dataesr/scientific-paragraphs-categorization huggingface dataset:
Pixels requiring data are those with < 100 % water PFT cover in the PFT product. Pixels that are designated as surface water in the landform dataset and have < 100 % water PFT cover in the PFT product are additionally filled with one of the terrestrial landforms (plains, hills, and mountains). Missing data generally occur along coastlines due to mismatches in the land-sea masks of the auxiliary datasets and the CCI MRLC data. The gap-filled datasets are combined to create a dataset of 1,531 unique combinations of landform, region, and climate zone. For each of the unique combinations, the areal cover of each of the tree cover classes with well-defined tree types (codes 50-82) is calculated using static pixels of those classes, and the majority tree type by area is identified for each unique combination.
4). In a separate assessment of the implications of the updated PFT distributions for model evaluation, JULES simulations of PFT distributions, created for the Inter-Sectoral Impacts Model Inter-comparison Project (ISIMIP;  Frieler et al. , 2017) , were used. This was done to compare evaluation results using both the CWT-derived PFT distributions (PFTglobal) and the updated PFT The CCI PFT dataset is provided as a companion product to the ESA CCI LC map series products with similar specifications with a global extent, a pixel size of 300 m and a Plate Carrée projection. However, climate models may need products associated with a coarser spatial resolution, over specific areas (e. g. , for regional climate models), and/or in another projection. To tackle the variety of requirements, a user tool has been developed that allows users to adjust the products in a way which is suitable to their models.
PFT layer description considering the CCI MRLC categories and the PFTglobal dataset Table  2  shows the global areal coverage of each PFT by class for 2010 for the PFTlocal product, and Table  A1  shows the equivalent data corresponding to the application of the most recent version of the CCI MRLC global CWT  (Lurton et al. 2020;  Table  A2 ) to the v2. 0. 8 CCI MRLC map for 2010 (hereafter called PFTglobal). Figure  A2  complements Table  A1  by illustrating the differences between the PFTlocal and the PFTglobal products globally at a spatial resolution of 0. 25 x 0. 25 degrees. For each class of PFTglobal, the global CWT specifies the fractional composition of contributing PFTs; in this approach, each pixel of a class is assigned the same fractional PFT composition regardless of its location on Earth.
The distribution for Africa is shown in Figure  A1 , where tree crops in the Sahel are readily apparent. Globally, class 10 pixels have 5. 1 % tree cover on average (Table  3 ). On average, class 12 pixels (rainfed croplandtree or shrub cover) have 18. 4 % tree cover. The auxiliary dataset used to derive tree cover for most classes in the PFTlocal product is based on Landsat 7 images  (Hansen et al. 2013) ; the artifacts associated with the failure of the Landsat 7 Scan Line Corrector  (Andrefouet et al. , 2003)  are visible in the 300 m PFT local dataset in some regions, particularly in west-central Africa.
Shrub cover The PFTlocal product indicates 4. 7 million km 2 of global shrub cover. The largest contributors to total shrub cover are broadleaved deciduous (44. 6 %) and needleleaved evergreen shrubs (25. In addition to the differences in the shrubland class pixels, a large part of the difference in total shrub cover between the PFTlocal product and the PFTglobal dataset can be ascribed to the fact that the PFTlocal product estimates shrub PFT only in pixels belonging to the shrubland classes (codes 120-122 and 180) due to a lack of appropriate datasets to apply to the other classes. The CWT estimates 9. 5 million km 2 of shrub cover in non-shrubland PFTs, and some of this shrub cover may indeed be missing from the PFTlocal product. However, because the PFTlocal product, which is based on quantitative estimation using auxiliary inputs, and the CWT, which is largely based on expert input, differed so strongly in the estimates of shrub PFT in the shrubland class pixels, some of the differences in the non-shrubland class pixels may likewise be due to bias in the CWT.
