dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we compare time series of monthly precipitation anomalies from the long-term mean in order to investigate the reproduction of intra-seasonal to inter-annual variations, and, hence, to assess the reliability of the datasets for climate monitoring. Some elements of this evaluation will be undertaken with a subset of the datasets only, because of limited time resolution or temporal extent in some of the datasets. Where meaningful, we summarize the correspondence between evaluated datasets and reference by quantitative scores. For this purpose we choose the conventional root-mean-square error (RMSE) and the Stable Equitable Error in Probability Space  (SEEPS, Rodwell et al. , 2010). SEEPS is a three-category score that values the distinction between dry, light and intense precipitation.
Compared to RMSE, the categorical nature of SEEPS places less emphasis on quantitative correspondence and avoids that the score is dominated by intense precipitation. In SEEPS, light and intense precipitation events are defined respectively as the lower two-thirds and the upper third of all wet days. The threshold (quantile) is defined individually for evaluated and reference data and, hence, SEEPS is not sensitive to biases in the frequency distribution of the evaluated datasets. Just as for the RMSE, a small value of SEEPS means better correspondence. In some parts of our evaluation, the comparison between evaluated data and reference is made grid-point by grid-point (at comparable resolutions, see below). This brings a risk that higher resolution datasets are excessively penalized for small misplacements in their precipitation patterns (e.
g. Ebert, 2008). Several of our analyses will therefore be conducted either at a coarser than the nominal resolution of the evaluated datasets or over climatologically and topographically distinct subdomains of the Alps. The subdomains are depicted in Figure  1  and their climatological characteristics are discussed in  Isotta et al. (2014). One of the domains was defined as a medium size river catchment (Aare without Reuss and Limmat, Switzerland, 11'900 km 2 ). It represents conditions with both high elevations and plains, typical when considering catchment mean precipitation.
Evaluation at different spatial resolutions Statistics of daily precipitation are sensitive to spatial resolution (e. g. Mearns et al. , 1995; Osborn and Hulme, 1997; Tustison et al. 2001; Frei et al. , 2003). To avoid inconsistencies we compare datasets with different original resolutions only after rescaling them to the same resolution and onto a common grid. Three different resolutions will be considered, at 0. 5 °(∼ 55 km), 0. 2 °(∼ 22 km) and 0. 05 °(∼ 5 km), using the grid structures of GPCC (longitude-latitude grid), HIRLAM and MESAN (rotated pole grids), respectively, as common grids. In most cases, the rescaling is done from higher to coarser resolution. This upscaling is carried out with the original daily fields by averaging, day-by-day, over all grid points of the fine resolution contained in a grid pixel of the coarser resolution grid.
In some cases, it will be interesting to compare datasets at fine resolution, when a coarse resolution dataset is crudely downscaled. This was done with a simple nearest neighbor interpolation, which was also adopted to re-grid the APGD dataset (originally developed on an ETRS kilometer grid) onto the 5 km rotated pole grid used for dataset comparisons (i. e. the MESAN grid coordinate system). The daily precipitation statistics are calculated finally from the rescaled datasets on the standard grids. As a result, fields of the wet-day frequency, for example, are available on three different grids for the high-resolution datasets MESAN, MESCAN and APGD, and an informative evaluation of the wet-day frequency in HIRLAM (22 km) is made by comparison with the frequency determined from the upscaled APGD.
We have briefly tested the sensitivity of the correspondence scores (RMSE and SEEPS) upon the regridding of the daily fields from MESCAN and APGD onto the MESAN grid, used as the standard at high resolution. To this end we calculated the scores in three different versions, using, in turn, the three native grid structures as common grid. The results for RMSE are shown in Table  2. Estimates of RMSE differ typically by 5 % or less between the different grids. The largest differences are found in regions SE-Massif Central (SEMC) and Ticino, regions with particularly strong precipitation gradients (see later). Nevertheless, the uncertainties in RMSE arising from the re-gridding seem to be clearly smaller than the variations of RMSE between regions and between datasets. Similar results were found for SEEPS.
