dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This paper is a joint effort of the working group "INTRACO" supported by  CESAB , the synthesis center of the  French Foundation for Research on Biodiversity (FRB)  and sDiv, the synthesis center of iDiv ( DFG FZT 118 ,  202548816 ). CGT's work is supported by a PhD grant provided by the  Laboratoire d'Excellence CEBA (Center for the study of Biodiversity  in Amazonia; http://www. labex-ceba. fr/en/). CEBA is funded by an " Investissement d'Avenir " grant of the  French National Research Agency  ( CEBA :  ANR-10-LABX-25-01 ). This work has been realized with the support of  MESO@LR-Platform at the University of Montpellier , and authors thank particularly  Thomas Arsouze  and  Philippe Verley  for their support in the use of the platform.
Updates include the airmass correction ( §7. 1), window to window averaging ( §7. 2), and scaling to tie to in situ calibration scales ( §7. 3). GGG2020 data is available through tccondata. org. A repository containing the full set of publicly available data is available through CaltechDATA (Total Carbon Column Observing Network (TCCON) Team, 2022). Each TCCON site's data record has its own unique DOI. On occasions that a site needs to reprocess and redeliver data already released to the public, the revised dataset will receive a new DOI with the revision number incremented. TCCON sites are permitted to withhold data from the public archive for up to one year from acquisition. This public archive is updated approximately once per month with newly delivered or released data. The TCCON data product is documented extensively through the TCCON Wiki (https: //tccon-wiki.
In situ bias correction summary A summary of the in situ correction factors, their errors, and what in situ calibration scales each product is tied to are given in Table  2. Because these correction factors are the mean TCCON/in situ ratio, dividing the airmass-corrected and windowaveraged values by these correction factors removes the mean TCCON-in situ bias. In the TCCON data, users will find two sets of X CO2 variables. Releasing the rescaled X CO2 as new variables, rather than creating a new TCCON data version with the existing variables rescaled, was chosen for several reasons. First, it is logistically simpler, allowing us to provide this update more quickly. Second, during this transitional period when existing CO 2 data is available on both the X2007 and X2019 scales, having both X2007 and X2019 X CO2 allows users to switch back and forth easily if they need to match up with other datasets on a mix of both scales.
Uncertainty budget To calculate an uncertainty in the GGG2020 dataset, we selected three days from the East Trout Lake dataset spanning a range of atmospheric water vapor, surface temperature and solar zenith angle (Figure  16 ). Each known source of uncertainty is modeled or perturbed by a realistic amount in the GFIT forward model (the quantitative amounts are described in the following paragraphs), and we compute the percent fractional difference in X gas between the perturbed and unperturbed value. set by a physical pinhole ranging from 0. 5-1. 3 mm drilled into a thin plate within the instrument, and its size can be in error by a few percent. Here, we increase FOV by 7% to reflect any uncertainty in the field stop diameter.
Acknowledgements. The authors gratefully acknowledge the use of GNU Parallel (Tange, 2011) in the GGG processing. The authors also thank  James Abshire  for providing CO2 data used in deriving the in situ correction ( §7. 3). A portion of this research was carried out at the  Jet Propulsion Laboratory (JPL), California Institute of Technology , under a contract with  NASA  ( 80NM0018D0004 ). Government sponsorship is acknowledged. Support for Caltech TCCON sites and partial support for JLL, MK, CMR, and POW provided by  NASA  grants  NNX17AE15G  and  80NSSC22K1066. Material from BBS and RFK is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement No. 1852977. MR and RS acknowledge funding by the  German Helmholtz Research Program  " Changing Earth -Sustaining our Future" within the Research Field "Earth and Environment ".
The  Paris TCCON  site has received funding from  Sorbonne Université , the  French research center CNRS  and the  French space agency CNES. The Cyprus TCCON site and AirCore flights have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 856612  and the  Cyprus Government. The TCCON site at Réunion Island has been operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-INIWRE  (Grant agreement ID:  313169 ), the ministerial decree for  ICOS  ( FR/35/IC1  to  FR/35/C6 ),  ESFRI-FED ICOS-BE  project ( EF/211/ICOS-BE ) and local activities supported by  LACy/UMR8105  and by  OSU-R/UMS3365 -Université de La Réunion. The Eureka TCCON measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network  for the.
