dataesr/scientific-paragraphs-categorization huggingface dataset:
Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12. 5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects.
Observational data Model simulations are evaluated using observational and reanalysis datasets. For daily precipitation and daily maximum temperature, we use the gridded station-based dataset E-OBS v15 on a 0. 5 • latitude-longitude grid  (Haylock et al. , 2008). This covers the European domain from 1950 to the present. Gridded datasets tend to reduce the magnitude of extremes compared to station data through smoothing effects but are more comparable to the grid box averages from GCMs  (Haylock et al. , 2008). E-OBS has a somewhat nonuniform underlying station density, with relatively high densities in Germany, Sweden, and Slovenia and low densities in other countries (e. g. Spain, France, Austria). It tends to underestimate precipitation extremes relative to higherdensity regional datasets, especially where it has poor coverage due to missed extremes which are local in scale  (Prein and Gobiet 2017; Herrera et al.
, 2019). However, such highresolution datasets are not available at a pan-European scale. As a compromise, results are repeated for precipitation extremes using the 5. 5 km resolution MESAN reanalysis  (Landelius et al. , 2016) , which adjusts a downscaled first guess from the 22 km resolution HIRLAM reanalysis  (Dahlgren et al. , 2016)  with a network of station-based precipitation observations. For much of Europe these are the same as those used for E-OBS, but with the addition of Swedish Meteorological and Hydrological Institute (SMHI) stations over Sweden and a high density of Météo-France stations over France  (Landelius et al. , 2016). MESAN provides daily precipitation data for the more limited period 1989-2010. Prein and Gobiet (2017)  find that it gives heavier extremes than E-OBS in some regions (France, Spain, the Carpathians) but generally not as high as the high-resolution regional datasets (except in France).
Neither dataset is corrected for gauge undercatch, which tends to be around 3 %-20 % for rain, up to 40 % for snow, and even 80 % for non-shielded gauges  (Førland et al. , 1996; Goodison et al. , 1997). Wind extremes tend to happen on sub-daily timescales, necessitating the use of sub-daily data to avoid missing as many events as with daily data (although events, or their peak magnitude, will still be missed). We use 10 m wind speed from three reanalysis datasets. These are the EURO4M DY-NAD  (Landelius et al. , 2016) , UERRA MESCAN-SURFEX  (Bazile et al. , 2017) , and ERA5  (Hersbach et al. , 2019)  reanalyses. The former is available at 6-hourly intervals on a 5. 5 km rotated grid over Europe for the period 1979-2013 and is computed through dynamical adaptation of a downscaled version of the 22 km resolution HIRLAM reanalysis to 5.
5 km resolution orography using DYNAD (a simplified version of HIRLAM). MESCAN is also available at the same spatial and temporal resolution over Europe from 1961 onwards but is computed through dynamical downscaling of the 11 km UERRA-HARMONIE reanalysis. Both HIRLAM and UERRA-HARMONIE are forced by the ERA-Interim global reanalysis (ERA40 before 1979 for the latter). Finally, ERA5 is available globally at 0. 25 • and at hourly resolution from 1979 onwards. We subsample ERA5 to 6-hourly data by taking every sixth value in order to be consistent with the other reanalyses.
Regridding In order to compare models of different resolutions with each other and with the observational datasets it was necessary to regrid variables to a common grid. Using a high-resolution grid for evaluation would preserve the finer spatial detail and localized extremes for high-resolution simulations, but it is sometimes considered unfair for coarse-resolution models which cannot be expected to simulate the same intensities of extremes even for a perfect simulation due to spatial smoothing effects. If processes are captured better at higher resolution, improvements should still be visible when regridded to coarser resolution  (Prein et al. , 2016; Fantini et al. , 2018). However, the finer spatial detail is an inherent advantage of high resolution and smoothing this out will result in partial information loss.