The difference in the water body class occurs because the PFTlocal product allows up to 14 % vegetation cover in this class whereas the CWT assumes 100 % water PFT. PFTs with significant global coverage in water body class pixels in the PFTlocal product include natural grasses (183,000 km 2 ) and needleleaved evergreen trees (31,000 km 2 ) with smaller contributions from the other tree types. 2 ). The global area-weighted mean bare soil percentages are 85. 9 % in sparse vegetation class pixels and 99. 9 % in bare area class pixels, which are nearly identical to the compositions suggested by the global CWT (85 % for sparse vegetation classes and 100 % for bare area classes). Cumulatively for these classes, the PFTlocal product suggests only 0. 2 % lower bare soil PFT coverage at the global scale relative to the assumed distribution in the PFTglobal dataset (difference of 65,000 km 2 ).
In the PFTlocal product, the bare area classes contain, in addition to bare soil PFT, inland water PFT (13,000 km 2 ) and tree cover (1,000 km 2 ). The PFT local product does not include bare soil PFT in the shrubland classes, while the PFTglobal dataset assumes 20 % bare soil for the non-flooded shrubland classes 120-122. Because the non-flooded shrubland class pixels have such a large extent globally (13. 3 million km 2 ), the PFTglobal dataset suggests 2. 7 million km 2 of additional bare soil in such pixels relative to the PFT product. Differences in the distribution of bare area between the PFT local product and the PFTglobal are especially pronounced in the U. S. intermountain west, parts of southern and eastern Africa, the northern coast of Australia, and the highlands of Argentina and Brazil, as these are regions with significant shrubland class cover.
Built fraction Both the PFTlocal product and the PFTglobal assign built PFT only to pixels of the urban class (code 190). The presence of a built PFT is not universal in land surface or Earth system models; for example, the current version of the ORCHIDEE land-surface model considers built areas to be 80 % bare soil and 20 % grasslands. The cross-walking of land cover classes to PFTs for the urban class strongly depends on the framework used to calculate surface fluxes in the urban environment and therefore intermodel variation in the global CWT may be stronger for the urban class than for vegetated classes. The global CWT used for this analysis assigns 100 % of the urban class as built PFT. For comparison, the JULES land surface model assigns urban class pixels as 75 % built and 25 % bare soil.
The PFTlocal product suggests 477,000 km 2 of built area globally (Table  2 ), which corresponds to an area-weighted mean composition of 73. 7 % built PFT in urban class pixels (Table  3 ). The auxiliary inputs suggest that about 1. 6 % of urban class pixels have 0 % built PFT coverage. This suggests a mismatch between the land cover classification and the auxiliary inputs for a small number of pixels, which could be related to a mismatch in the time stamp of the auxiliary inputs (2014) relative to the land cover dataset. Considering all urban class pixels, 6. 2 % have built PFT of 0-25 %, 7. 8 % have built PFT of 26-50 %, 31. 9 % have built PFT of 51-75 %, and 54. 1 % have built PFT of 76-100 %. As area-weighted means, the non-built portion of urban class pixels is 25. 1 % natural grass cover, 0.
Conclusion and perspectives The new PFT product (PFTlocal) was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year in the 1992-2020 era, consistent with the CCI MRLC map for the corresponding year. The PFTlocal dataset exhibits intraclass spatial variability in PFT fractional cover at the 300 m pixel level and is complementary to the CCI medium resolution multi-mission LC map series since the derived PFT fractions maintain consistency with the original LC class legend.
The PFTlocal dataset provides a more faithful representation of PFT distributions because it draws on high-resolution peerreviewed mapping of specific vegetation classes to refine global assumptions about PFT fractions. In many cases, the global CWT presented a reasonable approximation for estimating PFT fractions within many land cover classes as shown by the fractions estimated from the auxiliary products falling close to that suggested by the global CWT. Note that a recent study by  Marie et al. (2022)  followed the same objective of refining the global CWT (used to map the ESA land cover classes onto PFTs) but with a different approach. Instead of using the tree cover dataset from  Hansen et al. (2013) , they valorised a map of above-ground biomass over Africa  (Bouvet et al. , 2018)  to define local CWTs, using the information from AGB to better constrain the partition between tree and short vegetation PFTs, for each LC class.
