dataesr/scientific-paragraphs-categorization huggingface dataset:
CONTRIBUTIONS TO PUBLIC REPOSITO-RIES. The ERA-CLIM2 observations feed into existing repositories and are already used in other projects. This section gives a brief overview (Table  4  lists the contributions to existing repositories). By submitting data to existing repositories (from where they are retrieved for reanalyses projects), they become part of their update cycles. This guarantees that version numbers are unique and that older versions remain available. When reanalyses approach real time, observations will be taken from the real-time data stream and not from these repositories, but the data will still be available to the user via the OFA. The digitized surface data were submitted to ISPD (pressure) and ISTI (temperature). The ERA-CLIM2 upper-air data have been integrated into the latest version 2.
1 of the CHUAN [database [for version 2 see  Stickler et al. (2014) ], which is ASCII formatted (and can be downloaded from ftp://giub-torrent. unibe. ch /eraclim2/). Those data have been reformatted into the ECMWF Observation Database (ODB2) format and have been stored in the Meteorological Archival and Retrieval System (MARS) archive. In this format the data can be immediately read by the Integrated Forecast System. CHUAN2. 1 will be used in the back extension of the Copernicus Climate Change Services (C3S) ERA5 (http://apps. ecmwf. int/datasets/) from 1950, where it will provide an essential source of upper-air information. After assimilation the upper-air data only observations but also metadatabases and data holdings  (Thorne et al. 2017) , data rescue services  (Brönnimann et al. 2018, manuscript submitted to Geosci.
Data J. ) , and an active climate data community. The C3S contributes to make this undertaking sustainable via investment in services on data rescue and global database production. C3S is one of several dedicated information services recently established by the European Commission as part of its Copernicus Earth Observation Programme. The purpose of C3S is to provide open and free access to data, tools, and information needed to support climate adaptation and mitigation in Europe. Improving access to climate observations and reanalyses is a key objective. C3S is being implemented as an operational service similar to weather forecasting, which means that all data and products will be fully supported, quality assured, and reliably delivered on a well-defined schedule. This provides a unique opportunity to transfer some of the essential work on observations and reanalysis undertaken in ERA-CLIM and ERA-CLIM2 and related projects to a sustainable operational environment.
Various technical aspects of the work on observations described in this article are now included in C3S. For example, a C3S data rescue service is being developed that will build on the work started in ERA-CLIM on the collection and sharing of metadata on data rescue projects  (Brönnimann et al. 2018, manuscript submitted to Geosci. Data J. ). This service will also develop new tools for data rescue and promote best practices and in some cases provide direct support to data rescue communities (e. g. , in the form of capacity-building workshops). C3S is also taking steps to improve access to observations in existing climate data archives, by supporting the development and maintenance of a new global, quality-controlled database of historic surface observations, including land and marine data, building on existing holdings at NOAA/NCEI and many other sources  (Thorne et al.
2017). Similar activities will be initiated to ensure the maintenance and further development of a merged database of available upper-air meteorological observations, including all those collected in the ERA-CLIM and ERA-CLIM2 projects. EUMETSAT is continuing its recalibration and reprocessing activities within a dedicated C3S project in support of the next reanalysis of the satellite era. Production of global and regional model-based reanalyses, which until recently has relied almost exclusively on research funding, is now conducted as a fully supported operational activity under Copernicus. This includes ERA5, the latest ECMWF reanalysis, which will be completed in 2018, and incorporates various reprocessed satellite data records developed in ERA-CLIM. The next generation, ERA6, will likely be a coupled atmosphere-ocean reanalysis using data and methods developed in ERA-CLIM2.
CONCLUSIONS. Global dynamical reanalyses of the atmosphere and ocean rely on observations in various steps along the production chain. In addition to being assimilated into a reanalysis system, observations are used to constrain the model boundary conditions, for uncertainty determination of other observations, and for the evaluation of data products. In ERA-CLIM2, major efforts were devoted to observations, namely, data rescue, processing and management, compilation and quality control, and error estimation: • New sea surface and sea ice datasets were produced that serve as boundary conditions for reanalyses. • Hundreds of thousands of images were taken within ERA-CLIM and ERA-CLIM2 and 6. 4 million station days were digitized, amounting to tens of millions of data points. • Several snow products were generated, encompassing SWE and snow depth.