One drawback is that this makes robust statistics more challenging. In order to examine how well the climate models simulate extremes and the differences between different resolutions, we first examine the spatial patterns of the climatological mean values of the indices and their biases with respect to observational datasets. We then examine return period plots (see definitions below) for a number of regions for each index, which highlights any differences in the shape of the tails of the distribution of the extremes. The regions used are based on the PRUDENCE regions  (Christensen and Christensen, 2007)  and the IPCC SREX regions  (Seneviratne et al. , 2012)  and are shown in Fig. S2  and Table  S2. A subset of representative regions are presented here, with some comments about the others.
To avoid complications from missing data, grid cells in E-OBS with more than 5 d of missing data in any year during the period examined were masked for the whole period. Having one or more years missing would complicate the calculation of regional mean return periods and values. Models and observational datasets are masked to have the same spatial coverage, which is land only. A common time period across the models being examined and the observations they are being compared to is chosen to allow comparability. For the CMIP5 and CORDEX analysis 1970-2005 is used for temperature and precipitation and 1979-2005 for wind. For the UPSCALE runs we use 1985-2011 for temperature, 1989-2010 for precipitation to allow comparisons with MESAN (although 1986-2011 is used for the analogue analysis; see below), and 1986-2011 for wind.
In order to allow the shapes of the return period curves to be compared more easily between different types of models (i. e. CMIP5 and CORDEX at both resolutions), we first adjust each model to have the same climatological mean value of the extreme index in question. This effectively shifts the curves up or down but does not change their shape, which is the focus of these figures. Without such a shift, curves are too spread out to be able to discern differences in shape. Therefore, we cannot comment on mean biases of the extreme indices based on the return plots, but these biases are already shown and discussed based on map figures (see Sect. 3. 1). We implement this adjustment by subtracting the difference between the model climatology of the index in question and the climatology of the reference observational dataset for each model at a grid cell level.
We use E-OBS as the reference for temperature and precipitation and MESCAN for wind. The additional observational datasets shown in the return period plots are also adjusted in the same way. For the UPSCALE simulations, results can also be examined without the need to shift the curves to a common mean value because the same version of the same model is used for a given resolution, meaning that curves for individual simulations tend to cluster together instead of having large mean differences. In this way, differences in biases with resolution are also seen in the return period plots. Nevertheless, we also present UPSCALE results with the adjustment in Fig. S10  for comparison. Confidence intervals for the observational datasets are calculated using a bootstrapping method. If, for example, the analysis period was 1970-2005 (i.
11 • simulations. Using regional-scale very high-resolution datasets could improve agreement with the 0. 11 • simulations, since they tend to give heavier precipitation extremes  (Prein and Gobiet, 2017). Gauge undercatch will also contribute to the differ-ence, particularly for precipitation extremes associated with strong winds and in snow-dominated regions Figure  S5  shows results for individual models. Again, whilst models agree on the general pattern of precipitation extremes, i. e. wettest over mountains, there are considerable inter-model differences concerning the magnitude, particularly over complex orography. A number of CMIP5 models have extremes that are too light everywhere, but all underestimate precipitation extremes over mountainous regions to a greater or lesser extent. RCMs systematically simulate heavier precipitation extremes compared to their driving GCMs, particularly over mountains, and these extremes tend to become heavier when moving from 0.
CMIP5 has a dry bias, particularly over mountains, whilst CORDEX tends to be too wet relative to E-OBS, particularly at 0. 11 • , but results are sensitive to the observational https://doi. org/10. 5194/gmd-13-5583-2020 Geosci. Model Dev. , 13, 5583-5607, 2020 dataset used, with wet biases for CORDEX being reduced when compared to the higher-resolution MESAN dataset.
Wind extremes Finally, we examine annual maximum wind (WindXx). Figure  4  shows the multi-model medians of climatological mean annual maximum wind for CMIP5 and CORDEX at 0. 11 • compared to three reanalysis datasets. Data for CORDEX 0. 44 • were very limited and did not overlap with the models used at 0. 11 The CMIP5 driving model median shows a similar overall pattern of WindXx as the reanalyses, particularly ERA5, with a pattern of weaker winds in the north and south and a belt of stronger winds in the middle. However, CMIP5 does not tend to have stronger winds over mountains like in DYNAD and MESCAN. Using the whole CMIP5 ensemble gives similar results. The CORDEX multi-model medians show generally higher wind speeds than CMIP5 and capture the high wind speeds along western coastlines and over some mountainous terrain.
