dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite-based surveillance of volcanic hot spots and plumes can be coupled with modeling to allow ensemble-based approaches to crisis response. We complete benchmark tests on an effusive crisis response protocol aimed at delivering product for use in tracking lava flows. The response involves integration of four models: MIROVA for discharge rate (TADR), the ASTER urgent response protocol for delivery of high-spatial resolution satellite data, DOWNFLOW for flow path projections, and PyFLOWGO for flow run-out. We test the protocol using the data feed available during Piton de la Fournaise's April-May 2018 eruption, with product being delivered to the Observatoire du Piton de la Fournaise via Google Drive. The response was initialized by an alert at 19:50Z on 27 April 2018. Initially DOWNFLOW-FLOWGO were run using TADRs typical of Piton de la Fournaise, and revealed that flow at >120 m 3 /s could reach the island belt road.
FLOWGO has been initialized for and tested for lava channels at Piton de la Fournaise by  Harris et al. (2016)  and  Rhéty et al. (2017) , and-to allow improved model initialization, iteration and application-has been rewritten and rebuilt in Python as PyFLOWGO  (Chevrel et al. , 2018). It is this version of FLOWGO that we use here. As described in  Harris et al. (2017) , the response protocol is initialized with the alert of an imminent eruption and provision of the vent location provided by the OVPF as part of their mandated monitoring and response procedures. Subsequently, it involves calling each model in sequence and passing results between each actor, and then final product to OVPF, in as timely fashion as possible. The protocol also calls in ground truth (for vent locations, effusion rates, channel dimensions, flow lengths) provided by the OVPF as well as textural and chemical data (for eruption temperatures, vesicularity, crystallinity, rheological models) produced at LMV, to improve model uncertainty and syn-response validation.
Methodology While implementation of MIROVA and the ASTER urgent response protocol (URP) allow near-real time collection and processing of satellite thermal data for derivation of time-averaged discharge rate and mapping of a thermal anomaly, DOWNFLOW and FLOWGO (DOWNFLOWGO) allow the flow paths and potential run out distance to be projected. These models are called in sequence, where the call-down procedure is given in Figure  2. As part of this system, output and product are shared using a standardized reporting form (as given in Appendix A) which is shared between an email distribution list involving all actors in the response chain, and to OVPF for integration into surveillance and reporting duties. With each update, the group is issued an update email, flagging the field that has been updated and giving the time and date of the update as well as the name of the person responsible for the update.
The reporting form has four fields for: (i) current MIROVAderived TADR and time series; (ii) current vent location and DOWNFLOWGO projections; (iii) current ASTER thermal distribution map, with flow field evolution time series and report; (iv) InSAR-based flow length report and coherence images (Appendix A). Another field may be added to the reporting form including relevant OVPF data collection, e. g. , flow length from Structure from Motion (SfM), SO2 flux, sampling locations etc. This is left at the observatory's discretion to add depending on work loads and time commitment. MIROVA and ASTER were called using the observatory bulletin announcing implementation of alert level 1, that is an eruption is believed (on the basis of seismic and ground deformation data) to be "imminent" (in the next minutes/hours).
The active vent was apparent as a single pixel hot spot in the 15-m near-infrared data and the vent location was updated to 365280 m E; 7648835 m S (Appendix D), with the TADR for this day being 3. 6-4. 6 m 3 /s (Table  2 ). These details were updated in the reporting form, and the vent location for DOWNFLOWGO adjusted slightly (although this had no effect on the flow paths or LoSD). The following day (3 May), the second coherence map was produced. This revealed that the lava flow field had, at some point, reached the base of the caldera wall, turning SE to follow the base of the wall (Figure  6 ) having attained a length of 2. 5 km (Table  3 ). The shorter length of the active flows implied by the size of the thermal anomaly in ASTER on 2 May, as well as the 17 pixel (1530 m) long zone of cooler pixels beyond the front of the main hot spot indicated that flow front locations had begun to retreat back up flow by this time.
