dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicates that the COSMOS-Europe data could be beneficial for model applications at the continental scale despite the limited coverage in some areas of Europe, i. e. , even though the measurements are not area-wide such as remote sensing data. Furthermore, at present only a low number of CRNS stations are automatically transferring neutron count data to the TERENO database that hosts the COSMOS-Europe data. However, a near-real-time availability of the data would be necessary in particular for the use of the data for the improvement of flood models, e. g. , in context of the European Flood Awareness System (EFAS,  Smith et al. , 2016). Efforts should be made in the future to equip more stations with au-tomatic data transfer capabilities to enable rapid transfer of neutron counts to the TERENO database.
Here, the implemented automated routines for data pre-processing, correction, and neutron counts to soil moisture allow for immediate provision of COSMOS-Europe data products. Finally, we would like to point out that there are still alternative data products to some of the CRNS stations used here, which were processed using less comprehensive methods. Although our processing methods closely follow the commonly used method presented by  Zreda et al. (2012) , there are some important differences as we make use of the latest CRNS research findings from the last 10 years. For example, the US COSMOS database (http://cosmos. hwr. arizona. edu/, last access: 2 March 2022) does not contain corrections for air humidity, which were suggested by  Rosolem et al. (2013)  and  Köhli et al. (2021). Another main difference of our processing scheme is the weighting of the in situ calibration data according to neutron transport theory  (Schrön et al.
Data availability The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is stored in a common data format and shared via Forschungszentrum Jülich (https://teodoor. icg. kfa-juelich. de/ibg3butt/ibg. butt. download?FileIdentifier= 519e0691-7eb3-4351-aff5-c0a0335933ab, last access: 24 September 2021); https://doi. org/10. 34731/x9s3-kr48 (Bogena and Ney, 2021). Potential users can also access the data of the individual CRNS stations in a dedicated section for COSMOS-Europe in the TERENO data portal TEODOOR at https://ddp. tereno. net/ddp/dispatch?searchparams=keywords-Cosmic%20Ray (last access: 2 March 2022). Here, metadata information about the stations (e. g. , site owner) as well as the raw data and the processed data products can be accessed.
Conclusions and outlook In this data paper, we present soil moisture data from 65 CRNS stations that are distributed across Europe and cover all major land use types and climate zones. The raw neutron count data from the CRNS stations were processed using state-of-the-art methods in a harmonized way including correction of the raw neutron counts and conversion into soil moisture based on available in situ information. In addition, information on the data uncertainty is added to the datasetinformation that is particularly useful for remote sensing and modeling applications. It should be noted that the sites have individual heterogeneous conditions, which cannot always be adequately reflected by a standard processing scheme. In addition, the data processing used in this work represents the state of the art, but this may change as a result of future research.
We therefore provide raw data and will update the published dataset with an incremental version number if new processing procedures become accepted in the future. We show that the COSMOS-Europe dataset enables a good representation of the magnitude and distribution of drought events. However, so far, only the central part of Europe is particularly well covered by COSMOS-Europe, while there are still large gaps in the peripheral areas of Europe. The density of COSMOS stations in Europe is still not sufficient to completely represent soil moisture patterns across all parts of the continent. Thus, future efforts should invest in higher observational coverage. One emphasis in the further development of COSMOS-Europe must be to convince countries to put CRNS stations into operation that do not yet operate CRNS stations or hardly any.
In addition, efforts should be made in the future to equip more stations with automatic data transfer capabilities to enable near-real-time accessibility of soil moisture information, e. g. , to support flood forecasting. The data presented here can be used for a manifold of hydrological applications, such as drought assessment, flood risk assessment, and snow water estimation. Similar to COSMOS-Europe, several other large-scale COSMOS networks already exist in the USA, Australia, and India. The obvious next step is to build on the methods developed in this study to create a global network of continental COSMOS networks, similar to the FLUXNET initiative for eddy covariance measurements of land-atmosphere exchange fluxes  (FLUXNET, 2021)  or the International Soil Moisture Network (ISMN) that provides in situ soil moisture data from 2842 stations worldwide  (Dorigo et al.