CORDEX 0. 11 tends to be closest to DY-NAD and MESCAN, whilst CMIP5 is closest to ERA5. Figure  S9  shows that when using sfcWindmax, CORDEX 0. 11 has heavier tails than CORDEX 0. 44. In summary, winds tend to be somewhat stronger and with somewhat heavier tails at higher resolution, with a large spread between models. Reanalysis datasets give fairly diverse results.
Precipitation extremes For precipitation, Fig. 7  shows the ensemble mean climatological mean of Rx1day for the period 1989-2010 for the three UPSCALE ensembles and their differences relative to E-OBS and MESAN. The overall pattern of Rx1day in the simulations is similar to that in the observational datasets, with heavier precipitation extremes and finer spatial detail with increasing resolution over complex orography. All resolutions have bands of heavy precipitation either side of the Alps, but these move closer together as the Alps become better defined. All simulations are generally wetter than E-OBS across most of Europe. The dry bias over orography in the Alps, southern Norway, and the Scottish Highlands is reduced with resolution, whilst a wet bias on the southern edge of the Alps and the coastal side of the Dinaric Alps in the Balkans appears instead.
MESAN gives higher values for observed Rx1day, which improves agreement in regions where E-OBS is below the models and causes a higher-resolution subset to agree better in the  other regions (Fig. 6 ). For the curves that are adjusted to have the same climatological mean, E-OBS tends to lie just on the lower end of the ensemble for most regions, whilst MESAN gives slightly heavier tails and tends to improve agreement with models (Fig. S10 ). Results for Rx5day are broadly sim-ilar (except that both observational datasets lie above all the models for the British Isles). In summary, precipitation extremes are somewhat wetter and heavier tailed with increasing resolution, mostly in southern Europe and the Alps for HadGEM3-A. Dry oro-graphic biases decrease with resolution, but wet biases appear in the south next to mountain ranges instead.
e. isolating the downscaling effect, or conversely whether circulation differences explain any differences in extreme events whilst circulation-variable (e. g. precipitation) relationships stay the same, i. e. the upscaling effect. For each day in the lower-resolution simulations we pick the nearest circulation analogue from anywhere in the higherresolution simulations, providing it happens at the right time of year (i. e. within a 30 d window centred on the day of the year in question). We then record the associated temperature, precipitation, and wind values from the higher-resolution simulations to make a "u-chronic" dataset (e. g. Jézéquel, et al. , 2018)  that contains data from the high-resolution simulations but follows the daily sequence of circulation patterns from the low-resolution models.
For precipitation and wind the European domain used is -16 to 44 • E and 34 to 72 • N (roughly the same as the domain plotted in the map-based figures). For temperature, a larger domain is used, since the history and trajectory of air masses are important for temperature extremes. This domain is loosely based on the domain used by  Cattiaux et al. (2010)  and extends over the North Atlantic as well as Europe (-62 to 44 • E and 24 to 80 • N). However, results are very similar if the smaller domain is used (not shown). For the 5 d variables (Rx5day and TXx5day) the u-chronic dataset was smoothed using a 5 d running mean. We also tried smoothing the daily geopotential height, precipitation, and temperature datasets first and then performing the analogue analysis. The relationship between the different curves was largely consistent between the two approaches, but absolute values differed and the shape of the curves changed a little.