Results This section presents and discusses results of our evaluation both by visual comparison and quantitative analysis. We begin by comparing mean precipitation, its annual cycle, and selected statistics of daily precipitation (wet-day frequency, 95 % quantile, Section 5. 1), proceed to the distribution functions of daily precipitation (Section 5. 2) and compare results for a particular case of heavy precipitation (Section 5. 3). Section 5. 4 discusses scores of the correspondence between the datasets for daily precipitation. Finally, the ability of the datasets to reproduce the intra-seasonal to inter-annual variations is examined in Section 5. 5. Our description makes use of geographical notions for which the reader is referred to Figure  1. The APGD dataset will serve as reference but in our interpretations we take into account that this dataset itself has errors and uncertainties (see previously Section 3).
Where meaningful, results will be discussed for several resolutions (out of 5 km, 22 km and 50 km) and always ensuring consistency in spatial resolution between evaluated and reference datasets (see Section 4). The time period considered is partly constrained by the limited temporal coverage in some of the evaluated datasets. Therefore, apart from a long reference period  (15 years 1989-2003) , some of our analyses will be conducted for a single year only  (2008) , for which all datasets are available. Tests revealed that the pattern of the considered statistical parameters, as well as the characteristic differences between the datasets are fairly robust between the single-year and multi-year periods. Despite the limited temporal coverage in some of our analyses, the results can be considered representative of the climatological behaviour of the datasets.
Mean precipitation Figure  2  depicts mean annual precipitation at all three resolutions. The results provide a first overview of all datasets considered in this evaluation. The reference period differs between the left-hand column  (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003)  and the remaining columns (  2008 ), to ensure maximum comparability for datasets with limited temporal coverage. The reference dataset (APGD) is shown at all three resolutions (first row). Not surprisingly, the two versions at coarser resolution reproduce smooth versions of the distribution with much smaller peak values and less prominent mesoscale patterns, notably for the dry inner-Alpine conditions, the wet anomalies along the southern Alpine rim and the anomalies associated with the smaller-scale hill ranges (Black Forest, Vosges).
The three global datasets (ERA-INT, GPCC and CRU) reveal quite different representations of annual mean precipitation in the Alps. Patterns range from a smooth moist anomaly (ERA-INT) over a pronounced regional moist maximum (CRU) to a structured distribution with mesoscale anomalies (GPCC). GPCC reproduces the characteristic distribution of APGD most accurately, with signatures of the moist anomaly extending along the northern rim of the Alps, two regional anomalies along the southern rim, dryness in the interior of the ridge and patterns associated with the Massif Central and the small-scale hill ranges (Vosges, Black Forest). Some discrepancies to APGD are found in the Apennine mountains, south-eastern France and for the spatial extent of the inner-Alpine dry anomaly (Trentino in Northern Italy).
ERA-INT and CRU miss out many of these mesoscale patterns. While ERA-INT tends to underestimate domain mean precipitation, CRU exaggerates the magnitude of the Alpine precipitation anomaly. It is to be noted that the resolution of ERA-INT (75 km) is coarser than the 55-km reference considered here (panel a) and that the topography of the underlying global model may be even coarser, which may explain some of the discrepancies. As regards CRU, a possible explanation is that the three-dimensional spline interpolation used for the background climatology overestimates the height dependence of precipitation and produced maximum precipitation in the region of maximum elevation  (Hutchinson, 1995; New et al. , 1999). The European datasets at 22-km resolution (Figure 2, middle column) reveal more pronounced spatial variations and much larger peak values, compared to the global datasets.
g. in France and Austria, see http://eca. knmi. nl/). Mean annual precipitation for the datasets with highest resolution (MESAN and MESCAN, 5 km) are depicted in the right-hand column of Figure  2. The 5-km version of HIRLAM (obtained by nearest-neighbor downscaling, see Section 4) is included for comparison. The large resolution step from 22 to 5 kilometers reveals a further structuring of the pattern. Both downscaling datasets show precipitation anomalies associated with individual mountain massifs. Compared to HIRLAM, which provided the background to these OI systems, there is a generally better correspondence with APGD. Notably the overestimate by HIRLAM is substantially reduced. However, the degree of adjustment varies regionally. In France, MESAN and MES-CAN show a spatially refined pattern, which is in close agreement with APGD, except for a somewhat smoother appearance.
