dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , a feature that is not in the training dataset. The comparison against the reference Total Carbon Column Observing Net-work (TCCON) and the surface-air-sample-driven inversion of the Copernicus Atmosphere Monitoring Service (CAMS) remains very good, as in the first version of the NN. In addition, the difference with the CAMS model (also called innovation in a data assimilation context) for NASA Atmospheric CO 2 Observations from Space (ACOS) and the NN estimates is correlated. These results confirm the potential of the NN approach for an operational processing of satellite observations aiming at the monitoring of CO 2 concentrations and fluxes. The true information content of the neural network product remains to be properly evaluated, in particular regarding the respective input of the measured spectrum and the training dataset.
, 2019 ; see also Fig. 8 ). Indeed, the seasonal cycle of CO 2 together with the growth rate generates a set of XCO 2 samples with a well-known variability. The uncertainties on the modeling (≈ 1 ppm) are small with respect to the range of XCO 2 samples that is available in the multi-year dataset (20 ppm). As a consequence, although CAMS is not the truth, it may be used for supervised learning. Note that other 4D descriptions of the atmospheric composition could have been used for our work. We chose CAMS mostly for practical reasons; the same procedure may be attempted with another modeling dataset. In practice, we used a series of OCO-2 spectra from a 5year dataset for the NN training. We then applied the NN to the observations that were not used in the training and compared their estimates to both the same CAMS model used for the training and also the fully independent set of Total Carbon Column Observing Network (TCCON,  Wunch et al.
, 2011a)  observations. The results indicated an accuracy and precision that were similar to, if not better than, that of the ACOS algorithm. More recent results challenged our interpretation of the NN skill. In particular, the XCO 2 estimates of the NN did not show significant enhancement downwind of large power plants, unlike the product of the NASA Atmospheric CO 2 Observations from Space (ACOS) full-physics algorithm. This is shown in the following together with our interpretation. A new version of the NN resulted from this interpretation and retains the high accuracy of the first version, while being much more independent from the training dataset. In the following, Sect. 2 describes the main characteristic of the NN approach and the training procedure. Section 3 presents the limitation of the first version of the NN, as it shows no innovation with respect to the training dataset.
The XCO 2 from CAMS is used for both the training and the evaluation, although using independent datasets: the "training" dataset is a 3 % random sample of the full dataset. The observations that are used for the training are earmarked and not used for further evaluation. 3 Initial results and interpretation  David et al. (2021)  described a first version of the NN approach to estimate XCO 2. In this first version, the surface pressure was not used as input, and the training was made on observations acquired during even months, while the validation used observations of the odd months. The results were surprisingly good in that the statistical difference to both the CAMS modeling and the independent TCCON observations indicated an accuracy similar to or better than that of the CAMS product.
Meas. Tech. , 15, 5219-5234, 2022 that the NN did reproduce the seasonal variation in XCO 2 together with the growth rate but was unable to identify smallscale features. Since all observations are processed independently, we could not interpret this apparent incoherence. Second, we made an experiment where the training dataset is biased by 1 ppm for the observations acquired during a single month (within the full period of 50+ months). When applied to the validation dataset, the differences to CAMS show a bias of ≈ 0. 5 ppm, but only for the observations that are within a few weeks of the biased period (Fig. A1 ). This is rather surprising as the observation date is not an input of the NN. Still, these results provide a clear indication that this version of the NN is somehow sensitive to the observation date.
To investigate the issue, we developed and trained a new NN with the same inputs, but aiming at estimating the date, latitude, and longitude. For the training, we used the true values of these parameters, and we analyzed how the NN was able to make an estimate based on the inputs (the spectra and the observation geometry). Figure  3  shows the histograms of the errors when applied to the independent dataset. The results indicate that the NN approach is able to make a reasonable estimate of the location and date of the observation based on the spectra and the observation geometry. The standard deviation of the latitude error is on the order of 7 • , and there is no significant difference with the footprint. One may expect that this information is largely derived from the observation geometry that changes with the latitude (both the SZA and the azimuth do).