, 2018; Chiarelli et al. , 2020). This reference PET ET 0 = k c • PET, where k c is a crop-type and growing stages dependent parameter. These models also consider delivery losses and flows that return to natural reservoirs, i. e. they consider the total water withdrawal, by using empirical ratios or specific rules according to the irrigation methods  (Rost et al. , 2008; Jägermeyr et al. , 2015). The advantage of calculating the withdrawn volume is that it allows comparison and validation with datasets of reported values, for example the FAO AQUASTAT dataset  (Frenken and Gillet, 2012). GHMs explicitly represents water supply sources  (Döll et al. , 2012)  and allows the estimation of non-sustainable groundwater used for irrigation  (Wada et al. , 2012). Some GHMs also simulate water allocation (use of water by type of source) based on rules that use information of local infrastructure and environmental flow estimations  (Siebert et al.
Then we assess the modelling bias against observed datasets using a factor analysis, with and without irrigation, for ET and LAI. In large basin with extensive irrigation activities, we compare simulated and observed values of discharge, and total water storage anomalies (TWSA). We also show some results on the correlation between the irrigation bias and some landscape descriptors, as a first step to improve the realism of the scheme. Finally, we discuss the results and we present the main conclusions and perspectives.
ORCHIDEE v2. 2 ORCHIDEE describes the fluxes of mass, momentum, and heat between the surface and the atmosphere  (Krinner et al. , 2005). Here we use version 2. 2, which is close to the version used for CMIP6 (corresponding to 2. 0). Version 2. 0 has been largely described in many papers  (Cheruy et al. , 2020; Boucher et al. , 2020; Tafasca et al. , 2020)  and version 2. 2 only adds a few minor bug corrections. We summarize the main characteristics of the model that mediate in the simulation of irrigation. In each grid cell, vegetation is represented by a mosaic of up to 15 plant functional types (PFTs), including generic C3 and C4 crops, as well as generic C4 grasses, and tropical, boreal and temperate C3 grasses. The PFTs fractions are described by the LUHv2 dataset  (Lurton et al. , 2020) , and each PFT is characterized by a specific set of parameters, applied to same set of equations  (Boucher et al.
Input data for ORCHIDEE Firstly, ORCHIDEE is run at global scale in offline mode. We run the model for the period 1970 -2013, but we leave the first 10 years as warm-up, and we focus our analysis on the period 1980 -2013. We use the GSWP3 (van den Hurk et al. , 2016) as meteorological forcing (http://hydro. iis. u-tokyo. ac. jp/GSWP3/) with a resolution of 0. 5 degrees. We also run short simulations for the sensitivity analysis and parameter tuning (see below in Section 4). We prescribe the irrigated surfaces in transient mode, i. e. irrigated surfaces may change every year, based on the Historical Irrigation Dataset (HID) from  Siebert et al. (2015)  and on Land Use Harmonization 2 (LUHv2) dataset from  Hurtt et al. (2020). HID presents a map every 10 years before 1980 and every 5 years after at 5 arcmin resolution, and for each year, we use the nearest map in time to avoid data interpolation, LUHv2 presents a map every year with a 0.
25 degrees resolution. The main difference between the HID and LUHv2 maps is that HID prescribes the area that is equipped for irrigation (AEI), while LUHv2 prescribes the area that is actually irrigated (AAI). As a result, the HID dataset has a greater irrigated surface (3. 0 10 6 km 2 for HID, 2. 5 10 6 km 2 for LUHv2 at global scale around 2000). The performed simulations use uniform parameters over irrigated areas, main changes between simulations are summarized in Table  1. As a reference, we use a simulation with no irrigation, called NoIrr, while simulation Irr with irrigation activated, uses parameter values according to results from the sensitivity and tuning analysis (Section 4) and the HID maps. The simulation Irr_NoTuned also activates irrigation and uses the HID dataset as input, but it uses a-priori parameter values.