Using the PFTlocal as a benchmark improves the evaluation of every major surface type in the JULES-TRIFFID dynamic vegetation model, particularly shrub cover. This allows a new perspective on priorities for dynamic vegetation model development. The user tool described in  Poulter et al. (2015)  has been reformatted such that it can be applied directly to the new PFT map series to create user-specific ready-to-use inputs for LSMs. The user tool creates model-ready inputs at user specification, which greatly expands the ease of use of the product both within and beyond the modelling community. The PFT dataset is designed primarily for use in land surface and Earth system models. For the vegetated classes except for sparse vegetation, the entire non-water fraction of the 300 m pixel is assigned as vegetation, allowing the actual presence of grass vegetation to be determined by the land surface models.
For use outside of modelling, this could introduce some bias (e. g. , underestimating bare soil cover in some pixels and overestimating grass cover), but the fractions of the high biomass veg types (trees and shrubs) can be used for non-modelling use cases. Production of the PFTlocal product is dependent on the availability and quality of the auxiliary datasets at a spatial resolution higher than 300 m; this is especially critical for mapping the shrubland class. With the combined information of the phenological attribute of the ESA CCI LC classes, the percentage of tree canopy cover from  Hansen et al. (2013) , and the GEDI product  (Potapov et al. , 2021) , it was possible, for the first time, to map four shrubland classes at the global scale: broadleaved evergreen, broadleaved deciduous, needleleaved evergreen, and needleleaved deciduous.
Yet, further research is still needed to improve the estimation of shrubland class pixels north of 52°N (i. e. , outside of the extent of the GEDI product). The urban PFT would benefit from separating impervious surfaces from buildings. Finnally, the current workflow should further be tested against annual ancillary product updates as operational production of very-high-resolution datasets becomes the norm. The proposed methodology is automated so that the PFT dataset will be updated annually as new annual land cover maps are produced in C3S. Table 1. For each of the 22 global and 15 regional land cover classes of the CCI MRLC map series, listed are the set of 766 contributing PFTs with the possibility for non-zero fractional cover. The regional land cover classes with codes ending with 767 1, 2, or 3, are thematically richer than the global classes but can be found only at the regional scale depending.
associated with the forest class are estimated and PFT fractions associated with the cropland class are also estimated for the pixel. The method used to assign the PFT fractions depends on the time stamp of the class in relation to the time stamp(2010)     of the auxiliary dataset (Hansen et al. , 2013)  from which the tree cover fractions are derived. The PFT fraction of a pixel in 2010 was derived using the following class-specific methods described in Sect. 2. 2. 1. Any change of class occurring before or after 2010 leads to deriving new PFT fractions as the mean PFT fractions of all 300 m pixels of the same class pixels within the overlapping 0. 25° x 0. 25° window centred on the pixel of interest. The input pixels over which the mean is calculated are the 300 m pixels that did not experience land cover class change over the 1992-2020 era.
2 %). Shrub cover is 74 % lower in the PFT local product than in the PFTglobal dataset. Some of this difference arises because the PFTlocal product estimates lower shrub PFT in shrubland class pixels (codes 120-122 and 180) compared to the PFTglobal dataset, which estimates 8. 8 million km 2 of shrub PFT in shrubland classes. The area-weighted mean percentage composition of shrubs in shrubland class pixels is 30. 0 % for class 120 in the PFTlocal product, 26. 1 % for class 121, 34. 4 % for class 122, and 30. 7 % for class 180. The CWT suggests 60 % shrub cover for classes 120-122 and 40 % for class 180. The CWT estimates 0 km 2 of tree PFT cumulatively in these classes https://doi. org/10. 5194/essd-2022-296 Preprint. Discussion started: 16 September 2022 c Author(s) 2022. CC BY 4. 0 License.
compared to 630,000 km 2 in the PFT product. The uncertainty associated with the height estimation in the global canopy height product of Potapov et al. (2021) may contribute to the confusion of shrubs and trees in some cases. Nonetheless, the evidencebased PFTlocal product indicates a significantly lower estimate for global woody vegetation cover in pixels of the shrubland classes compared to the PFTglobal dataset, which was largely based on expert knowledge.