Acknowledgements. We thank  Peter Strauss  and  Gerhab Rab  from the  Institute for Land and Water Management Research, Fed-eral Agency for Water Management Austria, Petzenkirchen, Austria. We thank  Trenton Franz  from the  School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States. We also thank  Carmen Zengerle ,  Mandy Kasner ,  Felix Pohl , and  Solveig Landmark ,  UFZ Leipzig , for supporting field calibration, lab analysis, and data processing. We furthermore thank  Daniel Dolfus ,  Marius Schmidt ,  Ansgar Weuthen , and  Bernd Schilling ,  Forschungszentrum Jülich, Germany. The  COSMOS-UK  project team is thanked for making its data available to COSMOS-Europe. Luca Stevanato  is thanked for the technical details about the Finapp sensor. The stations at Cunnersdorf, Lindenberg, and Harzgerode have been supported by  Falk Böttcher ,  Frank Beyrich , and  Petra Fude ,  German Weather Service  (DWD).
The Zerbst site has been supported by  Getec Green Energy GmbH  and  Jörg Kachelmann  (Meteologix AG). The CESBIO sites have been supported by the  CNES TOSCA program. The ERA5-Land data are provided by  ECMWF  ( Muñoz Sabater ,  2021 ). The Jena dataset was retrieved at the site of The Jena Experiment, operated by  DFG  research unit FOR 1451.
Financial support. We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BioökonomieREVIER, Digitales Geosystem -Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK's Nathttps://doi. org/10. 5194/essd-14-1125-2022Earth Syst. Sci. Data, 14, 1125Data, 14,  -1151Data, 14,  , 2022.
Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection.
Here, the same approach is used for a different location (mid-latitude) and on a long time period to get robust statistics. To do so, the study is based on direct measurements from the SIRTA-ReOBS dataset  (Chiriaco et al. , 2018) , which includes many variables collected since 2002 at SIRTA (Site Instrumental de Recherche en Télédetection Active;  Haeffelin et al. 2005) , an observatory located in a semi-urban area in the southwest of Paris, France. This dataset is well suited to the current study objective because (i) it allows the use of a multi-variable synergistic compilation to study and compare different characteristics of the atmosphere or the surface (e. g. ,  Bastin et al. , 2018; Dione et al. , 2017; Cheruy et al. , 2013; Chiriaco et al. , 2014) , and (ii) it is located in western Europe and allows access to the hourly timescale, i.
e. , the scales of the local processes of the current study. The SIRTA-ReOBS dataset, along with some variables retrieved from ERA5, enables the development of a model to estimate all the terms involved in the near-surface temperature variations using predominantly real observations. The use of the model developed in the current study considers all the variables acting within the atmospheric boundary layer (ABL) and controlling surface temperature variations, all of them estimated almost exclusively from surface-based observations. Thus, it allows us to separately study the influence of each SEB term in a local scale. This indeed allows a realistic and reliable estimation of the contribution of each term (radiative fluxes, turbulent heat fluxes, etc. ) on hourly temperature variations, and it would be possible to have that at different sites since each term will present a different behavior and importance.
, 2005; Dewitte and Clerbaux, 2017; Willson et al. , 1981; Allan et al. , 2014; Cherviakov, 2016)  or at the surface  (Wild et al. , 2015; Hakuba et al. , 2013; Wild, 2017)  or both at the same time  (Hartmann, 1993; Kato et al. , 2012; Li and Leighton, 1993) , but a lack of studies investigating their impact on a smaller scale is noted due to limited reliable ground-based measurement availability. In this study, to understand how clouds influence the 1 h temperature variations, cases with a particular cloud effect on temperature variations are more deeply analyzed using lidar profiles. To achieve these two objectives, the current paper is organized as follows: the dataset is presented in Sect. 2. Section 3 (and Appendix A) describes how the different terms acting on hourly temperature variations are estimated and evaluates how well the model fits the hourly observations based on statistics.
