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Background: The majority of complete dentures are still conventionally manufactured using a flask-and-pack technique. However, the polymerization process may introduce a distortion of the denture body. The aim of this study was to evaluate the three-dimensional fit of the posterior palatal seal of maxillary complete dentures with the original impression, and to give recommendations for scraping. Methods: Four autopolymerising resins were used to manufacture 40 palatal plates each for high, medium and flat palates (total n = 120). The misfit was captured by taking a reline impression with a highly fluid silicone, the dimensions of which were measured with a flat-bed scanner. Results: The shape of the palate had a significant impact (median p = 0.0435), but not the resin type (median p = 0.2575). It was largest for the flat palate and smallest for the high palate. The largest misfit was observed in the palatal midline area (flat-palate average median: 685 µm; high and medium palates: 620 µm) decreasing towards the lateral and anterior regions. Conclusions: The results suggest compensating for the palatal misfit that occurs with autopolymerising resins by scraping a postdam of an approximately 0.7 mm depth to the master cast, decreasing towards the anterior and lateral areas. In high and medium palates, the scraping could be less pronounced. [ABSTRACT FROM AUTHOR]

The uncertainty of the precipitation parameter in the ECMWF twentieth-century (ERA-20C) centennial reanalysis is assessed by means of a comparison with the GPCC in situ product Full Data Monthly Version 7 (FDM-V7). For the spatial and temporal validation of ERA-20C, global temporal scores were calculated on monthly, seasonal, and annual time scales. These include contingency table scores, correlations, and differences in the trend, along with time series analyses. Not surprisingly, the regions with the strongest deviations correspond to regions with data scarcity, such as mountainous regions with their upwind and downwind effects, and monsoon regions. They all show a strong systematic bias (ERA-20C minus FDM-V7) and significant breaks in the time series. The mean annual global bias is about 37 mm, and the median is about 8 mm yr−1. Among the largest mean annual biases are, for example, 3361 mm in the southern Andes, 2603 mm in the Western Ghats, and 2682 mm in Papua New Guinea. However, if there is high station density, the precipitation distribution is correctly reproduced, even in orographically demanding regions such as the Alps. [ABSTRACT FROM AUTHOR]

The performance of the laser-optical Particle Size Velocity (PARSIVEL) disdrometer is evaluated to determine the characteristics of falling snow. PARSIVEL’s measuring principle is reexamined to detect its limitations and pitfalls when applied to solid precipitation. This study uses snow observations taken during the Canadian Cloudsat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Validation Project (C3VP) campaign, when two PARSIVEL instruments were collocated with a single two-dimensional disdrometer (2-DVD), which allows more detailed observation of snowflakes. When characterizing the snowflake size, PARSIVEL instruments inherently retrieve only one size parameter, which is approximately equal to the widest horizontal dimension (more accurately with large snowflakes) and that has no microphysical meaning. Unlike for raindrops, the equivolume PARSIVEL diameter—the PARSIVEL output variable—has no physical counterpart for snowflakes. PARSIVEL’s fall velocity measurement may not be accurate for a single snowflake particle. This is due to the internally assumed relationship between horizontal and vertical snow particle dimensions. The uncertainty originates from the shape-related factor, which tends to depart more and more from unity with increasing snowflake sizes and can produce large errors. When averaging over a large number of snowflakes, the correction factor is size dependent with a systematic tendency to an underestimation of the fall speed (but never exceeding 20%). Compared to a collocated 2-DVD for long-lasting events, PARSIVEL seems to overestimate the number of small snowflakes and large particles. The disagreement between PARSIVEL and 2-DVD snow measurements can only be partly ascribed to PARSIVEL intrinsic limitations (border effects and sizing problems), but it has to deal with the difficulties and drawbacks of both instruments in fully characterizing snow properties. [ABSTRACT FROM AUTHOR]

In this paper we present EMO-5 ("European Meteorological Observations", spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at 10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020). [ABSTRACT FROM AUTHOR]

In this paper we present EMO- 51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/ 0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for 'European Meteorological Observations', whereas the 5 denotes the spatial resolution of 5 km. [ABSTRACT FROM AUTHOR]

A dense monthly precipitation dataset of Ireland and Northern Ireland was homogenized with several modern homogenization methods. The efficiency of these homogenizations was tested by examining the similarity of homogenization results both in the real data homogenization and in the homogenization of a simulated dataset. The analysis of homogenization results shows that the real dataset is characterized by a large number of, but mostly small, non‐climatic biases, and a moderate reduction of such biases can be achieved with homogenization. Finally, a combination of the ACMANT and Climatol homogenization results was applied to improve the data accuracy before the trend calculations. These two methods were selected for their proven high accuracy, missing data tolerance and ability to complete time series via the infilling of missing values before the trend calculations. Metadata were used within the Climatol method. To facilitate this analysis the study area was split into smaller climatic regions by using the Ward clustering method. Five climatic zones consistent with the known spatial patterns of precipitation in Ireland were established. Linear regression fitting and the Mann‐Kendall test were applied. Low frequency fluctuations were also examined by applying a Gaussian filter. The results show that the precipitation amount generally increases in the study area, particularly in the northwestern region. The most significant increasing trends for the whole study period (1941–2010) are found for late winter and spring precipitation, as well as for the annual totals. In the period from the early 1970s the increase of precipitation is general in all seasons of the year except in winter, but the statistical significance of this increase is weak. [ABSTRACT FROM AUTHOR]

