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This article addresses the challenge of comparing in-service weatherability among newly developed coatings. The study aims to compare the durability of three thermoplastic polyurethane-based coatings specifically formulated for defense inflatables. It introduces a reliability model that incorporates two weathering stresses, namely, ultra-violet radiation and temperature, to predict the service life of the coatings. A life-stress relationship has been established from the accelerated aging tests, which facilitates the determination of material service life at use level conditions. Notably, the analysis underscores the significant improvement in service lifetime achieved with nanocompositebased coatings. The validity of the proposed model is established through comparison with real-world field test data, emphasizing the effectiveness of the approach in assessing and comparing the performance of the three coated samples. The insights gained from this research will surely contribute to enhancing the durability assessment of coated systems in real-world conditions for various fields of applications. Highlights * Introduces a unique method to compare weatherability of three polyurethane coatings. * Reliability model predicts service life under UV and temperature stresses. * Life-stress relationship via accelerated aging for accurate service life. * Nanocomposite coatings show longer service life than conventional ones. * Model validated with field data, confirming practical applicability. KEYWORDS comparative service life prediction, nanocomposite, reliability, TPU coatings, weathering degradation, 1 | INTRODUCTION Multifunctional polymeric protective coating has become a promising design technology to the material scientists all over the world. Particularly, the impermeable coated structures have a very crucial [...]

Assessing the information provided by coproduced climate services is a timely challenge, given the continuously evolving scientific knowledge and its increasing translation to address societal needs. Here, we propose a joint evaluation and verification framework to assess prototype services that provide seasonal forecast information based on the experience from the Horizon 2020 (H2020) Climate forecasts enabled knowledge services (CLARA) project. The quality and value of the forecasts generated by CLARA services were first assessed for five climate services utilizing the Copernicus Climate Change Service seasonal forecasts and responding to knowledge needs from the water resources management, agriculture, and energy production sectors. This joint forecast verification and service evaluation highlights various skills and values across physical variables, services, and sectors, as well as a need to bridge the gap between verification and user-oriented evaluation. We provide lessons learned based on the service developers' and users' experience and recommendations to consortia that may want to deploy such verification and evaluation exercises. Last, we formalize a framework for joint verification and evaluation in service development, following a transdisciplinary (from data purveyors to service users) and interdisciplinary chain (climate, hydrology, economics, and decision analysis). SIGNIFICANCE STATEMENT: Tools to communicate climate-related information to users, typically dam managers, irrigation consortia, or energy producers, are fast evolving to answer societal needs. It is crucial to estimate the quality of the provided information, along with economic, environmental, and/or societal gains. Here, we exemplify how to assess information quality and potential gains in five services that provide data and information for hydropower, solar power, irrigation, and water reservoirs in Europe and South America. Based on this work, we recommend 1) service developers to well anticipate such quality and value assessments, due to the number of actors to be involved; 2) flexibility when screening how to quantify quality and gain to account for decision contexts; and 3) sustained funding or collaborating platforms to ensure the iterative coevaluation process. KEYWORDS: Forecast verification/skill; Seasonal forecasting; Climate services; Economic value, 1. Introduction Climate and water services convey climate-relevant knowledge and information (processed, structured, and well-communicated data interpreted for decision-making contexts) for horizons ranging from weeks to decades ahead. Climate services, [...]

This paper provides an introduction to a new tool that is designed to provide operationally useful vulnerability information to National Weather Service (NWS) Weather Forecasting Offices (WFOs). The Brief Vulnerability Overview Tool (BVOT) is a shapefile containing local known, spatially specific, and weather-hazard-related vulnerabilities in a format that is easily integrated into the existing forecasting, warning, and decision support responsibilities and tasks of NWS WFO meteorologists. The methods for gathering vulnerability data and then building a BVOT for a WFO leverage and strengthen the relationships that NWS WFOs already have with their local emergency managers (EMs) and core partners to work together to identify operationally useful, local vulnerability knowledge. The BVOT is populated with discrete, known vulnerabilities to provide NWS meteorologists spatial situational awareness of those people, places, and things of greatest concern to their core partners. Crucially, the BVOT is a subsample of all potential vulnerabilities; its primary purpose is to make meteorologists aware of those weather-hazard-specific vulnerabilities that, as we posed to them, "keep them awake at night." Here, we describe the development of the BVOT as a social science-informed operational tool; how the BVOT methods have evolved and how it can be integrated into the culture of the NWS as a tool for building and maintaining relationships with partners; and how the BVOT is designed to be used and its impact on operational decision-making as observed in NOAA's Hazardous Weather Testbed. SIGNIFICANCE STATEMENT: Operational meteorologists in the National Weather Service often rely on everyday knowledge of vulnerabilities to shape their messaging to communities threatened by hazardous weather. This study seeks to test methods that will permit meteorologists to work with local communities and emergency managers to collect knowledge about local vulnerabilities so that they can be formally shared and integrated into everyday operations. The results of this research provide an illustrative pathway demonstrating how social science-informed research can be used to improve meteorological operations. Future work should investigate how other findings from social science can be translated into real, operational impacts. KEYWORDS: Social science; Operational forecasting; Communication/ decision-making; Decision support; Vulnerability, 1. BVOT: An inductive approach to actionable vulnerability decision support The mission of the U.S. NWS is to "provide weather, water, and climate data, forecasts, warnings, and impact-based decision support [...]

Operational agencies face significant challenges related to the verification and evaluation of weather forecasts. These challenges were investigated in a series of online workshops and polls engaging operational personnel from six countries. Five key themes emerged: inadequate verification approaches for both existing and emerging products; incomplete and uncertain observations; difficulties in accurately capturing users' real-world experiences using simplified metrics; poor communication and understanding of forecasts and complex verification information; and institutional factors such as limited resources, evolving meteorologist roles, and concerns over reputational damage. We identify nearly 50 operationally relevant scientific questions and suggest calls to action. Addressing these needs includes designing forecast systems with verification as a central consideration, enhancing the availability of observations, and developing and adopting community software systems. Additionally, we propose the establishment of an international community comprising environmental and social science researchers, statisticians, verification practitioners, and users to provide sustained support for this collective endeavor. KEYWORDS: Uncertainty; Forecast verification/skill; Numerical weather prediction/ forecasting; Operational forecasting; Communications/ decision-making, 1. Introduction Weather prediction (Fig. 1) is a continuously evolving enterprise. The rapid expansion of various types of data (including satellite and other kinds), coupled with increasing computer capabilities, advances [...]

Weather and climate models usually overlook a cloud process--the radiative effect on microphysics (REM)--that leads to a bias of 'excessive water vapor' and 'too dense clouds.' Since the bias distorts [...]

Developing and improving forecasting systems requires ongoing rigorous evaluation of their performance, using tools that examine performance aspects that are relevant for forecast developers and users. Until recently, a consolidated [...]

Existing traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, major events, traffic control, and metro failures. The current research still cannot fully use the information contained in these external factors. To address this issue, we propose a novel metro traffic flow prediction method (KGR-STGNN) based on knowledge graph representation learning. We construct a knowledge graph that stores factors related to metro traffic networks. Through the knowledge graph representation learning technology, we can learn the influence representation of external factors from the traffic knowledge graph, which can better incorporate the influence of external factors into the prediction model based on the spatiotemporal graph neural network. Experimental results demonstrate the effectiveness of our proposed model., Author(s): Shun Wang [1]; Yimei Lv [2]; Yuan Peng [3]; Xinglin Piao (corresponding author) [1]; Yong Zhang [1] 1. Introduction Accurate traffic prediction has become a core component of intelligent [...]