dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the surface emissivity over land is less well known and might induce local biases. Moreover, comparisons conducted between AEROIASI and AERONET coarse AOD retrievals showed distinct discrepancies between the two datasets in many sites over and downwind of the Sahara Desert  (Cuesta et al. , 2020)  and AEROIASI overestimations far away from desert dust sources  (Cuesta et al. , 2015). The biases in both cases reach or even exceed 0. 1 in absolute value. Additionally, the use of non-zero a priori values for dust abundance (equivalent to an AOD at 10 µm of ∼ 0. 03) is expected to induce positive biases in situations with both very low dust abundances and low sensitivities, as encountered for the relatively lower surface temperatures of mid-latitudes as compared to those near the tropics.
Developments for future versions of the product will aim at screening out these low sensitivity situations. In this study, we used coarse DOD over the period 2008-2016, provided by the AEROIASI Version 3 dataset, which was retrieved from MetOp-A/IASI data (IASI-A, Level 3), whose mission was completed in November 2021. The horizontal resolution of the AEROIASI dataset is 1°× 1°. The DOD at 10 µm was obtained by vertically integrating the extinction coefficient and then it was spectrally converted from 10 µm to 550 nm using a conversion factor of 1. 70, derived with a Mie code. The derived AEROIASI coarse DOD (at 550 nm) considers coarse dust particles larger than 0. 6 µm in radius. The spatial collocation between the two datasets was achieved by re-gridding MONARCH coarse DOD through bilinear interpolation using the coarser AEROIASI grid as a reference.
The algorithm fundamentally depends on the assumption that the coarse mode Ångström exponent and its derivative are close to zero. AERONET provides a long-term and continuous database of aerosol optical, microphysical and radiative properties, the best currently available on a global basis for aerosol research and characterization, validation of satellite retrievals and evaluation of aerosol models. The descriptions of the MIDAS, MISR, and AEROIASI dust products above summarized the features and the uncertainties of the total and coarse DOD products which depend upon the instruments' capabilities, the limitations of the retrieval techniques, and the validity of the assumptions made in order to separate mineral dust aerosols from other aerosol components. All four observational datasets (including AERONET) have their advantages and disadvantages, thus can be complementary to each other in order to overcome limitations regarding the quality of the dust retrievals and the spatiotemporal coverage: MIDAS provides total and coarse DOD observations both over land and sea with the finest spatial resolution (0.
1°× 0. 1°); MISR provides the most physically robust separation of DOD by discriminating dust aerosols based on actual retrieved particle shape information; AEROIASI has the most frequent sampling, covering the Earth twice a day, and it is the only dataset to provide nighttime measurements; AERONET ground-based measurements provide the finest temporal resolution (∼ 15 minutes), giving the possibility to assess MONARCH at its original 3-hourly time-scale. Moreover, the signal-to-noise ratio for the AERONET direct-sun measurements is high and the surrounding surface reflectance usually makes no significant contribution to the signal in most cases. This renders AERONET AOD the best available source for surface-based particle property retrieval results, therefore in this study the AERONET dust-filtered retrievals were used not only to assess the model outputs but also to validate the quality of the satellite-based dust products (see Sect.
3). In this study, we used AERONET Version 3 quality-assured data (i. e. , Level 2. 0) as a reference dataset  (Giles et al. , 2019). Since AOD includes contributions from different types of particles, a dust-filter method was applied to identify AOD observations in which dust is the dominant aerosol type. AERONET dust-filtered AOD (i. e. , DOD) is based on direct-sun AOD retrievals between 440 and 870 nm. Although direct-sun does not yield AOD at 550 nm, this variable is calculated from the AOD at 440, 675 and 870 nm and the Ångström Exponent at 440-870 nm (AE) using the Ångström's law. Then AE is used as a filter because it is inversely related to the average aerosol size. Lower AE values (< 1) indicate significant presence of coarse-mode particles (e. g. , mineral dust and sea-salt), whereas higher AE (> 1) values imply a large abundance of fine particles (e.
In total, retrievals from 238 stations were used for the present analysis. Total and coarse DOD datasets from the dust regional reanalysis were bilinearly interpolated over each AERONET station. AERONET data are acquired at 15-minute intervals on average; therefore, all AERONET measurements within ±90 minutes of the model outputs have been averaged for the comparison on a 3-hourly basis. Figure  2  shows the location of the AERONET sites with at least 30 temporally collocated pairs available.
