dataesr/scientific-paragraphs-categorization huggingface dataset:
125 • × 0. 125 •. This spatial resolution is higher than that of the heritage databases. The database includes a directional description of the surface reflection, as does the GOME-2 surface DLER database, but the angular dependence is improved with a third-order instead of a second-order parameterisation. The use of the TROPOMI instrument allows a wavelength band in the SWIR (at 2314 nm). The retrieval approach that was followed relies on heritage from previous climatologies, but has been improved in a number of ways. The retrieval algorithm employs a combination of active and statistical cloud filtering which results in less cloud contamination in the database. Also, cloud shadows are not ignored but removed using a recently developed cloud shadow detection technique  (Trees et al. , 2022).
This paper has the following structure. Section 2 briefly introduces the TROPOMI instrument. Section 3 introduces the theory behind (directional) Lambertian surface reflection. In Sect. 4 the algorithm set-up and the retrieval code are discussed. Examples of the anisotropic surface reflectivity observed by TROPOMI, and of cloud and aerosol features in the database, are presented in Sect. 5. Section 6 presents the results of a validation of the new TROPOMI surface DLER database. The paper ends in Sect. 7 with conclusions and an outlook to the future.
Algorithm set-up The algorithm set-up is close to algorithm set-ups described earlier  (Tilstra et al. , 2017 (Tilstra et al. , , 2021))  There are quite some differences of the algorithm compared to the previous algorithms. One important difference is the use of active cloud screening instead of relying purely on statistical cloud screening (see  Sect. 4. 3). This improves the quality of the database, especially for regions where cloud contamination is an issue. Note that the algorithm still uses statistical cloud screening as a second-stage cloud filter. As a result of the different cloud filtering procedure, the algorithm does not provide the so-called MIN-LER and MODE-LER fields that were part of earlier databases (e. g. Kleipool et al. , 2008; Tilstra et al. , 2017). Instead, the algorithm distinguishes between two types of grids representing snow/ice and snow/ice-free conditions (see Sect.
Wavelength bands First, we decide on the best set of wavelength bands for the DLER database. In Table  1 , the central wavelength and the bandwidth of the wavelength bands are presented, along with the instrument channel, or TROPOMI band, from which the wavelength bands originate. The 21 wavelength bands were mostly selected based on heritage considerations, i. e. their selection was motivated by their presence in the heritage databases mentioned in the introduction of this paper. For the calculation of the reflectance bands from the reflectance spectrum a triangular weighting function w is used, which is defined in the following The reflectance calculations are performed using spectral band integration or monochromatically. For all wavelength bands absorption by ozone, NO2, and O2-O2 is included.
, 2017). The result is therefore assumed to be the snow/ice surface LER spectrum of the grid cell in question. Both the "clear" and "snice" grids are incomplete because the requirements w. r. t. the presence or absence of snow/ice cannot always be fulfilled. Gaps are corrected afterwards by copying the missing information from one grid to the other. Figure  1  shows examples of the two grids. The four images show the "clear" and "snice" surface LER grids in the polar regions for two months. Figures  1a  and b  show the "clear" field for the months March and September, while Figs. 1c  and d  show the "snice" field for these months. The four figures illustrate that the surface condition extremes (snow or snow-free; ice or icefree) are contained in the TROPOMI surface DLER climatology. The user of the TROPOMI surface DLER database receives two complete grids to choose from.
Calculating the DLER The directional dependence of the surface LER is retrieved in the way best explained by Fig. containers and the normal surface LER retrieval is performed for each of these nine containers. This results in nine surface LER values, which, as a function of viewing angle θ v , are fitted by a third-order polynomial. The DLER can then be parameterised in the following way. We parameterise the DLER as in Tilstra et al. (  2021 ), but add a third order term: A DLER = A LER + c 0 + c 1 • θ v + c 2 • θ 2 v + c 3 • θ 3 v. (8) In Eq. (  8 ), the directional surface LER is expressed as an extension on top of the non-directional surface LER. The values of A LER and of the polynomial coefficients c 0 , c 1 , c 2 , and c 3 are contained in the database file. The polynomial coefficients are equal to zero for water surfaces.
