dataesr/scientific-paragraphs-categorization huggingface dataset:
For precipitation, it can happen that for low quantiles, the probability of precipitation is lower in the RCM than in the observation dataset (i. e. several null values in the RCM, which can correspond to different positive values in the observational data). In this case, a random draw is performed amongst the observation values within the same quantile. 6. Selection of analogue date for sub-daily disaggregation: for each day in the RCM dataset, an analogous date is chosen in the observational dataset, matching the following criteria: the month and the weather regime must be the same as in the RCM dataset, and whenever possible, consecutive time slices are chosen in the observational dataset in order to avoid artificial jumps in the final data linked to the choice of analogues. A further criterion is applied to ensure that the weather situations are even more comparable between the RCM date and the analogous date from the observational record, based on precipitation consistency (wet vs.
dry conditions). A threshold of 1 kg m -2 day -1 on total precipitation is applied to partition dates between dry and wet conditions. For the first RCM date, a random draw amongst all available observational dates is performed, then the dates are browsed through chronologically until one meets all the requirements outlined above. This analogous day is then used in the following step for all variables. If the following analogue day in the observations still meets all requirements, it is selected as analogue for the following day in the RCM (to ensure as far as possible consecutive time slices). A new random draw is only performed once the analogue fails to meet all requirements described above. 7. Sub-daily disaggregation: the adjusted RCM dataset is disaggregated from a daily integration period into an hourly time step by using the hourly observational data from each analogous date chosen in the previous step to reconstruct the daily cycle of the data: X h RCM (i) = a × X h OBS + b, (2) where X h RCM (i) is the hourly-adjusted RCM value of the variable X and X h OBS is the hourly observational value of the same variable from the chosen analogous date (step 6).
To avoid this,  Olsson et al. (2015)  separated temperature data into wet and dry days before adjustment. In our case an additional quantile mapping against the observational dataset is applied for daily cumulated adjusted RCM rainfall and snowfall separately. Hourly-adjusted RCM rainfall and snowfall (a 2 ) are then determined by applying the ratio between daily rainfall or snowfall after quantile mapping (A 2 ) and daily rainfall or snowfall before quantile mapping (A 1 ) to the hourly rainfall or snowfall before quantile mapping (a 1 ) a 2 = a 1 × A 2 A 1. (7) If A 1 = 0 and A 2 = 0, then a 2 = 0. If A 1 = 0 and A 2 = 0, then a 2 = A 2. 9. Final adjusted dataset: the resulting adjusted hourly time series for each variable are obtained for each snow year (from the 1st of August to the 31 July of the following year), matching the format of the observational dataset.
SAFRAN reanalysis and application of ADAMONT method using SAFRAN Although the ADAMONT method is highly generic and can be applied using any hourly-resolution observational dataset, in the following we focus on the use of ADAMONT using the SAFRAN reanalysis data as an observational dataset. We first describe SAFRAN, then we present specific features of the ADAMONT method when using SAFRAN as the observational dataset. The SAFRAN system is a regional-scale meteorological downscaling and surface analysis system  (Durand et al. , 1993) , which provides hourly data of temperature, precipitation amount and phase, specific humidity, wind speed, and shortwave and longwave radiation for each mountain region (or "massif") in the French Alps (23 massifs, as illustrated in Fig. 3 ) but also in the French and Spanish Pyrenees and Corsica.
, 2005)  with various meteorological observations including in situ mountain stations, radiosondes and satellite data. It was complemented after the end of the ERA-40 reanalysis (  2002 ) by large-scale meteorological fields from the ARPEGE analysis so that it now spans the period from 1959 to 2016, making it one of the longest meteorological reanalyses available in the French mountain regions. When the ADAMONT method is applied using the SAFRAN reanalysis, only one geographic coordinate is used for each massif, corresponding to the centre of the massif (see Fig. 3 ). However, for each massif several altitude levels are considered, which means that depending on the N factor considered different RCM grid points may be selected for a given massif and altitude. Also, in order to maximise the consistency between massifs after the adjustment process, the dry/wet analogue day criterion used for the time disaggregation of RCM-adjusted variables into hourly variables is computed generally for the entire SAFRAN dataset, here in the 23 French Alp massifs.
