dataesr/scientific-paragraphs-categorization huggingface dataset:
The temporal resolution of this product is once every 3 h, but here daily accumulations (22:30-22:30 UTC) are used for most investigations. In addition, outgoing longwave radiation (OLR) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the geostationary Meteosat Second Generation (MSG) satellites with a spatial and temporal resolution of ∼ 3 km at nadir and 15 min, respectively, are used as a proxy for convective activity  (Schmetz et al. , 2002). In particular, channel 9 (i. e. approximately 9. 80-11. 80 µm) of the thermal infrared band is taken to retrieve cloud-top temperatures and to ensure day and night coverage. Different types of clouds are analysed using information on cloud-top characteristics (CTX) and the cloud mask (CMA) from the Satellite Application Facility on Climate Monitoring (CM SAF).
Both the CTX and CMA subsets are part of the CLAAS-2  (Finkensieper et al. , 2016)  dataset, which is derived from information provided by SE-VIRI  (Stengel et al. , 2013). Therefore, CLAAS data have the same temporal and spatial resolution as the SEVIRI dataset. As this paper is meant to give a broad overview of meteorological and chemical conditions only, a detailed analysis of DACCIWA field campaign data is left to follow-up studies. The only exception is radiosonde data from Abidjan (for location, see Fig. 1 ) used to illustrate a period of unusual dryness during July 2016. Relative humidity was derived from (00:00, 06:00, 12:00 and 18:00 UTC) soundings four times daily using the high-resolution vertical profiles obtained from the MODEM radiosonde system. The analysis will concentrate on the main DACCIWA study region (8 • W-8 • E, 5-10 • N, see Fig.
Methods In order to better understand and characterise atmospheric variability during the DACCIWA campaign, a number of features important for SWA were objectively or subjectively identified: 1. Equatorial waves: the presence of CCEWs is identified using the wave filtering method in specific wavenumber-frequency domains as described in  Wheeler and Kiladis (1999). In addition to the CCEWs (Kelvin waves, MJO, mixed Rossby-gravity and equatorial Rossby waves), tropical depression-like disturbances (TD) are filtered following the method by  Roundy and Frank (2004). These often correspond to AEWs over West Africa. The filtering is applied to the 3-hourly TRMM 3B42 (v7) precipitation dataset (see Sect. 2. 1) within the northern equatorial band 5-15 • N, which contains the bulk of the convective precipitation during the campaign period but excludes some heavier oceanic rainfalls (see Fig.
, 2015). Figure  17  shows the fraction of low clouds (defined here as cloud-top pressure of 800 hPa and lower) daily at 00:00 UTC in the 5-10 • N, 8 • W-8 • E box (see Fig. 1 ) as analysed from the CLAAS-2 dataset (see Sect. 2). This fraction is relative to the number of pixels not obscured by higher clouds, which can be as high as 85 % (red curve in Fig. 17 ), indicating a large uncertainty in the low-cloud estimate. Despite this, a clear difference in low-cloud cover between the four phases can be seen. Typical fractions during Phase 1 range around 60 %. Given the large uncertainty, day-to-day variations should be regarded with caution. Thus, it is no surprise that the impact of individual synoptic features is generally hard to discern. Only the wet Feature A and dry Feature D stand out, with a high fraction of obscuring high clouds and a low fraction of low clouds for Feature A and the opposite for Feature D.
A summary of the latter is provided in Table  1. This analysis shed new light on the richness of synoptic-scale features affecting the region and their impacts on wind, precipitation, cloudiness, and the distribution of dust and biomass burning plumes. It serves as valuable background information for the more detailed examinations of the comprehensive dataset collected during the DACCIWA campaign from ground stations, aircraft, radiosondes, and satellites and as inspiration for a deeper analysis of the under-researched propagating synoptic systems of SWA and their dynamics. The 2-month DACCIWA period can be divided into four distinct phases: - A cyclonic disturbance (labelled D) with two centres develops, creating an area of strong, dry, dusty northerlies to the west, followed by a deep penetration of southerlies and re-moistening of the area, which finally creates the monsoon onset.