Post-processing the surface LER and DLER Several post-processing corrections are conducted to remove imperfections from the surface LER and DLER database. Cloud contamination over the oceans can be detected relatively easily, by checking if the surface LER value exceeds a certain threshold. This is done per band duo, at the longest wavelength band of the band duo. If cloud contamination is detected, then the post-processing correction starts looking for suitable, near-by donor cells. In all cases such a donor cell can be found and the surface LER from the donor cell is copied to the cloud contaminated grid cell. Flags are set to log the situation. This correction is particularly important for the ocean region near 60 • S, where the view to the surface is almost always obstructed by clouds.
Another issue that needs to be addressed is that of missing data due to polar night. Correcting for this phenomenon is mostly a cosmetic procedure, and not relevant for retrievals based on passive instruments. The issue of missing data (empty grid cells) is remedied by the post-processing step by searching for donor cells in other months at exactly the same location. The month nearest in time is used for that. Other tasks by the post-processing step are performing sanity checking and performing the error calculation. These tasks are described in the ATBD  (Tilstra et al. , 2023). Here, we analyse various properties of the surface LER and DLER database. First, in Sect. 5. 1 we study the anisotropy of the Earth's land surface reflectance. In Sect. 5. 2 we study the retrieved directional dependence for several surface type regions, as well as its seasonal dependence.
Surface anisotropy The directional dependence of the surface reflection can be studied by examining the surface anisotropy parameter. In this paper, this parameter is defined as the difference between the TROPOMI surface DLER at viewing angles θ v of -45 • (east viewing direction) and +45 • (west viewing direction). The results that are shown in Sect. 5. 2 confirm that this is a proper definition. In Fig. 3   For 670 nm, the surface anisotropy parameter derived from the TROPOMI surface DLER database is much smaller than at 772 nm. This is partly because the surface reflectance itself is smaller. For vegetated surfaces the anisotropy parameter now reaches 0. 04 (30-40%); for desert surfaces the numbers are 0. 04 (10-20%). At 463 nm, the land surface anisotropy is mild. Values reach about 0.
02 for both vegetation and desert, but because of the different reflectance levels the percentages are different (vegetation: 20-40%; desert: ∼10%). Looking at all these numbers and comparing these with previous values found for the GOME-2 surface DLER database, we conclude that the surface anisotropy observed by TROPOMI is smaller than that observed by GOME-2, but still significant. The surface anisotropy will be studied more closely in the next section.
Similar behaviour was also found earlier from the GOME-2 surface DLER database  (Tilstra et al. , 2021, Sect. 6. 2). In general, the surface anisotropy found for TROPOMI is weaker than that found for GOME-2. This is directly related to the different equator crossing times of the two instruments (TROPOMI: 13:30 LT; GOME-2: 09:30 LT). Results for some of the other wavelength bands can be found in Figs. S1-S3 in the Supplement. While producing images, the surface reflectivity was increased to better emphasise the presence of cloud and aerosol contamination, leading to saturation and discolouration over some of the desert areas. Certain regions show signs of cloud contamination. The impact of persistent aerosol plumes can also been seen in some cases. In window (b) signs of cloud shadow impact can be seen in the southern regions.
as the blue, green, and red components, respectively. Cloud contamination would reveal itself by leaving a grey haze in Fig. 5. From earlier analyses performed using previous surface LER databases (see e. g. Koelemeijer et al. , 2003; Kleipool et al. , 2008;  occur. In the same way, cloud contamination can be observed in Figs. 5b, c  near the coast of Guinea. Also in this case the cloud contamination is less strong compared to the previous surface LER databases. This is mainly owing to the smaller footprint size of the TROPOMI instrument, but also the use of active cloud filtering (on top of the statistical approach taken for the previous surface LER databases) may have resulted in less severe residual cloud contamination. Over the ocean, several examples of cloud contamination can be found, at locations depending on the calendar month.