snow precipitation. The one-dimensional multilayer physical snow scheme Crocus is able to simulate the evolution of the snowpack over time by accounting for several processes occurring in the snowpack, such as thermal diffusion, phase changes, metamorphism, etc. The SAFRAN-Crocus model chain has been operationally used for more than 20 years for avalanche hazard forecasting and extensively evaluated over the alpine domain in particular with snow depth observation stations  (Durand et al. , 1999 (Durand et al. , , 2009b;; Lafaysse et al. , 2013). Here we apply the Crocus model using either the SAFRAN reanalysis itself or adjusted fields from a given RCM using the ADAMONT method, in order to compute and compare snow conditions using either driving data. This is both a proof-of-concept of the applicability of the ADAMONT method to generate data appropriate to driving land surface models and a means to assess the intervariable consistency of the ADAMONT output given that Crocus is simultaneously sensitive to all meteorological fields and potentially disturbed by inconsistencies in the forcing dataset.
-Rain/snow quantile mapping: the method was applied with (default case) or without ("no corr") the last adjustment step operating on the rainfall and snowfall separately. -Raw RCM data: raw RCM simulations, without any adjustment, were considered for some of the variables (temperature and precipitation only) and compared to adjusted results. This can not be used in the case of snow depth because daily-resolved RCM output cannot be employed to run Crocus. -The impact of using 6 h input RCM data instead of daily data was also tested but yielded similar results (not shown). Only results based on daily RCM input are presented because GCM/RCM outputs are often available at this time step on data distribution platforms such as the one of EURO-CORDEX. The following indicators were analysed for temperature, total precipitation and snow depth: the seasonal average time series from 1980 to 2010 in the SAFRAN and the adjusted RCM datasets; the mean annual cycle over two distinct periods:  1980-1995 and 1995-2010  in the SAFRAN and the adjusted RCM datasets; the mean value for each elevation band over the evaluation period in the SAFRAN and the adjusted RCM datasets; the correlation and the ratio of standard deviations between time series of the SAFRAN and the adjusted RCM datasets for each variable and as a function of the integration window (from 1 day to several years) over the evaluation period; the cumulated PDF of daily variables over the evaluation period in the SAFRAN and the adjusted RCM datasets; the root mean square error (RMSE) and the mean bias over the evaluation period, computed over seasonal integration periods based on the SAFRAN and the adjusted RCM datasets (to evaluate the method performance in terms of reproducing amounts); scores specific to the detection of occurrence of precipitation events in the SAFRAN and the adjusted RCM datasets over the evaluation period: the probability of detection (POD = n hh /(n hh + n hd )), the false alarm rate (FAR = n dh /(n dh + n hh )), the probability of false detection (POFD = n dh /(n dh + n dd )) and the true skill score (TSS = POD -FAR), where n hh is the number of days which are wet in the SAFRAN and wet in the adjusted RCM, n dd the number of days which are dry in the reanalysis and dry in the adjusted RCM, n hd the number of days which are wet in the reanalysis but dry in the adjusted RCM and n dh the number of days which are dry in the reanalysis but wet in the adjusted RCM (a threshold of 1 kg m -2 d -1 was considered for the occurrence of precipitation); scores for the duration and persistence of precipitation events over the evaluation period  (Wilby et al.
Spatial variability and statistical characteristics of the variables This section provides the evidence needed to assess the performance of the ADAMONT method applied to a RCM driven by a global reanalysis (ERA-Interim) using the SAFRAN meteorological reanalysis as the observational dataset in the French Alps. Adjusted RCM data are compared to SAFRAN itself. Adequate performance of the method is attained when the two datasets match best. Figure  4  presents the location of the Vercors massif and its average temperature, precipitation and snow depth for each elevation band, for the evaluation period in the SAFRAN/Crocus reanalysis as well as adjusted RCM. The shape of the mean altitudinal evolution of all three variables is well represented compared to SAFRAN, which is also the case for other massifs (see the Supplement).