Simulation Irrigation Irrigated surfaces β [-] Ai [-] Imax [mm/h] Adduction [-] NoIrr No - - - - - Irr_NoTuned Yes HID 1. 0 0. 9,0. 0,0. 9 1. 0 0. 0 Irr Yes HID 0. 9 0. 9,0. 9,0. 9 3. 0 0. 05 Irr_LUH Yes LUHv2 0. 9 0. 9,0. 9,0. 9 3. 0 0. 05 Irr_NoAdd Yes HID 0. 9 0. 9,0. 9,0. 9 3. 0 0. 0 Irr_Beta Yes HID 0. 75 0. 9,0. 9,0. 9 3. 0 0. 05 Irr_Imax Yes HID 0. 9 0. 9,0. 9,0. 9 1. 0 0. 05 and finally the effect of changes in the I max value is considered in simulation Irr_Imax. All these simulations use HID to prescribe irrigated areas. We analyze the effect of large differences in prescribed irrigated areas on irrigation amounts by using the LUHv2 dataset as input (simulation Irr_LUH) and using the same parameter values as the Irr simulation.
Validation datasets and landscape descriptors The validation of the new irrigation scheme and its effect on the model bias is focused on five variables: evapotranspiration, leaf area index, discharge, irrigation withdrawal and total water storage anomalies. We also use two landscape descriptors datasets (see below). Irrigation water withdrawals -We use two datasets: first, we compare the simulated irrigation rates with values from the FAO-AQUASTAT database (https://www. fao. org/aquastat/en/) reported in  Frenken and Gillet (2012)  for irrigation volumes around 2000. AQUASTAT is based on reported values at the country scale, so it does not inform on seasonal values or their spatial distribution. In countries with a lack of information, data is completed using modelling outputs to estimate the plant requirement, and country-level ratios of irrigation efficiency to calculate the irrigation water withdrawal  (Hoogeveen et al.
, 2015). While the plant requirement corresponds to the increase of evapotranspiration, the irrigation water withdrawal is the volume that is abstracted from the natural reservoirs, and includes the losses and return flows. We also use the spatially explicit information of irrigation water withdrawal around the year 2000 from  Sacks et al. (2009). This reconstruction uses national-level census data, primarily from AQUASTAT, with maps of croplands by crop type, areas equipped for irrigation, and climatic water deficit. The result is a gridded map with a resolution of 0. 5 degrees. Evapotranspiration -We use two datasets: the first product is GLEAM v3. 3a, which combines satellite-observed values of soil moisture, vegetation optical depth, and snow-water equivalent, reanalysis of air temperature and radiation, and a multisource precipitation product  (Martens et al.
, 2017). The second dataset is FLUXCOM  (Jung et al. , 2019) , which merges Fluxnet eddy covariance towers with remote sensing (RS) and meteorological (METEO) data using machine learning algorithms. Here River discharge -We use monthly data from the Global Runoff Data Centre (GRDC, https://www. bafg. de/GRDC-/EN/ Home/homepage_node. html) in 14 large basins with strong irrigation activities. We choose the station nearest to the river mouth that also has data available for the study period (Figure  S4  shows the basins and its corresponding discharge station). Basin boundaries were delineated with the flow directions map used by ORCHIDEE (section 2. 1). Total water storage anomalies -We compare the total water storage anomalies (TWSA) from our simulations with three different monthly products of TWSA based on GRACE (Gravity Recovery and Climate Experiments) observations based on global mascon solutions, that are suitable for hydrologic applications  (Scanlon et al.
, 2016) : CSR  (Save et al. , 2016) , GRC Tellus, called here TELLUS  (Watkins et al. , 2015)  and NASA GSFC  (Loomis et al. , 2019). CSR has a spatial resolution of 0. 25 degree, while TELLUS and NASA GSFC have a resolution of 0. 5 degrees. As the differences between products at the large river basin scale are small, we use the average value of the three products. All the products cover the period from april 2002 to the end of the simulation in 2014. Landscape descriptors -We compare the simulation results with two landscape descriptors which are linked to irrigation and may contribute to the irrigation bias. We use the fraction of irrigated rice around year 2000 from MIRCA2000  (Portmann et al. , 2010)  (see spatial distribution and focus on Southeast Asia in Fig. S5 ), and the location and volume of major dams based on the Global Reservoir and Dams dataset, GRanD  (Lehner et al.
The objective of the sensitivity analysis and parameter tuning was to identify key parameters and reduce the underestimation of irrigation by tuning the uniform parameter values. Second, although the once-at-a-time method is suitable given the computational cost of running an ORCHIDEE simulation, it also has drawbacks and limitations in its analysis  (Song et al. , 2015) , for instance its qualitative 335 nature, and lack of quantification of individual interaction between parameters. Third, we use the LUHv2 map, which represents the areas actually irrigated, AAI (a lower value than the areas equipped for irrigation, AEI, which is used in other datasets). We do not consider the effect of prescribed data uncertainty nor the effect of meteorological forcing in this analysis.
