dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure and configuration Most of the DYAMOND models (Table  1 ) solve the fully compressible Navier-Stokes equations, with their representation of molecular diffusion modified to account for unresolved turbulent motions and for an atmosphere with a condensible (water) component. Exceptions are the System for Atmospheric Modeling (SAM) which uses the anelastic form of the non-hydrostatic equations and the Integrated Forecast System (IFS) which uses the (hydrostatic) primitive equations and is thus the only hydrostatic model. More generally, the numerical methods used by the different models to solve their governing equations depend on their choice of grid and a number of other factors, and thus vary considerably. These differences contribute to the richness of the simulation dataset as a basis for assessing the importance of model formulation choices.
For the boundary layer parameterization (denoted BL), the letters "T," "K," and "S" denote either a TKE-like model (including an additional prognostic equation), a diagnostic eddy diffusivity, or a Smagorinsky-like three-dimensional closure as is common for large-eddy simulation. Finally, some indication is given as to whether fractional cloudiness (denoted FC) is parameterized nearly a terabyte of information (664 GBytes, double precision) per sample. Hourly output for 40 days inflates this by a factor of a thousand. The practice of providing derived variables, e. g. , measuring energy and matter fluxes, inflates this by another factor of 3. Hence, approaching the output naively would imply data volumes of about 2 PBytes for each model. This contextualizes one motivation for DYAMOND, which was to develop strategies for dealing effectively with the large amount of information inherent in GSRMs.
The sheer size of the output fields also poses quite a challenge for their visualization. Even in the cases where we might be able to visualize model output using relatively conventional approaches, to interactively explore and visually analyze the full 3D data set over time, would require parallel visualization techniques. For yet larger simulations, in situ visualization techniques and/or an online compression and a level-of-detail (LoD) rendering approach  (Jubair et al. 2016; Bethel et al. 2012; Clyne et al. 2007)  would also be necessary. m ). Plotted is the height of the surface, relative to mean sea level, taken from ICON, IFS, and SAM to illustrate different representations of topography. Note that the land-sea mask does not correspond to the height of the 0 m contour, so even if regions may formally be just above, respectively below, sea level, this does not preclude them from being treated as sea, respectively land The German Climate Computing Center (DKRZ) agreed to host the output and serve as an analysis hub for the community, something made possible through close interaction with a European Commission funded Centre of Excellence in Simulation of Weather and Climate in Europe-the ESiWACE project.
An accidental ensemble There is a temptation to question what can be learned about the climate system from a single 40-day snapshot. Experience suggests a great deal. For instance,  Rodwell and Palmer (2007)  showed how initial tendency errorsidentified in a data-assimilation procedure-are informative of model biases. Simpler approaches, using hindcasts, have shown to be similarly indicative of climatological biases  (Williamson 2005; Williams et al. 2013). Quite apart from this backdrop, DYAMOND provides an additional means for testing to what extent differences among models are significant. It does so by including in the DYAMOND dataset an accidental ensemble  Each model's variables were remapped to a regular 2D LaLo grid, and these were in turn time-averaged for four different periods. The remapping was done to ensure that, at a minimum, the following information was available for each model of additional simulations.
Simulations "Palmer-Turing" test In an essay on modeling the climate system, Palmer (2016) introduced the notion of using Alan Turing's test as a measure of climate model fidelity. Turing's original idea was to use the extent to which a passive observer could distinguish a conversation between a human and a computer from a conversation between two humans as a measure of artificial intelligence. Palmer's variant of Turing's test is presented in Fig. 2 , which compares a snapshot of the Earth from a geostationary satellite, with a visualization of its condensate burdens using output from the nine DYA-MOND models. A second simulation by the IFS, at its operational resolution, is also included as a further reference. The visualizations are created using the condensate output of the models and rendered through the 3D visualization software ParaView in a way to visually mimic the true color output of the Himawari 8 reference data.