No such correlation was found. Thus, either our hypothesis is wrong or the description of the longitudinal variations in stratospheric CO 2 in CAMS offers a poor representation of the reality. Both hypotheses are plausible. These results clearly demonstrate that the input data to the NN provide indirect information on the date and latitude. Atmospheric simulations such as those of CAMS indicate that XCO 2 variations are mostly a function of time and latitude. Indeed, on average, the deviations of XCO 2 along the longitudes are on the order of 0. 5 ppm (standard deviation). They are however larger (≈ 1 ppm) over the Northern Hemisphere where most of the observations analyzed here are acquired. We hypothesize that our first version of the NN, as published in  David et al. (2021) , obtains a proxy of the latitude and date and outputs the corresponding CAMS value.
A new version of the neural network As shown above, the NN appears to use the wCO2 band to derive a proxy of the observation date, which makes it possible, together with the proxies of the location, to estimate XCO 2 based on the statistical distribution of the CAMS XCO 2. To avoid this feature, an option is to not use the information from the wCO2 band. We therefore developed a similar version of the NN but without this band (i. e. , only the O 2 and sCO2, together with the observation geometry). The NN is trained on the variations in XCO 2 caused by the atmospheric growth rate and the surface flux seasonal cycle. It identifies signatures in the spectra that relate to the CO 2 atmospheric content. These signatures can then be used for an estimate of XCO 2 , even for situations that are poorly reproduced in the training dataset.
2. We decided to increase the number of NN hidden layers to five (instead of one in  David et al. , 2021). Our experience indicates that, with a larger number of layers, there is less over-fitting of the training spectra; i. e. , there is a better agreement between the loss of the training and that of the test dataset. An increased number of hidden layers also leads to slightly better performance, in particular for the NN that was designed for the land-glint observations (see below). of (i) larger variations in the optical path than for the nadir mode and (ii) the Doppler effect that may affect the absorption line positions on the input spectra. This is why our first attempts focused on the nadir cases, but there is a need to also exploit the many observations acquired in glint mode. Figure  5  shows the inter-comparison of the XCO 2 estimated from CAMS, ACOS, and NN.
All three datasets are highly consistent, with a statistical difference around 1 ppm and little bias. Let us recall that there is no satellite data input to the version of CAMS that is used here, so that it is fully independent from ACOS. The 1. 06 ppm standard deviation of their differences demonstrates that both product precisions are better than this number. CAMS and the NN are not as independent because the latter is trained with the former (but using different space-time locations). Let us stress that any bias in CAMS may be transferred to the NN product. Thus, a high agreement between CAMS and the NN product is not a demonstration of the latter accuracy. Still, it has been shown that the NN retrieves features that are not in CAMS, which indicates some independence between the satellite product and the model.
It is difficult to elaborate further as the true nature of the cloud contamination in the cases classified as probably clear is unknown. Figure  8  is based on the satellite product innovation, i. e. , the difference to the model estimates. Indeed, one may consider that the model provides current knowledge on the XCO 2 distribution, constrained by surface air sample measurements and atmospheric transport. The satellite product has the potential to improve this knowledge, but only as much as the difference with the model estimate. Typical values are around 1 ppm. The interesting result shown by Fig. 8  is that the two satellite estimates are significantly correlated. This provides further evidence that the NN estimate is not only a reconstruction of the training dataset (CAMS) with some noise.
Indeed, when NN differs from the model, ACOS, the independent satellite product tends to agree. Finally, Fig. 9  shows a comparison of the model and remotely sensed estimates of XCO 2 against the reference retrievals of the TCCON network. Although the OCO-2 satel-  lite platform can be oriented so that the instrument field of view is close to the surface station, we only use nadir data here. Indeed, the NN was not trained on the target data and can only be used to process measurements that have been acquired in observation configurations that are similar to those of the training. We thus have to rely on nadir or glint measurements acquired in the vicinity of TCCON sites. In the following, we use nadir measurements that are within 5 • in longitude and 1. 5 • in latitude to the TCCON site. For the reference, we average the TCCON estimates of XCO 2 within 30 min of the satellite overpass.
