dataesr/scientific-paragraphs-categorization huggingface dataset:
Doppler Current Profilers HF radar data from the database of the University of California, San Diego (https://hfrnet-tds. ucsd. edu/thredds/ catalog. html) provide surface currents along the west coast of the United States, including the SCB. Seasonally averaged data from 2012 to 2020 were used to analyze trends of surface currents in the Bight compared to the model. Acoustic Doppler Current Profilers (ADCP) provide current data in the water column. ADCP measurement data from OCSD for the period June 1999 to June 2000 and Los Angeles County Sanitation District (LACSD) during November 2000 to June 2007 were used to validate vertical profiles of currents.
Acknowledgments This research was supported by the  National Oceanic and Atmospheric Administration (NOAA)  under grants for ocean acidification award  NA15NOS4780186 ,  ECOHAB  award  NA18NOS4780174 , and  Marine Debris  award  NA19NOS9990086. This work was supported by the  California Ocean Protection Council  grants  C0100400  and  C0831014. Computational resources were provided by the  Extreme Science and Engineering Discovery Environment (XSEDE)  through allocation  TG-OCE170017  and  TG-OCE030000N. I wish to extend my special thanks to the three anonymous reviewers for helping improving this manuscript.
2 ). We verified manually that this procedure defines knickpoints correctly by checking the computed positions on longitudinal profiles. We investigated in particular if the procedure is robust with respect to the time interval between successive profiles. We found that the record interval of 5 min is too small to produce well-defined erosional peaks, which lead us to identify knickpoint positions from a time interval of 10 min. Then, we built a first catalogue of knickpoint positions at different times from which we manually extract the successive positions of each individual knickpoint. We complemented the database by computing incremental retreat rates of knickpoints from their successive positions. DEMs were also used to compute hydraulic information (water depth, river width, discharge and shear stress) using the Floodos hydrodynamic model of  Davy et al.
Photogrammetry and remote sensing In order to assess the long-term shoreline evolution, we perform time-series analysis of both photogrammetric Landsat, SPOT1, SPOT4 and sentinel 2A, 2B orbital scenes completed by the aerial photography, bathymetric and topographic maps dating from  1936-1948-1952-1974-1984-1988-1999 and 2016. The obtained datasets are analyzed using the ESRI ArcGIS 10. 2. 2 software to calculate shoreline evolution of change statistics from multiple coastline positions. Additionally, we use the Digital Shoreline Analysis System (DSAS) software to calculate the rate-of-change at the specified time interval  (Thieler and Danforth, 1994). In our study area, transects were generated using DSAS every ∼ 1 km length with ∼ 200 m spacing to detect the high resolution changes  (Hzami et al.
In early century, the natural progression between 1936 to 1952 is marked by the deltaic plain progradation of the Medjerda River with a fluvial sedimentary unit mainly composed by silt and clay deposits rich in terrigenous components (D50 = 98 µm)  (Benmoussa et al. , 2018). The shoreline evolution shows a net accretion rate during this period with a positive rate of change NSM by +300 m ± 25 m. The flood event occurred in 1973 is marked by the dissymmetric morphology of the mouth River clearly observed on aerial photography of 1974 dataset (Fig. 2a ). The shoreline level shows accretion rate at the mouth of the new artificial channel, from 1952 to 1981 with an EPR average of +13 m ± 0. 15 m yr -1. Geomorphological dissymmetry observed on the multi-temporal scenes of 1973 is the re-sponse of the reworking of the tidal-inlet by the wave action during storm surge.
Acknowledgements. The authors are grateful to all the research members who contributed to the scientific and academic tasks during the project. The authors would also like to thank the Copernicus editorial support team for their editing that helped improve this manuscript. Financial support. This research has been funded by the  National Institute of Marine Sciences and Technologies in Tunisia, University of Carthage. This project was developed under the framework of the  PHC-UTIQUE-RYSCMED  (grant nos. 16G1005  and  34854QC ) project and the institutional DYNAMISME & MOSBIO-CLIM research projects funded by the  Tunisian High Ministry of Education and Scientific Research and Campus France.
