dataesr/scientific-paragraphs-categorization huggingface dataset:
This result reinforces the importance of the staff training and underlines the feasibility of implementing the program in other geographical areas with different teams. While the present paper provides and details the methods and implementation of a family-centered, home-based, educational pediatric obesity intervention, Figure  3  also suggests its efficacy in the short term when it comes to reducing the z-BMI of children with obesity. According to our results, the ProxOb program significantly reduced the z-BMI of children with obesity after 6 months of home-based intervention. Furthermore, the observed mean reduction of -0. 32 of the z-BMI is even higher than the one observed by Arsanan and colleagues in their recent meta-analysis (-0. 14 (-0. 26; -0. 08))  [11]. This encouraging result might be because our program combines the main details specified here again that have been shown to enhance the effect of such approaches as described above.
This improvement of the ProxOb program by using an e-health application and providing families with regular feedback and support should enhance the effect of the program and its maintenance, as previously suggested  [13, 17, 18]. Finally, while the ProxOb program rests on an educational approach to improve behaviors, future editions should consider the evaluation of some key metabolic parameters to consider the metabolic profile of the children and their families as a potential determining factor for the success of the intervention.
Acknowledgments: The authors and the ProxOb working group would like to thank all the practitioners that have been involved in the creation, development, and improvement of the  ProxOb  project. We would like to particularly thank the  CALORIS Obesity Specialized Center team , all the departmental coordinators, the involved practitioners, and physicians. The working group also wants to thank the  Contrats Locaux de Santé  and the  Clermont-Ferrand SSR Ugecam and Coeur de Bourbonnais à St Pourçain SSR Hospital ; le local Combrailles agency of the Doctors of the World; the  departmental council of Puy de Dome ; the sanitary service of the National Education Minister; and all the professional involved in the ProxOb teaching program. We would also like to thank the  Clermont-Ferrand University Hospital  and its biostatistics department.
They account for 26 times the leaf area, 34 times the number of leaves and 94 times the number of shoots than long shoots in the canopy. The three selected morphotypes were then used in simulations to ensure that our simulation conclusions refer to a diversity of background architectures. We then simulated the 3D architecture of trees to assess the quantity of incoming light on short shoots vs long shoots. We imputed the recorded morphological parameters to develop 3D tree mock-ups using AMAPSIM software  (Barczi et al. , 2008). AMAPSIM is a structural plant model that simulates plant architecture according to a set of architectural rules described in  Barth el emy & Caraglio (2007). We parameterized AMAPSIM to describe the three morphotypes using quantitative variables recorded on plants in the field.
All AMAPSIM parameter files and scripts used in this study are available in Supporting Information (Note S1). , 2008). A yield was computed for each morphotype (with leaf mass area and wood density set at 0. 01 g cm À2 and 0. 77 g cm À3 , respectively) as the intercepted light divided by the shoot mass. Short shoots of morphotypes 1, 2 and 3 intercept two, six and seven times more light, respectively, despite the fact less light is intercepted per unit area. In the same way, the total short shoot mass is higher than the total long shoot mass due to the higher number of short shoots. In the lower panel, the internode lengths of short shoots were increased to match those of long shoots. Increasing the length of the internodes of short shoots increases the light intercepted compared with short internodes.
Statistical analysis All recorded variables were averaged at the species level (calculated based on five individuals per species). The number of long and short shoots was compared between species using the Wilcoxon Mann-Whitney test for paired data sets, while long and short shoots were compared across species using the Wilcoxon test for independent data. The choice of nonparametric tests was justified by the nonhomoscedasticity of the data. All statistical analyses were performed in R 3. 6. 2 (R Core Team, 2013).
Acknowledgements MM received support from a  NRF-SASSCAL  grant (no. 118588 ). AHL received support from an  ANRT  ( ANRT-CIFRE no. 2021/0522 ) reissued funding. We wish to thank  Phumlani Zwane  for assistance in data collection and the staff from  Hluhluwe-iMfolozi Park Research Centre  for their logistical support and help they provided. We are grateful to the three anonymous reviewers and the editor who provided helpful suggestions that allowed us to improve our manuscript.
