dataesr/scientific-paragraphs-categorization huggingface dataset:
3  (denoted as DRE k,i,j , hereafter k indicates the size bin index and (i, j ) indicates the longitude-latitude grid index, unless specified otherwise). Note that we do not consider dust RI spatial variation and dust size vertical variation due to the lack of observation-based dust mineralogy and size estimation on a global scale. In DRE k,i,j calculations, we constrain the monthly mean dust extinction vertical distributions using the CALIOP-based climatological dataset of  Song et al. (2021). It is worth mentioning that our target in this section is DREE k,i,j calculations. Considering dust DRE is approximately linear to DAOD  (Satheesh and Ramanathan, 2000) , the DAOD used in dust DRE calculations will not significantly affect dust DREE results, we simply calculate dustDRE k,i,j with respect toDAOD 532 nm i,j from CALIOP-based DAOD climatology.
Considering DRE SW strongly depends on the solar zenith angle (SZA), we calculate DRE SW for every 1 h using the corresponding hourly SZA in the mid-month day. As a result, every three SZA share the same atmospheric and surface properties in DRE SW calculations due to their different temporal resolution. The definitions of variables and indices used to derive size-resolved dust DREE dataset are summarized in Table  2. Equation (1) shows the way of deriving 1-hourly monthly mean DRE SW : 1 h DRE SW k,i,j (tt) = DRE SW k,i,j (R (t), H 2 O (t), O 3 (t), CO 2 (t), ζ d , SZA(d mm , tt)), (1) where "t" indicates eight points at 3 h intervals given in UTC, 'tt' indicates 24 points at 1 h intervals given in UTC, "d mm " indicates the mid-month day of the month, and "R (t)" represents 3-hourly monthly mean surface albedo.
DREE SW or LW k,i,j = DRE SW or LW k,i,j DAOD 532 nm i,j. (5) Finally, we average the monthly mean size-resolved dust DREE (DREE k,i,j ) over 4 years to get monthly mean sizeresolved dust DREE datasets in addition to the associated interannual standard deviation (std). The std indicates the DREE uncertainty caused by interannual variation of monthly mean atmospheric and surface properties as well as dust vertical distributions. Finally, the dataset developed in this study contains monthly mean size-resolved dust DREE and its associated interannual std at TOA and surface with dimension of 10 bins, 12 months, 90 latitudes, 72 longitudes for each of 6 dust models in SW and LW. Figures  S1  and S2  in the Supplement demonstrate the global distribution of the monthly mean size-resolved DREE SW and DREE LW at TOA for June.
It is important to note that the dust DREE of each grid cell rarely depends on the DAOD because dust DRE is approximately linear with DAOD  (Satheesh and Ramanathan, 2000). Therefore, the choice of CALIOP-based or MODISbased DAOD climatology to derive the global (5 • × 2 • ) sizeresolved DREE dataset will not lead to a large difference. In other words, the size-resolved DREE dataset is rarely related to the robustness of the DAOD used in the derivation process. We select CALIOP-based DAOD to derive the size-resolved dust DREE dataset because the CALIOP-based dust climatology contains a dust vertical distribution, which is especially important for obtaining LW DREE. Nevertheless, using CALIOP-based dust retrieval to derive size-resolved dust DREE dataset has several limitations: (1) the size-resolved dust DREE dataset may miss some regions with tenuous dust layers that are below the CALIOP sensitivity.
(2) The LW DREE is related to the quality of dust vertical distribution retrieval. By contrast, dust DRE highly depends on DAOD, therefore we will use different DAOD climatological datasets retrieved from different sensors (i. e. , CALIOP and MODIS) to investigate global dust DRE in Sect. 5. 2. Furthermore, even though dust DREE of each grid cell is rarely related to DAOD, regional or global mean dust DREE will depend on the DAOD spatial distribution (i. e. , DAOD 2D distribution) in the region of interest (see details in Sect. 5. 2). Based on the monthly mean size-resolved dust DREE datasets derived above, we further calculate global annual mean size-resolved dust DREE SW and DREE LW at TOA and surface for the six dust models (Fig. 5 ). As already discussed the global mean dust DREEs depend on the DAOD spatial distribution and the global mean dust DREEs shown in Fig.