• Satellite data were recalibrated and reprocessed to provide consistent satellite data records from the 1980s until today. • The HadIOD and HadISD databases of qualitycontrolled surface and ocean measurements were extended and improved. A large fraction of the data were not assimilated into the reanalysis yet but fed into global repositories on which future reanalysis efforts will be based. The work on observations therefore is one cycle ahead of reanalysis production. The continuation of this effort is vital for the success of future reanalysis projects. ACKNOWLEDGMENTS. ERA-CLIM2 was funded by EU-FP7 (Grant Agreement 607029). The project collaborated closely with the Horizon2020 project EUSTACE (640171), as well as the Swiss National Science Foundation projects DECADE (147320) and CHIMES (169676).
Russian radiosonde data for 41 stations, 1938-64 167,401 167,401 167,401 Daily weather report, Germany, 1903-34 118,020 117,837 114,187 India Meteorological Department, 1928-36 113,779 113,779 113,779 Indian daily weather report, 1938-42 106,379 106,379 106,379 Pakistan daily weather report, 1949-56 101,714 101,714 101,714 Daily weather report, Cairo, Egypt, 1920-53 68,184 68,184 68,184 Aerological observations, Netherlands, 1909-40 59,054 59,054 59,054 International days, 1923-28 53,883 53,883 53,725 Monthly bulletin, Portugal (Mozambique), 1938-56 27,402 23,292 23,292 Annual bulletin, Finland, 1919-34 19,583 19,583 19,583 Moving upper-air data, 1888-1947 9,000 9,000 9,000 Météo-France database, 1948-58 0 33,818 33,818 Comptes Rendus, 1923-57 216,608 86,476 67,201 Other French sources, 1900-57 98,894 35,375 25,210 Spanish data, 1912-61 14,519 14,519 14,519 Portuguese annals, 1939-72 27,093 27,093 27,093 Other sources, upper air 77,632 75,746 75,762.
In North America, the National Urban Database and Access Portal Tool (NUDAPT) compiled this information for parts of more than 40 cities  (Ching et al. 2009 ), but in most places the data to derive UCPs are either not available/incomplete and/or available at poor spatial/ temporal resolutions. The absence of internationally consistent urban data for such purposes is recognized by global-to-urban climate science communities to be a significant impediment to scientific progress  (Jackson et al. 2010; Revi et al. 2014; Baklanov et al. 2009 Baklanov et al. , 2018)). Overcoming this impediment is the aim of the World Urban Database and Access Portal Tool (WUDAPT) project. In this paper, we review the concepts and operational methodologies that underpin WUDAPT  (Ching 2013; Ching et al. 2016 Ching et al.
(see Table  1  and Fig. 1b; Masson 2000; Martilli et al. 2002; Dupont et al. 2004; Otte et al. 2004; Oleson et al. 2008). Fourth, for worldwide applicability, UCPs should be collected using a scheme that is consistent and reliable. Finally, given the time frame, the generation of this database should be practicable and achievable on a reasonably short time frame for greatest impact. WUDAPT adopts a pragmatic approach to meet these criteria. The components of the urban landscape that are relevant to climate can be organized by scale into   Jason Ching, jksching@gmail. com The abstract for this article can be found in this issue, following the table of contents. (Stewart and Oke 2012). L1 data use the LCZ maps to provide a sampling context for acquiring and managing information at finer scales.
This information is used to classify Landsat scenes into LCZ maps using a random forest (RF) classifier implemented in the System for Automated Geoscientific Analyses (SAGA) software  (Conrad et al. 2015). The quality of the L0 data relies on the skill of the experts that create the TAs, and considerable effort has been placed on training experts and on independent assessment of the TA data. The current quality control scheme emphasizes the statistical reliability of a city database by randomly dividing   Table 2. the TAs into a set for training and a set for evaluation purposes. With each iteration, an LCZ map is generated for a given TA set, and the resulting LCZs are compared with the evaluation set; overall accuracy (OA) is measured as the percent of LCZ values that are predicted correctly.