The Rayleigh scattering of the atmosphere was thereby imitated by the rendering process. White color is used for both cloud water and cloud ice, along with a zero to one clamped weighted logarithmic opacity table that is adjusted to the data ranges of each model and by visual comparison to the Himawari 8 reference data. On a superficial level, the fact that the image of Himawari 8  (Bessho et al. 2016)  is not distinguishable from the model output suggests that the simulations pass, at least in a weak form, the "Palmer-Turing" test. Himawari 8 was chosen as the basis for comparison because it was the most advanced geostationary satellite in orbit in 2016. The images are constructed from the output at 0400 UTC on 4 August 2016, which corresponds to 10:37:12 local time at 140. 7 °E where Himawari 8 is located and the images are centered.
GEOS-3. 3 km produces much less (3. 2 mm d 1 ) precipitation. This appears to be related to an output error, and a new set of simulations has been performed for inclusion in subsequent analysis. At the time of our analysis, however, this data was not available, so GEOS-3. 3 km is not included in either the tabulation or the computation of the multi-model mean. Compared to the cluster of models in Fig. 5 , the observed precipitation (3. 50 mm d 1 ) for the same period-as estimated from calibrated microwave measurements to retrieve precipitation globally at 10 km spatial and hourly temporal scales-is at the two-sigma range of this group of models. The observations and models differ by an amount that is larger than the weekto-week variability just south of the Equator, where the simulations have a secondary peak in precipitation that is more pronounced than is observed, and in the very high latitudes of the Southern Hemisphere.
To test this simple approach, it was compared to a more comprehensive algorithm  (Kodama et al. 2015) , based on the threedimensional temperature and wind fields, applied to 30 years of data from a previous 14-km NICAM simulation. Application of the simple algorithm to the NICAM output resulted in an identification of nearly 90 % of the tropical cyclones identified by the more complex algorithm. 3 Some outliers associated with persistent features with anomalously low pressure (< 840 hPa) were additionally filtered (mostly in the FV3 output), as these were believed to arise from cyclones interacting with land which were not filtered by the CDO land mask. The algorithm was applied to the entire 40-day period for 8 simulations, resulting in the identification of 76 tropical cyclones, or just over 9 per simulation.
In dealing with this output, domain-specific analysis tools, like the CDOs, proved to be invaluable. Our experience was, in contrast to what is commonly believed, that different output strategies, file names, variable names, units, etc. were not a great impediment to analysis, but that having standard conforming data dimensions is essential for reaping the benefits of Fig. 8  Water vapor anomaly correlation. Zonally averaged anomaly correlation of hourly (0. 1 degree) precipitable water between ICON-2. 5 km and GEOS-3. 3 km normalized by zonally and temporally averaged water vapor variance from ICON-2. 5 km plotted as a function of time and latitude domain-specific analysis tools. Use of compressed data formats greatly reduced the data volumes, but was subject to the dynamic range of the data, which in some cases was artificially inflated by inadequate filtering of insignificant values.
Any attempt to give a comprehensive overview of models that contain such a fantastic range of scales for a fluid whose dynamics are as diverse and heterogeneous as Earth's atmosphere will be necessarily superficial. Our hope is that by documenting the DYAMOND simulations, curating subsets of their output, and by making this, as well as their full output, available for analysis by the broader research community, a more specific, and thereby in-depth, analysis will follow. Toward this end, we have also used this manuscript to highlight differences in model formulation which makes their comparison interesting, as well as features of the simulations that we noticed and believe merit further study. To organize the dissemination of findings from studies of these and related questions, a subset of the present authors have also organized a Fig.
Acknowledgements BS thanks the AORI at the  University of Tokyo  who supported an extended visit to the  University of Tokyo  which facilitated the writing of this manuscript. Prof. Yukari Takayabu  is thanked for her input on and provision of the GSMaPv6 precipitation retrievals. The NICAM simulations were performed on the Earth Simulator at the  Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Peter Caldwell  is thanked for constructive comments on an earlier draft.
