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Abstract NOAA’s Hazardous Weather Testbed (HWT) is a physical space and research framework to foster collaboration and evaluate emerging tools, technology, and products for NWS operations. The HWT’s Experimental Warning Program (EWP) focuses on research, technology, and communication that may improve severe and hazardous weather warnings and societal response. The EWP was established with three fundamental hypotheses: 1) collaboration with operational meteorologists increases the speed of the transition process and rate of adoption of beneficial applications and technology, 2) the transition of knowledge between research and operations benefits both the research and operational communities, and 3) including end users in experiments generates outcomes that are more reliable and useful for society. The EWP is designed to mimic the operations of any NWS Forecast Office, providing the opportunity for experiments to leverage live and archived severe weather activity anywhere in the United States. During the first decade of activity in the EWP, 15 experiments covered topics including new radar and satellite applications, storm-scale numerical models and data assimilation, total lightning use in severe weather forecasting, and multiple social science and end-user topics. The experiments range from exploratory and conceptual research to more controlled experimental design to establish statistical patterns and causal relationships. The EWP brought more than 400 NWS forecasters, 60 emergency managers, and 30 broadcast meteorologists to the HWT to participate in live demonstrations, archive events, and data-denial experiments influencing today’s operational warning environment and shaping the future of warning research, technology, and communication for years to come. 

The processing–structure–property relationship using poly(lactic acid) (PLA) and poly(ethylene terephthalate) (PET) is explored. Specifically, both pre‐extension and preshear of amorphous PLA and PET above their glass transition temperaturesT_g, carried out in the affine deformation limit, can induce a specific type of cold crystallization during annealing, i.e., nanoconfined crystallization (NCC) where crystal sizes are limited to a nanoscopic scale in all dimensions so as to render the processed PLA and PET optically transparent. The new polymer structure after premelt deformation can show considerably enhanced mechanical properties. For example, premelt stretching produces geometric condensation of the chain network. This structural alternation can profoundly change the mechanical characteristics, e.g., turning brittle PLA ductile. In contrast, after preshear of amorphous PLA aboveT_g, the NCC containing PLA remains brittle, showing the importance to have geometric condensation from processing. Both AFM imaging and SAXS measurements are performed to verify that premelt deformation of PLA and PET indeed results in NCC from annealing that permits the strain‐induced cold crystallization to take place on the length scale of the mesh size of the deformed chain network.

 

This paper synthesizes multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications. ML has recently exploded in popularity in many fields, including meteorology. Although ML has been successful in meteorology, it has not been as widely accepted, primarily due to the perception that ML models are “black boxes,” meaning the ML methods are thought to take inputs and provide outputs but not to yield physically interpretable information to the user. This paper introduces and demonstrates multiple MIV techniques for both traditional ML and deep learning, to enable meteorologists to understand what ML models have learned. We discuss permutation-based predictor importance, forward and backward selection, saliency maps, class-activation maps, backward optimization, and novelty detection. We apply these methods at multiple spatiotemporal scales to tornado, hail, winter precipitation type, and convective-storm mode. By analyzing such a wide variety of applications, we intend for this work to demystify the black box of ML, offer insight in applying MIV techniques, and serve as a MIV toolbox for meteorologists and other physical scientists.