The agreement is not too surprising considering that the high-resolution station data available here contributes a bulk of added information. However, in Switzerland, MESAN and MESCAN largely replicate the spatial distribution of HIRLAM without adding much detail (except for the correction of bias). Like in HIRLAM, the precipitation contrasts in the Aare catchment are overemphasized and the dryness of the Valais and upper Inn valleys are too broad. A similar lack in small-scale variability is found for Northern Italy. It seems that the coarse station network available in these regions has limited the ability of the downscaling methods to correct for errors in the background field and to compensate for the lack of spatial resolution. Between the two downscaling datasets the results are very similar.
Differences can be noted over the Apennines and the Massif Central. More insight into how the various datasets represent mean precipitation is provided in Figure  3 , which depicts the annual cycle averaged over the Alps and over three subdomains. The comparison is restricted to datasets with a multi-year coverage. Generally, the main features are well captured by all datasets. Notably, they reproduce the distinct courses with a summer (convective) maximum at the northern Alpine rim (Bavaria) and two seasonal maxima (spring and autumn) in the southern regions (SE-Massif Central and Ticino). The underestimation by E-Obs and ERA-INT is evident in all domains (except Bavaria) and around the year. In E-Obs, the underestimation is particularly large at the southern rim (Fig. 3c ) and relates to the poor representation of the prominent mesoscale anomalies there (see also  Figure 2).
In ERA-INT the underestimates are of compa-rable magnitude throughout the year. GPCC follows the annual cycle of the reference closely but underestimates its amplitude along the southern rim (Fig. 3c ). The overestimate by HIRLAM is particularly from winter, spring and summer. In Ticino, this dataset (as well as CRU and ERA-INT) misses the intermediate reduction of mean precipitation in summer. Finally, the MESAN downscaling dataset reproduces the annual cycle very well and, hence, has corrected for much of the overestimates in HIRLAM. In Ticino, however, MESAN also underestimates the amplitude of the seasonal variation.
Wet-day frequency In the Alpine region wet days are more frequent over the ridge and over the northern and western foreland of the Alps in Germany and France (APGD, Figure  4a ). Along the French Mediterranean coast, the Po Valley and Eastern Austria the frequency is only about half that over the ridge. Lower frequencies are also found in interior parts of the Alps. This general pattern is qualitatively evident in all evaluated datasets, but with considerable quantitative discrepancies and mismatches in spatial detail. E-Obs has a very smooth distribution with several of the topography-related patterns missing or misplaced. Over France, where the interpolation has to bridge large distances between stations, wet-day frequency is generally overestimated. The two regional reanalyses (HIRLAM and UKMO) overestimate the frequency of wet days considerably over the entire domain.
Residual signals from HIRLAM are, however, clearly discernable in other regions, notably in Northern Italy, where MESAN and MESCAN overestimate and misplace the gradient between Po valley and the Southern Alpine rim. A similar analysis for individual seasons (not shown) reveals that the characteristic discrepancies of all evaluation datasets are similar in all seasons. Quantitatively, the overestimate in wet day frequency by the regional reanalysis HIRLAM is particularly large in spring and summer, the more convective seasons. A similar result is found for UKMO, however, this comparison may be less robust due to only one year of available data. As regards E-Obs, the overestimation in the Alps is much stronger in the winter half-year.