Funding This work was supported by the  Max Planck Society , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  ESiWACE No 675191 , and the  High-Definition Clouds and Precipitation for Climate Prediction  ( HD(CP) 2  )project of the  German Ministry for Education and Research (BMBF)  within the  framework program  " Research for Sustainable Development (FONA) ", www. fona. de,under the number FKZ:  01LK1211A/C. MS, CK , and RS are supported by the  FLAGSHIP2020  within the priority study4 (Advancement of meteorological and global environmental predictions utilizing observational "Big Data") and the " Integrated Research Program for Advancing Climate Models (TOUGOU program )" from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
Ground based in-situ measurements Aerosols were sampled through a PM10 inlet and delivered to a comprehensive suite of instruments. Absorption coefficient measurements were performed every minute by an AE16 aethalometer (Magee Scientific, 1 wavelength: 880 nm) at dry relative humidity (lower than 40% during 85% of the time with a maximum of 53% reached on  April 19, 2015). Aethalometer data have a known scattering interference from particles deposited on the collection filter, and the measurements were post-corrected following the new correction developed by Collaud  Coen et al. (2010). Finally the corrected absorption coefficients allowed retrieving BC concentrations  (Rivellini et al. , 2017). Dry scattering coefficient measurements were also made at 1 Hz using a three-wavelength nephelometer (Aurora 3000, ECOTECH) operating at 450, 525, and 635 nm at dry Relative Humidity (RH < 40% during 85% of the time).
A full description of the instrument is available in  Rivellini et al. (2017). Under ambient conditions, mass concentrations of particulate organics, sulfate, nitrate, ammonium, and chloride are obtained with a detection limit < 0. 2µg/m 3 for 30 min of signal averaging. Middlebrook et al. (2012)  algorithm was applied to ACSM mass concentrations to obtain a time-dependent correction of Collection Efficiency (CE) ranging from 0. 45 to 0. 83  (Rivellini et al. , 2017). A minor fraction (3. 2%) of the data was excluded from the dataset due to unstable parameters, which were generally observed after restarting the instrument following power outages. Positive Matrix Factorization (PMF) of the ACSM organic aerosol spectra was performed using the Multilinear Engine  (Paatero and Tapper, 1994)  and the Source Finder (SoFi, v.
5. 3)  (Canonaco et al. , 2013)  operated with IgorPro 6. 37 (Wavemetrics). It allows the deconvolution of the organic mass spectra into factors such as Hydrocarbon-like Organic Aerosol (HOA), Cooking Organic Aerosol (COA) and Oxygenated Organic Aerosol (OOA) concentrations. Additionally the latter one can correspond to different oxidization levels (More-Oxidized OOA or MO-OOA, Less-Oxidized OOA or LO-OOA), depending on the fragments attributed to these factors and reflecting their faster or longer processing time in the atmosphere  (Lee et al. , 2015; Xu et al. , 2017). Meteorological data including temperature, water vapour mixing ratio, and solar radiation were also measured every 10 mn at the sampling site using a meteorological tower (DAVIS Inc weather station, Vantage Pro 2).
Automated particle analysis was run for about 500 particles per impaction stage using the commercially available Esprit 2. 0 system (Bruker®). Spectral acquisitions were performed at 15 kV, 300 pA for 20 s. The identification of individual particles is based on their elemental composition obtained from SEM/EDX data as previously described in  Derimian et al. (2017). In summary, the analysed particles were classified among four different groups: Dust, Marine, Mixed Dust/Marine, and Other. Particles sorted into the 'Dust' particle group were composed of aluminosilicate (Al-and Si-rich), silicate (Si-rich), calcite (Ca-rich), gypsum (Ca-and Srich), dolomite (Ca-and Mg-rich), and Ti-rich particles. The 'Marine' particle group comprises fresh (Na-and Cl-rich) and aged (Cl-depleted) sea salts. Particles that contain sea salts internally mixed with crustal elements were classified into the 'Mixed Dust/Marine' particle type.
The meteorological characteristics of the air flows were deduced using the approach of Allwine and Whiteman (1994), which consists in evaluating specific atmospheric transport and diffusion conditions by means of three indicators concepts: stagnation, recirculation, and ventilation. These indicators are not real measures of an air mass transport (except under idealized homogeneous wind conditions) but they should be used as characteristics of the flow at the measurement point  (Allwine and Whiteman, 1994). They are widely used to estimate flow meteorological characteristics at a study site influenced by local effects such as SB or slope breeze in complex terrain  (Al-Khadouri et al. , 2015; Chithra and Shiva Nagendra, 2014; Crescenti, 1997; Venegas and Mazzeo, 1999). To identify these three types of airflow conditions, integral quantities are defined over a certain 'transport period', on the basis of the meridional vj and zonal uj components of the wind as observed at time tj (j being the index of the point in the dataset) with the sampling interval τ of N discrete data points.