Excellent agreement is also seen at Darwin, Edwards, Park Falls, and Bremen. The comparison with TCCON does not allow favoring of one satellite estimate versus the other. Focusing on the stations with a large number of observation (25 overpasses or more), the NN estimates appear slightly better than ACOS at Darwin, Edwards, Garmisch, Orléans, and Białystok, while they are the opposite at Saga, Park Falls, and Sodankylä. The figure (and table) also clearly shows that the CAMS product offers a better agreement with the TCCON data than any of the satellite estimates in most cases. The high quality of the CAMS modeling used in this paper, at least over the TC-CON site, provides further justification of its use as a training dataset. We have applied a very similar procedure to the OCO-2 observations acquired in glint mode over land.
Discussion and conclusion This paper follows on from  David et al. (2021) , in which we described a neural-network-based technique to estimate XCO 2 and the surface pressure from the OCO-2 spectral measurements. An important message is that our interpretation of the results in that earlier study was incorrect. The NN developed in that paper reproduced the statistical variations of the training dataset (CAMS) and was unable to generate features, such as plume from emission hot-spots. Thus, contrary to our claims, the NN method, as presented in that paper, could not be used to process OCO-2 and generate XCO 2 estimates with any real value. 1. Figure  A4  provides similar results for the glint case. of the observation with a reasonable accuracy. This was unexpected as we wrote in  David et al.
(2021)  Let us recall that the NN input does not contain any information on the location or date of the observation. This is a strong indication that the information is derived from the spectra as the NN does not 'know' the CAMS value that corresponds to the observation location. Our interpretation was wrong. In fact, the NN input can somehow be used by the NN for a fairly accurate estimate of the latitude and date. Because most XCO 2 variations are a function of latitude and date, this information could be used by the NN to generate a reasonable estimate, i. e. , one that mimics the main variations in the training dataset. A question remains on the indirect information that is used by the NN to estimate the observation date. The fact that the precision on the date estimate is much better when using a combination of the O 2 +wCO2 rather than of the O 2 +sCO2 suggests that the information lies in the wCO2 band.
The spatial and temporal variations in the surface pressure, at the scale of the potential accuracies on the date and location, are much larger than this number, so that the NN estimate cannot rely on this kind of indirect information. This provided a strong indication that the NN method has the potential to extract meaningful information from the spectrum itself. We have therefore developed a new version of the NN excluding the wCO2 band from the inputs. In this version, the behavior of the NN is much different from the earlier version as it generates features that are not in the training dataset. This clearly shows that the NN uses the signature of XCO 2 contained in the sCO2 spectra to make an XCO 2 estimate. The accuracy of this estimate is similar to the one obtained with the first version of the NN and similar to that of the ACOS products.
This is confirmed by the comparison of the XCO 2 estimates against the TCCON retrievals. Another strong argument that the NN XCO 2 estimate contains true information and is not only a noisy copy of the training dataset is that the innovations of the two satellite estimates, i. e. , the differences to the model data, are significantly correlated (Fig. 8 ). Note that we use here a single neural network for the eight footprints of the OCO-2 instrument. We analyzed whether the result performance, assessed as the standard deviation of the differences with CAMS, is a function of the footprint. The statistics are very similar for all, except for footprint 2, which shows slightly higher deviation for both the ACOS and the NN satellite products (a difference of ≈ 0. 1 ppm to the mean of ≈ 1 ppm). These results confirm that the NN technique has a strong potential to process the OCO-2 observations, as well as those from forthcoming missions aiming at the observation of CO 2 from space such as the forthcoming MicroCarb  (Pascal et al.
