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The safety and efficacy of vaccination is a subject contentious in the public mind. Despite overwhelming evidence of their benefits to public health, COVID-19 and human papilloma virus (HPV) vaccines have been the focus of intense concerns. While the original phase III trials and post-market phase IV studies have continued to show their benefits and positive safety profile, some authors have attempted to reassess the original trial data, purporting to showing hidden harms for both COVID-19 and HPV vaccines. It is critical to ascertain why such divergent claims could stem from analysis of the same data, and this work accordingly examines these reports. In both cases, we find that erroneous statistical assumptions and unwarranted inferences are likely to have influenced the conclusions drawn, and identifies choices that would tend to result in spurious findings. This work also examines the wider issues with unregistered posthoc examinations on known trial data without preregistration, and how this may result in data-dredging, the torturing of data until it confesses to non-existent relationships, and how we might prevent against this on vaccine science. [ABSTRACT FROM AUTHOR]

[Display omitted] • Models based on kriging with external drift on elevation were the optimal choices. • Additional RMI stations improved the accuracy but not the robustness of the models. • Meteorological reanalysis used as predictor did not improve spatial interpolation. • Spatial interpolation is a promising option to improve current agricultural DSSs. • Agromet.be is an application for spatial interpolation of weather data in Wallonia. Food production will have to increase in the future to face the growing world population. Agricultural decision support systems (DSSs) are part of the solution since they aim at protecting crops against fungal diseases, a significant contributor to yield losses, while minimising pesticide use. DSSs are mainly driven by weather data which, currently, are usually obtained from the nearest available weather station. Since the latter is sometimes located far away from a farmer's field, this can lead to inaccurate recommendations. In order to provide better local weather data, spatial interpolation is a solution. However, since it must be delivered in near real-time, integrating a spatial interpolation process into an operational application necessitates addressing four constraints: Accuracy , Robustness , Reliability and Latency. This study aimed at developing an operational application for a near real-time spatial interpolation of air temperature and relative humidity at hourly and daily timescales. The first objective was to select the best spatial interpolation models among five algorithms: nearest neighbour, inverse distance weighting, multiple linear regression, ordinary kriging and kriging with external drift. The best models were based on kriging with elevation as external drift. They largely reduced the mean absolute error (MAE) compared to using the nearest station: for hourly air temperature MAE dropped from 0.93 °C to 0.59 °C. It performed also better than multiple linear regression (MAE = 0.68 °C). The second objective was to evaluate the impact of increasing station density by adding stations from the Belgian synoptic network. Additional stations improved Accuracy (MAE = 0.57 °C) but to a lesser extent than expected and had no clear impact on Robustness. The third objective was to assess the interest of using reanalyses (i.e. climate model outputs) as dynamic predictor variables. Reanalyses did not improve Accuracy (MAE = 0.62 °C) because, compared to elevation, they did not provide useful additional information that can be leveraged by the interpolation models. Using such a dynamical input also impacted Reliability negatively due to potential availability issues. Kriging models presented the highest computing times. However, Latency caused by the interpolation process itself was very small compared to the entirety of data processing. The selected models were implemented on an online application " Agromet.be ". Near real-time dissemination of interpolated weather data enables to produce local warnings helping farmers to take better decisions about spraying schedules. As a future improvement of spatial interpolation, integrating numerous personal weather stations owned by farmers seems promising. [ABSTRACT FROM AUTHOR]

Atmospheric reanalyses are the only source of spatial and temporal gridded wind information at wind turbine height providing data over several decades in the past. The application potential of reanalyses in the renewable energy sector depends strongly on the quality of the meteorological quantities. While global reanalyses have a resolution of typically 50 km, new regional reanalyses COSMO-REA6 and COSMO-REA2 have about 6 km and 2 km horizontal grid spacing, respectively. Here, we investigate the added value of the new regional reanalyses for the renewable energy sector, especially their application potential for site assessment. Four well established wind towers in Europe are used as reference for this purpose. We find regional reanalyses performing significantly better or at least similar to global reanalyses. Especially marginal distributions show significant improvements e.g. the most extreme temporal wind changes (ramp rates) at typical hub-heights are underrepresented by global reanalyses between −80 and −43% while COSMO-REA2 represents them with relative errors between −14 and +9%. Considering biases, mean absolute errors, and correlations most significant improvements occur close to ground and in areas with complex terrain. Moreover, vertically extrapolated wind measurements which are commonly used for site assessment show a stronger site dependency in their performance than reanalyses. • Reanalyses (REA) are a valuable tool for providing gridded wind speed at hub-height. • Low level wind profiles from regional reanalyses outperform global reanalyses. • COSMO-REA2 represents extreme ramp rate occurrences at hub-height best. • The performance of REA is more robust than those of vertical extrapolation methods. [ABSTRACT FROM AUTHOR]

