Search for: All records

This project developed a pre-interview survey, interview protocols, and materials for conducting interviews with expert users to better understand how they assess and make use decisions about new AI/ML guidance. Weather forecasters access and synthesize myriad sources of information when forecasting for high-impact, severe weather events. In recent years, artificial intelligence (AI) techniques have increasingly been used to produce new guidance tools with the goal of aiding weather forecasting, including for severe weather. For this study, we leveraged these advances to explore how National Weather Service (NWS) forecasters perceive the use of new AI guidance for forecasting severe hail and storm mode. We also specifically examine which guidance features are important for how forecasters assess the trustworthiness of new AI guidance. To this aim, we conducted online, structured interviews with NWS forecasters from across the Eastern, Central, and Southern Regions. The interviews covered the forecasters’ approaches and challenges for forecasting severe weather, perceptions of AI and its use in forecasting, and reactions to one of two experimental (i.e., non-operational) AI severe weather guidance: probability of severe hail or probability of storm mode. During the interview, the forecasters went through a self-guided review of different sets of information about the development (spin-up information, AI model technique, training of AI model, input information) and performance (verification metrics, interactive output, output comparison to operational guidance) of the presented guidance. The forecasters then assessed how the information influenced their perception of how trustworthy the guidance was and whether or not they would consider using it for forecasting. This project includes the pre-interview survey, survey data, interview protocols, and accompanying information boards used for the interviews. There is one set of interview materials in which AI/ML are mentioned throughout and another set where AI/ML were only mentioned at the end of the interviews. We did this to better understand how the label “AI/ML” did or did not affect how interviewees responded to interview questions and reviewed the information board. We also leverage think aloud methods with the information board, the instructions for which are included in the interview protocols. 

Abstract Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are “black boxes” and thus end-users are hesitant to apply the machine learning methods in their everyday workflow. To reduce the opaqueness of machine learning methods and lower hesitancy toward machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression, logistic regression, decision trees, random forest, gradient boosted decision trees, naïve Bayes, and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyze the use of machine learning in meteorology.