Acknowledgement This study was carried out with the support of the  European Space Agency Climate Change Initiative  under the contract  ESA/No. 4000126564Land_Cover_cci. The  ESA CCI  supported the methodological development and generation of the global PFT map series 1992-2020 as well as the climate modelling analysis. We thank  Olivier Arino  and  Fabrizio Ramoino  for their long-term support in the  Land_Cover_cci  project. We thank  Clement Albergel  for his fresh look on the work done and his review. We thank  Benjamin Goffart  for tailoring the  CCI Land Cover  web interface to visualize and interact with the  CCI PFT dataset 2010.
Further,  Ovtchinnikov and Kogan (2000)  used simulated cloud data to conclude that the combination of radar reflectivity with liquid water path from a microwave radiometer can significantly increase the accuracy and the robustness of the retrieval. 2005 ) calculated the drop size, liquid water content, and liquid water flux of drizzle using the synergy of cloud radar and backscattering information from lidar. This technique was applied to the drizzle below the cloud base, as the lidar beam is strongly attenuated when it penetrates the cloud. To improve the quality of LWC retrievals in clouds and drizzle,  Löhnert et al. (2008)  implements a target classification scheme using certain thresholds determined by radar reflectivity and ceilometer extinction profile. Some LWC profile retrievals in the literature are applicable to both precipitating and non-precipitating clouds, although they may have their own set of limitations.
The data from the highest range resolution are gridded, while the values from the lower-resolution range are distributed using the closest value. Due to their higher sensitivity, the highest-range-resolution data are utilized, and background noise is eliminated. In this study, 39 cloud cases with the L2 product of BASTA measurements at the SIRTA location are used. During the SOFOG-3D field experiment, the vertically pointing BASTA radar was deployed in a fog-prone region to acquire high-resolution observations of the fog's characteristics. The L2 product of BASTA observations is used to evaluate the performance of the algorithm for retrieving the LWC of lowlevel fog. Due to the coupling of the radar antenna, the minimum detectable range was 40 m above the ground for the L2 product.
MWRs are sensitive to the total liquid water content in the cloud column  (Ware et al. , 2002). In general, statistical methods (linear or quadratic regressions or neural networks) trained from simulated MWR observations from a database of radiosoundings or model analyses are used  (Cimini et al. , 2006). Optimalestimation retrievals combining an a priori estimate of the atmospheric state with observations through an iterative process can also be used  (Martinet et al. , 2020). In this study, LWP retrievals based on MWR observations have been retrieved through quadratic regressions trained from a database of radiosoundings for SIRTA, while for SOFOG3D, neural networks trained from AROME short-term-forecasts have been used. Humidity profiles can be retrieved with a limited vertical resolution due to the smoother weighting functions for K-band channels.
Discussion of the retrieval uncertainty Other sources of error in the retrieval algorithm are discussed in this section. Doppler radars also detect boundary layer insects, large dust particles, and pollen suspended in the air as a result of the convective boundary layer that grows in the morning hours and matures shortly after midday  (Geerts and Miao, 2005). These so-called airborne plankton contaminate the reflectivity profile as a result of the formation of the convective boundary layer. Therefore, the unwanted signal in the radar reflectivity due to airborne plankton must be removed before estimating LWC. In this data set, the majority of liquid clouds are observed below 2500 m. We selected the cloud cases where cloud height remained below 2500 m as the clouds above are anticipated to be mixed-phase or ice clouds.
Analysis of the method when the microwave radiometer is available This section describes the analysis of retrieval when applied to various cloud types. As detailed in Sect. 3, the retrieval technique is applied to reflectivity data from 95 GHz BASTA radar with LWP estimates from a co-located RPG HATPRO microwave radiometer for various cloud cases from SIRTA. Between November 2018 and May 2019, 39 cloud and fog cases at the SIRTA observatory were selected to address the algorithm's implementation on warm clouds. The data set contains a relatively large number of cloudy cases, including fog and light drizzle. A detailed discussion of retrieval and algorithm implementation is elaborated for a typical example of a liquid cloud in the next subsection. Illustration of the retrieval for the 5 February 2019 case at SIRTA A case study of one of the selected cloudy cases from SIRTA on 5 February 2019 is presented in Fig.