(2021). The first challenge is the cloud detection. All the analysis described in this paper relies on the ACOS cloud detection, and only the observations identified as clear are processed. Our analysis demonstrates the potential of the NN approach but is currently not independent from ACOS. We are currently evaluating independent approaches for the cloud detection. Although the NN described here aims at an estimate of XCO 2 , we have shown earlier that the same tool can be used for an estimate of the surface pressure with a 1σ precision on the order of 3 hPa for clear-sky cases. Numerical weather analyses are actually better than that  (Salstein et al. , 2008). Thus, one may use the comparison of the surface pressure estimate from the NN to the numerical weather data for an easy identification of perturbations to the spectra that are linked to cloud or large aerosol contamination.
This would allow an easy and rapid quality indicator for the selection of observations that may be used for XCO 2 estimates, either using a physics-based algorithm or a NN approach. This idea remains to be evaluated. The second challenge concerns the absence of a quantitative indication of the amount of information that the NN takes from its prior information (contained in the training database) vs. the amount of information that the NN takes from the measured spectra. For Bayesian full-physics retrievals, these weights are represented by the averaging kernel  (Rodgers, 1990) , which allows a clean comparison of each retrieval with 3D atmospheric models, at least in theory (see the discussion about the practical difficulties in  Chevallier, 2015). The NN training targets the CO 2 column with a homogeneous weighting along the vertical, but this can hardly be achieved without some contribution from the prior information.
g. ,  Chau et al. , 2022) , or analytical estimates  (Aires et al. , 2004 ) should be explored. The last challenge concerns the need for a high-quality training dataset, in the context of increasing XCO 2. The comparison against the TCCON observations (Fig. 8 ) demonstrates that the CAMS inversion product meets this requirement. In fact, there are strong indications that CAMS remains better than the satellite products, at the very least in terms of global precision. However, because of the atmospheric growth rate of CO 2 , the training must be regularly updated. Indeed, with a frozen training dataset, the true realtime XCO 2 progressively leaves the training range. The NN approach requires a training dataset that is representative of the observation and would then lead to underestimates. For quasi-near-real-time data assimilation (e.
g. ,  Massart et al. , 2016) , the training dataset must therefore gradually integrate recent high-quality XCO 2 data, but without sacrificing robustness. As a final remark, we call for caution. We have been tricked by the NN ability to generate a consistent description of the atmospheric XCO 2 in our first analysis. It is difficult to ensure that we are not tricked again. The source of the information that leads to a fairly accurate estimate of the date, when using the weak CO2 band, remains unclear. As a consequence, although it is demonstrated that the new version of the NN generates structures that are not in the training dataset, there may be biases in the CAMS modeling that have a significant influence on the NN product.
Acknowledgements. This work was in part funded by  CNES , the French space agency, in the context of the preparation for the Mi-croCarb mission, and, to a smaller extent, by the  Copernicus Atmosphere Monitoring Service , implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission. OCO-2 L1 and L2 data were produced by the  OCO-2  project at the  Jet Propulsion Laboratory, California Institute of Technology , and obtained from the  ACOS/OCO -2 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center. TCCON data were obtained from the  TCCON Data Archive.
Code and data availability. The codes used in this paper and the CAMS model simulations are available, upon request, from the author. The OCO-2 can be downloaded from the NASA OCO-2 archive depository (https://disc. gsfc. nasa. gov/datasets?keywords= oco-2, last access: 1 February 2022), and TCCON data can be downloaded from the TCCON Data Archive (https://tccondata. org/, last access: 4 February 2021).
Acknowledgements. This work is a contribution to the  HyMeX program  ( HYdrological cycle in the Mediterranean EXperiment)  through  INSU-MISTRALS  support and the  Med-CORDEX program  ( COordinated Regional climate Downscaling EXperiment -Mediterranean region ). It was also supported by a joint  CNRS-Weizmann Institute of Science  collaborative project. The authors acknowledge  Météo-France  for supplying the data and the HyMeX database teams ( ESPRI/IPSL  and  SEDOO/Observatoire Midi-Pyrénées)  for their help in accessing the data. Financial support. This research has been supported by the  Centre National de la Recherche Scientifique (CNRS)  in the framework of a collaborative project shared between the  CNRS  and the  Weizmann Institute of Science. Review statement. This paper was edited by  Markus Meier  and reviewed by two anonymous referees.