Data used for the temperature variation estimation model This study is mainly based on the SIRTA-ReOBS dataset analysis. The SIRTA observatory  (Haeffelin et al. , 2005)  is located in a semi-urban area 20 km southwest of Paris (48. 71   1 , allowing a multi-year analysis. Some variables that were retrieved from measurements at the SIRTA observatory sometimes present gaps due to instrumental issues  (Chiriaco et al. , 2018; Pal and Haeffelin, 2015). In particular F lat and F sens variables are limited (with only 5 % and 73 % availability, respectively): Sect. 3 and Appendix A show how this issue is handled. Figure  1  shows the complete dataset availability in the SIRTA-ReOBS dataset for the 5-year study period. The availability of data is quasi-homogeneous around 60 % for all hours (Fig. 1a ).
Most of the time gaps are due to the absence of the mixing layer depth (MLD) variable for some complete days, extended sometimes for more than 2 months (not shown), restricting 71 % of MLD data availability. Other gaps are caused by the absence of radiative variables (9 % of missing data; see Table  1 ). Summer is the season with the best data coverage (70 %), and winter has the least data coverage (56 % -Fig. 1b ), whose absences are due precisely to gaps in the MLD variable. To complete this dataset, hourly ERA5-Land (horizontal resolution of 0. 1 • × 0. 1 • ; Copernicus Climate Change Service, 2019) is used to estimate the horizontal wind at 10 m and the temperature near the surface T 2 m , as required for the advection term (see Sect. 3 and Appendix A). Furthermore, the temperature at the mixing layer depth (T MLD ) is also retrieved from the ERA5 (spatial resolution of 31 km and 137 levels up to 1 hPa; ERA5, 2020).
In this section, a model which estimates these four terms is presented, based on several meteorological observations retrieved from the SIRTA-ReOBS dataset. Each term involved in this model is described (further descriptions are presented in Appendix), and then a statistical evaluation is performed to assess how well the model follows the real observations of temperature variations. Finally, the mean monthly-hourly and annual cycles of the averaged contribution of each term and the same for 1 h temperature variations ( ∂T 2 m ∂t obs ) are presented (split into day and night).
2, i. e. , predominantly based on observations retrieved from SIRTA-ReOBS, but also on ERA5 datasets. Concerning the advection term, the computation of this term requires the extraction of the temperature at 2 m (T 2 m ) and northward (v 10 m ) and eastward (u 10 m ) wind components at 10 m, at the SIRTA grid point and the surrounding grid points. The temperature at the mixing layer depth T MLD is retrieved using SIRTA-ReOBS combined with ERA5 datasets. First, radiosoundings available in SIRTA-ReOBS (twice a day) are used to get the pressure at the MLD level, and then T MLD is retrieved from the vertical temperature profile in ERA5 at the nearest grid box from SIRTA Observatory (48. 7 • N and 2. 2 • E) and at this pressure level but at the closest time. Note that uncertainty remains in T MLD because it is the temperature at a certain pressure level, which is only available twice a day.
The two other terms (HG and Adv) have a similar but weak impact on the temperature variation model at this timescale. In addition, the PDF of the ∂T 2 m ∂t mod is compared to ∂T 2 m ∂t obs in 1 h in Fig. 2b : differences occur for cases where the temperature decreases during the hour, but these differences correspond to some cases where the model presents more negative values than the observations, around -1 • C h -1. The modeled PDF fits very well with the observed PDF for cases where temperature increases during the hour (a smaller number of cases as shown by the PDFs). Figure  2c  shows the scatter plot of the observations versus the model. The correlation between the two datasets is good (0. 79) and the bias remains small (-0. 20 • C h -1 ). However, the model has difficulties in reproducing the extremes of temperature change within an hour, e.