The general definition of the R 2 regression score function is R 2 = 1 - SS res SS tot , ( 12 ) where SS res is the residual sum of squares, and SS tot is the total sum of squares. 3. Mean absolute percentage error measures the accuracy of the retrieval in percentage: MAPE = 100 n n 0 LWC true -LWC ret LWC true , (13) where LWC ret and LWC true are retrieved and true LWC, respectively, and n is the number of data points. Analysis of each sensitivity parameter is presented in the next section. et al. (2021). LWC of a fog structure from AROME shortterm forecasts at the nearest grid location of SIRTA is considered the true atmospheric state. In this case, we are considering only liquid droplets, with no overlapping of liquid or ice clouds aloft. Profiles of true LWC are used to synthesize observation parameters like radar reflectivity using the previously defined power law relation and the liquid water    7 ) (see Fig.
Comparison of LWC retrieval with in situ data In situ measurements of cloud and fog are required to validate the distribution of LWC with time and height. In general, in situ measurements of cloud microphysical parameters are collected using a research aircraft mounted with sensors flying inside the cloud. During the SOFOG-3D field experiment, a tethered balloon equipped with in situ sensors was used to collect the microphysical parameters of fog. This approach is much more economical than the research aircraft flying inside the cloud; however, the trajectory of the balloon cannot be fully controlled, and the measurements are limited to the lowermost 1-2 km level. Simultaneous measurements using remote sensing instruments like BASTA cloud radar, a microwave radiometer, and automatic weather stations were also collected for various fog cases  (Martinet et al.
Statistical analysis of retrievals to derive climatology The primary objective of this statistical analysis is to derive a climatology of LWC and ln a in order to allow the algorithm to be able to retrieve LWC for fog and low-level liquid clouds even when additional measurements are not available. A comparison of retrieved LWC with in situ LWC measurements for fog and stratus clouds from the SOFOG-3D experiment is already presented in Sect. 5. 1. Therefore, the climatology is developed from the retrieval technique discussed in Sect. 3. 4 using the larger data set from SIRTA measurements for a variety of cloud and fog incidents. Statistical analysis to derive a climatology of LWC and the scaling factor is presented in this section. The histogram of the retrieved scaling factor ln a (Fig. 11d ) indicates that the highest values of occurrence are around -3, which is close to the ln a a priori value from  (Atlas, 1954)  the empirical relation plotted as the red line, but it is not precisely the same.
The algorithm is implemented in a set of cloud and fog instances observed at SIRTA, and the analysis is discussed in this paper. These retrievals have been used to develop a climatology of LWC and the scaling factor for warm clouds and fog. The application of derived climatology to estimate LWC for stand-alone radar observations is also presented in this paper. By utilizing the climatology of the scaling factor, this stand-alone radar method can provide continuous retrieval of LWC for warm clouds even in the absence of radiometers and other additional measurements. Although this climatology is developed using measurements from the SIRTA observatory for limited cloud scenarios, a more extensive data collection from several measurement locations might be used to generate a more robust climatology of the scaling factor.
Acknowledgements. This research has been supported by the  Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)  and  Université Paris-Saclay. The authors would like to acknowledge  SIRTA  for providing the BASTA radar and MWR data used in this study. The authors would like to acknowledge the assistance team of the SOFOG-3D campaign for the execution of the field experiments. Financial support. This research has been supported by the  French Association Nationale de la Recherche et de la Technologie (ANRT)  and the company Meteomodem (Convention CIFRE). Review statement. This paper was edited by  S. Joseph Munchak  and reviewed by four anonymous referees.
Code and data availability. Data used from SIRTA and SOFOG-3D are publicly available from http://sirta. ipsl. fr/ (last access: 22 February 2023, SIRTA, 2023) and https://sofog3d. aeris-data. fr/ (last access: 22 February 2023, SoFog3D, 2023), respectively. Requests for cloud radar data can also be sent to julien. delanoe@latmos. ipsl. fr. The code developed in this study can be made available upon request to the authors.