An interesting idea to be explored further in this context is interactions between AEWs and mixed Rossby-gravity waves as suggested by  Cheng et al. (2017). Data availability. The majority of data used in this study are publicly available through third parties: -ERA-I  (Dee et al. , 2011) , ECMWF operational analyses and dust and CO fields from CAMS-IFS  (Inness et al. , 2013 ) can be retrieved from the ECMWF website http://www. ecmwf. int or ECMWF's Meteorological Archival and Retrieval System (MARS). -Reynolds SST data  (Reynolds et al. , 2007)  can be retrieved from http://www. ncdc. noaa. gov. -TRMM product 3B42 v7  (Huffman et al. , 2007)  can be retrieved from https://giovanni. gsfc. nasa. gov. -CLAAS-2 CTX and CMA products  (Stengel et al. , 2013; Finkensieper et al. , 2016)  can be found at https://doi.
org/doi: 10. 5676/EUM_SAF_CM/CLAAS/V002. -SEVIRI OLR data  (Schmetz et al. , 2002)  can be accessed via https://www. eumetsat. int/website/home/Data/ MeteosatServices/0DegreeService. -Dispersion of city plumes based on HYSPLIT using GDAS winds is described at http://www. ready. noaa. gov. The only data used that are original to DACCIWA are the high-resolution radiosonde measurements from Abidjan. These are available from the DACCIWA database at http://baobab. sedoo. fr/Data-Search/?datsId=1656&project_name=DACCIWA. They do not have a DOI yet. At lower, standard TEMP resolution, they are available via https://www. ncdc. noaa. gov/data-access/ weather-balloon/integrated-global-radiosonde-archive. The Supplement related to this article is available online at https://doi. org/10. 5194/acp-17-10893-2017supplement.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no. 603502. AS has been supported from subproject " C2 -Prediction of wet and dry periods of the West African monsoon " of the  Transregional Collaborative Research Center SFB-TR 165 "Waves to Weather " funded by the  German Research Foundation  (DFG), MG by the  LABEX  project funded by  Agence Nationale de la Recherche  ( French National Research Agency , grant  ANR-10-LABX-18-01 ), and TB by  BMBF  grant no. 01LP1520D  ( MIKLIP-PROMISA ). The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. ready. noaa. gov)used in this publication.
Oceanic and Atmospheric Administration, and two Asian initiatives. The entire data record spans 98,677 instrument hours for CCN data, 157,880 for particle size distribution and 70,817 for chemical composition, and represents nine different environments. The general instrumental set-up and locationrelated specifics are described in the Methods section. The records are available as Network Common Data Form (NetCDF) files (Data Citation 1) whereby each file contains data from one instrument (CCNC, MPSS or AMS) and station. These NetCDF records are additionally available through the ACTRIS data portal (http://actris. nilu. no/Content/Products). The harmonized multi-year data of three key variables (cloud condensation nuclei, particle number size distribution and chemical composition) for aerosol-cloud interactions is a unique collection to test satellite retrieval methods and to evaluate global climate models targeting the reduction of the related uncertainty in radiative forcing.
At most stations more data of one or two variables are available, however, those time periods are not included here if CCN data were not available. Figure  3  presents the general instrumental set-up with cloud condensation nuclei counters and mobility particle size spectrometer measurements for the poly-and monodisperse operation alternatives, and the optional chemical composition measurement. In the case of polydisperse CCN measurements, the aerosol is sampled and all sizes are simultaneously measured by the CCNC. In the monodisperse case, a size selection prior to the CCN analysis occurs. While different information can be gained by the two measurement options, this aggregated dataset considers only the time series of CCN number concentrations at various supersaturations (SS, see Table  2 ).