Other studies suggest that country-scale higher-resolution precipitation datasets give even heavier precipitation extremes, which may agree best with the 0. 11 • simulations (e. g. Prein and Gobiet, 2017). Similarly, for mean precipitation,  Prein and Gobiet (2017)  find that RCM biases are of a similar size as the differences between different observational estimates. For extreme precipitation, various studies find that a number of as-pects (biases, frequency-intensity distributions, spatial patterns) of mean and extreme precipitation improve in EURO-CORDEX at 0. 11 • compared to 0. 44 • when compared to such high-resolution datasets (e. g. Prein et al. , 2016; Torma et al. , 2015; Fantini et al. , 2017). Prein et al. (2016)  ascribe this mostly to the better representation of orography at higher resolution, but also the ability to capture the larger scales of convection.
e. not reaching the same high resolutions as CORDEX 0. 11 • but also not as coarse as some CMIP5 models. Other global model studies also tend to find an increase in precipitation extremes with increased resolution for Europe, which is continent-wide in summer and concentrated in mountainous regions in winter  (Volosciuk et al. , 2015; Wehner et al. , 2014). This sometimes improves agreement with observational data (e. g. Kopparla et al. , 2013; Wehner et al. , 2014, for winter)  but can overestimate summer extreme precipitation if parameterization schemes are not retuned  (Wehner et al. , 2014). For wind extremes, higher resolution gave somewhat stronger winds and heavier tails for most regions for both the CORDEX vs. CMIP5 analysis and to a lesser extent for HadGEM3-A, except for regions dominated by coasts for the latter, for which large coastal grid boxes at lower resolution brought strong ocean winds further over land.
, 2019; Jacob et al. , 2020) , will enable the benefits of high resolution and its effect on European climate projections to be explored more thoroughly. The former will allow a systematic exploration of the effects of increased resolution for multiple GCMs through coordinated experiments simulating the past and future climate. The latter will include a first-of-its-kind large multi-model ensemble at convectivepermitting resolution for decadal time slices in the present and future for a large domain covering central Europe and part of the Mediterranean. Code and data availability. The CMIP5 and CORDEX data used for this analysis are available from the Earth System Grid Federation portals and are detailed in Table  S1. The HadGEM3-A UPSCALE simulations are available from the CEDA-JASMIN platform.
E-OBS can be downloaded here: https://www. ecad. eu/download/ensembles/download. php (ECAD E-OBS gridded dataset, 2017). MESAN is available here: http://exporter. nsc. liu. se/ 620eed0cb2c74c859f7d6db81742e114/ (SMHI EURO4M MESAN data, 2020). ERA5 and MESCAN are available from the Copernicus Climate Data Store (2020) at https://cds. climate. copernicus. eu, whilst DYNAD winds are available from Tomas Landelius at SMHI. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-5583-2020-supplement. Author contributions. CI, RV, and SJ conceptualized the study. CI carried out the analysis and wrote the paper. JS managed the CRECP project together with CH and BE, and all co-authors were involved in discussions to prepare the study and helped improve the paper.
For CMIP, the US Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System science portals. We thank the modelling team that produced the UPSCALE simulations and acknowledge the JASMIN and IPSL mesocentre computing clusters on which this analysis was performed. We thank Tomas Landelius from SMHI for making the DYNAD wind data available. We also acknowledge helpful input from the CRECP project scientific advisory board and useful discussions with UK Met Office scientists, in particular Malcolm Roberts and Carol McSweeney. We thank Abhishekh Srivastava and three other anonymous reviewers for their helpful comments and suggestions. Financial support.
• ; therefore, those results are not shown. The MESCAN and DYNAD reanalyses show strong extreme winds over the UK, the Norwegian mountains, and the NW coastline of France through to Denmark. Relatively strong winds are also seen over the Spanish plateau, as is a belt of strong winds running zonally across central Europe between somewhat slower winds to the north and south. The datasets differ in the magnitude of the winds, with DYNAD having more contrast between areas of low and high wind. MES-CAN should be the more accurate of the two (Tomas Lan-.