Variability of the irrigation rates due to parameter values and input data The global annual irrigation volumes (Fig. 5-a ) show a large uncertainty across the simulations due to changes in the parameter values (for instance, -24. 7% between Irr_NoTunned and Irr). The parameter set used in the Irr simulation manages to increase the irrigation rate and to markedly reduce the irrigation bias when compared to the Irr_NoTuned simulation. Also, a positive trend in the annual irrigation volume is observed in all simulations. It is caused by the increase in irrigated area, observed in both HID and LUHv2 datasets (see simulations Irr and Irr_LUH). The irrigated area has been identified by  Puy et al. (2021)  as the main driver of irrigation water withdrawal, and the increase of the prescribed irrigated area in the simulations explains in part the positive trend in the irrigation rate (see Fig.
9-b ) if we retire a single outlier corresponding to the Nile river basin (r value without the outlier is -0. 55). The correlation between paddy rice and irrigation bias suggests the need to explicitly represent paddy irrigation at global scale. Thus, we add an assessement of the β value and irrigation bias, using the short simulations used on the parameter tuning (see section 4). We use all simulations with changes on the β parameter from Table  2. Then, we build a composite map of the β value that minimizes the irrigation bias at a grid-cell scale (Fig. 9-c ) and then we show the corresponding irrigation bias as compared to  Sacks et al. (2009)  dataset. The results roughly show at least two classes for β: the first with values of 1. 2 and 1. 4 (for instance in China and north India) and the second with values of 0.
9 ), and water withdrawal for other economic sectors and other uses could explain the differences in seasonal discharge dynamics between ORCHIDEE and observed data in some basins  (Pokhrel et al. , 2016). Effect of irrigation on simulated TWSA is weak. In some large river basins, we observed increases in low values in areas with significant surface water supply. But even when irrigation is activated, ORCHIDEE is not able to follow the trends exhibited by GRACE datasets, for instance in Huang He and Indus river basin, two heavily irrigated areas where water depletion has been related to groundwater pumping for irrigation  (Rodell et al. , 2018; Yin et al. , 2020). There are probably multiple causes for inability of LSMs to capture large negative decadal water storage trends  (Scanlon et al.
Acknowledgements. This research was part of the PhD project of  Pedro F. Arboleda , funded by  EUR IPSL CGS  ( ANR Investissements d'avenir. Grant Number:  ANR-11-IDEX-0004-17-EURE-0006 ). The work also received support from project  BLUEGEM  ( ANR-21-SOIL-0001 ). The simulations were done using the IDRIS computational facilities ( Institut du Développement et des Ressources en Informatique Scientifique, CNRS, France) , under the allocation 2022-[ AD010113599R1 ]. We thanks  Bill Sacks  for sharing the gridded maps on irrigation withdrawal for year 2000. 27 https://doi. org/10. 5194/egusphere-2023-1323 Preprint. Discussion started: 14 July 2023 c Author(s) 2023. CC BY 4. 0 License.
ornl. gov/cgi-bin/dsviewer. pl?ds_id=1653. Monthly discharge data from the GRDC is available at https://www. bafg. de/GRDC/EN/Home/homepage_node. html. For TWSA, CSR dataset is available at https://www2. csr. utexas. edu/grace/RL06_mascons. html, Tellus is available at https://podaac. jpl. nasa. gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_ RL06. 1_V3, and GSFC is available at https://earth. gsfc. nasa. gov/geo/data/grace-mascons. We thanks William Sacks for providing the gridded irrigation rates dataset. HID dataset is available at https://mygeohub. org/publications/8/2 and LUHv2 is available at https://luh. umd. edu/. MIRCA2000 is available at https://zenodo. org/record/7422506. GRanD dataset is available at https://sedac. ciesin. columbia. edu/data/set/ grand-v1-dams-rev01. For analysis, we used standard packages from R v.
9 https://doi. org/10. 5194/egusphere-2023-1323 Preprint. Discussion started: 14 July 2023 c Author(s) 2023. CC BY 4. 0 License. we use RS+METEO products, specifically the averages of RS+METEO WFDEI and RS+METEO CRUNCEP v8 , to cover the analysis period. Leaf area index -We use the LAI3g dataset(Zhu et al. , 2013)  climatological values for the period 1983-2015. This dataset applies a neural network algorithm on satellite observations of the Normalized Difference Vegetation Index (NDVI) 3g to estimate LAI.