Time series are also provided for the submicrometer aerosol number size distribution and, where available, chemical species including particulate sulfate, nitrate, ammonium, organics, chloride and sea salt. In some cases, all three types of instruments shared the same aerosol inlet, while at other stations, separate, yet closely positioned, inlets were used. All data originators (instrument principle investigators) submitted data in their preferred format (Nasa Ames 1001 from EBAS or instrument specific format) to this effort from which the first author constructed the time series and converted all data to standard temperature and pressure (STP) where necessary. Temperature and pressure data were either available from the data originators or in the EBAS data base (http://ebas. nilu. no). Thereafter, all data types were averaged to the full hour with the time stamp being the end of the measurement interval and the time series were converted to UTC.
In case of the Puy de Dôme observatory (PUY), averages are over 4 h due to longer CCN monodisperse scan times. Further data treatment associated with quality assurance is explained in the 'Technical Validation' section. Hereafter, this dataset is called 'aggregated' or 'secondary' dataset. Several of the primary measurement datasets, this is without the processing and aggregation by the first author, were archived in and are available from the EBAS data base. EBAS is the primary data repository for all ACTRIS near-surface data, also hosting GAW-World Data Center for Aerosols (GAW-WDCA, http://www. gaw-wdca. org) and all European Monitoring and Evaluation Programme (EMEP) data amongst others.
Generally, the systems consist of a sampling head (total aerosol, PM 10 , PM 2. 5 or PM 1 ), a short stainless steel tube with a laminar sample flow, and dryers to keep RH below 40%. Note that the station abbreviation 'FIK' is used in the GAW system, while 'FKL' is used in ACTRIS and other protocols and 'GR0002R' in the EMEP database.
Such local influences of pollution have been removed from the dataset by visual inspection to make the dataset representative of the regional background conditions  22. In the Sphinx laboratory on Jungfraujoch, the heated (~20 °C) inlet collects all aerosol particles and cloud droplets up to 40 μm. As the cold sample flow (July mean temperature is around -1 °C) enters the laboratory which is kept at approximately 25 °C, all condensed water evaporates, hence interstitial as well as activated aerosol particles are measured. Losses for the whole inlet system are below 5% for particles between 10 and 750 nm diameters. Inside the laboratory, the dried aerosol sample is distributed to a series of instruments including the permanently installed CCNC and scanning mobility particle sizer (SMPS), as well as to the time-of-flight aerosol chemical speciation monitor (ToF-ACSM) for the respective measurement period.
Generally, the target accuracy for SS>0. 2% is ±10%, in relative terms, and ΔSS ≤ 0. 03%, in absolute terms. During measurements, the actual supersaturation can deviate from the target setting. For such instances, the recommendation  38  is to linearly interpolate to the target supersaturation for deviations o20%. This was applied to this dataset. For larger deviations, data is reported as missing. Importantly, supersaturation calibrations need to be carried out at the flow rate and pressure level at which the instrument will be operated. Ref. 38 provides information on data processing procedures for how to include temperature readings and treatment of diffusion losses, and, for monodisperse measurements specifically, the correction for multiply-charged particles and inversion routines (see paragraph on particle size spectrometers).
In case of high CCN number concentrations (>5000 cm -3 ), the supersaturation and droplet sizes can decrease, because of the higher water vapor depletion  45. This can affect the derived CCN number concentration. This dataset has not been corrected for this potential effect. Reliability and comparability of the datasets presented here were ensured by the application of the ACTRIS technical standards as described above (if not described otherwise below). In the case of the non-ACTRIS stations and earlier measurements, the same procedures were followed nevertheless as they had been established as 'good practice' within the community beforehand. For each station's CCN measurements, Table  2  provides details on the data treatment protocol which includes information on the deployed instrument model, its operation mode, and acquisition and data processing software used to create the data record.
References describing data acquisition and processing following the protocols are included. Protocols for CCN datasets are named 'P_CCNC_nameofstation'. Station specific CCNC remarks BRW. The instrument had been calibrated at a lower pressure level (840 hPa) than the operational level (sea level). Therefore the uncertainty in the supersaturation determination is between 10 and 20%. CES. Specific particle losses due to the inlet line from the manifold and within the instrument have been calculated. Transmission of particles starting at the size range expected to activate at 1% supersaturation (roughly 50 nm) is >90%. The losses have been taken into account. FIK. At Finokalia, the CCNC is connected to a PM 1 head. The instrument is calibrated regularly with sodium chloride particles. Differences to the ACTRIS SOP calibration with ammonium sulfate are described in detail in ref.