Data processing Growth rates were determined using Eq. (  1 ): µ = ln(OD2) -ln(OD1) t2 -t1 (1)  (Arriada and Abreu, 2014) Where: µ is the specific growth rate, OD 1 and OD 2 are the optical densities at 680 nm at time t 1 and t 2 , respectively. Doubling time t D , were OD 2 = 2 * OD 1 , was determined using Eq. (2): t D = ln(2) µ (2) Where: µ is the specific growth rate, t D is the doubling time. To determine optimal salinities for each species, both growth rates and final cellular concentrations reached during stationary phases were considered. Optimums were determined when both growth rates and final OD 680 values where higher than 80% of the maximum values measured for each species. To determine the half maximal effective concentration (EC 50 ) of the different compounds, a four-parameter dose-response curve (Hill equation, Eq.
Toxicity of metals Toxicity tests were conducted on barium, iron, copper, chromium, zinc, arsenate, benzene, naphthalene, phenol and acetate to detect their potential effect on cell growths. Freshwater Chlorella vulgaris was also investigated and considered as reference for literature comparison purposes. Growth rates were calculated for each concentration. Each strain had a different growth rate observed for the control, corresponding to 0. 57 ± 0. 05 d -1 for C. vulgaris, 0. 26 ± 0. 02 d -1 for D. salina, 0. 44 ± 0. 09 d -1 for N. oceanica, 0. 45 ± 0. 11 d -1 for T. suecica, 0. 38 ± 0. 03 d -1 for P. costavermella, 0. 29 ± 0. 03 d -1 for S. rubescens and 0. 04 ± 0. 04 d -1 for C. simplex. For each metal or metalloid and for each strain, Hill equation (Eq. 3) was used to fit the inhibition data and determined the EC 50 and EC 10 at 96 h.
This aircraft is developed for 60 passengers and an intermediate transport range of approximately 9000 km (e. g. , Paris-Tokyo;  Defoort et al. , 2012). -LAPCAT is short for Long-Term Advanced Propulsion Concepts and Technologies. It is the joint effort of many European institutes, resulting -amongst others -in an aircraft model meant to travel at Mach 8 with scramjet technology, to carry up to 300 passengers, and for long-range flights of approximately 18 000 km (e. g. , Brussels-Sydney;  Steelant and Langener, 2014; Steelant et al. , 2015). The LAPCAT version is based on the technology level developed in the French high-speed propulsion program PREPHA. While we focus on H 2 -driven aircraft in this study, the HIKARI emission inventory includes carbon-based emission data as well. In general, H 2 -driven aircraft with air-breathing engines emit H 2 O and NO x but potentially include unburned H 2 fuel.
2. The data is taken from the HIKARI project's emission inventory. Table  2. HIKARI emission inventory. Annual emission of trace gas species H 2 O, NO x , and H 2. The upper three rows contain amounts of trace gases emitted in the whole atmosphere, while the lower three rows contain amounts of trace gases emitted at stratospheric altitudes only (above 100 hPa). Note that for ZEHST and LAPCAT, a part of the subsonic aviation is replaced by hypersonic transport; hence, the numbers depict whole air traffic scenarios. After 15 years of the continuous operation of hypersonic fleets, the total emission of trace gases for ZEHST and LAP-CAT scenarios at stratospheric altitudes are 206 and 319 Tg of H 2 O, 0. 3 Tg and 0. 5 TgNO 2 of NO x , and 2. 3 and 3. 5 Tg of H 2 , respectively. During the 15 years, the emitted trace gases, while being chemically converted, are continuously transported to tropospheric altitudes, and only parts of the total emitted trace gases remain as perturbation.