The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5. 5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.
e. after the TROPOMI algorithm update to version 02. 02. 00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i. e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions.
The NRT global CAMS system has used CAMS_GLOB_ANT anthropogenic emissions  (Granier et al. , 2019)  since July 2019 and used MACCity anthropogenic emissions  (Granier et al. , 2011)  before then. Biomass burning emissions have come from the Global Fire Assimilation System V1. 4 (GFAS;  Kaiser et al. , 2012)  since October 2020 and were GFAS V1. 2 before. Biogenic emissions are from CAMS_GLOB_BIO from July 2019 onwards, based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN;  Guenther et al. , 2006) , and from MEGAN_MACC, a different application of the MEGAN model, before then (see also Table  S1  in the Supplement for CAMS model changes). The IFS uses an incremental four-dimensional variational (4D-Var) data assimilation system going back to  Courtier et al. (1994). In the current CAMS 4D-Var setup, a cost function that measures the differences between the model fields and the observations is minimized to obtain the best possible forecast through the length of the assimilation window by adjusting the initial conditions.
Several atmospheric composition fields, including CO, are included in the control vector and minimized together with the meteorological control variables. The data assimilation methodology for the atmospheric composition fields remains unchanged compared to the one described in  Inness et al. (2015b) , but the background errors for CO were updated in July 2019 (Fig. 1 ). CAMS uses 12 h assimilation windows from 03:00 to 15:00 and 15:00 to 03:00 UTC and two minimizations at spectral truncations T95 (∼ 210 km) and T159 (∼ 110 km). TCCO products from IASI and MOPITT instruments are routinely assimilated in the CAMS NRT system (see Table  1 ). The MOPITT data used by CAMS are the NRT thermal infrared (TIR) MOPITT retrievals from the 4. 7 µm band  (Deeter et al. , 2017 (Deeter et al. , , 2019 (Deeter et al.
, , 2022)). IASI TCCO is retrieved from the same band (spectral range 2143-2181. 25 cm -1 ), and the data used during the period covered in this paper are produced by Eumetsat using LATMOS/ULB's Fast Optimal Retrievals on Layers for IASI (FORLI, v20151001) algorithm, documented in  George et al. (2009)  and  Clerbaux et al. (2009). The TIR retrievals have the largest sensitivity to CO in the mid-troposphere  (Deeter et al. , 2013; George et al. , 2015). In the CAMS system, IASI and MOPITT TCCO data are thinned to a horizontal resolution of 0. 5 • × 0. 5 • by randomly selecting one observation in each grid cell. The observation operator for TCCO in the CAMS system applies the averaging kernels of the observations to the model fields and calculates the model equivalent at the observation location and time, giving departures between the observations and the model as d = y -Ĥ (x m ).
VarBC is applied to the IASI TCCO data, with three predictors: (1) the thickness of the 1000-300 hPa layer, (2) the thermal contrast between the surface temperature and the temperature of the lowest model level over land, and (3) a global constant. The thickness parameter was chosen to allow for latitudinal variations of the bias correction. MOPITT TCCO data are used to "anchor" the bias correction, i. e. are assimilated without bias correction. Experience has shown that it is important to anchor the variational bias correction scheme, to avoid drifts in the model fields  (Inness et al. , 2013). MOPITT is used as the anchor because it was the first instrument assimilated in the early CAMS system, and the assimilation of IASI CO was added later. To prepare the CAMS system for the eventual loss of the MOPITT instrument, and because IASI and CAMS CO show good agreement (see  Figs.
7 and 8 below)  in the next CAMS model upgrade (planned for Q2/2023), the VarBC settings will be changed to use IASI-C as anchor and to bias-correct MOPITT and IASI-B CO. The CAMS NRT model and data assimilation system is continually improved as the NWP model and the chemical scheme are further developed, and new satellite data are added. Table  S1  lists the model upgrades that were implemented since the monitoring of TROPOMI CO data began in the CAMS NRT system in November 2018. The change to model CY46R1 on 9 July 2019 had a big impact on the CO analysis field and led to increased negative biases (see Fig. 3  below), likely to be linked to a change of the emission inventories. In the CAMS 4D-Var data assimilation system, the background error covariance matrix is given in a wavelet formulation  (Fisher, 2004 (Fisher, , 2006)).