Evaluation strategy The evaluation metrics that were used to quantify the level of agreement between the model simulations and the observations are the mean bias (MB), the root mean square error (RMSE), the fractional gross error (FGE) and the correlation coefficient (CC), the definitions of which are given in Appendix A. The inter-comparison of total and coarse DOD was conducted over two different temporal scales (annual and seasonal) and over two different spatial scales (grid-point and regional). All the statistical indicators (Table  A1 ) were computed on an annual scale, considering all the different model and satellite datasets collocated pairs for the period 2007-2016 of the reanalysis, and on a seasonal scale where the collocated data of a certain season were compared throughout the years according to the following classification: boreal winter (December-January-February: DJF), boreal spring (March-April-May: MAM), boreal summer (June-July-August: JJA) and boreal autumn (September-October-November: SON).
The seasonal subdivision of the datasets allows for the assessment of the MONARCH reanalysis performance in reproducing the annual cycle and the seasonal patterns of the total and coarse DOD. The aforementioned temporal aggregations were generated for each grid-point of the reanalysis-satellite collocated data and for each individual AERONET station. Moreover, the evaluation statistics were produced at a regional scale in order to assess model performance over regions with distinct characteristics. The study geographical domain has been divided in ten specific sub-regions (Fig. 1 ) where the model scores were computed considering all the modelled and satellite-based dust product pairs contained in each one of them, giving the opportunity to identify any dependencies between the different model and satellite datasets and the features of each region.
The ten sub-regions are mainly classified into three groups: (i) continental regions that contain the mineral dust Mediterranean Sea, Arabian Sea and Sub-Saharan Africa (hereafter TroAtl, MedSea, AraSea and SubSah). The latter are subject to seasonal DOD variation. Furthermore, the borders between the regions are defined so that every region consists mainly of one surface type (i. e. , land or sea). This rough approximation can improve the interpretation of the regional results, considering that the surface type is associated with the retrieval algorithms used to derive AOD from the satellite observations. Nevertheless, differences between the inter-comparison results obtained from the different datasets do exist in some cases. These discrepancies may be due to several possible reasons related to the features of the datasets and the region.
Finally, a multi-sensor aggregation comparison based on the considered satellite-based dust datasets (i. e. , MIDAS, MISR and AEROIASI) is applied to get an overall assessment of the MONARCH performance. We excluded AERONET from the multisensor aggregation at a regional scale because the representativeness of the computed regional metrics remains questionable due to the uneven distribution of stations in the various sub-regions both quantitatively and spatially (Fig. 2 ). For example, a large number of network sites sufficiently covers the Mediterranean region and Northern Europe, whereas only one station corresponds to the Arabian Sea, which is additionally located at the edge of the sub-region. The regional evaluation metrics of each satellite dataset were averaged to one final value weighted by the number of the model and satellite-based dust product pairs that each dataset contributes within each sub-region.
Each satellite dataset was spatially averaged over the AERONET sites, and the AERONET time-series were temporally averaged centered on the satellite overpass time at the site. The criteria of spatiotemporal coincidence are ±2 hours for AERONET and ±1°of latitude and longitude for AEROIASI, ±0. 5°for MISR and ±0. 2°for MIDAS, according to the spatial resolution of each satellite dataset used. The time-series that emerged from the collocation were then compared to each other using the metrics defined in Table  A1. In addition, DOD time-series retrieved from MIDAS and MISR (hereafter MIDAS+MISR), as well as coarse DOD retrievals from MIDAS and AEROIASI (hereafter MIDAS+IASI), were combined at station level and then compared to AERONET with the aim to investigate if an aggregated satellite multi-sensor product could statistically mitigate the weaknesses of each sensor and the biased values they introduce into the individual products.