3a  and 4c ). The advection term does not present a strong monthly-hourly cycle compared to the other terms, although one can distinguish a mean negative action (still very low) to local temperature variations at all seasons with a mean minimum in July in the afternoon of -0. 12 • C h -1 , as shown in Fig. 4e. Lastly, the residual, defined as the difference between the model (sum of all terms) and the observations (i. e. , ∂T 2 m ∂t mod -∂T 2 m ∂t obs ), also has a seasonal variability and is mainly negative, but a minimum difference of -0. 45 • C h -1 between the two datasets is found in April between 07:00 and 09:00 UTC, which is partly related to the negative increase in the HA term at that time. This is due to the important absence of latent heat flux data especially for this month, which implies an increase in the bias when τ a is estimated (see Table  1  and Appendix A).
General behavior of the weight of the different terms Firstly, the random forest method is used to establish which term dominates the temperature changes in the model ( ∂T 2 m ∂t mod ) during day and nighttime. The predictor importance estimate value is defined as the sum of the mean square error (MSE) of each term averaged over all decision trees used and normalized by the standard deviation taken over the trees. This principle consists of permuting the values of a term -predictor -in the decision trees where this term was left out-of-bag and assessing how much worse the MSE becomes after the permutation  (James et al. , 2013, Chap. 8 ). Thus, the larger this value, the more important the term. This importance estimation feature from the random forest method has been previously used to calculate the variable importance in different datasets for many applied problems in sciences or health fields for both regression and classification studies (e.
Discussion on the specific role of clouds in temperature variations Section 4 shows that clouds are the main modulator of solar radiation on hourly temperature variations during the day (and the main contributor during the night). Knowing how and in what measure each term contributes to temperature variations, a deeper analysis is performed in this section in order to better understand the role of clouds. This analysis is performed by considering other variables available in the SIRTA-ReOBS dataset to characterize both the atmosphere and the clouds. In the following, only cloudy cases are considered. These cases are identified based on a criterion on the absolute value of cloud radiative effect (CRE) that must be higher than 5 W m -2 in SW and LW  (Chiriaco et al. , 2018)  during daytime, whereas for nighttime only LW is considered for this criterion.
5 (bin 3). The cases with negatives or values of R CS very close to zero (10 % of the cases), which occurred in early morning and late afternoon, are excluded since they affect the sign of the distribution or give divergent values. This histogram along with the one used in Sect. 5. 2 are presented in Appendix C. Figure  8a  shows the values of the observed temperature variations divided by R CS , and Fig. 9  shows the distribution (represented as box-and-whisker plots) of different relevant meteorological observations available in the SIRTA-ReOBS dataset, for each bin created, split into seasons for daytime. To add cloud information, lidar data are also considered, but because they are not available all the time, and in particular when it is raining (see Sect. 2. 2), they are presented as additional boxplots (light color ones) that correspond to the lidar sampling (whereas the dark-color boxplots are for the total cloudy sampling).
7 • C h -1 ), whereas clouds cool (warm) up to -3. 7 • C h -1 (1. 4 • C h -1 ). The atmospheric heat exchange becomes predominant in the late afternoons when turbulent fluxes develop due to the increase in atmospheric instability. Some of the biases found are due to the difficulty of the model to reproduce smaller-scale processes such as the cold pool events well. The use of a random forest analysis makes it possible to identify which term dominates over the others. Clear-sky radiation most influences the 1 h temperature variations during the day, whereas the cloud radiation has the most influence during the night followed by the ground heat exchange term. Nevertheless, separating the dataset into hours and seasons shows that the cloud radiation effect becomes predominant in several hours for all the seasons because it presents a greater variation in its hourly values than the clear-sky radiative effect, especially in spring and summer during daytime when more local cloud cover variability occurs.