In September (Fig. 5c ) a clear grey feature near Angola can be seen. This feature is related to aerosol and cloud presence. The aerosols are biomass burning aerosols which are transported westward over the ocean. Note that the grey feature is also visible over the continent itself, not just over the ocean. In Fig. 5a ,b one can also see a grey feature between the north of Africa and the north of South America. These are dust aerosols originating from the desert regions in the north of the African continent and travelling over the Atlantic Ocean. In conclusion, cloud and aerosol contamination are both present in the TROPOMI surface LER database, although less severe than in previous LER databases.
Impact of cloud shadows The impact of cloud shadows is not clearly visible in Fig. 5. This is a direct consequence of the cloud shadow screening method introduced in Sect. 4. 4. To prove this, Fig. 6  demonstrates the necessity for including cloud shadow filtering in the retrieval. In the left window, a 494-nm "clear" surface LER field is shown which was retrieved from TROPOMI observations from February 2021. The field is based on only one month of data and no post-processing corrections were applied. The field is therefore a very raw field, derived from a low number of observations. This is needed for Fig. 6 , however, to be able to In this section we compare the non-directional TROPOMI surface LER with heritage databases based on the GOME-1, OMI, SCIAMACHY, and GOME-2 instruments. These databases are non-directional, except for the GOME-2 DLER database.
Results Figure  7  presents global maps of the differences between the TROPOMI surface LER and the SCIAMACHY surface LER databases. The differences were determined for the month March and for the four indicated mutual wavelength bands. For the TROPOMI database the snow/ice-free "clear" field was selected; for the SCIAMACHY database the "MIN-LER" field was selected. A grey colour is used for grid cells in the TROPOMI surface LER database which were flagged as corrected for missing data. These are typically grid cells that encountered snow cover during the entire month. From Fig. 7  it can be seen that there is good agreement over the oceans but for the longer wavelengths there are issues over land. For 758 nm, TROPOMI shows higher values over the desert areas, but lower values over vegetated regions.
Here the deviations could be caused by the different overpass times of the TROPOMI and SCIAMACHY satellite instruments, because the surface reflection strongly depends on the solar geometry. For 2314 nm, the differences are harder to explain and surface type alone cannot be the only explanation for the differences because Sahara desert (blue, negative) and Australian desert (red, positive) deviate very differently. In Fig. 8  the results for the comparison with the OMI surface LER are shown. Note the different set of wavelength bands compared to Fig. 7. For the shortest wavelength, 380 nm, the differences are similar to the TROPOMI-SCIAMACHY result for 380 nm shown in Fig. 7. This is not surprising as the SCIAMACHY and OMI surface LER databases were found to agree quite well in the past (see e.
g. Tilstra et al. , 2017, Fig. 12 ). Nevertheless, the agreement with OMI is clearly better. For the longer wavelength bands, the agreement between TROPOMI and OMI is also good, also for both land and sea surfaces. The good agreement between TROPOMI and OMI could be expected, because of the orbital similarities and the similar equator passing times. Some mild features are nevertheless visible in Fig. 8. Finally, Fig. 9  presents results from the intercomparison between surface LER databases from TROPOMI and GOME-2. There are now some stronger differences visible. At 380 nm, the thick blue haze suggest a systematic difference between TROPOMI and GOME-2 surface LER. The difference, of up to about -0. 02, is caused primarily by a known offset of 0. 01-0. 02 in the GOME-2 surface LER due to calibration errors  (Tilstra et al.
It is at this point a bit premature to blame the GOME-1 surface LER database for the differences. Nevertheless, the GOME-1 surface LER database is the most likely candidate to blame because similar behaviour was seen when this database was compared to the SCIAMACHY, GOME-2, and OMI surface LER databases  (Tilstra et al. , 2017). Fig. 10b  presents the differences between the TROPOMI surface LER and the GOME-2 surface LER. Here the distributions are less wide and, more importantly, they are becoming less wide with increasing wavelength. This is the expected behaviour, because the surface albedo over water decreases rapidly with increasing wavelength. Additionally, the centre positions of the histograms are close to zero, with the exception of the 380-nm histogram. As reported earlier  (Tilstra et al.