MEL. A DMA is used to perform size-segregated CCN measurements. The aerosol to sheath air flow rate is kept at 1:10. Multiply-charged particles cannot be avoided in the selection process, hence CCN data is corrected by applying the bipolar charge distribution  46. To quality check CCN data, particle number size distributions from a separate dual mobility particle size spectrometer (described below) have been used. NOT. Monodisperse CCN analysis was performed following the Scanning Mobility CCN Analysis (SMCA) system  29, 47. The sample air was dried with two silica gel diffusion dryers. Subsequently particles were selected by their mobility diameter with a DMA. The monodisperse aerosol sample flow was split between a CPC and the CCNC for measurements of total particle and CCN number size distributions.
50  which serves as guideline within ACTRIS and WMO-GAW among others. All stations followed these guidelines including non-ACTRIS sites, if not stated otherwise. In Table  3 , datasets from station operators that participated in the intercomparison described in ref. 50 are marked with '*'. In addition to their instruments, also inversion routines have been tested. Despite compliance with the protocols, deviation of results between instruments is expected. In a number of intercomparison workshops at the WCCAP (ref. 50) it was found that uncertainties within 10% can be expected for particles in the size range 20 to 200 nm, while deviations become significantly larger for smaller particles. Also at the higher end, divergence was observed. This should be kept in mind when interpreting particle size distributions and size resolved number concentrations.
The Q-ACSM has been used extensively and validated within the ACTRIS network stations  59. In this work, four datasets originate from the Q-ACSM, and one each from the ToF-ACSM and HR-ToF-AMS. Below follows a joint description of all mass spectrometer types, while specificities are presented under the station headings. In general, all AMS and ACSM types sample aerosol through a critical orifice and an aerodynamic lens system. Typically, the critical orifice has a diameter of 100 μm, restricting the sample flow to 0. 08 l min -1. The lens system focuses the particles into a narrow beam and concentrates them before they are accelerated into a vacuum chamber and hit a vaporizer operated at 600 °C. Particles in the size range from 150 to 450 nm are transmitted by the aerodynamic lens to about 100% efficiency, while also a significant fraction of the particles between 70 and 1000 nm are transmitted  60.
Description of size distribution data acquisition for each site including the instrument type, scan width and steps, acquisition and data processing software and references from the literature. *Operators at these stations participated in intercomparison workshops at the WCCAP  50. aerodynamic diameter of the particles can be measured. This type of information, however, is not included in this work as most chemical composition measurements were conducted by ACSMs and particle size distribution data from the MPSS is available. Finally, the obtained mass spectra are deconvoluted by applying a so-called fragmentation table based on laboratory experiments  61  to report the typical inorganic and organic aerosol components. More detailed descriptions of the functioning of the various AMS and ACSM types can be found in refs 52-54,57,58.
To ensure reliable results, several well-established procedures  52, 59, 62  including calibrations were routinely carried out by instrument operators. In addition, all Q-ACSMs operated within ACTRIS participated in an intercomparison exercise  59, 62  (marked with '*' in Table  3 ) at the Aerosol Chemical Monitor Calibration Center. Generally, the latest acquisition and analysis software were used. After instrument transport, the lens system needs to be aligned such that the particle beam hits the vaporizer unit centrally and all voltages need to be tuned for optimal ion detection in the mass spectrometer. Protocols are described in refs 51,57. The instrument intercomparison described in these references took place in 2013 during many of the reported periods in this dataset. Data from this period are not reported.