InSAR data processing The InSAR method consists of computing an interferogram by subtracting the phase between two SAR images acquired of the same area at different epochs. The phase recorded on a SAR image depends both on the radar wave round trip travel time between the instrument and the ground, and on the interaction between the radar wave and reflectors on the ground surface. Provided that this last component remains stable between two successive acquisitions, the differential phase displayed on the interferogram will only reflect changes in the radar wave travel time between the two acquisitions and can be exploited to measure possible ground surface displacement occurring between the two acquisitions. This is the classical application of InSAR that has seen a succesful development in the field of volcanological application since the pioneering work of  Massonnet et al.
This is typicaly the case at Piton de la Fournaise when a lava flow is emplaced in the (largely) vegetation-free Enclos Fouqué caldera (the upper part of the volcano) where the interferometric coherence is very high (>70 %). Here, by detecting the boundary between dark and light-toned areas one can obtain a map of the lava flow contour (e. g. ,  Rowland et al. 2003; Dietterich et al. 2012; Bato et al. 2016). In this study, we exploited interferometric coherence images to produce an early map of the April-May 2018 lava flow. To provide relevant data for input in the response protocol in as timely fashion as possible, we used only ESA Sentinel-1 data. Sentinel-1A/B data are acquired for La Réunion island every six days both during ascending and descending passes, alternatively in Interferometric Wide Swath mode (IW, range spacing = 2.
33 m, azimuth spacing = 14. 06 m) and in Stripmap mode (SM, range spacing = 2. 66 m, azimuth spacing = 4. 15 m). The data are made freely available by the European Space Agency (ESA) via the Sentinel-1 Data Hub between 4 and 24 hours after acquisition. As it is not possible to produce an interferogram by combining IW with SM data, so the shortest time period between two usable interferograms is 12 days. We compute the interferograms and the coherence images using the Doris 5. 0. 3 InSAR processor  (Kampes and Usai 1999; Kampes and Hanssen 2003). To georeference the interferometric products, we used a 5 m resolution Digital Elevation Model (DEM) produced by the French Geographic Institute from two airborne LiDAR sur-veys carried out over La Réunion in 2008 and 2009. After georeferencing, the coherence maps derived from IW data have a 15 m pixel size, and those derived from SM data have a 5 m pixel size.
To discriminate, in the coherence images, areas covered by lava flow from other poorly coherent areas (e. g. , due to air fall or to changes in the soil moisture between the two radar acquisitions) we developed a three step procedure. The first step consists of applying a median filter on the coherence image. Then, in a second step, we perform a clustering-based image thresholding approach: the Otsu algorithm  (Otsu 1979 ). The resulting binary image is used to trace the lava flow boundary (including boundaries of kipukas) using the bwboundaries Matlab© function. Finally, the lava flow surface area is estimated with associated uncertainty taking into account the pixel surface and the probability that each pixel belongs to the lava flow knowing its coherence. As the April -May 2018 eruption lasted more than one month, we were able to compute several coherence images combining, for a given acquisition geometry and mode (ascending or descending pass, IW or SM mode), a unique master image with several slave images.
Acknowledgments. This work comes out of a three day workshop and exercise held in Clermont Ferrand in June 2018 funded by  ANR-LAVA. This work was funded by the  Agence National de la Recherche  (ANR: www. agence-nationale-recherche. fr) through project  ANR-LAVA  ( ANR Program :  DS0902 2016 ; Project:  ANR-16 CE39-0009 , PI:  A. Harris , Link: www. agence-nationalerecherche. fr/?Project=ANR- 16-CE39-0009 ; https://sites. google. com/view/anrlava/). We would like to thank the Sentinel-1 ESA team, especially  P. Potin  and  Y. -L. Desnos  for having made possible routine Sentinel-1 StripMap acquisition on La Réunion Island. This is ANR-LAVA contribution no. 5 and IPGP contribution no. XXX.
Abstract. Low-level clouds (LLCs) cover a wide area of southern West Africa (SWA) during the summer monsoon months and have an important cooling effect on the regional climate. Previous studies of these clouds have focused on modelling and remote sensing via satellite. We present the first comprehensive set of in situ measurements of cloud microphysics from the region, taken during June-July 2016, as part of the DACCIWA (Dynamics-aerosol-chemistry-cloud interactions in West Africa) campaign. This novel dataset allows us to assess spatial, diurnal, and day-to-day variation in the properties of these clouds over the region. LLCs developed overnight and mean cloud cover peaked a few hundred kilometres inland around 10:00 local solar time (LST), before clouds began to dissipate and convection intensified in the afternoon.