Conclusions The new method ADAMONT is able to statistically adjust daily regional climate model projections and to provide hourly-adjusted outputs of temperature, precipitation, wind speed, humidity and short-and longwave radiation necessary to force energy balance land surface (impact) models. The method processes daily outputs from an RCM and adjusts them with a sub-daily (typically hourly) observational dataset. The method was evaluated using outputs from the ALADIN-Climate RCM driven by ERA-Interim reanalysis for the time period 1980-2010, using the SAFRAN meteorological reanalysis in the French Alps as an observation dataset. The direct outputs of the ADAMONT method, namely temperature and total precipitation, as well as an indirect output, namely snow depth, computed by the Crocus model from meteorological variables corrected independently of each other were evaluated.
e. after a first quantile mapping on total precipitation, an additional quantile mapping against the observational dataset is applied for daily cumulated adjusted RCM rainfall and snowfall separately) is unambiguously demonstrated. Using a grid point selection technique relying on spatial but also altitudinal proximity between SAFRAN massif centre points and RCM grid points either had no impact on the performance indicators or degraded them for altitudes higher than 2100 m a. s. l. As a consequence, the simple spatial grid point neighbour selection technique will be retained for future applications of the method. The ADAMONT method is generic and can be applied to any observational dataset. Its application using the SAFRAN reanalysis as the observation dataset is somewhat a specific case, initially tailored for French mountainous regions  (Durand et al.
, 2009a). However, beyond the French mountain regions, the method could be applied in France using the SAFRAN-France gridded reanalysis  (Vidal et al. , 2010). A Spanish version of SAFRAN was also developed recently  (Quintana-Seguí et al. , 2017). The method could also be applied to other observational datasets or meteorological reanalyses, such as ERA-Interim surface fields  (Dee et al. , 2011)  or MESCAN  (Soci et al. , 2016). The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-10-4257-2017supplement.
Acknowledgements. This study benefited from funding from the  French Ministry for Ecology (MEEM)  through the  GICC program  and  ONERC , in the framework of the  ADAMONT  project. It also forms part of the  Interreg  project  POCTEFA/Clim 'Py. CNRM/CEN is part of  LabEX OSUG@2020  ( ANR10 LABX56 ). This work contributes to the  CDP-Trajectories  project, supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). We thank  S. Bernus ,  P. Lassegues ,  F. Besson  and  V. Gouget at Météo-France  for helping to make the method more robust. We thank the two anonymous reviewers for useful and constructive comments. Edited by: James Annan Reviewed by: two anonymous referees.
Photogrammetry has recently seen a rapid increase in many applications, thanks to developments in computing power and algorithms. Furthermore with the democratisation of UAVs (Unmanned Aerial Vehicles), close range photogrammetry has seen more and more use due to the easier capability to acquire aerial close range images. In terms of photogrammetric processing, many commercial software solutions exist in the market that offer results from user-friendly environments. However, in most commercial solutions, a black-box approach to photogrammetric calculations is often used. This is understandable in light of the proprietary nature of the algorithms, but it may pose a problem if the results need to be validated in an independent manner. In this paper, the Damped Bundle Adjustment Toolbox (DBAT) developed for Matlab was used to reprocess some photogrammetric projects that were processed using the commercial software Agisoft Photoscan.
INTRODUCTION Close range photogrammetry has often been used to acquire 3D data (e. g. shape, position, and size) from images  (Grussenmeyer et al. , 2002). The rise in the use of UAVs (Unmanned Aerial Vehicles) and rapid developments in imaging technology and image processing have increased the use of close range photogrammetry for mapping purposes  (Murtiyoso and Grussenmeyer, 2017). This relatively low cost solution  (Barsanti et al. , 2014)  for mapping and reality-based 3D modelling is often complemented by commercial, easy-to-use photogrammetric and/or SfM (Structure-from-Motion) software packages. Although some open source software alternatives exist  (Pierrot-Deseilligny and Clery, 2012; González-Aguilera et al. , 2016) , commercial software such as Eos System's Photomodeler, Pix4D, and Agisoft Photoscan remain very popular, especially outside the photogrammetric community due to their simplicity in creating fairly accurate results  (Grussenmeyer et al.