Code availability. The licence of the software code Esri ArcGIS 10. 2 is provided by the Hydroscience of Montpellier Research website (HSM, Montpellier) (https://app. umontpellier. fr/securum/public/centralAuthentication, last access: 19 October 2021). Data availability. Data set related to the physical parameters of the wave propagation are provided by the database of COADS (1964) (https://shen. sdsu. edu/researchgroup/coads. htm, last access: 20 October 2021).
As part of the EUREC 4 A (Elucidating the role of cloud-circulation coupling in climate) field campaign, the German research aircraft HALO (High Altitude and Long Range Research Aircraft), configured as a cloud observatory, conducted 15 research flights in the trade-wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and metadata describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by timestamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight conditions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights.
In this paper we elaborate on HALO's contribution to EUREC 4 A, independent of the other platforms. We do so by describing how HALO was deployed during EUREC 4 A, both in standard narrative form and through the provision of auxiliary data and metadata, including flight segmentation data, animated geostationary satellite data with flight tracks, and curated photographs (Sect. 2). Through the provision of aircraft state information and the construction of a multi-sensor cloud mask product, Sect. 3 gives a synthetic overview of HALO's scientific payload and the varying cloud conditions it observed. Section 4 outlines how to access and use the HALO measurements as part of a developing data concept. Links to the data and a brief summary are provided in Sect. 6.
org/10. 5281/zenodo. 4900003. This section provides a description of the YAML files and the reasoning behind their structure and method. By adopting non-exclusive segments, a timestamp can belong to multiple segments that differ in kind. For example, timestamps belonging to the kind "lidar leg" will also belong to the kind "straight leg" if they match the definition of the latter. Segment start and end times were first roughly categorized based on timestamps from the flight reports and aircraft navigation features such as roll angle, altitude, pitch, etc. However, the final attribution of timestamps to segments was performed manually by the listed contact in the YAML files. At least one other person later tested the segmentation for errors or avoidable deviations from the segment definitions (Table  2 ).
This was done to maintain the objective segment classification whenever possible so that the user of these data can expect the segments to match the definition as closely as possible. As the segmentation was performed manually, segments are defined by the time intervals that are assigned to them, rather than by their kind. The flight segments in the YAML files also contain a field called "dropsondes", which provides a list of the dropsondes, whose times of launch are associated with the respective segment. The dropsondes are provided with classifications of good, bad, and ugly, based on their QC classification types from the EUREC 4 A dropsonde dataset JOANNE (Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments;  George et al. , 2021). The list is in the form of unique dropsonde IDs that correspond to the variable "sonde_id" provided in JOANNE and are the "cf_role" variable therein.
Photographs During all research flights photographs were taken to visually document the conditions being sampled (Fig. 4 ). Most photos were taken by the principal investigator, through either the left or the right window in the middle of the cabin forward of the wing; a few were taken from the cockpit. A subset (50-100 per flight) of these photos has been selected and further curated as described below. These photos, with their extended metadata, are included as part of EUREC 4 A's HALO dataset. The data curation involved manually correcting camera timestamps by calibrating the camera's internal clock with photographic evidence of flight-level time data from GPS watches or instrument panels synchronized with the aircraft sensor system time (BAHAMAS -BAsic HALO Measurement And Sensor system, Sect.
3. 1). GPS location and altitude tags are added to each photo using BAHAMAS location data at the capture time. For photographs taken on the apron, where aircraft position data are not available, the position of the usual parking position (13. 08 • N, 59. 4828 • W) was used. With a cruising air speed of 200 m s -1 , the estimated 1 min accuracy of the capture time implies a GPS location accuracy of about 12 km. Additional metadata were added using standard IPTC (International Press Telecommunications Council) metadata conventions. The IPTC tag "description" is used to describe the scene photographed. The IPTC tag "keywords" contains information about the orientation (viewing direction), the platform HALO, pictured cloud types, or other notable objects. In cases where the orientation could not be determined, a default is adopted, usually to the left or right of the PI seat.