Quality of CAMS CO analysis fields CAMS NRT data are routinely validated, and validation reports are produced every 3 months (https://atmosphere. copernicus. eu/global-services, last access: 28 October 2022, for example,  Errera et al. , 2021). These reports show that the seasonality of the CO field is reproduced well by the global CAMS NRT system, i. e. currently still without the assimilation of TROPOMI data, when compared with independent data. However, the CAMS CO data generally have a negative bias. Compared to IAGOS (In-service Aircraft for a Global Observing System; https://www. iagos. org/, last access: 28 October 2022) aircraft CO data  (Nedelec et al. , 2015)  the NRT CAMS data show the largest underestimations in the lower troposphere, while upper layers show a better agreement.
, 2021). Errera et al. (2021)  document that biases against GAW CO surface observations are within -8 % for European GAW stations and Asian stations and around -16 % for stations located in the Southern Hemisphere. Their comparisons with EEA AirBase surface observations in Europe show high temporal correlations, small biases over Belgium, Germany, Austria, and Switzerland, and larger negative biases over Spain (-30 %), Estonia (-30 %), Poland (-50 %), the Czech Republic (-60 %), and Bulgaria (-65 %). Compared to NDACC FTIR tropospheric column CO data (Fig. 3 ) the CAMS NRT system underestimates the tropospheric CO column in the Northern Hemisphere (NH). This negative bias increased in July 2019 after the CAMS model upgrade to CY46R1 to between -3 % to -15 % for most stations and is larger than the reported 3 % measurement uncertainty.
Compared with MOPITT and IASI TCCO data, the CAMS validation reports (e. g. Errera et al. , 2021)  find negative biases of up to -20 % and -30 % respectively. These also increased after the CAMS model upgrade to CY46R1 in July 2019. The negative CO bias of the CAMS model is even more pronounced in experiments without CO data assimilation  (Errera et al. , 2021).
TROPOMI TCCO data The monitoring of TROPOMI TCCO data in the global CAMS NRT system began on 19 November 2018. TROPOMI has a local overpass time of 13:30 UTC, a spatial resolution of 5. 5 km × 7 km in nadir (7 km × 7 km before 6 August 2019) for data retrieved from the SWIR band, and a swath of 2600 km, and it provides daily global coverage with 14 orbits per day. For the work in this paper, we use NRT TROPOMI TCCO data produced with a retrieval algorithm developed by SRON, the Netherlands Institute for Space Research, and provided by ESA/Eumetsat for the pe-riod 19 November 2018 to 31 December 2021. These NRT data are usually available with 3 h of the observations being taken. Information about updates to the TROPOMI CO retrieval algorithm since November 2018 is given in Table  S2. The TROPOMI TCCO retrieval is documented in Landgraf et al.
Since the update to v01. 03. 02 on 3 July 2019 the NRT TROPOMI data are of the same quality as the offline retrieval. The operational TROPOMI CO retrieval deploys a profile scaling approach described in detail by  Borsdorff et al. (2014) , where a CO reference profile is scaled to fit the TROPOMI reflectance measurements. For this, global, monthly averaged (3 • ×2 • ) vertical CO a priori profiles from the chemical transport model TM5  (Krol et al. , 2005)  are used. The forward calculation of the measurement accounts for light scattering by clouds and aerosols in the atmosphere and thus simultaneously retrieves trace gas columns and effective parameters describing the cloud contamination of the measurements (height scattering layer, scattering optical thickness) as demonstrated by  Vidot et al.