More absorptive dust starts to warm the earth system in SW at smaller dust size, and vice versa. In addition, our results suggest that DREE SW is generally not sensitive to dust shape. Specifically, dust shape is not important for DREE SW in most size bins, while it is important in the fourth size bin (D : 0. 79 -1. 58 µm) with DREE SW of spheroidal dust obviously higher (less negative) than spherical dust. In the DREE LW , dust shape is almost as important as RI for several size bins. Our size-resolved dust DREE dataset is unique in many aspects: first, our DREE dataset is derived from CALIOPbased dust 3D distributions. Size-resolved DREE is derived for all grids with CALIOP-based DAOD ≥ 0. 01. Second, our size-resolved DREE dataset covers a wide range of dust di-ameters, specifically, they include dust DREE for 10 dust diameter size bins ranging from 0.
1 to 100 µm. This is challenging, if not impossible to obtain from global models because these models generally simulate dust particles with diameters only up to 20 µm and coarse dust particles in models deposit quickly and could not be sustained in the atmosphere and transport to the remote transport regions  (Huneeus et al. , 2011; Adebiyi and Kok, 2020)  where coarse particles have been observed by in situ measurements  (Weinzierl et al. , 2017). As a result, our size-resolved DREE dataset achieves a wide spatial coverage for a large range of dust sizes. This is critical for investigating the impacts of coarse dust and even giant dust particles on dust DRE on both regional and global scales. Third, considering that the dust vertical distribution is important for quantifying DRE LW , we constrain dust vertical distribution using CALIOP-based dust retrievals in DREE LW computations.
Fourth, our size-resolved dust DREE dataset accounts for dust LW scattering in DREE LW calculations since scattering capability is available through the DISORT in RRTM_LW  (Stamnes et al. , 1988). Dufresne et al. (2002)  suggest that dust LW scattering enhances the dust LW warming effect at TOA by a factor of up to 50 %. However, dust LW scattering is generally not considered in most global models. Therefore, many previous studies artificially account for dust LW scattering by increasing the radiative perturbation due to LW absorption by a certain fraction. For example,  Kok et al. (2017)   On the other hand, our size-resolved dust DREE dataset has several limitations. First, possible vertical and horizontal variations of dust particle size in each grid box (5 • × 2 • ) are not accounted for in our calculation.
, 2020). The spheroidal shape model assumption thus might produce systematic errors. Overall, the size-resolved dust DREE dataset is useful in many dust-related studies. First, with our size-resolved dust DREE dataset, dust DRE could be calculated efficiently for any DAOD magnitude, any DAOD spatial pattern and any dust PSD for any regions of the globe (see details in  Sect. 4. 1). Second, our size-resolved DREE dataset is derived for different RI and different dust shapes. As a result, we could estimate dust DRE uncertainty coming from DAOD, PSD, RI, and shape separately to better understand major uncertainty sources in dust DRE estimations. Third, our sizeresolved DREE dataset could be used to evaluate model simulated DREE for each size bin.
DRE calculation based on DREE dataset With the size-resolved dust DREE dataset derived in Sect. 3. 2, DRE of dust with any PSD and DAOD could be computed very efficiently without performing radiative transfer simulations as we do in the conventional method. This section introduces the methodology of applying the sizeresolved DREE dataset to calculate DRE of dust with any PSD and DAOD. The DRE of the full size range of dust can be expressed as the sum of DRE from each size bin (DRE k ). Dust DRE k is approximated to be linearly proportional to DAOD of kth size bin (DAOD k )  (Satheesh and Ramanathan, 2000). A similar concept of calculating dust DRE has been used in previous studies e. g. ,  Kok et al. (2017). Equation (  6 ) shows the pro-cess of computing dust DRE using the size-resolved DREE dataset: DRE = k DRE k = k DREE k × DAOD k = k DREE k × f k × DAOD, (6) where "DRE" represents dust DRE induced by the full size range of dust with an optical depth of "DAOD" and f k is the fraction of the DAOD contributed by the kth size bin.
Each variable in Eq. (  6 ) can be obtained or derived from datasets developed in this study and other studies. For example, the size-resolved DREE dataset (DREE k,i,j ) derived in this study is essential for utilizing this efficient and novel DRE calculation method. The DAOD can be obtained from CALIOP-based or MODIS-based DAOD climatological datasets  (Song et al. , 2021)  and f k can be derived from the dust extinction efficiency (Q e ), the geometric cross-sectional area (A) and dust PSD (dN/dD) based on Eq. (  7 ): f k ≡ DAOD k DAOD = D k+ D k-Q 532 nm e (D) A (D) dN dD dD D max 0 Q 532 nm e (D) A (D) dN dD dD , (7) where Q e is defined according to Q e ≡ σ e A , where σ e is the extinction cross-section and the geometric cross-sectional area of the particle (A) can be expressed as A = π r 2.