For con-venience, Table  6  provides links to all of the data assets published with this paper. Not all datasets from the instruments that were on board HALO are assets to this paper. Most of the datasets are published separately (e. g. , the dropsonde dataset JOANNE by  George et al. , 2021). We only describe a subset of all observed data that were taken on HALO during EUREC 4 A. In addition, How to EUREC4A provides a much more flexible and comprehensive, but for now volatile, description of EUREC 4 A's HALO's data. This book, first developed for HALO, is (as the name suggests) being extended to other instrument platforms deployed during EUREC 4 A. It is also being adopted by planned HALO campaigns. We hope that it will contribute to a new foundation for the treatment and dissemination of Earth system science data.
A1 HAMP microwave radiometer brightness temperatures HAMP microwave radiometer brightness temperatures observed during EUREC 4 A extend the dataset of previous campaigns such as NARVAL and NARVAL2, which are published by  Konow et al. (2019). Compared to previous campaigns a technical update of the radiometers and their data acquisition system resulted in a reduction in the number of frequency channels from 26 to 25; i. e. , the 183. 3 ± 12. 5 GHz was omitted. The quality of the original brightness temperature measurements was evaluated by comparing the measurements with synthetic ones simulated from dropsonde profiles provided in the JOANNE dataset considering the suggested humidity correction  (George et al. , 2021). Systematic differences which can arise from insufficient pre-flight calibration are corrected using a linear relation between recorded and synthetic brightness temperatures, which is estimated for each flight and radiometer channel.
The high number of dropsondes released during each EUREC 4 A flight allowed the implementation of a linear correction as an update from the previous processing  (Konow et al. , 2019) , which used a simple offset. The radiometer data were recorded on three independent data acquisition computers. The clocks of all systems were configured such that they occasionally synchronize with the central HALO BAHAMAS system clock. However, when inspecting the time series of different recording computers, clear time offsets on the order of seconds can be identified. To correct for these time offsets, the brightness temperature time series were carefully inspected and time series of the different modules were compared with each other, the WALES data, and the radar. Doing so, we could identify offsets of between -7 and +2 s, with the exception of one module's clock running 141 s behind during the flight HALO-0119, which were subsequently corrected.
In addition, we undertook a manual inspection of the brightness temperature time series for non-atmospheric signals. This means that signals coming for example from thermal receiver instabilities and emission signals observed over transient objects like ships, which have a much higher microwave emissivity than the ocean, are discarded. The brightness temperature and time offsets are corrected. Due to the new data acquisition system, data with a high temporal resolution of a 4 Hz sampling rate are available on request in addition to the quality-controlled and published dataset.
A2 HAMP microwave radiometer retrievals The retrieval methods developed by  Jacob et al. (2019)  are applied to the EUREC 4 A observations, and the retrieved time series of the integrated water vapor, liquid water path, and rainwater path are published in  Jacob (2021c). For this dataset, we updated the training database for the artificial neural network retrieval using ICON model simulations for the EUREC 4 A period.
Due to a receiver update and a frequency change from 35. 5 to 35. 17 GHz, the antenna gain (G a = 49. 0 dBi) and the receiver noise figure (NF = 8. 4 dB) had to be redetermined in laboratory measurements. After correcting measured σ * 0 for gaseous attenuation, they could be compared to modeled σ 0 using horizontal wind speed data from collocated dropsonde soundings analogously to in  Ewald et al. (2019). In Table  A1  the absolute calibration offsets found, σ 0 = σ * 0 -σ 0 , are summarized for each successful calibration pattern. In addition, Table  A1  compares the horizontal wind speed u drop measured by the dropsondes with the horizontal wind speed u fit retrieved from the angular pattern of σ * 0. In summary, an absolute calibration offset of +1. 7 dB was found for the EUREC 4 A deployment of the HAMP cloud radar and subsequently subtracted from the radar reflectivity for the unified dataset.