, 2017) , there is a radiometric calibration error impacting the retrieved GOME-2 surface LER for wavelengths up to 400 nm. This explains the shift of the 380-nm histogram with respect to zero. For the longer wavelength bands, however, there clearly is good agreement. In Fig. 10c  the surface LER difference histograms of the TROPOMI-SCIAMACHY intercomparison are presented. Note that a fifth histogram for the 2314-nm wavelength band was added. The agreement is good for all wavelength bands, with systematic deviations smaller than 0. 01 in all cases. Finally, in Fig. 10d  the histograms from the TROPOMI-OMI surface LER comparison are shown. The agreement is again good for all wavelengths and the systematic deviations are within 0. 01. For each of the reference databases, histograms were analysed for each calendar month and the central position and width (FHWM) of the distributions were recorded for each wavelength band.
Discussion of results In general, there is good agreement between the TROPOMI surface LER and the heritage surface LER databases based on GOME-1, OMI, SCIAMACHY and GOME-2. From the global difference maps presented in Figs. 7-9 we concluded that the best agreement is found when the TROPOMI surface LER is compared with the OMI surface LER. This can be understood by looking at the orbital and instrumental similarities of the two satellite instruments. The TROPOMI and OMI instruments have similar designs and their orbits have comparable local equator crossing times (13:30 and 13:45 LT, respectively). The similarity in overpass time means that the solar geometries are more or less the same. This means that the viewing and solar angles that define the surface BRDF are similar, resulting in a reliable comparison.
But also the agreement with the SCIAMACHY and GOME-2 surface LER databases is satisfactory. More quantitative results, derived from histograms such as the ones presented in Fig. 10 , were derived for all calendar months. For the wavelength bands from 328 to 494 nm we find accuracies within 0. 01-0. 02 when we compare with the OMI and SCIAMACHY surface LER databases. This is well within the accuracy requirements of 0. 03+10%. For the longer wavelengths (670-2314 nm) we found biases below 0. 003, which is better and well within the accuracy requirements of 0. 02+10%. A dependence on calendar month was not found. Detailed results can be found in the Supplement (Figs. S4-S6 and Tables  S1-S3 ). Good agreement between TROPOMI surface DLER and MODIS surface BRDF was found for a range of surface type regions.
Another factor may be the existence of radiometric calibration errors. Calibration errors were found in the past for TROPOMI spectral band 3 and 4, with reported errors between 6% and 10%  (Tilstra et al. , 2020). However, these numbers apply to an older version of the TROPOMI level-1 data (v1. 0. 0). The calibration should have been improved significantly for the version of the level-1 data that were used for the creation of the DLER database (v2. 1. 0). Moreover, the DLER wavelength bands that were compared with MODIS BRDF were derived from TROPOMI spectral bands 5-7, not from spectral bands 3 or 4. In the comparison the MODIS BRDF from one day was compared with the DLER representative for the entire month. This is not optimal and can cause differences. Another factor to take into account is the fact that DLER and BRDF are fundamentally different properties (cf.
Sect. 6. 2. 1). There are, in practice, many factors that prevent a perfect comparison between TROPOMI DLER and MODIS BRDF. However, the goal of the comparison was not to perform a very reliable validation. The comparison The surface anisotropy and the seasonal cycle captured by the DLER were studied for various surface types. The behaviour was found to be similar to behaviour also found earlier in the GOME-2 surface DLER climatology. Differences in behaviour were also found but these could be related to the difference in solar position due to difference in the overpass times of GOME-2 and TROPOMI. The TROPOMI surface DLER database was validated first by intercomparison of the non-directional surface LER with traditional surface LER databases based on GOME-1, OMI, SCIAMACHY and GOME-2. The comparisons with OMI and SCIAMACHY showed good qualitative and quantitative agreement for all wavelength bands and for all calendar months.
The accuracies were found to be below 0. 01-0. 02 for the shorter wavelengths (328-494 nm). These accuracies are below the requirements that were set (0. 03+10%). For the longer wavelengths (670-2314 nm) we found biases below 0. 003. These accuracies are well below the accuracy requirements (0. 02+10%). The comparison with GOME-2 showed good agreement for the longer wavelengths. For the shorter wavelengths, biases were found which could be linked to systematic biases in the GOME-2 surface LER database for these wavelengths. These systematic biases for the GOME-2 surface LER database were reported earlier  (Tilstra et al. , 2017). For the TROPOMI surface LER database, the overall conclusion is that all requirements are met. Next, the TROPOMI surface DLER database was compared with MODIS surface BRDF for a collection of scenes.