To ensure comparability between CDNC measurements on the different platforms, we performed a statistical analysis of all CDNC measurements made between 0 and 100 km inland over Togo/Benin at altitudes below 1 km. CDNC measured on the different platforms showed excellent agreement, with the medians and quartiles all agreeing within 5 %. We also consider cloud drop effective radius, defined as where n(R) is the number concentration of drops with radius R. R Eff = ∞ 0 R 3 n(R) dR ∞ 0 R 2 n(R For R Eff , average vertical profiles between 0 and 100 km inland showed the median values agreed within ∼ 1 µm, which is within the uncertainties of the instruments, and the interquartile ranges were similar for each platform. Data were considered in cloud when the measured liquid water content (LWC) was greater than 0.
129 on the Twin Otter and Falcon), and a scanning mobility particle sizer (SMPS) on board the ATR. Further details on the AMS and SMPS are provided by  Brito et al. (2018)  and  Haslett et al. (2019b). From the GRIMM measurements we consider only the total concentration of particles larger than 250 nm (N 250 ), and from the SMPS we use aerosol size distributions in the range 20-500 nm. The strength of the GRIMM dataset is that all three aircraft had instruments running on every flight, making it a useful dataset for measuring changes in accumulation mode aerosol concentrations. The SMPS data coverage is more limited and is restricted to straight and level runs, but it provides a more detailed measurement of the aerosol size distribution. Aerosol data were screened for cloud using a threshold for LWC of 0.
01 g m -3 and CDNC of 10 cm -3 , and data exceeding these thresholds were removed. Carbon monoxide (CO) concentrations were measured using infrared absorption spectrometry by an Aero-Laser AL5002 on the Twin Otter, SPectromètre InfraRouge In situ Toute altitude (SPIRIT,  Catoire et al. , 2017)  on the Falcon, and a Picarro Analyzer G2401-m on the ATR. We also consider vertical velocity, which was measured using wind probes on the ATR and Falcon.
Regional variation in cloud microphysics To investigate the effect of local emissions on cloud microphysical properties, we collated cloud measurements below 1 km in altitude from all three aircraft and compared them with distance to the coast (and the population centres nearby) on a north-south axis, as shown in Fig. 4a. Figure  4b  shows corresponding normalised histograms in the different regions. The limited spatial area of the aircraft sampling means that regional-scale orographic effects discussed in Sect. 3. 2 are not likely to have a large effect on in situ cloud properties. The heterogeneous nature of the dataset means that some areas and some days have large numbers of data points recorded, while others have relatively few. In particular, cloud measurements over the sea were sparse compared to inland.
Some clouds were observed with CDNC over 1500 cm -3. These long tails on the distributions are due to the effect of thick urban plumes, but they only represent a relatively small fraction of the inland data. The large majority of CDNC measurements inland were in the range 200-840 cm -3 (10th and 90th percentiles), and medians were 470 cm -3 up to 50 km from the coast and ∼ 430 cm -3 further inland. It is possible that some of this small difference in the medians may be due to measurement of more convective sea breeze clouds near the coast, but it is relatively minor overall. To investigate the effect of local pollution on CDNC, Fig. 4c  shows the distribution of CO concentrations measured in inland clouds. The inland CO data show a mode with mean and standard deviation of 141 ± 17 ppbv, but the distribution is asymmetric, with a tail extending to higher values above around 160 ppbv.
7, but data were only available for 9 d inland and 2 d offshore. The GRIMM data therefore show a similar trend to the SMPS, but with greater coverage. The mean and standard deviations in daily N 250 are 170 ± 50 cm -3 offshore and 230 ± 50 cm -3 inland, meaning the average variabilities were similar. Together with the good correlation, this suggests that aerosol imported from offshore had a strong influence on the average amount, and day-to-day variability, of accumulation mode aerosol inland. The day-to-day variability in the aerosol was not strongly represented in the CDNC measurements in Fig. 6b. There was a degree of correlation in the median CDNC and N 250 measurements (R 2 = 0. 72) after the 5th of July, but there was no correlation in the first half of the campaign. On a cloud-tocloud basis this correlation was dwarfed by the inherent local variability in CDNC.