10 https://doi. org/10. 5194/egusphere-2023-1323 Preprint. Discussion started: 14 July 2023 c Author(s) 2023. CC BY 4. 0 License. In addition to direct comparison at the grid cell, zonal or basin scale, we perform a factor analysis to reveal relationships between modelling bias and landscape descriptors. We use the fraction of irrigated areas around 2000 from HID, as well as the fraction of irrigated rice from MIRCA2000, both interpolated to the ORCHIDEE resolution. We categorized grid cells into six classes by irrigation fraction levels based on the two datasets, followingMizuochi et al. (2021): Class 1: 0%, Class 2: 0 to 5%, Class 3: 5 to 10%, Class 4: 10 to 20%, Class 5: 20 to 50% and Class 6: 50 to 100%.
Abstract-Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 70s. Enormous progress has been made since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently the data were processed pixel-by-pixel or taking a few neighboring pixels into account. Deep learning has obtained remarkable improvement in the computer vision field, and offers a whole new way to tackle the rain retrieval problem. The Global Precipitation Measurement (GPM) Core satellite carries similarly to TRMM, a passive microwave radiometer and a radar that share part of their swath. The brightness temperatures measured in the 37 and 89 GHz channels are used like the RGB components of a regular image while rain rate from Dual Frequency radar provides the surface rain.
I. MOTIVATION We have developed a rain retrieval algorithm, DRAIN, based on the use of deep-learning techniques. The architecture and principles driving DRAIN are presented in more detail in  [1]  but, namely, a database of co-located rain rates from the Dual frequency Precipitation Radar (DPR) and brightness temperatures from the Global Precipitation Measurement Microwave radiometer (GPM-GMI) were fed into a U-net  [2]. This type of convolutional network was successfully used to de-clutter radar images  [3]  and thus appeared to be well adapted to detect the contours of rain events. The main difference between more classical retrieval algorithms, either Bayesian-based  [4, 5]  or machine learning-based  [6] [7] [8] [9] [10] [11] [12] , and DRAIN arises from the fact that the latter processes the data as images while the formers work on a pixel-by-pixel basis.
In the present paper, we will not go into the details of DRAIN which have already been presented in  [1] , but we will focus on a more thorough validation of a more mature version of the algorithm. The two main improvements that must be highlighted between the initial version presented in  [1]  and the current version are described hereafter. First, the database was increased from about 52,000 images to about 103,000 allowing us to build a training database of 70,000 images for training and 33,000 images for validation. Data from the whole years 2014 to 2018 and a few months from 2020 and 2021 are used but the whole year 2019 was kept separate for the performance assessment (test) and most results presented hereafter are computed for that year. This large database is meant to dampen the effects of seasonal and interannual variability of rain.
Second, DRAIN retrieves now a set of 99 quantiles instead of a simple averaged rain rate as in  [1]. These quantiles represent the probability that the rain rate is below a certain threshold. Hereafter, when unspecified, the quantile 50 % (median) is used as rain proxy. The loss function for quantile regression is the one proposed in  [13]. Retrieving quantile for rain is interesting because as shown hereafter, it is possible to infer a confidence interval for the results. It would also be possible from the retrieved Cumulative Distribution Function (CDF) of rain intensity to deduce a Probability Density Function (PDF, not presented here). Section II will give a short presentation of the database construction while section III gives some elements about the methodology and the associated cost function.
II. DATABASE GMI is a conically-scanning radiometer with channels at 10. 65, 18. 7, 23. 8, 36. 6, 89. 0, 166. 0, 183. 3+/-3 and 183+/-7 GHz. All the channels are measured in both Horizontal (H) and Vertical (V) polarization except for 23. 8 and the two 183. 3 GHz sounding channels (V only). In the present study, only the two 36. 6 (hereafter noted 37 GHz) and 89. 0 GHz channels were used. This choice was driven by the idea that most conicalscanning passive microwave radiometers for rain retrieval do have channels in the 37 and 89 GHz region thus making a transposition of DRAIN to other platforms potentially easier. These two frequencies were also selected despite being mainly affected by ice scattering, because they offer a good spatial resolution with well-defined horizontal gradients. For the GMI, the pixel resolutions are respectively: 15.