This encounter of air masses (whether it is the warm air that encounters cold air or the opposite) tends to form mostly high-level clouds. Similar behavior has been found by  Chakroun et al. (2018)  during nighttime and  Mariotti et al. (2015)  over the Euro-Mediterranean area where the variability of cloud fraction (CF) is driven mostly by the encounter of two different air masses coming from northern Europe and southern Africa. During nighttime, even when clouds have a positive important radiative effect, temperatures are low and they are more controlled by large-scale processes since surface turbulent fluxes are low and there is no shortwave radiation most of the time. Since neither cloud fraction variable nor lidar data are available during nighttime, no information about the percentage of cloud presence can be known, yet variability on LWCRE is still found between clouds whose warming effect is weak and those whose warming effect is strong.
A2 Atmospheric heat exchange term This term is estimated as HA = T MLD -T 2 m τ a. (A12) In this equation, τ a can be calculated at equilibrium by setting the left side of the Eq. (A2) to zero and neglecting the ground heat exchange and advection terms (i. e. , for the temperature not to vary, a balance between these two latter terms must be set), leading to τ a = ρc p MLD (α + 1) • (T MLD -T 2 m ) F NET. (A13) For the atmospheric heat exchange, F NET = F lat + F sens. However, F lat has lots of missing values in the SIRTA-ReOBS dataset (material defection) and therefore does not allow us to perform a complete analysis for the 5 years of study. Hence, an hourly and monthly look-up table (LUT) is created to have an estimation of τ a. Moreover, T MLD is not directly available in the SIRTA-ReOBS dataset.
To retrieve that temperature, the mixing layer depth is first retrieved from the SIRTA-ReOBS dataset, by looking for the nearest radiosounding in time (two per day) and finding the pressure corresponding to this altitude; then it is possible to obtain the temperature at that pressure level by looking at them in the ERA5 dataset. Figure  A3  shows an average τ a for all the months of the year. During night, the relaxation timescale shows a higher month-to-month variability mainly due to the absence and gaps of the latent and sensible heat fluxes, which does not allow the reflection of a marked tendency of τ a , whereas for daytime a more clear behavior of τ a is spotted for all the months thanks to an increase in the availability of the data for these hours (not shown). Sometimes during nighttime, this relaxation time exceeds 30 h, probably because the turbulence is so weak at that time that the heat does not completely reach the surface boundary layer (SBL) established.
A4 Advection term Adv = u 10 ∂T 2 m ∂x + v 10 ∂T 2 m ∂y (A16) The advection term is estimated as presented in Eq. (A16) using the ERA5 dataset of horizontal wind components at 10 m and temperature at 2 m above the ground. The purpose of using this dataset is to have another point in each horizontal axis to calculate the transport of the mass of air in zonal and meridional directions, between the SIRTA observatory and the immediately following grid box. For the period and timescale considered, the advection term mostly plays a minor role, as shown in Fig. A2e , compared to the other terms.
Detailed information on how this method works is given here. The training algorithm for random forests applies the general technique of bagging, where the key to bagging is that trees are repeatedly fit to bootstrapped subsets of the observations. The bootstrapped term refers here to randomly choosing data that can be chosen several times to build decision trees (no selection restrictions). A training dataset is chosen randomly with replacement (bootstrapping) from the original dataset to create a decision tree. In regression techniques, the training dataset correspond to about two-thirds of the total of the sample. The remaining one-third of data not used to train that decision tree are used later as testing data but also to determine the importance of a specific term  (James et al. , 2013).
This procedure is repeated for all the decision trees used in the random forest. Finally, it is not necessary to have a validation dataset in this study because the main interest of using this machine learning is to determine the importance of the terms for the model developed, as it is known that random forest protects against overfitting by constructing training samples through bootstrapping. Some hyperparameters are tuned in order to optimize the analysis. -The random forest method is set to have 150 decision trees, because at that number the error converges to a small value, as seen in Fig. B1  (converging value of 0. 25 during daytime and 0. 12 during nighttime). -For the split criteria, since our model is a simple 1 • regression, the method is set to use the mean square error (MSE) to do the split at each leaf.