B1 WALES For the WALES cloud mask, lidar raw data at a temporal resolution of 5 Hz were used, which corresponds to 40 m horizontal spacing. The vertical resolution of the backscatter data is 7. 5 m. The cloud flag is inferred from a step of the lidar backscatter ratio at 532 nm to values bigger than 10 while searching from the aircraft downward. The limit value of 10 is higher than what is expected from dry aerosol at this time of the season and thus indicates that considerable water uptake has occurred. To further facilitate the discrimination of optically thin clouds and opaque ones, the atmospheric optical depth between the cloud top and the sub-cloud layer is included in the dataset. Values above 2 to 3, depending on the background light situation, are only rough estimates but indicate the presence of an optically thick, opaque cloud.
B2 HAMP (cloud radar) The HAMP radar cloud mask uses radar reflectivities measured by the HAMP cloud radar and calibrated as described in Sect. A3. The data are provided at a 1 Hz time interval and with a 30 m vertical resolution. Reflectivity data are first filtered for clutter. Any signal above the noise level at 200 m above sea level or higher is considered a possible cloud. Signals originating from an object of at least 4 contiguous pixels are classified as most likely a cloud; otherwise they are classified as probably a cloud signal. The cloud mask is published by  Jacob (2021b)  and can be downloaded from the database (https://doi. org/10. 25326/222).
If sun glint is present, this method H. Konow et al. : EUREC 4 A's HALO is applied to all pixels classified as either probably or most likely cloudy. Pixels with a strong vapor absorption signal are set to cloud-free. The cloud mask dataset has been published  (Pörtge et al. , 2021)  and is accessible for download (https://doi. org/10. 25326/166).
B4 VELOX (IR imager) A two-dimensional cloud mask from VELOX has been derived with a similar method using brightness temperature measurements from the broadband channel of the instrument at a temporal resolution of 1 Hz. In this case, four thresholds (0. 5, 1. 0, 1. 5, and 2. 0 K) are used to determine a cloud mask flag for each spatial pixel in the field. As with the KT19 cloud mask, if one threshold is exceeded, the pixel is flagged as probably cloudy, whereas all thresholds must be exceeded for the pixel to receive a most likely cloudy flag. Furthermore, a maximum and minimum possible cloud cover within the field of view has been calculated for each time step based on the number of probably cloudy and most likely cloudy pixels, respectively. This cloud mask dataset has also been published  (Schäfer et al.
B5 VELOX (KT19) The cloud mask from the KT19 is derived by comparing the measured brightness temperature to simulated measurements in cloud-free conditions. Using three thresholds (0. 7, 1. 0, and 2. 0 K) based on the difference between the measurements and simulations, a measurement is flagged as cloudfree when no threshold is reached, probably cloudy when one threshold is reached, or most likely cloudy when all three thresholds are reached. The cloud mask from the KT19 has been published  (Schäfer et al. , 2021a)  and can be found in the database (https://doi. org/10. 25326/162).
The cloud mask is published by  Jacob (2021a)  and can be downloaded from the database (https://doi. org/10. 25326/223). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-13-5545-2021-supplement. Author contributions. FA, SB, GG, SG, MJ, BM, MM, SS, BS, JV, RV, and MWe acted as principal investigators on one or more research flights. WALES operation and data quality control were performed by SG, MG, and MWi. The SMART, VELOX, and BAC-ARDI team, who took care of instrument design, operation, and/or data quality control, is made up of AE, AEL, MS, MWe, KW, and MZ. FA, SC, FE, GG, MH, LH, MJ, FJ, MK, HK, MM, JR, HS, and AW made up the HAMP team, with different responsibilities for instrument design and improvement, instrument calibration, instrument operation, data quality control, and product generation.
