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The Global Motivation Scale (GMS) is an 18-item self-report questionnaire. It measures a multidimensional conceptualization of motivation that falls along the self-determination continuum. The aim of the study was to evaluate the psychometric properties of the Polish version of the GMS, and to investigate its structure and reliability in a group of 537 subjects, aged 18–86 (M = 36.19; SD = 15.20). The bifactor modelling didn’t validate the theoretical six-factor model of the GMS, thus an exploratory analysis was conducted to determine an optimal model across age, gender and education. The adopted four-factor model matched three original GMS subscales: intrinsic motivation, external regulation and amotivation; the fourth factor represented identified and integrated regulations simultaneously (introjected regulation wasn’t included). Correlations among the factors didn’t confirm the simplex pattern, while the composite reliability coefficients were low (.55–.66). It is insufficient to analyze the assessment of the self-determination continuum only for statistical correctness – linguistic and cultural contexts should also be considered.

Before Palm Pilots and iPods, PCs and laptops, the term \"computer\" referred to the people who did scientific calculations by hand. These workers were neither calculating geniuses nor idiot savants but knowledgeable people who, in other circumstances, might have become scientists in their own right. When Computers Were Human represents the first in-depth account of this little-known, 200-year epoch in the history of science and technology. Beginning with the story of his own grandmother, who was trained as a human computer, David Alan Grier provides a poignant introduction to the wider world of women and men who did the hard computational labor of science. His grandmother's casual remark, \"I wish I'd used my calculus,\" hinted at a career deferred and an education forgotten, a secret life unappreciated; like many highly educated women of her generation, she studied to become a human computer because nothing else would offer her a place in the scientific world. The book begins with the return of Halley's comet in 1758 and the effort of three French astronomers to compute its orbit. It ends four cycles later, with a UNIVAC electronic computer projecting the 1986 orbit. In between, Grier tells us about the surveyors of the French Revolution, describes the calculating machines of Charles Babbage, and guides the reader through the Great Depression to marvel at the giant computing room of the Works Progress Administration. When Computers Were Human is the sad but lyrical story of workers who gladly did the hard labor of research calculation in the hope that they might be part of the scientific community. In the end, they were rewarded by a new electronic machine that took the place and the name of those who were, once, the computers.

Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns. Plain Language Summary Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts (“hindcasts”) from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts. Key Points Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscaling Downscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpart This downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations

This study investigates the ability of the Surface Water and Ocean Topography (SWOT) satellite to capture storm‐surge‐driven high sea levels in coastal regions. For the first time, the evolving process of storm surges has been observed in 2D through SWOT satellite data, providing a unique spatial perspective on these local extreme events. To validate satellite‐derived sea level measurements, we compared SWOT observations with SCHISM hydrodynamical model and tide gauge records from the Baltic, North Sea, and Gulf of Mexico, spanning micro‐to macro‐tidal environments. ERA5 wind and pressure fields were used to verify atmospheric conditions driving surge events. Excellent agreement between SWOT, in situ, and SCHISM model data was observed. These findings underscore SWOT's transformative potential for advancing coastal hazard monitoring, filling observational gaps, tracking cyclone‐driven sea level effects and enhance our understanding of how these events influence coastal dynamics.

Severe storms produce ocean waves with periods of 18–26 s, corresponding to wavelengths 500–1,055 m. These waves radiate globally as swell, generating microseisms and affecting coastal areas. Despite their significance, long waves often elude detection by existing remote sensing systems when their height is below 0.2 m. The new Surface Water Ocean Topography (SWOT) satellite offers a breakthrough by resolving these waves in global sea level measurements. Here we show that SWOT can detect 25‐s waves with heights as low as 3 cm, and resolves period and direction better than in situ buoys. SWOT provides detailed maps of wave height, wavelength, and direction across ocean basins. These measurements unveil intricate spatial patterns, shedding light on wave generation in storms, currents that influence propagation, and refraction, diffraction and reflection in shallow regions. Notably, the magnitude of reflections exceeds previous expectations, illustrating SWOT's transformative impact. Plain Language Summary Wind storms at sea make waves that increase in size with wind speed, and with the distance over which the high winds have been able to amplify the waves. Once generated these waves propagate as swell around the world ocean: in that stage the wave period remains constant while the wave height decay away from the source. Waves with periods longer than 18 s are relatively infrequent, but they are an important source of seismic waves and coastal impacts. However, current remote sensing techniques miss long waves under 0.2 m high. The Surface Water Ocean Topography (SWOT) satellite mission changes this, spotting 25‐s waves with heights as low as 3 cm. SWOT maps wave height, wavelength, and direction worldwide, revealing the influence of winds, currents and water depth. For example, We found stronger than expected coastal reflection, which will help revise wave forecasting models and their application in seismology. Key Points Surface Water Ocean Topography (SWOT) data provide the first open ocean spatial measurements of phase‐resolved swells with wavelength 500–1,050 m Swells with heights as low as 3 cm are well detected by SWOT, allowing tracking across oceans Swell reflection off the coast can be separated from incident waves

Sea-level measurements from radar satellite altimetry have reached a high level of accuracy and precision, which enables detection of global mean sea-level rise and attribution of most of the rate of rise to greenhouse gas emissions. This achievement is far beyond the original objectives of satellite altimetry missions. However, recent research shows that there is still room for improving the performance of satellite altimetry. Reduced uncertainties would enable regionalization of the detection and attribution of the anthropogenic signal in sea-level rise and provide new observational constraints on the water–energy cycle response to greenhouse gas emissions by improving the estimate of the ocean heat uptake and the Earth energy imbalance.Satellite radar altimetry enables the detection of sea-level changes by collecting data that have exceeded early expectations. This Perspective discusses potential advances that would enhance the data, allowing regional detection and attribution of sea-level change and improving ocean heat uptake estimates.