Repeatedly sampling (i. e. , bootstrapping) from the TAs allows us to measure the robustness of the LCZ map, that is, the consistency of the LCZ map when using different sets of training areas. A WUDAPT committee that oversees the quality of the L0 data examines the final LCZ map to ensure that it provides an accurate depiction of the urban landscape. There are currently more than 80 cities that are in the WUDAPT database; the reader should refer to the website (www. wudapt. org) for updates. Each LCZ map encodes UCP values that can be used in models [a subset of the list of parameters is shown in Table  2  and in  Stewart and Oke (2012)  and its supplemental material]; these UCPs are used in models and climate analyses. As an example, Fig. 2  shows the LCZ map for the Chicago, Illinois, area alongside a map of the pervious fraction that has been generated from a lookup table  (Table 2 ); note that LCZ types are associated with ranges of UCP values.
Establishing the veracity of the derived data is not straightforward, as it requires independently derived information that is comparable in scope and spatial resolution. Experiments on a few cities have shown good agreement, but these tests are, to this point, limited to plan area fractions in western cities  (Mills et al. 2015 (Mills et al. , 2017a,b),b). The WUDAPT portal. The portal is designed to support climate research that requires urban information  (Ching et al. 2015). Critically, it should allow users to extract relevant data at an appropriate spatial scale for modeling purposes. Currently, WUDAPT provides tools that can utilize the L0 data (Fig. 3a ), but other tools that require L1/L2 data are being designed; here we describe two portal tools, WUDAPT to WRF (W2W; Fig. 3b ) and SCALER (Fig.
The cross sections show two simulations based on a generic "urban" category (Fig. 9a ) using UCP values from  Zhang et al. (2010a,b)  and based on W U DA P T L 0 d at a (Fig. 9b ). The observable differences are the result of the simulated wind fields that ref lect advanced urban physics parameterizations in WRF that can take advantage of the quality of urban data provided. Also, preliminary work on modeling air quality over São Paulo  (Dirce et al. 2018 , manuscript submitted to Urban Climate) confirms that significant spatial and temporal variability in the complex 3D flows and mixed layer height variations across the city are evident when more precise urban data (i. e. , L0 data) are provided. While WUDAPT continues to acquire L0 data for additional cities, the longterm strategy recognizes the need for a multidimensional approach to data gathering and processing with an emphasis on gathering additional socioeconomic and surface variables.
As part of the Human Influence Experiment (HUMINEX) initiative,  Bechtel et al. (2017b)    In related work, cross evaluations are being undertaken with comparable urban land-cover information where it is available, such as the impermeable surface cover recorded in Europe's Urban Atlas and the built cover available in the Global Human Settlement Layer  (Pesaresi et al. 2013). This work also has the potential to provide more precise UCP values for LCZ types, which are currently based on the information presented in Table  2. The objective of this endeavor is to generate guidance for assigning the most probable values of UCPs by LCZs to each grid in the modeling domain. Actions to acquire higher-level data. Developing richer urban databases, in terms of both spatial detail and adding other relevant variables (such as building and vegetation characteristics), is a goal for the next phase of WUDAPT  (Ching et al.
2017a ). The information on buildings will be gathered using an approach similar to that for gathering L0 data, that is, to develop and employ an international building typology with associated physical and functional properties. Data acquisition will rely on crowdsourcing techniques such as smartphone and web-based tools and will utilize the WUDAPT community  (See et al. 2015). The paradigm for this initiative is based on the MApUCE project, which employs France's building database to extract detailed UCPs related to building dimensions, construction materials, and occupation patterns  (Masson et al. 2015 (Masson et al. , 2017)). Members of the Passive Low Energy Architecture (PLEA) community are helping to create the WU-DAPT building typology  (Ching et al. 2017b ). The existing L0 data (i.
