform the feature-based verification of impactful meteorological phenomena. Second, real-time output for these three models is visualized on our web page (https://aiweather.cira.colostate.edu) along with output from the GFS and the IFS. This allows users to easily compare output between each AIWP model and traditional, physics-based models with the goal of familiarizing users with the characteristics of AIWP models and determine whether the output aligns with expectations, is physically consistent and reasonable, and/or is trustworthy. We view these two efforts as a first step toward evaluating whether these new AIWP tools have a place in forecast operations. 

Abstract Artificial intelligence (AI) can be used to improve performance across a wide range of Earth system prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a categorization of bias for AI in the Earth sciences. This categorization can assist AI developers to identify potential biases that can affect their model throughout the AI development life cycle. We highlight examples from a variety of Earth system prediction tasks of each category of bias.