Acknowledgements LHR's PhD grant and the SHADOW campaign have been financially supported by the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , which is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 ", by the  Regional Council "Hauts-de-France " and the  European Regional Development Fund (ERDF). Al-Khadouri , A. ,  Al-Yahyai , S. , Charabi, Y. , 2015. Contribution of atmospheric processes to the degradation of air quality: case study ( Sohar Industrial Area, Oman). Arab. J. Geosci. 8, 1623Geosci. 8, -1633.
45 ±3. 40 ppt  [3]. Because of this atmospheric persistence, CF 4 is regulated by the Kyoto Protocol of the United Nations Framework Convention on Climate Change (UNFCCC)  [4, 5, 6]  as a substance whose atmospheric concentration should be supervised and reduced. Besides its natural origin  [7, 8, 9] , the second source of CF 4 is linked to aluminium production and to the manufacturing of semiconductor  [10]. The spectroscopic databases like HITRAN  [11, 12]  or GEISA  [13, 14]  only contain a limited range of data for CF  4  , without intensity analysis. The spectroscopy of CF 4 which is essential for quantitative measurement in the atmosphere still remains relatively poorly studied because of the complexity of analyses. Some detailed high-resolution studies exist, however they mostly concern line positions.
This was the case of the analysis of the ν 3 fundamental with the 2ν 3 overtone  [15]  and the interaction of this stretching state with the 2ν 4 overtone  [16]. Recent work was already dedicated to the analysis of some rovibrational bands of this molecule including some hot bands around 1283 cm -1  [17] , the data being included in the Dijon database  [18]. First accurate and complete ab initio based line lists for CF 4 up to 400 K in the range 0-4000 cm -1 have been recently published by the Reims-Tomsk groups  [19] , resulting in 2 billion rovibrational transitions. In Ref. [19] , although the variationally-predicted were not computed at the spectroscopic accuracy, the error on line intensities was only of few % allowing a correct global description of the overtones and combination bands up to 6 vibrational quanta validated by direct comparisons with experimental PNNL spectra.
Spectrum simulations, line assignments and the fitting process were carried out using the MIRS  [37, 38]  and SpectrAssign softwares based on an iterative semi-automatized comparison of synthetic and experimental spectra. The T d formalism is implemented in MIRS that thus provides all vibrational and rotational quantum numbers. The SpectrAssign program was used for assignments and visualization. It allows a simultaneous display of predicted lines from MIRS output and of an experimental spectrum. A limited number of observed lines were assigned by comparing the positions and intensities of the observed and simulated lines calculated from the initial values of the spectroscopic parameters. Afterwards, new assignments were made leading to a new set of fitted tensorial parameters. This process was performed iteratively until no more lines could be assigned.
Conclusion In this work, we have built a tensorial model that permitted for the first time a detailed analysis of the (ν 2 +2ν 4 , ν 2 +ν 3 , 4ν 2 , ν 1 +2ν 2 , 2ν 1 ) band system of CF 4 by combining non-empirical CT Hamiltonian, ab initio based variational predictions and experimental information. The present analysis supplies a first accurate and complete description of the infrared high-resolution spectrum of CF 4 in the region 1600-1800 cm -1. The final set of effective Hamiltonian/dipole moment parameters allows to reproduce simultaneously (i) quite accurately the cold bands and (ii) in a qualitative manner the main hot band transitions. The global fit of parameters and calculation of infrared spectrum was carried out with the MIRS and SpectrAssign softwares. Assignments was made up to J max = 70 and a set of 48 tensorial parameters has been adjusted.
The accuracy of the final empirically optimized calculated line list is satisfactory. We have obtained a root mean square deviation RMS of about 1. 590×10 -3 cm -1 for 1831 cold transitions. The standard deviation for line intensities for the cold bands {ν 2 +2ν 4 , ν 2 +ν 3 , 4ν 2 , ν 1 +2ν 2 , 2ν 1 } is of 1. 1%, with respect to the ab initio intensities, while, for the hot bands {2ν 2 +2ν 4 -ν 2 , 2ν 2 +ν 3 -ν 2 } and {ν 2 +3ν 4 -ν 4 , ν 2 +ν 3 +ν 4 -ν 4 }, the standard deviation is of ≈ 8 % and 5 %, respectively. The main objective of this study was to construct a CF 4 line list for various applications. This final list will include new assignments in the forthcoming versions of the spectroscopic HITRAN and GEISA databases. The line list for the assigned transitions is available as supplementary material to this paper.