The non-directional TROPOMI LER, which is also available in the DLER database, was also compared with MODIS BRDF to study the improvement in going from LER to DLER. The LER shows fair to reasonable correlation with MODIS BRDF, but the DLER is clearly better and has good to very good correlation with MODIS BRDF. The comparison was performed for various wavelength bands, but only the results from the comparison at 670 nm can be interpreted in a fairly quantitative way. Even for this wavelength band there are complications preventing a quantitative comparison, such as the difference in wavelength between the TROPOMI wavelength band used (670 nm) and the MODIS BRDF wavelength band used (645 nm). Nevertheless, the numbers that are found point to a good correlation between TROPOMI DLER and MODIS BRDF, and the conclusion is that the DLER is an important improvement on the traditional, non-directional LER.
1. Figure 10. 10 Figure10. Histograms of the differences between the TROPOMI surface LER database and (i) the GOME-1 surface LER database, (b) the GOME-2 surface LER database, (c) the SCIAMACHY surface LER database, and (d) the OMI surface LER database. The histograms were determined for land and water surfaces, for the month of March, for the indicated wavelength bands. To avoid the polar regions, the histograms were based only on grid cells between 60 • S and 60 • N. In general, there is good agreement.
https://doi. org/10. 5194/amt-2023-222 Preprint. Discussion started: 1 November 2023 c Author(s) 2023. CC BY 4. 0 License. with MODIS BRDF does clearly show that the DLER is a considerable improvement on the LER. In the Supplement, more detailed results can be found in Figs. S7-S15. 7 Conclusions This paper introduced a new surface albedo climatology of directionally dependent Lambertian-equivalent reflectivity (DLER) observed by the TROPOMI instrument on the Sentinel-5 Precursor satellite. The database contains monthly fields of DLER for 21 wavelength bands at a relatively high spatial resolution of 0. 125 • × 0. 125 •. The anisotropy of the surface reflection is handled by parameterisation of the viewing angle dependence.
https://doi. org/10. 5194/amt-2023-222 Preprint. Discussion started: 1 November 2023 c Author(s) 2023. CC BY 4. 0 License. retrievals of aerosol layer height and aerosol optical thickness. The TROPOMI surface DLER database can also be used for the support of retrievals from observations made by the OMI instrument, because the overpass times of TROPOMI and OMI are very similar. The DLER algorithm setup that was presented in this paper can be adapted to retrieve DLER from other instruments as well, for instance, from Sentinel-5 and 3MI on the MetOp-SG-A1 satellite to be launched in 2025, from OLCI on the Sentinel-3 satellites, and from the future CO2M mission. development phase of the project. PS and MdG helped with the radiative transfer modelling. All authors discussed the results and commented on the manuscript.
, 2014). Based on this method,  Geng et al. (2015)  estimated that as much as 45-53% of snow nitrate was lost after deposition during the last glacial time in the GISP2 100 ice core record. However, it's difficult to justify the assumed δ 15 N of deposited nitrate under different climates, and the method cannot correct for post-depositional modification of Δ 17 O(NO3 -). Erbland et al. (2015)  developed a 1-D snow photochemistry model (TRANSITS, https://github. com/JZxxhh/TRANSITS-model) that quantifies the effects of post-105 depositional processing on the preservations of nitrate and its isotopes in ice cores. The model comprises a series of physicochemical processes, including UV photolysis of snow nitrate, emission of NOx to the overlying atmosphere, local oxidation and nitrate deposition.
Acknowledgements : 840 L. G. acknowledges financial support from the  National Natural Science Foundation of China  (Awards:  41822605  and  41871051 ) and the  Strategic Priority Research Program of Chinese Academy of Sciences  ( XDB 41000000 ), and the  National Key R&D Program of China  ( 2022YFC3700701 ). This work was partially supported by the  French national programme LEFE/INSU (IMAGO) , the  ANR  grants  ANR-15-.