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results. [ABSTRACT FROM AUTHOR]

Abstract: CERA‐20C is a coupled reanalysis of the twentieth century which aims to reconstruct the past weather and climate of the Earth system including the atmosphere, ocean, land, ocean waves, and sea ice. This reanalysis is based on the CERA coupled atmosphere‐ocean assimilation system developed at ECMWF. CERA‐20C provides a 10 member ensemble of reanalyses to account for errors in the observational record as well as model error. It benefited from the prior experience of the retrospective atmospheric analysis ERA‐20C. The dynamical model and the data assimilation systems initially developed for NWP had been modified to take into account the evolution of the radiative forcing and the observing system. To limit the impact of changes in the observing system throughout the century, only conventional surface observations have been used in the atmosphere. CERA‐20C improves the specification of the background and the observation errors, two key elements to ensure a consistent weighting of the uncertainties across geophysical variables, space, and time. The quality of CERA‐20C has been evaluated against other centennial reanalyses and independent observations. Although CERA‐20C inherits some limitations of ERA‐20C to represent correctly the tropical cyclones in the first part of the century, it shows significant improvements in the troposphere, compared to ERA‐20C and 20CRv2c (the twentieth century reanalysis produced by NOAA/CIRES). A preliminary study of the climate variability in CERA‐20C has been carried out. CERA‐20C improves on the representation of atmosphere‐ocean heat fluxes and mean sea level pressure compared to previous uncoupled ocean and atmospheric historical reanalyses performed at ECMWF. [ABSTRACT FROM AUTHOR]

The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 1951-2000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCC's 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 1982-2015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCC's uncorrected precipitation climatology for 1951-2000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2%-3% increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 1931-1960 up to 1981-2010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets. [ABSTRACT FROM AUTHOR]

The precipitation event in July 1342, which happened over an area now situated in central Germany, most probably exceeded in damage any other reported event in historic and recent times in Central Europe. According to historic sources, the event lasted several days and resulted in widespread and extreme soil erosion with gullies up to 10 m deep which devastated landscapes and ruined farmers. In several regional records the resulting river levels were the highest of their entire records. Remarkable are also observations of extreme flood levels also in regions without significant rainfall. Occasionally the spatial extent of the flooding was reported to reach from the North Sea coast to northern Italy, but other studies hint at missing flood reports from catchments in-between. Due to these and other partially contradicting reports we critically review the body of evidence in order to come to a more conclusive synopsis of the event. Based on confirmed reports on floods and related rainfall, the millennium flood, we come to the conclusion that the event was generated by an extended convective system mainly confined to the catchment of the River Main and adjacent headwaters of tributaries of the Rivers Danube, Elbe, Weser and, Lahn. The intensity of the rainfall was sufficient to cause devastating soil erosion and extreme flood magnitudes along the river catchments even far downstream the precipitation area. Based on circulation patterns most likely related to extreme summer precipitation over the area indicated by those reports to which we attribute the highest confidence a Central European trough (TRM) configuration seems more plausible than the frequently postulated “Vb-track cyclone” (Genoa Low). Peak discharge estimations along the Rivers Main (Würzburg, Frankfurt) and Rhine (Mainz, Cologne) suggest values up to approximately twice the largest flood ever observed by a gauge; for most downstream gauges the values are the largest of the entire historic record. Several findings previously related to this flood event fit to our explanation, when considering that during the year 1342 four additional severe floods took place in Central Europe. [ABSTRACT FROM AUTHOR]

Sub-daily rainfall observations are vital to help us understand, model and adapt to changing climate extremes. However, gauge records often have quality issues, for example due to equipment malfunctions and recording errors. This paper presents a new, open-source quality control algorithm (GSDR-QC) to identify these issues in hourly rainfall data, along with an application of the algorithm to the Global Sub-Daily Rainfall (GSDR) observational dataset. The algorithm is based on 25 quality checks, which are combined into a simple rule base to remove suspicious data. The quality checks and rule base are adaptable to help incorporate local or regional information. Comparison with manually quality-controlled gauge data shows that the procedure results in an overall improvement to the quality of the GSDR dataset. A UK case study further demonstrates the performance of the GSDR-QC procedure, while showing how region-specific data and understanding can be incorporated into the quality control process. • New open-source quality control algorithm (GSDR-QC) for sub-daily rainfall data. • 25 quality checks and 11 rules used to exclude suspicious values. • Applied to the global GSDR hourly rainfall observations dataset. • Improves GSDR correspondence with manually quality-controlled GPCC data. • UK case study highlights flexibility to incorporate regional knowledge. [ABSTRACT FROM AUTHOR]