95 % quantile A reliable reproduction of the occurrence of intense and heavy precipitation is crucial in many applications of climate datasets. Here we investigate this element in terms of the 95 % quantile, i. e. the daily precipitation amount that is exceeded on average every 20 days. The quantiles were determined grid-point by grid-point and, hence, are representative of intense area-average precipitation (not point values). Consistency in spatial scale is established by upscaling higher resolution datasets (Section 4). Results from the 22-km versions of the datasets and for year 2008 are depicted in Figure  5. The distribution in APGD (Fig. 5a ) is very similar to that in a long-term climatology (see  Isotta et al. , 2014). Large values are found along the northern and southern Alpine rim, particularly in two regions to the south (Ticino and Julian Alps) that are well known for experiencing heavy precipitation frequently.
The south-eastern slopes of the Massif Central and the Ligurian coast (Apennines) are also affected frequently. Signatures of this characteristic pattern are evident in all datasets. The prominent anomalies along the southern rim and many other small-scale anomalies (Jura, Vosges and Black Forest), all of which are missed by the coarser resolution ERA-INT (not shown), are clearly evident in the regional reanalyses (HIRLAM, UKMO) and in the downscaling datasets  (MESAN and MES-CAN). Discrepancies to APGD are mostly with regard to the exact location (e. g. Ticino and Julian Alps anomalies shifted in HIRLAM), the spatial extent (anomalies in UKMO are broader) and relative amplitude (Massif Central in UKMO) of the anomalies. The downscaling datasets (MESAN and MESCAN) add spatial information over France and correct for the overestimate of HIRLAM over Southern Germany.
However, the Ticino anomaly is excessively reduced and, particularly in MESAN, the quantile is distinctly underestimated there. E-Obs shows more substantial differences to APGD than the regional reanalysis and downscaling datasets. Several of the mesoscale anomalies are missing and there is a tendency for underestimates generally over the domain. These discrepancies resemble those of mean precipitation (see Fig. 2 ) and may be similarly inter- preted as a consequence of the coarse station network, the attendant smoothing and the exaggeration of height gradients by the interpolation method. A similar analysis for the four seasons (not shown) reveals that major features of the seasonal variation in heavy precipitation (high quantiles in autumn, low quantiles in winter) are found in all datasets.
Distribution function The frequency distribution of daily precipitation offers further insight into the error structure of the various datasets. Figure  6  depicts these distributions for several subdomains of the Alps by pooling the correspond-ing grid points (see Figure  1 ). Comparability between the various datasets is established by upscaling higherresolution datasets to the 22-km standard grid before calculating the distributions (see Section 4). The results for ERA-INT are not strictly comparable (because of coarser resolution) but are added for comparison. There are marked differences in the frequency distribution between the domains owing to the spatial variation in precipitation intensity. For example, the regions Ticino and SE-Massif Central exhibit distributions with much longer tails (note the different scales between the panels).
All regional datasets reproduce these characteristic differences between subdomains, corroborating the finding of the previous subsection that major spatial variations in precipitation intensity in the Alps are captured. A discrepancy common to many of the datasets is the tendency to overestimate the frequency of light and underestimate that of heavy precipitation (see e. g. panels a and b). The overestimate at small thresholds is particularly marked for the model-based regional reanalyses (HIRLAM and UKMO). This is not merely the result of too frequent drizzle; the overestimate is still evident at moderate precipitation thresholds. This bias is related to the overestimate of mean precipitation and wet-day frequency in these datasets (see Figure  2  and 3 ). On the other hand, the underestimation of high intensities is visible in all the dataset types.
E-Obs underestimates the frequency after about 10 mm distinctly (note the log scale) and in all domains. In SE-Massif Central the frequency decrease is even steeper than that for the coarser resolution ERA-INT. An obvious explanation for this is the smoothing involved with the interpolation in data poor regions. Underestimates of the frequency of intense events are also found for the downscaling datasets (MESAN more so than MESCAN, see panels b, e and f). In some domains this underestimation is even more substantial than in HIRLAM (panel e, f). This has similar reasons like in E-Obs, because the underestimation is strongly reduced in region SE-Massif Central where these datasets build on high-resolution station data. The result that MES-CAN has a generally more long-tailed frequency distribution than MESAN, despite similar input data, may be attributable to a different form and the assumption of a shorter length scale in the structure function for background errors (35 km in MESCAN versus 240 km in MESAN), and, hence, more local corrections and less smoothing.