(2012). The TROPOMI TCCO datasets contain total-column averaging kernels for individual measurements that describe the vertical sensitivity of the retrieved CO columns. The CO retrieval under clear-sky atmospheric conditions shows a good sensitivity throughout the atmosphere, with minor variations due to the observation geometry of the satellite. Retrievals from cloudy measurements exhibit a reduced vertical sensitivity caused by shielding of the cloud in the observation geometry of the satellite. Figure  4  shows examples of TROPOMI averaging kernels for clear-sky and cloudy data over land as well as for cloudy data over sea for the NH, tropics, and SH averaged over the period 19 November 2018 to 31 December 2021 and illustrates the different vertical sensitivities. Because the TROPOMI CO retrieval is based on the profile scaling inversion  (Borsdorff et al.
, 2014) , the total-column averaging kernel A S5p cannot smooth the vertical CO a priori profile that is used for scaling within the inversion, and the validation equation (Eq. 2) simplifies to y = A S5p x true + ε, where x true is the true CO profile. This means that the CAMS model equivalent of the TROPOMI observations can be calculated as x S5P = A S5P x mod + ε = A S5P H (x m ) + ε , (4) where x mod is the CAMS model profile on the vertical grid of the TROPOMI a priori, A S5P the TROPOMI total-column averaging kernel, and x S5P the resulting CAMS CO column smoothed by the TROPOMI total averaging kernel A S5P. As recommended in the TROPOMI readme file (https://sentinels. copernicus. eu/documents/247904/3541451/ Sentinel-5P-Carbon-Monoxide-Level-2-Product-Readme- File, last access: 2 March 2022), we only use data with quality assurance values (qa_values) > 0.
A destriping algorithm has been included for offline TROPOMI data with the upgrade to v02. 02. 02 in July 2021, but this is not applied to the NRT data. TROPOMI also suffers from instrumental effects in the area of the South Atlantic Anomaly (SAA). According to the TROPOMI product readme file (https://sentinels. copernicus. eu/documents/247904/3541451/ Sentinel-5P-Carbon-Monoxide-Level-2-Product-Readme-File. pdf/f8942626-ffb6-4951-90fc-a16b6589e39e?t=1639982223246, last access: 27 April 2022) and the validation reports, the TROPOMI NRT CO product (which is used in the global CAMS system and in this paper) had an additional positive bias of 3 %-4 % before July 2019, but since processor version 01. 03. 02, the same configuration settings are used for the NRT and offline data processing streams, and therefore the data products are of the same quality.
TROPOMI super-observations for use in CAMS system and monitoring of TROPOMI CO in the NRT CAMS system Because the horizontal resolution of the TROPOMI TCCO data (5. 5 km × 7 km) is higher than the model resolution of T511 (about 40 km × 40 km), the TROPOMI data are not spatially representative for the model grid boxes. To overcome this representativeness error, the data are converted into so-called "super-observations" before they are included in the CAMS system. For this, the data are averaged to the T511 resolution of the model. Our method to create superobservations simply calculates the averages of the observations in a grid box and hence differs from a method described by  Miyazaki et al. (2012) , who also weigh the individual observations depending on the data coverage and take error correlations among the data into account.
We average the data separately for different surface types (e. g. land, ocean, ice) and for clear and cloudy data, and the observation errors and averaging kernels of the data are averaged in the same way as the observations. This averaging reduces the random errors in the data and also the representativeness errors due to unresolved small-scale features that are seen in TROPOMI data but not resolved in the model. TROPOMI CO data have been monitored passively in the CAMS system since November 2018, and results are shown in the Supplement (Figs. S1 to S4). In the global mean, CAMS TCCO analysis values are about 10 % lower than TROPOMI TCCO when only IASI and MOPITT TIR TCCO data are assimilated (averaged over the period November 2018 to December 2021), with the largest relative differences (11 %-14 %) found in the polar latitude bands, i.
The differences between ASSIM and CTRL allow us to assess the impact of the TROPOMI TCCO data on the CAMS CO analysis. The model cycle used for the experiments was CY47R3, meaning that the CTRL setup corresponds to the operational CAMS NRT configuration that was operational from October 2021. The TROPOMI data in both experiments were super-obbed to the model resolution of T511, as described in Sect. 2. 3. In ASSIM, TROPOMI TCCO was used without bias correction because the TROPOMI SWIR retrieval has different sensitivity to CO in the atmosphere than the IASI and MOPITT TIR retrievals, and it would not make sense to anchor a bias correction for TROPOMI to MOPITT or IASI.