e. , LCZ maps) will be used to provide a context for the data gathered and manage sampling across the urban landscape. The quality evaluation will require other independently derived data such as those available in some national censuses. Where possible, advanced satellite data and processing algorithms can provide high-definition data on building form  (Wang and Dai 2015) ; the feasibility of this has already been demonstrated by  Xu et al. (2017a,b). These sources could also provide UCPs, such as building volume density, ground coverage ratio, frontal area density, open spaces, and greenery coverage ratio. Portal tools. WUDAPT tools are being developed to make maximum use of these data as they emerge. Priority capabilities under consideration include tools that 1) link the database to the wide variety of urban climate models in stand-alone configurations (e.
g. ,  Grimmond et al. 2010)  or as components to largerscale models such as WRF, 2) allow weather data gathered at World Meteorological Organization (WMO) standard stations outside cities to be transferred to urban locations (e. g. ,  Erell and Williamson 2006) , 3) combine with other available modeling software (e. g. ,  Vanegas et al. 2012a,b)  and land-cover data to create future urban growth scenarios, and 4) evaluate urban risks associated with current and future climate hazards, (e. g. ,  Hanna et al. 2015). Further enhancements may be possible through links enabled through WUDAPT (www. wudapt. org), given the rich datasets afforded by variety and types of remotely sensed datasets beyond the traditional and basic Landsat land-use-land-cover classifications. Inclusion of such information in WUDAPT would be as ancillary and auxiliary data for enhanced analyses (e.
ACKNOWLEDGMENTS. We thank the  International Association of Urban Climate (IAUC)  for their encouragement and collaborations of IAUC experts to this initiative. The results to date are in large part due to the many urban experts from the IAUC who have unselfishly contributed their time as collaborators without compensation. We also thank  Professors Tim Oke  and  Andreas Christen  for contributing to Fig. 1. We gratefully acknowledge the support of the  Argonne National Laboratory  for supporting the first WUDAPT " Proof of Concept " workshop held in 2013 at  UCD, Dublin, Ireland. We acknowledge  IIASA  for its continued support and encouragement to WUDAPT and for hosting the WUDAPT website. Several authors were partially supported by ( i) the  Vice-Chancellor's One-Off Discretionary Fund of the Chinese University of Hong Kong  titled "World-wide  Urban Data Bank on Physical Forms of Cities & Impact Studies of Urban Forms on Human Comfort, Health and Environment Well -being" and by three  General Research Fund Project Grants of the Hong Kong Research Grants Council :  RGC-GRF 14611015 ,  14643816  and  16300715 ; (ii) the  Cluster of Excellence CliSAP  ( EXC 177 ),  University of Hamburg , funded through the  German Science Foundation (DFG) ; (iii)  US NSF CAREER AGS-0847472 ,  NSF   AGS-1522492  on extreme urban rainfall,  ESSO-MoES, India  (Grant/Project  MM/SERP/CNRS/2013/INT-10/002 ) under  Monsoon Mission; (iv) U.
S. NSF  Grant  AGS-1243095   Collaborative Research: EaSM2 linking human and Earth Systems model to Assess Regional Impacts and Adaptation in Urban Systems  and their Hinterlands; (v) NSF  CBET 1250232  on  STRONG Cities-Simulation Technologies for the Realization of Next Generation Cities ; (vi) National  Nature Science Foundation of China  ( 41425020 ,  91644215 ); (vii) the Spanish government in the framework of the  EXCLUR  project ( CGL2016-80154-R ), (viii) the  EU   FP7-funded ERC grant CrowdLand  ( 617754 ); and (ix) the  REACT  project funded by the  STEREO-III program of the Belgian Science Policy (BELSPO ,  SR/00/337 ). Finally, we wish to thank all authors' institutions for their support to WUDAPT with in-kind resources and scientists time, most without external support.
Costeffective approaches are of great interest for monitoring on several spatial and temporal scales. Before the advent of remotely piloted aircraft systems (RPASs), developments in remote sensing technology had already brought very high-resolution topographic data to the earth sciences community. Among these data, airborne lidar constituted a breakthrough, allowing for the characterisation of terrain surfaces with metre-size details. Such dense topographic data are of major importance for the description of hydrological-oriented geomorphological features  (Vaze et al. , 2010). These even allowed for the development of the first algorithms for automatic gully detection. Evans and Lindsay (2010)  used a 2 m lidar DEM to detect gullies as zones with high curvature and low altitude relative to the average surrounding elevation computed within a moving window.