Parcel model simulations The limited offshore dataset and the possibility of differing dynamics inland makes it difficult to assess the impact of local aerosol emissions on cloud properties based on measurements alone. To further investigate the relative sensitivities of CDNC to aerosol properties and updraft velocities, we conducted a series of parcel model simulations using ACPIM, described in Sect. 2. 1. 2. We initialised the model with representative thermodynamic conditions and varied the updraft velocity and aerosol size distribution and composition based on the measurements made in the field campaign.
Under such conditions,  Flamant et al. (2018a)  and  Deroubaix et al. (2019)  have shown that Lagos did not influence the air quality to the west (i. e. Benin and Togo). It is possible that local emissions have more of an effect on CDNC in Nigeria, but the baseline CDNC is still likely to have been high enough that this will have a reduced effect on cloud albedo compared to a clean background. Based on the satellite data there was no obvious effect on LLC fraction downwind of Lagos and the Niger delta, suggesting geographical variations in cloud formation and lifetime were still largely controlled by dynamics, orography, and the transport of cool, humid air inland by the maritime inflow. Over the next few decades, the populations of coastal cities such as Accra, Lomé, and Cotonou are expected to rise dramatically, as are their respective emissions.
Conclusions This study has assessed factors affecting properties of lowlevel clouds over southern West Africa during the West African summer monsoon, using an approach combining satellite observations with in situ measurements from the novel DACCIWA dataset. Satellite observations of low-level cloud (LLC) cover suggested regional variation in the cloud field was determined largely by the local orography and transport of cool, humid air inland by the maritime inflow, and no obvious impact of was observed in the large-scale cloud field downwind of major population centres. An as-sessment of cloud drop number concentration (CDNC) based on aircraft data acquired during the DACCIWA field campaign, as well as parcel modelling, showed that local emissions had a reduced effect on CDNC on the regional scale, as biomass burning pollution transported from the Southern Hemisphere dominated regional aerosol concentrations  (Haslett et al.
A diurnal cycle was observed, and CDNC increased by 45 %-60 % as clouds began to break up and become more convective in the afternoons compared to measurements made in the early morning. A systematic increase in CDNC was observed in CO plumes near the coastal cities, reaching over 1500 cm -3 in some cases. However, these plumes represented only a small fraction of the data measured across the region, and are unlikely to be the major factor determining CDNC inland. Offshore, the limited measurements of CDNC were split into clouds with around 100-200 cm -3 and those with around 300-700 cm -3. However, relatively few cloud penetrations were made offshore, so it is difficult to assess whether any differences between offshore and inland clouds are statistically representative. In no region, and at no time of day, were clouds observed that were representative of those found in pristine environments, and our measurements were all affected by the ubiquitous biomass burning pollution transported to the region from offshore.
Code and data availability. The DACCIWA field measurements are available on the DACCIWA SEDOO database (http://baobab. sedoo. fr/DACCIWA/, last access: 20 June 2019). SEVIRI optimal cloud analysis data and associated product guides are available to download from EUMETSAT (https://www. eumetsat. int/ website/home/Data/Products/Atmosphere/index. html, last access: 20 June 2019). ACPIM is available to download from the University of Manchester (https://personalpages. manchester. ac. uk/staff/ paul. connolly/research/acpim01. html, last access: 20 June 2019).
Acknowledgements. We thank everyone involved in DACCIWA, including the  British Antarctic Survey (BAS , operator of the Twin Otter), the  Service des Avions Français Instrumentés pour la Recherche en Environnement  (SAFIRE, a joint entity of  CNRS ,  Météo-France , and  CNES  and operator of the  ATR 42) , and the  Deutsches Zentrum für Luft-und Raumfahrt  (DLR, operator of the Falcon 20) for their support during the aircraft campaign. We also thank  Norbert Kalthoff  and  Bianca Adler  for providing the Savé ceilometer data, as well as  Lynn Hazan  for the ATR CO data. Financial support. This research has been supported by the  Seventh Framework Programme  (DACCIWA ( 603502 ) and BACCHUS (603445)), the  Natural Environment Research Council  (PhD studentship), the  Helmholtz Association  (grant no.