(Luhmann et al. , 2014). The primary aim of this paper is to test whether the open source toolbox DBAT (Damped Bundle Adjustment Toolbox)  (Börlin and Grussenmeyer, 2013)  can be used to perform the bundle adjustment reprocessing of terrestrial and UAV photogrammetric projects that were previously processed using the commercial software Agisoft Photoscan (PS). Compared to classical aerial photography, images provided by terrestrial and UAV close range acquisitions present a particular problem absent in traditional aerial photography, in that the image and control point configuration is often irregular. It is therefore in the interest of some users to understand the results in a more detailed manner. In addition, the projects used in this paper contain up to a few hundred images, which is higher than the number of images tested for DBAT in previously published studies.
SOFTWARE AND RELATED WORK The UAV was originally designed for military purposes, but has since seen many civilian uses in recent years. Photogrammetry by UAV opens many possibilities for its application in closerange situations. It complements terrestrial acquisition of 3D information  (Nex and Remondino, 2014). In its role as an aid to photogrammetric work, the UAV has seen many applications in various fields, such as disaster management  (Achille et al. , 2015; Baiocchi et al. , 2013) , 3D building reconstruction  (Roca et al. , 2013) , surveying/mapping  (Cramer, 2013) , and heritage documentation  (Chiabrando et al. , 2015; Murtiyoso and Grussenmeyer, 2017). The Damped Bundle Adjustment Tools (DBAT) is a series of functions developed in the Matlab language that enables the user to reprocess bundle adjustment projects generated by Photomodeler or Photoscan.
This paper uses DBAT version 0. 6. 5. 5. Agisoft Photoscan (PS) is a 3D reconstruction software which may be used for both aerial and terrestrial images. PS offers an easy-to-use graphical interface and workflow, with further possibilities to perform automation using Python scripts. PS employs a computer vision-leaning approach to generate 3D models. This presents a particular challenge to compare it to photogrammetric conventions since some terms  (Granshaw, 2016)  and definitions  (Hastedt and Luhmann, 2015; Murtiyoso et al. , 2017a)  are different. DBAT has recently been developed to accommodate these differences between photogrammetry and computer vision, particularly in terms of lens distortion parameters. This paper uses the PS version 1. 3. 4 build 5067.
Data sets Two data sets were used in this paper; a UAV data set and a terrestrial close range data set. The UAV data set was of the St-Pierre-le-Jeune church which has previously been modelled using several software solutions  (Murtiyoso et al. , 2017a)  and will serve as a basis for the reprocessing using DBAT. The St-Pierre-le-Jeune church is situated at the recently enlisted UNESCO World Heritage Site of Neustadt, in the city of Strasbourg, France. Although the church in its entirety has been documented in 3D, for the purposes of this research only the principal façade will be reprocessed in DBAT. The St-Pierre data set consisted of 239 images (Figure  1 ) each with a 38 MP resolution. Among these images, 67 were taken from a perpendicular point of view while the rest were oblique images taken with the sensor oriented upwards, downwards, to the left, and to the right.
In addition to this field acquisition, the Albris sensor was calibrated beforehand using a set of coded targets that was put in a dedicated room. The coded-targets were measured using a total station in order to give a rigorous setup for the calibration. The sensor was then calibrated in PS. The precalibrated values were used in one of the scenarios tested in this paper, while tests using approximate values derived from the images' EXIF file were also performed. The second data set, "Lacey", was a terrestrial close range acquisition of several World War I graffiti in the Maison Blanche underground site in Neuville-Saint-Vaast, northern France. The graffitis have a special historical interest as they were made by Canadian soldiers several days prior to the Battle of Vimy Ridge on April 1917.