With a lidar data set with a point density of 4 points m -2 ,  Baruch and Filin (2011)  performed curvature analyses to detect gully candidates in segments and then connect them to a complete network. Another example of curvature analysis is the work of  Rengers and Tucker (2014) , who identified gully headcuts from a 1 m lidar DEM as zones showing negative profile curvature below a given threshold and having a drainage area greater than 5000 m 2. Höfle et al. (2013)  proposed a method adapted to gullies of cushion peatlands using terrestrial lidar. In their work, gullies were delineated as polygons by detecting breaklines in the lidar DEM, and then artificial dams were manually positioned on the DEM, and finally, the formed sinks were filled. Occlusion effects due to the steep slopes of gully banks and the low-altitude point of view were noted by the authors.
Wundram and Loeffler (2008)  compared a DEM computed from kite aerial imagery to a ground survey and classified vegetation in mountainous areas with favourable results. Smith et al. (2009)  also demonstrated the potential of kite aerial photography for DEM production over small areas (i. e. less than 1 ha) using off-the-shelf cameras and professional photogrammetry software. More recently, 3-D modelling from kite imagery was carried out by a small number of authors with SfM + MVS software. Dandois and Ellis (2010)  have compared this technique (called "Ecosynth" by the authors) to lidar data for deriving elevation data and canopy-height models. Bryson et al. (2013 Bryson et al. ( , 2016) )  performed centimetre 3-D mapping of vegetation in coastal areas and mapped coastal changes. Wigmore and Mark (2017)  assessed the accuracy of SfM + MVS DEMs acquired with kites in comparison to lidar data in mountainous areas, where conditions limit the use of RPASs.
Automatic triggering was performed with a gentLED-Auto 05C intervalometer (Fig. 2a ). A time interval of 5 s between each image was chosen to ensure sufficient overlap. Complete coverage of the targeted area in the Kamech catchment was achieved within two flights of 3 h each. A total of 752 images were used to cover an area of 3. 18 km 2. The maximum flight altitude of 500 m led to a maximum estimated ground pixel size of 0. 13 m (see Table  2  for a summary of all these data). The upstream part of the catchment being crossed by a power line, we avoided having the kite line near it for safety reasons. As a result, a small area of the catchment was not covered by multi-view imagery. However, more of the area downstream and outside the catchment was reached. As a result, the data set covered an area of 3.
With this algorithm, the automated localisation of gully heads detected by high positive plan curvatures presented flaws. We observed that different threshold values -including the proposed default value -resulted either in an excessive number of missing gully heads or in categorising many anthropogenic depressions as gully heads, such as village streets or spaces between trees in orchards. As noted by  Orlandini et al. (2011) , the automatic detection of channel heads is indeed most problematic for small-scale features such as some of those targeted by our work. Thus, gully heads were digitised from a shaded view of the DEM with the same type of expertise as one would use in the field. This approach was used by  Höfle et al. (2013)  to produce their validation data set. The entire digitisation of the gully heads on the DEM was achieved within less than 2 h.
DEM quality The kite DEM quality was evaluated on an independent validation data set composed of 469 points (see Fig. 1c  for their localisation) and the median error, mean error and standard deviation of error were used as evaluation criteria. This control data set was surveyed with the same Topcon GR-3 RTK DGPS used for GCPs. These data came from a recurrent operation of bathymetry and topography of the reservoir performed a few weeks before image acquisition and from which points covered by vegetation were excluded. A qualitative assessment was also performed with a visual inspection of the kite DEM at full resolution.
Gully 3-D morphology Finally, we tested the ability of the DEM to derive 3-D information that allows for gully morphology monitoring. This evaluation was based on a profile comparison of the kite DEM with a reference DEM derived from an intensive field topographic survey of a mid-size gully. This reference DEM was calculated on a 0. 05 by 0. 05 m grid from a very dense point data set acquired in 2009 using a total station that had an (X, Y, Z) accuracy better than 0. 01 m  (Khalili et al. , 2013). Standard statistics on the deviation between the kite DEM and the reference DEM were derived on an elevation profile of a path composed of a series of line segments. 3 Results.