• ; therefore, those results are not shown. The MESCAN and DYNAD reanalyses show strong extreme winds over the UK, the Norwegian mountains, and the NW coastline of France through to Denmark. Relatively strong winds are also seen over the Spanish plateau, as is a belt of strong winds running zonally across central Europe between somewhat slower winds to the north and south. The datasets differ in the magnitude of the winds, with DYNAD having more contrast between areas of low and high wind. MES-CAN should be the more accurate of the two (Tomas Lan- Niermann et al. (2017)nication, 2020). ERA5 has notably slower winds, particularly over mountainous regions, but a similar overall zonal tripole pattern can be seen. Niermann et al. (2017)found that MESCAN underestimates extreme winds compared to station data over Germany.
An extensive review of the underlying theory and of many applications of SHSs is provided in books  [19, 17] , and a review of different possible semantics for this model is provided in  [34]. Applications of SHSs are numerous: a few examples are air traffic management  [40, 41] , communication networks  [26] , biochemical processes  [33, 42]. The software tool UPPAAL  [20]  implements a model of SHS similar to the one studied in this article and uses numerical methods to compute reachability probabilities, through numerical solving of differential equations and Monte Carlo simulation. Reachability problems have also been considered in an alternative semantics with discrete time; a numerical approach is for instance provided in  [1, 2]. Notations. We write R + = {x ∈ R | x ≥ 0} for the set of non-negative real numbers.
Our model is very close to the one of the software tool UPPAAL  [20]. In Section 3. 3, we will identify the need to restrict the definition of some components of SHSs to ensure their definability; this is however not required for the results of Section 3. 2. When referring to an SHS, we make in particular use of the same terminology as for hybrid systems (e. g. , runs, enabled edges, allowed delays I(•)) to describe its underlying hybrid system. In order to apply the theory developed in Section 2, we give the semantics of an SHS H as an STS T H = (S H , Σ H , κ H ). The set S H is the set L × R n of states of H , and Σ H is the σ -algebra product between 2 L and the Borel σ -algebra on R n. To define κ H , we first explain briefly the role of each probability distribution in the definition of SHS.
SThe next decade promises to be an exciting time for astronomers. Large volumes of astronomical data are continuously collected from highly productive space missions. This data has to be efficiently stored and analyzed in such a way that astronomers maximize their scientific return from these missions. Recognizing the need to better handle astronomical datasets, we designed ASTROIDE, a distributed data server for astronomical data. We analyze the peculiarities of the data and the queries in cosmological applications and design a new framework where astronomers can explore and manage vast amounts of data. ASTROIDE introduces effective methods for efficient astronomical query execution on Spark through data partitioning with HEALPix and customized optimizer. ASTROIDE offers a simple, expressive and unified interface through ADQL, a standard language for querying databases in astronomy.
• The query performances remain limited due to unsuitable data partitioning scheme. Therefore, there is a need to redesign astronomical operations while taking advantage of the steady progress in big data technology and tools. The target system should provide an effective, fast and linearly scalable data management for astronomical surveys. In this work, we present ASTROIDE, a distributed data server tailored for the management of large volume of astronomical data. ASTROIDE is designed as an extension of Apache Spark, and takes into account the peculiarities of the data and the queries related to astronomical surveys and catalogs. Queries are expressed in Astronomical Data Query Language (ADQL)  [9] , an SQL extension with astronomical functions. Various logical and physical optimization techniques for the ADQL execution are proposed, and integrated to Spark SQL thanks to the extensibility of its optimizer.
• We provide a high level access and manipulation by supporting the unified query language ADQL, widely used in the domain. • In order to speed up query processing, we combine data partitioning with HEALPix pixelization of spherical data. • We implement a query optimizer within ASTROIDE and provide a set of customized strategies for the optimization of astronomical query plans. • Experiments on real datasets of up to 1. 2 billion objects have demonstrated that ASTROIDE achieves high scalability for querying big astronomical datasets. The rest of this paper is organized as follows. Section 2 introduces the necessary background. Section 3 discusses the related works. Section 4 provides the system architecture overview of ASTROIDE. In Section 5, we explain the execution details of the main astronomical queries.