LF, TK, BM, VP, and TZ operated the specMACS instrument and ensured data quality control and product generation. AG and MZ operated the BAHAMAS system and took care of data quality control. FE updated the HAMP radar calibration for EUREC 4 A. MJ and AW derived the HAMP radiometer calibration data. VP coordinated the joint cloud mask product. MJ, FG, VP, MS, and MWi derived the cloud mask products for their instruments. MP derived the original flight segmentation. Flight segments were manually identified by GG, LH, TK, HK, TL, TM, and MP. HS created dataset satellite images and movies. RV and HK organized photo collection and publication. MK created and published the BAHAMAS data subset. TM, JR, and TK created the How to EUREC4A book. SB and BS devised and coordinated the EUREC 4 A campaign.
FA led the participation of the German Research Foundation (DFG) partners. HK and BS devised and wrote the manuscript with text input from FE, GG, MJ, MK, TK, AEL, TM, VP, JR, MS, HS, and MWi. All co-authors contributed with their ideas and comments to the development of the manuscript. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Elucidating the role of clouds-circulation coupling in climate: datasets from the 2020 (EUREC4A) field campaign". It is not associated with a conference. operations team.
Citation Flight segments https://doi. org/10. 5281/zenodo. 4900003 Prange et al. (2021) Satellite movies https://doi. org/10. 25326/225 Schulz et al. (2021) Photographs https://doi. org/10. 25326/229 Konow et al. (2021) Instrument information https://doi. org/10. 25326/232 Kölling et al. (2021) Aircraft state https://doi. org/10. 25326/161 Klingebiel (2021) HAMP radiometer cloud mask https://doi. org/10. 25326/223 Jacob (2021a) HAMP radar cloud mask https://doi. org/10. 25326/222 Jacob (2021b) specMACS cloud mask https://doi. org/10. 25326/166 Pörtge et al. (2021) VELOX KT19 cloud mask https://doi. org/10. 25326/162 Schäfer et al. (2021a) VELOX IR imager cloud mask https://doi. org/10. 25326/163 Schäfer et al. (2021b) WALES cloud mask https://doi. org/10. 25326/216 Wirth (2021) How to EUREC4A https://howto.
While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter-and intra-annual agricultural dynamics of parcel classification. Along with simple training adjustments, our model provides an improvement of over 6. 6 mIoU points over the current state-of-the-art of crop classification. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300 000 annotated parcels.
Multiple recent studies  [21, 13, 15, 32, 14]  have solidified the PSE+LTAE (Pixel Set Encoder & Lightweight Temporal Attention) as the state-of-the-art of crop type classification for these reasons. Furthermore, this network is particularly parsimonious in terms of computation and memory usage, which proves well suited for training on multi-year data. Finally, the code is available (https://github. com/VSainteuf/lightweight-temporalattention-pytorch, accessed on 10/10/21). For these reasons, we choose to use this network as the basis for our analysis and design modifications. [11] , handcrafted rules  [9] , or statistical analysis  [18] , while other models are based on a physical analysis of the soil composition  [7]  such as the nitrogen cycle  [8]. Aurbacher and Dabbert also take a simple economic model into account in their study  [1].
More sophisticated models combine different sources of knowledge for better suggestions, such as ROTOR  [2]  or CropRota  [33]. The RPG Explorer software  [23]  uses a second-order Markov Chain for a more advanced statistical analysis of rotations. Given the popularity of these tools, it is clear that the careful choice of cultivated crops can substantially impact agricultural yields and is the object of meticulous attention from farmers. This is reinforced by the multi-model, multi-country meta-study of Kollas et al. [20] , showing that multi-year modeling allows for a large increase in yield prediction. Consequently, we posit that a classification model with access to multi-year data will learn inter-annual patterns to improve its accuracy. Multi-Year Crop Type Classification. Multi-year crop type classification refers to leveraging information (satellite observations, past declarations) to improve the classification of the grown crop type in agricultural parcels.
• We introduce a modified attention-based temporal encoder able to model both interand intra-annual dynamics of agricultural parcels, yielding a large improvement in precision. • We present the first open-access multi-year dataset  [25]  for crop classification based on Sentinel-2 images, along with the full implementation of our model. • Our code is open-source and accessible at the following repository: https://github. com/felixquinton1/deep-crop-rotation.