As a large international consortium of 26 countries and 110 higher-education institutions (HEIs), we successfully developed and executed an online student survey during or directly after the initial peak of the COVID-19 pandemic. The COVID-19 International Student Well-being Study (C19 ISWS) is a cross-sectional multicountry study that collected data on higher-education students during the COVID-19 outbreak in the spring of 2020. The dataset allows description of: (1) living conditions, financial conditions, and academic workload before and during the COVID-19 outbreak; (2) the current level of mental well-being and effects on healthy lifestyles; (3) perceived stressors; (4) resources (e.g., social support and economic capital); (5) knowledge related to COVID-19; and (6) attitudes toward COVID-19 measures implemented by the government and relevant HEI. The dataset additionally includes information about COVID-19 measures taken by the government and HEI that were in place during the period of data collection. The collected data provide a comprehensive and comparative dataset on student well-being. In this article, we present the rationale for this study, the development and content of the survey, the methodology of data collection and sampling, and the limitations of the study. In addition, we highlight the opportunities that the dataset provides for advancing social science research on student well-being during the COVID-19 pandemic in varying policy contexts. Thus far, this is, to our knowledge, the first cross-country student well-being survey during the COVID-19 pandemic, resulting in a unique dataset that enables high-priority socially relevant research.

Abstract. We revised the current status of the apsidal-motion test to stellar structure and evolution. The observational sample was increased by about 50% in comparison to previous studies. Classical and relativistic systems were analyzed simultaneously and only systems with accurate absolute dimensions were considered. New interior models incorporating recent opacity tables, stellar rotation, mass loss, and moderate core overshooting were used as theoretical tools to compare the predicted with the observed shifts of the position of the periastron. The stellar models were computed for the precise observed masses and the adopted chemical compositions are consistent with the corresponding tables of opacities to avoid the inherent problems of interpolation in mass and in (X, Z). The derived chemical composition for each individual system was used to infer the primordial helium content as well as a law of enrichment. The values found are in good agreement with those obtained from various independent sources. For the first time, the effects of dynamic tides are taken into account systematically to determine the contribution of the tidal distortion to the predicted apsidal-motion rate. The deviations between the apsidal-motion rates resulting from the classical formula and those determined by taking into account the effects of dynamic tides are presented as a function of the level of synchronism. For systems close to synchronisation, dynamic tides cause deviations with respect to the classical apsidal-motion formula due to the effects of the compressibility of the stellar fluid. For systems with higher rotational angular velocities, additional deviations due to resonances arise when the forcing frequencies of the dynamic tides come into the range of the free oscillation modes of the component stars. The resulting comparison shows a good agreement between the observed and theoretical apsidal-motion rates. No systematic effects in the sense that models are less mass concentrated than real stars and no correlations with the evolutionary status of the systems were detected.

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Satellite precipitation products are becoming increasingly useful to complement rain gauge networks in regions where these are too sparse to capture spatial precipitation patterns, such as in the Tropical Andes. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (TPR) was active for 17 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and has generated one of the longest single-sensor, high-resolution, and high-accuracy rainfall records. In this study, high-resolution (5 km) gridded mean monthly climatological precipitation is derived from the raw orbital TPR data (TRMM 2A25) and merged with 723 rain gauges using multiple satellite-gauge (S-G) merging approaches. The resulting precipitation products are evaluated by cross validation and catchment water balances (runoff ratios) for 50 catchments across the Tropical Andes. Results show that the TPR captures major synoptic and seasonal precipitation patterns and also accurately defines orographic gradients but underestimates absolute monthly rainfall rates. The S-G merged products presented in this study constitute an improved source of climatological rainfall data, outperforming the gridded TPR product as well as a rain gauge-only product based on ordinary Kriging. Among the S-G merging methods, performance of inverse distance interpolation of satellite-gauge residuals was similar to that of geostatistical methods, which were more sensitive to gauge network density. High uncertainty and low performance of the merged precipitation products predominantly affected regions with low and intermittent precipitation regimes (e.g., Peruvian Pacific coast) and is likely linked to the low TPR sampling frequency. All S-G merged products presented in this study are available in the public domain.

An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.18, 0.258) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space-time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite-gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite-gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products.