(2017)  has a much larger value than spherical dust for dust D ≥ 1 µm (see their Fig. 1b ). This discrepancy is probably due to the different Q e definitions used in the two studies. Kok et al. (2017)  defined Q e as dust extinction per unit crosssection of a volume-equivalent sphere. Figure  6b  shows that f k of a specific PSD is not sensitive to dust RI and dust shape, this is also suggested by the similar Q 532 nm e vs. geometric diameter (D) trends of the six dust models shown in Fig. 6a. In contrast, f 4 (i. e. , f k for the fourth size bin with D ranging from 0. 79 to 1. 58 µm) is more sensitive to dust shape than other size bins, this is in line with the larger difference in Q 532 nm e with the shapes shown in Fig. 6a. In summary, the size-resolved dust DREE dataset provides an efficient way to compute DRE for any dust PSD and any DAOD by using Eqs.
3. 2 (Eqs. 1-4), while the DREE integration method is based on the monthly mean size-resolved DREE dataset derived from the 4-year (2007-2010) data as described in. The excellent agreement in monthly mean dust DRE between the two methods validates the DREE integration DRE calculation methodology. The shaded areas associated with DREE integration DRE correspond to the one standard deviation of DREE caused by the 4-year  (2007) (2008) (2009) (2010)  interannual variation of factors except dust microphysical properties such as monthly mean atmospheric and surface properties as well as dust vertical distributions (hereafter these factors are referred to as non-dust factors for short). The narrow shaded areas along DREE integration DRE suggest that non-dust factors cause very small uncertainty in dust DRE estimations.
However, the small effects of the 4-year interannual variation of non-dust factors may not necessarily be representative due to the limited number of years considered. Section 2. 1 discusses in detail the reason for choosing 2007-2010 to derive the size-resolved DREE dataset. To check the representativeness of 4-year interannual variation for non-dust factors, we compare the 4year  (2007-2010) and 10-year (2007-2017)  interannual standard deviation (std) of monthly mean non-dust factors (e. g. , surface albedo, surface temperature and dust vertical distribution) in Fig. 9. To evaluate the interannual variation of dust vertical distribution, we define dust mean extinction height (Z α ) referring to  Koffi et al. (2012)  as Z α = n i=1 β ext,i ×Z i n i=1 β ext,i , where β ext,i is the dust extinction coefficient at 532 nm at level i, and Z i is the altitude of level i.
Nevertheless, the 10-year std is slightly larger than 4-year std, they are both close to zero and of the same order of magnitude. As such, even though our monthly mean size-resolved DREE dataset is derived from 4-year  (2007) (2008) (2009) (2010)  data, they could be used to represent DREE and calculate DRE for other years considering the small sensitivity of monthly mean dust DRE to interannual variation of non-dust factors.
Regional and global dust DRE based on size-resolved DREE dataset After the validation of the DREE integration method in Sect. 4, we use the DREE integration method to calculate regional and global dust DRE in this section. There are three main objectives in this section: (1) the most important objective throughout this section is to demonstrate the usefulness of the size-resolved DREE dataset for calculating regional and global dust DRE for any given dust PSD, (2) the second objective is to validate the size-resolved DREE dataset by comparing with regional dust DREE reported by field studies based on satellite and ground-based observations (Sect. 5. 1) and (3) the third objective to assess the sensitivity of dust DRE to DAOD spatial pattern (Sect. 5. 2) as well as dust microphysical properties such as dust PSD, RI and shape (Sect.
The state of the art airborne observations of Saharan dust from the Fennec field campaign (Fennec-Fresh) and transported Saharan dust over the tropical eastern Atlantic within the Saharan air layer (SAL) from both AER-D and Fennec fieldwork campaigns are adopted  (Ryder et al. , 2013a (Ryder et al. , , b, 2018 (Ryder et al. , , 2019 ) (see Fig. 7 ). Both campaigns included giant dust particles, measuring up to 100 µm diameter for AER-D and up to 300 µm for Fennec. The wide coverage of dust diameters in our size-resolved DREE dataset al-lows dust DRE calculations for giant dust up to 100 µm over both dust source and transported regions where giant particles were observed in those campaigns. This is an advantage of our size-resolved DREE dataset compared to modeled dust DREE, because climate models generally cut off dust diameter at 20 µm and could not sustain coarse dust to remote transport regions due to several missing mechanisms in models  (Van Der Does et al.