Abstract In the United States, Central California has gained significant interest in offshore wind energy due to its strong winds and proximity to existing grid connections. This study provides a comprehensive evaluation of near-surface wind datasets in this region, including satellite-based observations (QuikSCAT, ASCAT, and CCMP V2.0), reanalysis (NARR and MERRA), and regional atmospheric models (WRF and WIND Toolkit). This work highlights spatiotemporal variations in the performance of the respective datasets in relation to in-situ buoy measurements using error metrics over both seasonal and diurnal time scales. The two scatterometers (QuikSCAT and ASCAT) showed the best overall performance, albeit with significantly less spatial and temporal resolution relative to other datasets. These datasets only slightly outperformed the next best dataset (WIND Toolkit), which has significantly greater temporal and spatial resolution as well as estimates of winds aloft. Considering tradeoffs between spatiotemporal resolution of the underlying datasets, error metrics relative to in-situ measurements, and the availability of data aloft, the WIND Toolkit appears to be the best dataset for this region. The framework and tradeoff analysis this research developed and demonstrated to assess offshore wind datasets can be applied in other regions where offshore wind energy is being considered. Highlights • Comprehensive assessment of near-surface wind datasets for Central California. • Seasonal and diurnal error metrics and spatial differences analyzed. • Framework for assessing wind data products for offshore wind energy. [ABSTRACT FROM AUTHOR]

Ability tilts refer to within-individual differences between two abilities, e.g. math ability – verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables. • GH = genetic heritability; NSE = non-shared environmental factors • Ability tilts (ATs) refer to within-individual differences between two abilities. • ATs have been found to be affected by GH and by NSE. • ATs have been claimed to be genuine human characteristics and due to niche-picking. • Effects of GH/NSE on tilts are spurious consequences of the constituent variables. [ABSTRACT FROM AUTHOR]

Output from meteorological reanalyses are used extensively in both academia and industry for modelling wind power. Recently, the first batch of the new ERA5 reanalysis was released. The main purpose of this paper is to compare the performance of ERA5 and MERRA-2 (a commonly used reanalysis today) in terms of modelling i) the aggregated wind generation in five different countries and ii) the generation for 1051 individual wind turbines in Sweden. The modelled wind power generation was compared to measurements. In conclusion, ERA5 performs better than MERRA-2 in all analysed aspects; correlations are higher, mean absolute and root mean square errors are in average around 20% lower and distributions of both hourly data and changes in hourly data are more similar to those for measurements. It is also shown that the uncertainty related to long-term correction (using one year of measurements and reanalysis data to predict the energy production during the remaining 1–5 years) is 20% lower for ERA5. In fact, using one year sample data and ERA5 gives slightly more accurate estimates than using two years of sample data and MERRA-2. Additionally, a new metric for quantifying the system size and dispersion of wind farms is proposed. [ABSTRACT FROM AUTHOR]