36 for both MIDAS and AERONET). Similarly, MISR presents overall underestimation (MB < -0. 07), particularly at all the stations along the coastlines of Africa and the Middle East (MB < -0. 1) (Fig. 2h ). In the Mediterranean Sea and continental Europe, MISR shows smaller differences against AERONET associated with the lower DODs in these long-range transport regions (annual DOD mean up to 0. 09 for MISR and AERONET) with RMSE up to 0. 08 (Fig. 2k ), MB ranges between -0. 04 and 0. 04 and FGE achieving maximum values (up to 2; Fig. 2n ). Lastly, the combination of MIDAS and MISR DOD at station level into a single time series (i. e. , MIDAS+MISR) compared to AERONET do not show major deviations with respect to the independent MIDAS and MISR datasets, and at most sites they are identical to MIDAS scores.
3 ). The MIDAS+IASI product was derived from the aggregation of the two datasets (Fig. 3e ), to which they contribute equally at stations located at lower latitudes, whereas AEROIASI's impact is bigger at sites in Northern Europe and Russia, owing to the higher number of IASI measurements available in those regions (Fig. 3s-u ). MIDAS+IASI CC (Fig. 3r ) provides low correlation at all AERONET stations in Northern Europe (latitudes > 45°N). MIDAS+IASI shows strong underestimation (MB < -0. 1) in the southwest of the Sahara Desert and on the coast of Pakistan, whereas overestimation is observed in Morocco (MB > 0. 1) and Europe (MB up to 0. 07 in northern latitudes; Fig. 3i ). These overestimates in Europe are directly associated with the strong overestimations of AEROIASI (Fig. 3h ). MIDAS+IASI is in good agreement with coarse DOD AERONET along the northern coast of Africa and the Red Sea, and in most sites across the Mediterranean Sea (-0.
MONARCH reanalysis assessment In this section the assessment of total DOD and coarse DOD products of the MONARCH reanalysis for 2007-2016 is analyzed. Firstly, MONARCH is compared versus each observational-based dust dataset (i. e. , AERONET, MIDAS, MISR and AEROIASI) at station level in the case of AERONET and at grid-cell level considering the individual grids for each satellite dataset. Then, the comparison was made at regional scale by generalizing the results based on the ten sub-regions shown in Fig. 1. The regional scores were computed at two different temporal scales as well: annual and seasonal. Finally, an overall assessment is attempted through the aggregation of the regional results that were obtained by the evaluation against the satellite datasets.
Independent dataset analysis Starting with the MONARCH DOD assessment, Fig. 4  shows the results of the comparison with DOD products retrieved from the space-based and ground-based observations. At first glance, MONARCH seems to capture the DOD spatial distribution obtained by the all three observational datasets (i. e. , MIDAS, MISR and AERONET), reproducing the major dust hotspots and the dust transport pathways in the area (Fig. 4a, c  and e ). More specifically, the reanalysis DOD exceeds 0. 27 over all the dust sources listed in Fig. 1 , with values exceeding 0. 36 over the western Sahara Desert, the Bodélé Depression (Fig. 1 , "E"), the Sahel and the Arabian Peninsula (Fig. 4a  and e ). Moreover, a pronounced dust plume is simulated stretching across the Tropical Atlantic Ocean. The magnitude and latitudinal extent are greatest over the west African coastline with the maximum DOD up to 0.
4s-t ). The reanalysis is highly correlated with AERONET observations over sites affected by medium-range dust transport, whereas the CC diminishes (< 0. 4) towards the northern latitudes of the study region, especially close to coastal areas (Fig. 4r ), where the number of observations used for the comparison is < 250 (Fig. 4u ). Furthermore, RMSE (Fig. 4j-l ) and FGE (Fig. 4m-o  Overall, the MONARCH reanalysis tends to underestimate DOD except in desert dust source regions where MONARCH and the observational datasets show some discrepancies. In the comparison with MIDAS (Fig. 4g ), the reanalysis shows overall overestimations in Northern Africa, the Arabian Peninsula and the part of Kazajistan, Uzbekistan and Turkmenistan with larger overestimation (MB > 0. 1) in the dust sources of Algeria, over the Sahel, the Bodélé Depression and the Ustyurt Plateau (Fig.