(2003)  to 0. 003 as used in WRF-Chem based on  Zhao et al. (2010). Third, in this study Kok2017-PSD is used to represent dust PSD in each dust grid and applied to our size-resolved dust DREE dataset to calculate global dust DRE. In contrast, the model-simulated dust DREE in  Kok et al. (2017)  has reduced cooling from SW scattering and enhanced warming from SW absorption effects because the short lifetime of coarse dust in models concentrates these particles over bright deserts. Fourth, the two studies use different dust shape models,  Kok et al. (2017)  accounts for a more nonspherical shape model (i. e. , triaxial ellipsoids). Here we do not compare our global mean DRE LW clear-sky with DRE LW all-sky suggested in  Kok et al. (2017)  because the lack of knowledge in DRE LW cloudy-sky prevents us from converting DRE LW clear-sky to DRE LW all-sky.
Summary and conclusion This study developed a clear-sky size-resolved dust DREE dataset in both SW and LW from CALIOP-based dust DAOD climatology and dust vertical distributions. The dataset contains global monthly mean dust DREE at TOA and surface with 5 • (longitude) ×2 • (latitude) spatial resolution for 10 size bins ranging from 0. 1 to 100 µm diameter, for three state of the art dust RI representing more, mean and less absorptive dust, and for two dust shapes representing spherical and spheroidal dust. The size-resolved DREE dataset allows us to calculate dust DRE of any DAOD climatology and dust PSD efficiently by using the DREE integration method presented in Sect. 4. 1 without involving radiative transfer simulations. The DREE integration method is proven to be in good agreement with conventional DRE calculations.
With the DREE integration methodology, we firstly calculated clear-sky regional mean DREE SW and DREE LW over the Sahara Desert and tropical Atlantic. The comparison of our calculations with those reported by field studies based on satellite and ground-based observations shows reasonable agreement. Secondly, we estimated global mean dust DRE with two satellite-based DAOD climatological datasets and two different global dust PSDs. We found that the global mean DAOD magnitude difference between the two DAOD climatological datasets is more important than the subtle difference in spatial patterns. Nevertheless, after scaling to the same global mean DAOD there is still more than 10 % difference between the two dust DRE SW , with CALIOP-based being the more positive one. Moreover, our results explicitly show the uncertainty induced by each dust microphysical property (i.
The two DAOD climatological datasets result in distinct dust DRE spatial patterns as shown in Fig. 10, which is consistent with the DAOD spatial patterns shown in Fig. 1 suggesting CALIOP DAOD is more concentrated over "dust belt" regions than MODIS DAOD. The global mean dust DRE SW , DRE LW and DRE NET based on the two DAOD climatologies are significantly different (Table 4), which is mainly caused by two factors. The first is the difference in DAOD magnitude. The CALIOP-based global mean DAOD is 0. 032, while the MODIS-based DAOD is 0. 047. The other factor is the difference in the DAOD spatial pattern. After we scale dust DRE to the same global mean DAOD (DAOD = 0. 03) to eliminate the effect of DAOD magnitude differences (values in parentheses in Table.
Acknowledgements. We would like to thank  Claire L. Ryder  for providing the Fennec and  AER-D dust PSDs. Qianqian Song  and  Zhibo Zhang  cordially acknowledge funding support from the  Future Investigators in NASA Earth and Space Science and Technology (FINESST). Zhibo Zhang 's research is supported by an  NASA  grant ( 80NSSC20K0130 ) from the  CALIPSO and CloudSat program. S. Albani  received funding from  MIUR (Progetto Dipartimenti di Eccellenza   2018-2022 ). The computations in this study were performed at the  UMBC High Performance Computing Facility (HPCF). The facility is supported by the  US National Science Foundation  through the  MRI program  (grant nos. CNS-0821258  and  CNS-1228778 ) and the  SCREMS program  (grant no. DMS-0821311 ), with substantial support from  UMBC. Financial support.