The entire Maison Blanche has similarly been modelled in a previous research  (Murtiyoso et al. , 2017b) , and in this paper only a segment will be reprocessed in DBAT. The Lacey comprises of 346 images at 50 MP acquired by a Canon EOS 5DR camera with a 28 mm lens. Five coded-target GCPs were measured on this data set. In order to perform verifications for the results, two additional check points were measured indirectly from the laser scanning point cloud of the same site. The laser scanner used to this end was a FARO Focus X330. The Lacey data set was taken from an average camera-to-object distance of 2 meters, giving a theoretical GSD of 0. 3 mm. The main difference between the scenarios lies in the self-calibration parameter configuration. The scenarios marked beginning with an "S" signify the processing of the St-Pierre data set, while those marked with "L" at the beginning signify the processing of the Lacey data set (see Table  1 ).
RESULTS AND DISCUSSIONS An illustration of the result of the bundle adjustment in the two algorithms tested is shown by Figure  2. In general, all algorithms managed to reach convergence in their computation and orient all images in all of the proposed scenarios. Several analyses regarding the results of the self-calibration process and the performance of each algorithm will be described in this section. The self-calibration analysis of the UAV sensor will be emphasized in this paper, while the GCP and CP analysis will be performed for both data sets. The UAV sensor selfcalibration is of particular interest since it consists of a small low-cost sensor in a parallel geometry acquisition, compared to the convergent geometry of the DSLR camera in the Lacey data set.
Differences with the precalibrated values are to be expected since the conditions during the calibration are not exactly the same as the conditions during the real acquisition. Furthermore, some difference between PS and DBAT may be expected because PS most probably performs a free network adjustment followed by a conformal 3D transformation, whereas DBAT includes GCPs in its bundle adjustment computation. It is also interesting to note that PS and DBAT arrived at the same calibration values in S1 (with EXIF initial values) and S4 (with precalibrated values as initial values). However, it should be noted that the case of the St-Pierre data set presents a particular case where oblique photos were also included in the bundle adjustment process; this increases the strength of the acquisition network geometry.
GCP and CP verification Results of the bundle adjustment show that for the UAV St-Pierre data set a maximum difference of 0. 3 mm for the GCP RMS between DBAT and PS were observed. The maximum difference of CP RMS for the same data set was also 0. 3 mm, for a theoretical GSD of 1. 4 mm. For the Lacey data set, the maximum GCP RMS difference was 0. 5 mm and 0. 7 mm for CP RMS for a theoretical GSD of 0. 3 mm. In the Lacey data set, a higher value of RMS can be explained by the fact that the GCPs were measured using a coded-target, which would have had a very small marking precision (the default PS value was 0. 1 pixels). This influences the weighting in the bundle adjustment, as shown also on the value of the σ 0. Indeed, the σ 0 value of 4. 5 on average indicates that one of the a priori standard deviation was heavily underestimated, which is true in this case.
Furthermore, the even higher values for the CP RMS in the Lacey data set is due to the fact that the CPs were indirectly measured from the laser scanner data set, and concern points of interest rather than clear artificial marks or coded targets. The centimetric result for the CP RMS is therefore expected. In the St-Pierre data set, several other factors also contribute to the final RMS result. The GCPs were distributed evenly on the façade; however the lack of depth variation between the GCPs may contribute to the final RMS. Furthermore, the noise present on the images also generates another source of error. However, the main objective of the experiment was to compare the performance of PS and DBAT. In this regard, DBAT has managed to reprocess PS projects under approximately the same conditions and weighting, although a slight difference is always to be expected when dealing with a black-box solution.
Quality control using DBAT In terms of correlation values, Table  4  shows the high correlation values between the different calibration parameters as well as the number of automatic tie points with high correlation values in all the scenarios tested. In the cases where K 3 is calculated in both data sets, the results show a strong correlation between the radial distortion coefficients. The standard deviation values given by DBAT for the calibration parameters are also useful to assess the quality of the selfcalibration process. As regards to the automatic tie points, the St-Pierre data set shows that over a quarter of its tie points have a strong correlation of more than 95%. The Lacey data set only has 0. 24% of its tie points which has a correlation of more than 95%. A strong correlation may mean that the parameters in the bundle adjustment were not solved correctly.