IASI-BC analysis departures are also reduced in ASSIM during July and August and of similar size to the ones in CTRL for the  rest of the time series. MOPITT departures in ASSIM are more negative than in CTRL (as already seen in Fig. 7b ). The increased MOPITT departures in ASSIM (Figs. 7  and 8 ) do not imply that there are problems with the MOPITT dataset. MOPITT data have been extensively validated (e. g. Deeter et al. , 2019,  show that MOPITT V8 products generally have biases of less than about 5 %) and are used with good results in the CAMS reanalysis, where their assimilation leads to a CO analysis that agrees well with independent observations  (Inness et al. , 2019b; Wagner et al. , 2021). The underlying problem is the current negative CO bias of the CAMS model, which increased with the implementation of model cycle CY46R1 in July 2019.
The CAMS CO analysis in ASSIM is improved when TROPOMI data are assimilated as TROPOMI has a small positive bias with respect to observations (see Sect. 2. 2), which compensates for the negative bias of the underlying CAMS model and gives an analysis that agrees better with independent observations than if MO-PITT is used as the main instrument. There are differences between the CO retrievals from TROPOMI, MOPITT, and IASI, whose investigation is beyond the scope of this paper. A study by  George et al. (2015)  using older versions of the MOPITT and IASI retrievals showed that using the same a priori profile in the MOPITT and IASI retrievals led to better agreement between the datasets in source regions and during periods of low sensitivity but that differences between the retrievals remained.
They attributed these to differences in time and location of the observations, differences in the vertical sensitivity of the instruments, and differences in auxiliary parameters used in the retrievals (such as temperature, humidity, and cloud cover). MOPITT is likely still to be the instrument of choice for a future CAMS2 reanalysis because of the long dataset going back to 2000 and the long-term stability  (Deeter et al. , 2019). Figure  9  takes a closer look at the analysis departures in the Arctic from IASI-B and IASI-C. Here we see a much improved fit in July and August when the high CO columns retrieved by IASI-BC, which are the result of strong boreal wildfires in Siberia and North America, are not well captured in CTRL (see also  Sect. 3. 2. 4). The improved fit to IASI-BC in ASSIM continues throughout September and October, i.
GFAS uses fire radiative power observations from the two Moderate Resolution Imaging Spectrometer (MODIS) instruments on the NASA Terra and Aqua satellites to produce daily global estimates of emissions from biomass burning, with a spatial resolution of 10 km × 10 km, and is one of the emission input datasets for the CAMS system (see Sect. 2. 1). GFAS emission estimates are likely to be an underestimation of the real biomass burning emissions, and uncertainties (which are also valid for other biomass burning emission datasets) arise from (1) the detection limit of the sensors for observing smaller fires or low temperature fires (such as in peatlands) and (2) the knowledge of fuel types (i. e. vegetation types and peat) and associated emission factors -particularly in Eurasia/Siberia. The CAMS global NRT forecasts captured the transport of smoke from the persistent large-scale Siberian and North American wildfires.
The NCAR Fourier transform spectroscopy (FTS) observation programmes at Thule, GR, Boulder, CO, and Mauna Loa, HI, are supported under contract by the National Aeronautics and Space Administration (NASA). We wish to thank the Danish Meteorological Institute for support at the Thule site and NOAA for support of the MLO site. The NDACC FTIR stations (Bremen, Garmisch, Izaña, and Ny-Ålesund) have been supported by the German Bundesministerium für Wirtschaft und Energie (BMWi) via DLR under grants 50EE1711A, 50EE1711B, and 50EE1711D. The multi-decadal FTIR monitoring programme of ULiège at the Jungfraujoch station has been primarily supported by the F. R. S. -FNRS (Brussels, Belgium) and by the GAW-CH programme of MeteoSwiss. The International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) supported the facilities needed to perform the FTIR observations.