Also, the instrument-specific flow rates were determined regularly as this directly affects the mass concentration calculation. The mass-to-charge ratios (m/z) are calibrated using three or more peaks for all ToF mass spectrometers and two peaks for the Q-ACSM, typically including m/z 28 (N 2 + ). This procedure was either automated with the help of the data acquisition software or carried out every couple of days. To determine aerosol mass concentration, the ionization efficiency (IE) is calibrated using ammonium nitrate, and all other species' mass concentrations are expressed in nitrate equivalent mass. Relative to the ionization efficiency of nitrate (IE NO3 ), ionization efficiencies of sulfate and ammonium (RIE SO4 , typically between 0. 6 and 1. 2, RIE NH4 , typically between 2. 5 and 5 (ref.
58 )) were determined. Refs 52,59 describe in detail the theory and the standard calibration procedures, the latter specifically for Q-ACSMs as suggested by the ACTRIS intercomparison facility. The IE and RIEs were determined at the beginning of the measurement and then repeated regularly. Based on the high number of field and laboratory measurements with the aerosol mass spectrometers, it is known that not all particles flash vaporize but can bounce of the heater and are hence not detected  52. To account for this, a correction factor, called collection efficiency (CE), is applied. For ambient measurements, CE is typically ~0. 5. However, factors such as particle water content or the nitrate fraction also play a role and a particle compositiondependent CE can be calculated  63. The CE applied to each station's dataset is provided in Table  4.
The detection limit is typically determined by sampling particle-free air through a filter. The signal oscillates around zero and the detection limit is defined as three times the standard deviation specific to a certain time resolution. Table  5  shows the one-hour detection limits for all relevant mass spectrometer types. Data below the detection limit are not reported in the datasets. Generally, results obtained from the Q-ACSM, ToF-ACSM and HR-ToF-AMS are in good agreement with R 2 >0. 9 for all species except chloride  59. There is some uncertainty in the determination of the organic aerosol (OA) mass concentration with the Q-ACMS due to variability in the detection of the ion CO 2 + at m/z 44. This ion typically constitutes the largest single contribution to OA (f 44 ), but as discussed in a Q-ACSM intercomparison paper  62  , this f 44 contribution can vary from 8.
5 to 18. 2% between instruments for the same aerosol. One factor contributing to the f 44 variability is the generation of a CO 2 + signal from the thermal decomposition of inorganic species on the instrument's vaporizer and filament  64. Table  4  provides details on the chemical composition data treatment protocol which includes information on the deployed instrument model, its operation mode, and acquisition and data processing *These instruments participated in the 2013 intercomparison  59. software, including the collection efficiency used to create the data record. References describing data acquisition and processing following the protocols are included. Protocols for chemical composition datasets are named 'P_typeofmassspectrometer_nameofstation'. Station specific remarks CES. Contrary to the other CES instruments, the Q-ACSM sampled from a 5 m high inlet with a PM 2.
In the NetCDF files, the version number and revision date are included. The 11 data records from cloud condensation nuclei measurements can be found in files named 'CCN_Instrument_Project[Database]_PlatformType_PlatformName[StationName]_Startdate_Enddate. nc'. Table  2  describes the measurement methods, operational parameters and applied codes to create the CCN time series. The file contains the time series of CCN concentrations in cm -3 at each available supersaturation. The 11 data records for particle size distribution measurements are available separately for each station named 'NSD_Instrument_Project[Database]_PlatformType_PlatformName[StationName]_Start-date_Enddate. nc'. Table  3  provides details on the measurement instruments, operational parameters and applied codes to produce the size distribution time series.
The files contain the time series of particle concentrations per size bin in the format dN/dlog 10 (d p ) in cm -3 , with N being the particle number concentration and d p the bin diameter. The midpoint diameter of each size bin is provided in nm. From the size distribution the total number of particles was calculated and is provided in the files named 'N_Instrument_Project[Database]_PlatformType_PlatformName[StationName]_Startdate_Enddate. nc'. Chemical composition data from 6 stations is available in separate files named 'Comp_Instrument_-Project[Database]_PlatformType_PlatformName[StationName]_Startdate_Enddate. nc'. Table  4  lists the instrument types, operational parameters, applied codes and collection efficiencies to create the time series. The mass concentrations in μg m -3 for each chemical component are provided.