Changes in climate extremes affect socioeconomics and natural systems in northeastern Argentina (NEA) and may increase its vulnerability leading to unprecedented disasters. This study investigates the long-term changes and interannual variability of daily temperature and precipitation climate extremes and assesses to what extent global reanalyses reproduce the observed variability in the recent past. Datasets include quality-controlled observations (1963–2013) and ERA-Interim and NCEP2 reanalyses (1979–2011). Climate extremes are characterized spatially and temporally by 15 indices proposed by the Expert Team on Climate Change Detection and Indices. The leading modes of the area-averaged index time series were obtained by means of a Singular Spectrum Analysis, while the spatial distribution of mean changes was estimated by fitting nonparametric linear trends to each index time series. The results show that temperature extremes are changing towards warmer conditions. The number of warm days has been increasing since 1990 while the number of cold days has been decreasing. Warm and cold nights show a significant signal of warming that seems to be stabilizing in recent decades. Heat waves almost double the frequency and duration of cold waves, and the duration of heat waves increased while cold spells decreased in last decades. Longer heat waves are related to longer dry spells. On the other hand, the number of frost days remained stable although they exhibit high interannual and decadal variability. As well, intense precipitation events in most of the region increased steadily since 1970. The annual maximum amount of 1-day and 5-day precipitation events increased from the 1970s to the 2000s, stabilizing in recent years. The ERA-Interim and NCEP2 reanalyses represent climate extremes with different success. ERA-Interim can recognize temperature extremes in time and space, while the older NCEP2 presents systematic positive errors and has some difficult to replicate the interannual variability of the number of summer days. Both reanalyses reproduce dry spells and the annual maximum 5-day precipitation with large biases, which are particularly noticeable at each observation station. Although reanalyses would be expected to add information for climate extremes in areas of scarce observations like northeastern Argentina, they still need to be used with great caution and only as a complement to observations, especially in studies focusing on precipitation extremes. [ABSTRACT FROM AUTHOR]

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 o S to 60 o S) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts “Interim” reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979–2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year −1 . For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution. [ABSTRACT FROM AUTHOR]

Different offshore wind datasets were compared with buoys measurements in the Iberian Peninsula Atlantic coast, aiming to assess the best alternatives to ocean surface measured winds for offshore wind energy assessment and other applications. Results show that although ASCAT (Advanced SCATterometer) high-resolution product showed the lowest wind speed temporal variability and wind power flux estimations errors, a WRF (Weather Research and Forecast) model high-resolution simulation can be considered as the best alternative for offshore wind energy applications. WRF's simulation showed wind power flux estimations errors very similar to ASCAT, the best representation of the wind speed mean state and Weibull probability density functions, and provide offshore wind data at typical turbines hub heights. CCMP (Cross-Calibrated Multi-Platform Ocean Surface Wind Vectors) can also be seen as a valid and readily available alternative of offshore wind data. The findings presented here can be of great value for offshore wind energy applications that focus on ocean areas where measured wind data are not available, or are insufficient for the desired purposes. An informed choice of the most accurate offshore wind databases will provide more realistic wind energy production estimates that will positively impact the preliminary planning stages of offshore wind energy exploration projects. [ABSTRACT FROM AUTHOR]

Remote sensing data and model simulations of precipitation complemented the traditional surface measurements and offer unprecedented coverage on a global scale. However, the substantial heterogeneity among data products has hindered this unique opportunity to obtain a robust quantification of the climatologic properties of precipitation globally. Herein we present pRecipe , a package developed in R with reproducible science as our standard. pRecipe provides functions to download, explore, process, and visualize a database of 24 precipitation data sets at monthly time step and 0.25 ∘ resolution (20CR v3, CHIRPS v2.0, CMAP, CMORPH, CPC-Global, CRU TS v4.06, EM-EARTH, ERA-20C, ERA5, GHCN v2, GLDAS-CLSM, GLDAS-NOAH, GLDAS-VIC, GPCC v2020, GPCP v2.3, GPM IMERGM v06, MSWEP v2.8, NCEP/DOE R2, NCEP/NCAR R1, PERSIANN-CDR, PREC/L, TerraClimate, TRMM 3B43 v7, UDel v5.01). • A toolbox and database for multi-source assessment of precipitation climatology. • Provides simple and aesthetic data visualization in R. • An example comparing multi-source precipitation climatology in Czechia is presented. [ABSTRACT FROM AUTHOR]