1 , "A", "B", "D", "E" and "J") and underestimation (MB < 0) in the Persian Gulf and the arid regions of Iran and Afghanistan (Fig. 1, "K" ). Regarding the comparison with AERONET (Fig. 4i ), MONARCH presents an overall underestimation, with MB < -0. 1 in western part of the Sahel and near to the coast of the Gulf of Guinea, except in the downwind sites of the Bodélé Depression and over the Great Sand Sea (Fig. 1 , "F") where maximum overestimations (MB > 0. 1) are observed. Over continental Europe and Russia, near-zero MB is observed compared to both datasets, because of the relatively lower DOD by MIDAS and AERONET and simulated by MONARCH in those regions. Over the maritime regions, the comparison with MIDAS, MISR and AERONET shows similar results. The reanalysis strongly underestimates the dust transport towards the Gulf of Guinea and in Cape Verde (MB < -0.
1), whereas it moderately underestimates (down to -0. 07) and overestimates (up to 0. 04) over the Tropical Atlantic and the central and eastern Mediterranean Sea, respectively. Particularly over the North Atlantic and the Arabian Sea, the comparison of MONARCH with MISR shows higher RMSE values (up to 0. 16; Fig. 4k ) and larger underestimations (-0. 04 < MB < -0. 07; Fig. 4h ) compared to MIDAS (-0. 01 < MB < -0. 04; Fig. 4g ), because of the relatively higher DOD recorded by MISR in these regions (Fig. 4d ). This difference between MISR and MIDAS DOD can be traced to the difference between MISR and MODIS total AOD, as in previous studies MISR AOD was found to be generally larger than MODIS AOD over water  (Guo et al. , 2013; Abdou et al. , 2005; Kahn et al. , 2010). The comparison of MONARCH DOD with each observational dataset was made at regional level as well, based on the ten sub-regions shown in Fig.
This dust transport seasonality is also confirmed by the seasonal values of MONARCH and MISR over the maritime regions (Fig. S2 ), which are fully covered by MISR dark-water retrievals. On the other hand, the low sensitivity of MISR non-spherical AOD over the Lastly, the regional means obtained from MONARCH versus AERONET comparison (Fig. S3 ) should be used with caution because the sub-regions are not evenly represented by AERONET stations in terms of amount and spatial distribution (see Fig. 2 , 3 rd column). The best coverage is found in MedSea and NorEur, in MidEas there are fewer stations but well distributed, whereas in NorAft the majority of the stations are located at the edges of the Sahara. The DOD seasonality is again identified over the dust emission and transport regions; however, huge biases between the two datasets like those obtained in SubSah during DJF (MB = -0.
S5  and Fig. S6 ). The intra-annual variability of the coarse dust particles long-range transport is well represented by MIDAS and MONARCH seasonal coarse DOD over TroAtl, AraSea and MedSea (maxima in JJA, JJA and MAM, respectively). The two datasets are in good agreement too, providing very low seasonal and annual MB values in those sub-regions. On the other hand, AEROIASI does not exhibit any seasonality over the maritime and the remote regions located north of 40°N, with no season-on-season changes in coarse DOD, which remain consistently greater than 0. 1 (NorAtl, NorEur and Russia), probably linked to lack of sensitivity. AEROIASI shows a weak performance for detecting low DOD values in seasons and in regions where minimal dust activity is expected, as already shown in Fig. 3h. As a consequence, large biases (MB < -0.
Assessment through multi-sensor aggregation Overall, the DOD comparison results against the three independent datasets (i. e. , MIDAS, MISR and AERONET) shows that over the areas of most interest, with high dust activity throughout the year and greater number of measurements, the evaluation scores are consistent, despite the different features of each dataset, namely the raw data, the dust separation assumptions, the uncertainties and the spatiotemporal resolution and coverage. Consequently, the validity of the evaluation results is enhanced, leading to safer conclusions about the performance of the model. Nevertheless, differences between the results obtained from the different datasets do exist in some cases. In particular, the results obtained by MIDAS and MISR over the North Atlantic differ in MB, RMSE and CC, and the same applies to CC between MIDAS and AERONET.