Data availability. The size-resolved dust DREE dataset and the codes to calculate dust DRE for any given dust PSD and DAOD are available at https://drive. google. com/drive/folders/15_ e28Y9JiSWiJnIM_2flEmt2u6i9phEY?usp=sharing, (last access: 28 September 2022) CALIOP-based and MODIS-based DAOD climatological datasets are available at https://drive. google. com/ drive/folders/1aQVupe7govPwR6qmsqUbR4fJQsp1DBCX?usp= sharing, (last access: 28 September 2022; Song et al. , 2021)    Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-13115-2022-supplement.
The SPectrogram Analysis and Cataloguing Environment (SPACE) tool is an interactive python tool designed to label radio emission features of interest in a time-frequency map (called "dynamic spectrum"). The program uses Matplotlib's Polygon Selector widget to allow a user to select and edit an undefined number of vertices on top of the dynamic spectrum before closing the shape (polygon). Multiple polygons may be drawn on any spectrum, and the feature name along with the coordinates for each polygon vertex are saved into a ". json" file as per the "Time-Frequency Catalogue" (TFCat) format along with other data such as the feature id, observer name, and data units. This paper describes the first official stable release (version 2. 0) of the tool.
Previous catalogues have been using square boxes to define the time and frequency intervals containing the radio signal (such as  Leblanc, 2020a; Leblanc, 2020b; Leblanc, 2020c; Leblanc, 2020d; Leblanc, 2020e) , but to be able to automatically disentangle the emissions when using the catalogue, it is then needed to construct catalogues with polygon vertices and with distinct labels. This was done by a few authors, primarily using tools built in IDL to construct such catalogues of Jupiter radio emissions  Marques et al. (2017) ;  Zarka et al. (2021). Once catalogues are built they can comprise training sets which form the basis of supervised machine learning approaches to classify larger samples of unseen data. Here we present a Python user interface tool to allow the drawing of polygons around features in dynamic spectra.
The SPACE package The SPectrogram Analysis and Cataloguing Environment (SPACE) tool is an interactive python tool designed to label radio emission features of interest in a time-frequency map (called a "dynamic spectrum"). The program enables users to create and edit the vertices of a polygon on the dynamic spectrum plot, before naming and saving it as a 'feature' in a catalogue for future analysis. Full usage documentation is available on the GitHub repository of the code  (Louis et al. , 2022)  and directly at https:// space-labelling-tool. readthedocs. io.
Installation The code is available on an open-source GitHub repository  (Louis et al. , 2022) , with full installation instructions present on the repository page and on the ReadTheDocs webpage, and packed with the tool. It can easily be downloaded using git and installed using pip, with all prerequisites included in the provided requirements. txt file: 1 git clone https://github. com/CorentinLouis/ SPACE_labelling_tool. git 2 pip install -e SPACE_labelling_tool Listing 1: Download and Install SPACE using git and pip. Installing in editable mode ("-e") will allow the scripts to read any new or modified versions of the configuration files. The code is also available from PyPi 1  and can be directly install using pip: 1 pip install SPACE-labelling-tool Listing 2: Download and Install SPACE with pip.
g. , intensity) appear simultaneously on the other views once they have been named (see bottom panel of Figure  1A ), allowing users to easily see how a feature presents in multiple measurement types. Once a region has been labelled, a user can pan their viewing window back and forth through the time range within the dataset by clicking on the Prev or Next buttons (see Figure  1 ), with an overlap applied between each view in order to facilitate   Gurnett et al. , 2004)  data  (Lamy et al. , 2008 (Lamy et al. , , 2009)). The two panels show Intensity and Polarization data, respectively. At the top right of the top panel one can see a polygon that has just been drawn, with the window for naming the feature appearing at the top left of the graphics window. Other features have already been labelled, and appear in both intensity and polarisation views, with their names overlaid.
The data displays in panel (B) are the estimated flux density  (Louis et al. , 2021a,c)  from Juno/Waves measurements  (Kurth et al. , 2017)  labelling features that lie on the edge of a window. Once finished, the labelled regions can be saved as a TFCat (Time-Frequency Catalogue) formatted JSON 2  file  (Cecconi et al. , 2022, this issue)  by clicking on the Save button, and used later. If a user reopens the same data file, or another data file with the same naming structure (e. g. , observations_20180601_v02. cdf and observations_20180602_v02. cdf) saved features from previous sessions will be pre-loaded (see Figure  1 ). Full usage documentation is available on the GitHub repository of the code  (Louis et al. , 2022 ) and directly at https:// space-labelling-tool. readthedocs. io.