Five successive reference missions, TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and more recently Sentinel-6 Michael Freilich, have ensured the continuity and stability of the satellite altimetry data record. Tandem flight phases have played a key role in verifying and ensuring the consistency of sea level measurements between successive altimetry reference missions and thus the stability of sea level measurements. During a tandem flight phase, two successive reference missions follow each other on an identical ground track at intervals of less than 1 min. Observing the same ocean zone simultaneously, the differences in sea level measurements between the two altimetry missions mainly reflect their relative errors. Relative errors are due to instrumental differences related to altimeter characteristics (e.g., altimeter noise) and processing of altimeter measurements (e.g., retracking algorithm), precise orbit determination, and mean sea surface. Accurate determination of systematic instrumental differences is achievable by averaging these relative errors over periods that exceed 100 d. This enables for the precise calibration of the two altimeters. The global mean sea level offset between successive altimetry missions can be accurately estimated with an uncertainty of about ±0.5 mm ([16 %–84 %] confidence level). Nevertheless, it is only feasible to detect instrumental drifts in the global mean sea level exceeding 1.0 to 1.5 mm yr−1 due to the brief duration of the tandem phase (9 to 12 months). This study aims to propose a novel cross-validation method with a better ability to assess the instrumental stability (i.e., instrumental drifts in the global mean sea level trends). It is based on the implementation of a second tandem flight phase between two successive satellites a few years after the first one. Calculating sea level differences during the second tandem phase provides an accurate evaluation of relative errors between the two successive altimetry missions. With a second tandem phase that is long enough, the systematic instrumental differences in sea level will be accurately reevaluated. The idea is to calculate the trend between the systematic instrumental differences made during the two tandem phases. The uncertainty in the trend is influenced by the length of each tandem phase and the time intervals between the two tandem phases. Our findings show that assessing the instrumental stability with two tandem phases can achieve an uncertainty below ±0.1 mm yr−1 ([16 %–84 %] confidence level) at the global scale for time intervals between the two tandem phases that are higher than 4 years or more and where each tandem phase lasts at least 4 months. On regional scales, the gain is greater, with an uncertainty of ±0.5 mm yr−1 ([16 %–84 %] confidence level) for spatial scales of about 1000 km or more. With regard to the scenario foreseen for the second phase between Jason-3 and Sentinel-6 Michael Freilich planned for early 2025, 2 years and 9 months after the end of the first tandem phase, the instrumental stability could be assessed with an uncertainty of ±0.14 mm yr−1 on the global scale and ±0.65 mm yr−1 for spatial scales of about 1000 km ([16 %–84 %] confidence level). In order to achieve a larger benefit from the use of this novel cross-validation method, this involves regularly implementing double tandem phases between two successive altimetry missions in the future.

More than 29 years of altimeter data have been recently reprocessed by the multi-satellite Data Unification and Altimeter Combination System (DUACS) and made available under the name of DT2021 through the Copernicus Marine Service (CMEMS) and the Copernicus Climate Change Service (C3S). New standards have been applied and various geophysical correction parameters have been updated compared to the previous release in order to improve the product quality. This paper describes the assessment of this new release through the comparison of both the all satellites and the two satellites product with external in situ tide gauge measurements in the coastal areas of the European seas for a time period from 1 January 1993 to 31 May 2020. The aim is to quantify the improvements on the previous DT2018 processing version for the retrieval of sea level in the coastal zone. The results confirmed that the CMEMS product in the new DT2021 processing version better solves the signal in the coastal band. The all satellites dataset showed a reduction of 3 % in errors when compared with tide gauges and of 5 % in the variance of the differences between the datasets compared to DT2018 reprocessing. Moreover, the all satellites dataset provided more accurate sea level measurements when making a comparison with tide gauges with respect to the climatic two satellites dataset due to the better performance of the former for the assessment of higher than climatic frequency signals. By contrast, the two satellite dataset is the most suitable product for the assessment of long-term sea level sea surface height (SSH) trends in the coastal zone due to its larger stability to the detriment of the all satellites dataset.

The NASA-CNES altimetry mission SWOT (Surface Water and Ocean Topography) deployed in December 2022 embarks a Ka-band Radar Interferometer (KaRIn), providing a 120 km-wide swath sea level measurement. On 19 May 2023, SWOT was able to record a 2D signature of the tsunami generated by the Mw 7.7 earthquake southeast of the Loyalty Islands (southwest Pacific Ocean), about 1 h after the earthquake, on a straight SSW-NNE path. Comparison between numerical models and real measurements was performed to assess SWOT's ability to monitor tsunami waves. A uniform coseismic slip rupture model allows to satisfactorily fit the regional observations. Testing models against a dynamic representation of the tsunami wavefield (instead of static) show a good phase agreement, but simulated amplitudes are lower than the measurements. However, this SWOT unprecedented 2D observation critically inform on tsunami propagation and modelling, and offer a breakthrough perspective for better predictions.

This article presents an algorithm for registering the arrival of tsunami waves based on the operational data of sea level measurements. The algorithm was developed using the fuzzy mathematics approach and implies an expert assessment while the procedure of adjustment and tuning. Its adaptive capabilities allow to function in accordance with the current preceding the arrival of a tsunami wave. The presented algorithm tends to be a universal tool that can be used for detecting the restructuring of processes according to measurements of their characteristics in time.