As shown in Fig. 4 , in most regions that are subject to high DOD levels and frequent dust intrusions, the assessment results are not affected by the features of each dataset; instead, datasets from different sensors can be used in a complementary way to provide more solid insight into the performance of the MONARCH reanalysis. In particular, the regional results obtained from MIDAS (Fig. S1 ) and MISR (Fig. S2 ) were averaged to a final weighted mean (MIDAS+MISR), considering as weight the corresponding number N as it is described in Sect. 2. 6, using the equations shown in Table  A2. As mentioned earlier, the two datasets not only do not overlap but complement each other in terms of space and time, favoring the combination of their results. In particular, MIDAS covers Aqua's overpass time (13:30 LT), both over land and sea whereas MISR considers Terra's overpass time (10:30 LT) over sea.
Moreover, MISR and MIDAS have different spatial and temporal resolution. Consequently, https://doi. org/10. 5194/acp-2022-655 Preprint. Discussion started: 1 November 2022 c Author(s) 2022. CC BY 4. 0 License. the two reanalysis samples obtained by collocation with the satellite datasets are complementary to each other. The combined regional results from MIDAS and MISR are presented by region and by temporal scale, in Fig. S4 ; furthermore, the annual scores are displayed in maps in Fig. 6 , to better understand the geographical distribution of the results. The MONARCH DOD (Fig. 6a ) and the multi-sensor aggregation DOD (Fig. 6b ) present similar patterns, with small discrepancies in DOD values over and around the dust sources. In most sub-regions MB (Fig. 6c ) ranges between -0. 02 and 0.
On the contrary, in northern sub-regions, patterns in seasonality are less pronounced because of the low presence of dust. The sign of the seasonal MB agrees with the sign of the annual MB, with the exception of SubSah, where low underestimations during JJA (MB = -0. 02) are likely associated with mesoscale convective dust storms (e. g. , Haboobs) that the model cannot simulate. The reanalysis performance in reproducing the regional dust cycle can be also assessed by the normalized parameters of Following the same methodology, the coarse DOD comparison results from MIDAS (Fig. S5 ) and AEROIASI (Fig. S6 ) were combined on a regional scale. Again here the satellite datasets complement each other since the MODIS equatorial overpass time is 13:30 LT while IASI crosses the equator twice a day at 09:30 and 21:30 LT.
This again should be attributed to MERRA-2 fine/coarse DOD ratio which eventually underestimates MIDAS coarse DOD, especially over areas of high dust activity (Fig. 3g ). In conclusion, the discrepancies between MONARCH coarse DOD and both satellite datasets over the African continent are most likely due to underestimations in MIDAS and AEROIASI coarse DOD retrievals. Any seasonality in the performance of the reanalysis in reproducing the coarse DOD can be assessed by the seasonal values of the metrics computed at regional scale (Fig. S8 ). The reanalysis simulates very well the annual cycle of coarse dust at both emission and transport regions where coarse DOD seasonality is intense and peak activity occurs in MAM or in JJA, depending on the sub-region, exactly as was observed by the two satellite instruments.
Instead of using a single dataset as reference, in the present analysis different observational-based products were combined, which together provide better coverage of the model's spatiotemporal domain. However, each satellite sensor has its own strengths, limitations, and uncertainties. The total and coarse DOD products of the reference datasets (i. e. , MIDAS, MISR, and AEROIASI) were obtained following different retrieval techniques and assumptions; limitations on each dust characterization technique introduce uncertainties into the DOD products. Therefore, an additional advantage of using different observational reference datasets is the ability to perform cross-validation of the model's performance, based on the results obtained from each dataset. By collating the comparison results obtained from the different datasets we can identify biases caused by retrieval uncertainties and assess their contribution to the evaluation results.
Moreover, prior to the reanalysis assessment, we checked the quality of the satellite-based dust products by applying collocated inter-comparison among the different datasets, using AERONET observations as the reference dataset. Significant discrepancies between satellite and ground-based products over certain regions should be considered as a potential source of skewness for the subsequent model assessment. More specifically, MIDAS and MISR tend to underestimate total DOD in areas Taking into account these outcomes, the MONARCH reanalysis assessment was based on the comparison against AERONET and the satellite products, highlighting the similarities among the obtained results for drawing safer conclusions. According to our findings, the MONARCH reanalysis reproduces very well the spatial distribution of atmospheric dust across the NAMEE region, identifying the major dust emission hotspots located in the Sahara Desert and the Middle East, and the main dust transport pathways toward the adjacent maritime regions of the Atlantic Ocean, the Arabian Sea and the Mediterranean Sea.