Reanalyses are important sources of meteorological data. Recent studies have shown that precipitation and temperature data from reanalysis present a strong potential for hydrological modelling, especially in regions with a sparse observational network. The objective of this study is to evaluate the impacts of the combination of three global atmospheric reanalyses – ERA-Interim, CFSR and MERRA – and one gridded observation dataset on the accuracy of hydrological model discharge simulations. Two combination approaches were used. The first one combined reanalyses and the observational database using a weighted average of the precipitation and temperature inputs. The second one consisted in using all meteorological inputs separately and combining the simulated hydrographs. The combinations were performed over 460 Canadian watersheds (representing regions with a low density of weather stations) and 370 US watersheds (representing regions with a higher density of weather stations). Results showed significant improvements in the simulated discharges for 68% and 92% of the Canadian watersheds for the input combinations and output combinations, respectively. Moreover, both approaches led to significant improvements in the simulated discharges for 72% of the US watersheds studied. For all watersheds where simulated discharges using observational data had a Nash Sutcliffe efficiency (NSE) lower than 0.5, the combination with reanalyses resulted in a median NSE increase of 0.3. This indicates that reanalysis can successfully compensate for deficiencies in the surface observation record and provide significantly better hydrological modelling performance. [ABSTRACT FROM AUTHOR]

Specific humidity is generally thought to decrease with height in the troposphere. However, here we document the existence of specific humidity inversions in five reanalyses: the National Centers for Environmental Prediction (NCEP) second reanalysis (NCEP-2), the European Centre for Medium-Range Forecasts (ECMWF) 40-year reanalysis (ERA-40), the Modern Era Retrospective Analysis for Research Applications (MERRA), NCEP's Climate Forecast System Reanalysis (CFSR), and the ECMWF interim reanalysis (ERA-Interim). These inversions are most frequent in the polar regions. Inversions do occur elsewhere, most notably over the subtropical stratus regions, but are less frequent and likely overproduced depending on the location. Polar inversions are the most persistent in winter and the strongest (as defined by the humidity difference divided by the pressure difference across the inversion) in summer or autumn with low bases (at pressures > 900 hPa). Winter humidity inversions are lower, being near-surface, due to the persistence of low-level temperature inversions associated with these humidity inversions, while summer humidity inversions tend to be located near cloud top providing moisture to prevent the melt season stratus from evaporating. The most important contributions to affect humidity inversions in MERRA are dynamics, turbulence, and moist physics. However, local advection may not play as much of a role as regional humidity convergence. The subtropical stratus inversions are as thick as polar humidity inversions but with higher bases generally at pressures < 900 hPa. These inversions are confirmed by rawinsonde data, but there are discrepancies between the observed annual and diurnal cycles in inversion frequency and those portrayed in the reanalyses. [ABSTRACT FROM AUTHOR]

Highlights: [•] Simulated winds and wind energy estimates forced by different reanalysis were evaluated in Portugal. [•] ERA-Interim reanalysis is the one that likely provides the most realistic initial and boundary data. [•] NCEP-FNL and NCEP-GFS analyses showed better results than the other reanalyses datasets tested. [•] New generation reanalysis provide considerable improvement in the near surface wind simulation. [•] NCEP-FNL and NCEP-GFS analyses are the best alternatives to ERA-Interim. [ABSTRACT FROM AUTHOR]

Abstract: Statistical characteristics of daily precipitation in the reanalyses of the National Centers of Environmental Prediction (NCEP1 and NCEP2) and the European Center for Medium Range Weather Forecasts (ECMWF; ERA15 and ERA40) are intercompared with each other and with the in situ data assembled from different collections of station observations. Intercomparison is performed over the European continent. Precipitation statistics analyzed were the precipitation intensity, the parameters of Gamma distribution and the 99% percentiles of daily precipitation. NCEP1 and NCEP2 reanalyses show the higher occurrence of heavy precipitation than ECMWF products. Station data in comparison to the reanalyses show significantly higher estimates of heavy and extreme precipitation. Among the four reanalyses, NCEP2 demonstrates the closest to the station data estimates of extreme precipitation. The analysis of linear trends of statistical characteristics of heavy precipitation in ERA40 and NCEP1 for a 43-year period shows similarity of the trend patterns in winter and identifies strong local disagreements, resulting in the trends of opposite signs during summer. Interannual variability of statistical characteristics in different reanalyses is quite consistent over the Northern and Eastern Europe than in the mountain regions of the Southern Europe. Correlation between statistical characteristics of precipitation in different reanalyses and between the reanalyses and station data is 20–30% higher during the winter season. [Copyright &y& Elsevier]