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Abstract The benefits of collaboration between the research and operational communities during the research-to-operations (R2O) process have long been documented in the scientific literature. Operational forecasters have a practiced, expert insight into weather analysis and forecasting but typically lack the time and resources for formal research and development. Conversely, many researchers have the resources, theoretical knowledge, and formal experience to solve complex meteorological challenges but lack an understanding of operation procedures, needs, requirements, and authority necessary to effectively bridge the R2O gap. Collaboration then serves as the most viable strategy to further a better understanding and improved prediction of atmospheric processes via ongoing multi-disciplinary knowledge transfer between the research and operational communities. However, existing R2O processes leave room for improvement when it comes to collaboration throughout a new product’s development cycle. This study assesses the subjective importance of collaboration at various stages of product development via a survey presented to participants of the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. This feedback is then applied to create a proposed new R2O workflow that combines components from existing R2O procedures and modern co-production philosophies. 

Abstract As an increasing number of machine learning (ML) products enter the research-to-operations (R2O) pipeline, researchers have anecdotally noted a perceived hesitancy by operational forecasters to adopt this relatively new technology. One explanation often cited in the literature is that this perceived hesitancy derives from the complex and opaque nature of ML methods. Because modern ML models are trained to solve tasks by optimizing a potentially complex combination of mathematical weights, thresholds, and nonlinear cost functions, it can be difficult to determine how these models reach a solution from their given input. However, it remains unclear to what degree a model’s transparency may influence a forecaster’s decision to use that model or if that impact differs between ML and more traditional (i.e., non-ML) methods. To address this question, a survey was offered to forecaster and researcher participants attending the 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE) with questions about how participants subjectively perceive and compare machine learning products to more traditionally derived products. Results from this study revealed few differences in how participants evaluated machine learning products compared to other types of guidance. However, comparing the responses between operational forecasters, researchers, and academics exposed notable differences in what factors the three groups considered to be most important for determining the operational success of a new forecast product. These results support the need for increased collaboration between the operational and research communities. Significance StatementParticipants of the 2021 Hazardous Weather Testbed Spring Forecasting Experiment were surveyed to assess how machine learning products are perceived and evaluated in operational settings. The results revealed little difference in how machine learning products are evaluated compared to more traditional methods but emphasized the need for explainable product behavior and comprehensive end-user training. 

Abstract MetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general road map for future planned development for MetPy is also discussed.