One part of the project included airborne measurements of flaring emissions in order to fill the data gap on oil extraction activities in SWA. Those in situ measurements are, as far as we know, the first reported data on oil extraction activities in this region. We use here the Lagrangian transport model FLEXPART (FLEXible PARTicle dispersion model;  Stohl et al. , 2005)  in combination with an inventory of emissions dedicated to flaring emissions and created in the framework of the DAC-CIWA project  (Deetz and Vogel, 2017)  to reproduce the aircraft measurements. We focus on evaluating the sensitivity of the model to two parameters: the emission flux and the injection height of the flaring emissions. After a brief description of the DACCIWA project and the platform in Sect. 2, we present in Sect.
We focus on CO, NO 2 , SO 2 and aerosol measurements during two flights on 10 and 14 July. Both CO and NO 2 were measured by SPIRIT (SPectromètre InfraRouge In situ Toute altitude;  Catoire et al. , 2017). This infrared absorption spectrometer uses continuous-wave distributed-feedback roomtemperature quantum cascade lasers (QCLs) allowing online scanning of mid-infrared rotational-vibrational lines with spectral resolution of 10 -3 cm -1. In the present campaign, the ambient air is sampled in a multipass cell with a path length of 134. 22 m and in micro-windows around the 2179. 772 cm -1 line for CO and the 1630. 326 cm -1 line for NO 2. A home-made software using the HITRAN 2012 database  (Rothman et al. , 2013)  is used to deduce the total molecule abundance. The overall uncertainties are 4 ppbv for CO and 0.
, 2005). The particle counter was mounted inside the fuselage behind the Falcon isokinetic aerosol inlet. The large particle cut-off diameter imposed by the inlet has been found to be between 1. 5 and 3 µm, depending on flight altitude  (Fiebig, 2001). The lower cut-off diameter of the CPC was at ∼ 10 nm. Particle losses due to diffusion effects were minimised by using a standard 5. 75 L min -1 bypass flow to the instrument. Using the particle loss calculator described in von der Weiden et al. (  2009 ), we estimate the particle losses in the tubing to be less than 10 % for the relevant size range. Flight sequences inside clouds are known to cause sampling artefacts and have therefore been removed from the dataset.
04 kg s -1 for 10 July in a3 and 0. 035 kg s -1 for 14 July in b3). For all panels, triangles represent the data for all the peaks measured and squares represent the mean from these data. The slope, standard error values for the slope coefficients, the F statistic and the confidence interval (I 95 % or I 90 % only for panel b1 and b2) are added for all the plots. and e. They show that NO 2 concentrations are already very low (< 1 ppbv) from 40 km from the source on 10 July and even closer (from 20 km from the source) on 14 July. With the distance between the coast and the emission source following the wind direction being 70 km, only pollutants with a relatively long lifetime or secondary pollutant like O 3 can impact the air quality of the coast. Considering the very low SO 2 flux, the FLEXPART simulations in the CTRL run induce insignificant SO 2 concentration at an aircraft-sampled location (results not shown).
If this relationship is possible, a general relationship between the emissions and the radiant heat could thus allow estimating the emissions of all flaring processes detected by satellite. Data availability. The aircraft data used here can be accessed using the DACCIWA database at http://baobab. sedoo. fr/DACCIWA/ (last access: October 2018;  Brissebrat et al. , 2017). A 2-year embargo period applies after the upload. External users can request the release of datasets before the end of the embargo period. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-11401-2019-supplement. Author contributions. HS was the mission scientist for the Falcon 20 aircraft and VC the principal investigator (PI) of the EUFAR2-APSOWA campaign. VC, SC and VB participated in the SPIRIT measurements aboard the aircraft, with remote assistance from CR for the SPIRIT maintenance.