Case study of an extreme precipitation event To illustrate the degree of correspondence between the various observation datasets we briefly illustrate precipitation distributions for a particular case of heavy precipitation (Figure  7 ). The case consisted of two episodes  (11-17 and 20-22 May 1999)  with intense and enduring rainfall, succeeding shortly one after the other. The first -a quasi-stationary frontal system -affected parts of the northern Alpine rim and adjacent foreland regions in Switzerland, Austria and Southern Germany (Fig. 7a ). The second -moist Mediterranean air masses lifted over a blocked cold-air layer -was even more intense and slightly shifted eastwards (Fig. 7e ). In combination with rapid snowmelt, the two rainfall episodes caused flooding and costly damages particularly in eastern Switzerland, but also in Germany and Austria (Bundesamt für Wasser und Geologie, 2000).
The variations between datasets are quite different in the two episodes. In the first case, location and intensity of the precipitation peaks differ considerably. HIRLAM misses the core of the heavy precipitation system entirely. MESAN partly corrects for this, but the sevenday total is still underestimated by more than 30 %. In this episode, E-Obs shows the best correspondence with APGD. It correctly positions the peak intensity, however, again underestimates the totals. For the second period, the estimated fields are surprisingly similar. Still, the three evaluated datasets underestimate the peak intensity. In this case HIRLAM shows the best agreement with APGD. The 22-km version of MESAN, however, shows a clear underestimate of the peak intensity. The depicted cases summarize impressions that we gained from a visual comparison of the studied datasets for a series of heavy precipitation cases in the Alps.
Scores Results from a quantitative comparison of daily precipitation in the evaluated datasets against APGD are shown in Figure  8  and 9. Figure  8  depicts the spatial distribution of root mean square error (RMSE, mm per day) and the Stable Equitable Error in Probability Space  (SEEPS, Rodwell et al. , 2010, dimensionless). The scores were determined from the daily precipitation series of year 2008 for each gridpoint of the 22-km versions of the datasets (see Section 4). Figure  9  compares mean values of the two scores averaged over subdomains (see Fig. 1 ) and stratified between winter half-year (defined here as the period from October to March) and summer halfyear (April-September). For most of the datasets the spatial distribution is different between the two scores (Fig. 8 ). RMSE tends to be larger in areas with a more vigorous precipitation climate (Massif Central, Ticino, Julian Alps, cf.
Fig. 5 ), because it weights similar relative errors more heav-ily at high compared to low intensities. On the other hand, SEEPS tends to show more evenly distributed patterns due to its implicit calibration to the local climate (categories defined by quantiles). RMSE is considered here primarily to quantify the magnitude of typical local  errors, while SEEPS is used for comparing the degree of correspondence between different regions of the domain (see also  Rodwell et al. , 2010). For the European interpolation dataset (E-Obs) and the downscaling datasets (MESAN and MESCAN) the density of input data seems to be a key factor of dataset reliability. In E-Obs this manifests in clear contrasts of SEEPS between Germany and Slovenia on the one hand, and France and Italy on the other hand (see Fig.
8 , and regions BAV and SEMC in Fig. 9 ). These are exactly the regions with particularly dense and coarse station coverage respectively in the underlying ECA&D dataset (see Section 2). In MESAN and MESCAN, where highresolution observations were also used over France (on top of the ECA&D data), the SEEPS score replicates the pattern of data density too and shows pronounced contrast across country borders, such as the French-Italian border (Fig. 8 ). Both downscaling datasets reveal very small values both for RMSE and SEEPS over France (region SEMC in Fig. 9 ), indicating very close correspondence with APGD. This is not surprising considering that over this part of the domain MESAN and MESCAN used the same high-resolution station data like APGD, except for differences in the data quality procedures.