Acknowledgements This work has received funding from (i) the  European Research Council (ERC)  under the seventh  Framework Programme  of the  European Union  ( ERC Advanced Grant , grant agreement No  290864 , RHEOLITH), (ii) the  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ) and (iii) the program "Sakura" by the  French Ministry of Foreign Affairs. We thank the Editor and the two anonymous reviewers for their constructive comments on an earlier version of the manuscript.
Acknowledgments We acknowledge funding support from the  National Key Research and Development Program of China  (grant  2018YFC1507700 ) and  Basic Scientific Research and Operational Practice Funding of Chinese Academy of Meteorological Sciences  (Grant No. 2017Y006 ). The authors declare that all data that support the findings are publicly available. CMIP5 model outputs are accessible via the website (https:// cmip. llnl. gov/cmip5/data_portal. html). The CESM simulations under stabilized warming scenarios could be accessed at the website (https://www. earthsystemgrid. org/dataset/ucar. cgd. ccsm4. lowwarming. html).
A recent study of Swart et al (2018) performed a regression-based approach to detection and attribution of temperature and salinity changes in the Southern Ocean, using a large ensemble of the CanESM2 model at 0-2000 m depth, separating the anthropogenic influence on ocean warming to individual forcings (i. e. aerosols-only, ozone-only, greenhouse-gas only). Thermosteric sea level rise also has been attributed to anthropogenic forcings (including individual attribution to greenhouse gas forcing and aerosol forcing), using global mean thermosteric sea level inferred from the observed ocean heat content interpolated datasets (that account for regions with missing observational coverage), and compared with the direct global thermosteric sea level output from climate models (Slangen et al 2014).
Our sensitivity analysis (figure  S4 ) with inflated variance may at least partly account for that, and shows larger uncertainty rages (5%-95%) on the scaling factors. We use only one observational dataset Levitus et al (2012), as results from other detection and attribution studies shown to be relatively insensitive to the use of other observational data sets  (Gleckler et al 2012 , Bilbao et al 2019 , Slangen et al 2014). The Levitus et al (2012) dataset provides a raw (non-interpolated) three-dimensional ocean temperature data set, which is needed for detection and attribution analysis presented here (figure  S6 ). Alternative datasets (e. g. Cheng et al 2017) are available, however, in their mapping method (to infill missing data regions in 0-700 m layer), they make use of spatial covariance of CMIP model outputs  (Cheng et al 2017).
Acknowledgments We are grateful for helpful comments from  Jonathan Gregory ,  Francis Zwiers ,  Sybren Drijfhout ,  Jennifer Mecking , and  Andrew R Friedman. We also thank  Tim Boyer  for assistance with understanding the observational data. KBT and GCH were supported by the  UK NERCfunded SMURPHs project  ( NE/N006143/1 ). GCH was further funded by the  Wolfson Foundation  and the  Royal Society  as a  Royal Society Wolfson Research Merit Award  ( WM130060 ) holder. G C H and A P S were supported by the  ERC  funded project  TITAN  ( EC-320691 ), and the  UK NERC  under the  Belmont forum, Grant PacMedy  ( NE/P006752/1 ). We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output.
The vast majority of these are the sections covered during the World Ocean Circulation Experiment and the Joint Global Ocean Flux Study (WOCE/JGOFS) in the 1990s, but data from important "historical" cruises were also included, such as from the Geochemical Ocean Sections Study (GEOSECS), Transient Traces in the Ocean (TTO), and South Atlantic Ventilation Experiment  (SAVE). The second version of GLODAP, GLO-DAPv2, was released in 2016  (Key et al. , 2015; Lauvset et al. , 2016; Olsen et al. , 2016)  with data from 724 scientific cruises: those included in GLODAPv1. 1, those amassed for the Carbon in the Atlantic Ocean (CARINA) data synthesis Table  1. Variables in the GLODAPv2. 2019 comma separated (csv) product files, their units, short and flag names, and corresponding names in the individual cruise exchange files.