CAS Common Chemistry ( ) is an open web resource that provides access to reliable chemical substance information for the scientific community. Having served millions of visitors since its creation in 2009, the resource was extensively updated in 2021 with significant enhancements. The underlying dataset was expanded from 8000 to 500,000 chemical substances and includes additional associated information, such as basic properties and computer-readable chemical structure information. New use cases are supported with enhanced search capabilities and an integrated application programming interface. Reusable licensing of the content is provided through a Creative Commons Attribution-Non-Commercial (CC-BY-NC 4.0) license allowing other public resources to integrate the data into their systems. This paper provides an overview of the enhancements to data and functionality, discusses the benefits of the contribution to the chemistry community, and summarizes recent progress in leveraging this resource to strengthen other information sources.

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The limited areal extent and evolving nature of convective storms over a small catchment near Johannesburg were investigated and an analysis of 21 storm events over the 10.4 km 2 suburban catchment was carried out. Shapes of storms were modelled by a numerical surface fitting technique chosen after several alternative methods were compared for suitability. A model was developed, utilizing the chosen inverse squared distance technique, which enabled storm intensities to be simulated over the study catchment. The results of the study suggest that a spatial description of storm events at discrete time steps is a valuable approach for understanding the cellular composition of storm events and that the spatial variability of storms should be incorporated when studying the rainfall-runoff process. Variation spatiale de l'intensité des pluies durant les orages de courte duréeRésumé La surface limitée de l'étendue et de l'évolution d'orages convectifs au dessus d'une petite région près de Johannesburg a été étudiée et l'analyse de 21 orages, sur une étendue de 10.4 km 2 de cette région péri-urbaine a été faite. La forme de la superficie couverte par les orages a été relevée par un système technique de surface numérique, après que plusieurs méthodes alternatives aient été comparées pour leur acceptabilité quant à l'étude. Un modèle a été développé en utilisant la technique du choix inverse du carré de la distance, qui permet la simulation de l'intensité des orages au dessus de la région d'étude. Les résultats de cette étude suggèrent qu'une description spatiale des événements d'un orage à différentes étapes, est une approche valable pour comprendre la composition cellulaire des événements d'un orage et que la variation spatiale d'orages devrait être pris au compte lors des études du processus pluies-debits.

Hydrologic models operated by the National Weather Service call for an accurate, consistent, high‐resolution, multi‐decade, continental‐scale record of hydrometeorological fields to serve as forcing data for model calibration. To serve this purpose, the Analysis of Record for Calibration was developed, and version 1.1 of the dataset is described in this study. Geospatial and scientific requirements, methods used in dataset generation, and input data sources are described. Given the prominent role of precipitation in model calibration, accurate and consistent precipitation is a particularly high priority for the analysis. To evaluate the analysis from this perspective, its daily precipitation is compared with surface observing stations over 43 years. The analysis exhibits low bias compared with other similar products. It also displays nonstationary bias behavior after 2015 due to the lack of a climatological constraint, as well as frequent occurrences of heavy‐to‐extreme precipitation that are often difficult to verify. These findings should be taken into account when the product is used for model calibration.

Mountains of the arid Great Basin region of Nevada are home to critical water resources and numerous species of plants and animals. Understanding the nature of climatic variability in these environments, especially in the face of unfolding climate change, is a challenge for resource planning and adaptation. Here, we utilize an Embedded Sensor Network (ESN) to investigate landscape-scale temperature variability in Great Basin National Park (GBNP). The ESN was installed in 2006 and has been maintained during uninterrupted annual student research training expeditions. The ESN is comprised of 29 Lascar sensors that record hourly near-surface air temperature and relative humidity at locations spanning 2000 m and multiple ecoregions within the park. From a maximum elevation near 4000 m a.s.l. atop Wheeler Peak, the sensor locations are distributed:(1) along a multi-mountain ridgeline to the valley floor, located ∼2000 m lower; (2) along two streams in adjoining eastern-draining watersheds; and (3) within multiple ecological zones including sub-alpine forests, alpine lakes, sagebrush meadows, and a rock glacier. After quality checking all available hourly observations, we analyze a 12-year distributed temperature record for GBNP and report on key patterns of variability. From 2006 to 2018, there were significantly increasing trends in daily maximum, minimum and mean temperatures for all elevations. The average daily minimum temperature increased by 2.1 • C. The trend in daily maximum temperatures above 3500 m was significantly greater than the increasing trends at lower elevations, suggesting that daytime forcings may be driving enhanced warming at GBNP's highest elevations. These results indicate that existing weather stations, such as the Wheeler Peak SNOTEL site, alone cannot account for small-scale variability found in GBNP. This study offers an alternative, low-cost methodology for sustaining long-term, distributed observations of conditions in heterogeneous mountainous environments at finer spatial resolutions. In arid mountainous regions with vulnerable water resources and fragile ecosystems, it is imperative to maintain and extend existing networks and observations as climate change continues to alter conditions.