dataesr/scientific-paragraphs-categorization huggingface dataset:
Note also a layer around θ = 320 K in the extratropical WISE data (Fig. 5d ) with low N 2 O values marking signatures of stratospheric intrusions. This layer is much more pronounced in CLaMS simulations than in the observations, and is consistent with a similar feature in CO profile (Fig. 4b ). Mean CO 2 profiles (Fig. 5b  and e ) can be understood as a result of a coupled tropospheric and stratospheric transport folded in time and space with the evolution of the CO 2 sources (and sinks) in the PBL while both transport and sources show a pronounced seasonality  (Diallo et al. , 2017). CO 2 being almost chemically inert is a well-suited tracer for validation of transport in the models if the lower boundary condition is realistically parameterized. However, the (assimilated) CarbonTracker data set used for the lower boundary of CLaMS does not reproduce well the CO 2 observations in the lowest part of the troposphere where air dominated by regional sources was sampled (Kathmandu/Nepal for Strato-Clim and Shannon/Ireland for WISE).
Code and data availability. MESSy is continuously further developed and applied by a consortium of institutions. The usage of MESSy and access to the source code is licensed to all affiliates of institutions which are members of the MESSy Consortium. Institutions can become a member of the MESSy Consortium by signing the MESSy Memorandum of Understanding. More information can be found on the MESSy Consortium Website (http://www. messy-interface. org, last access: 30 June 2022). The CLaMS-2. 0/MESSy code presented here is based on MESSy version 2. 54 and accessible for MESSy Consortium members at https://gitlab. dkrz. de/MESSy (last access: 1 October 2021). ERA5 and ERA-Interim model level reanalysis data are available from the ECMWF as deterministic forecasts (atmospheric model): ERA5 via https://apps.
ecmwf. int/data-catalogues/era5/?class=ea (last access: 1 January 2022) and ERA-Interim via https://apps. ecmwf. int/archive-catalogue/?class=ei (last access: 1 January 2022). The CarbonTracker data (version CT-NRT. v2017) can be downloaded from the NOAA ftp server: http://ftp. cmdl. noaa. gov, see: /products/carbontracker/co2/CT-.
A total of 339 measures have been rejected due to the report of hazy skies or the presence of a few clouds and 1,134 measures have been used to build the 23 histograms shown in Fig. 1. For most of the locations, several NSB measuring sessions at different times of the year were performed. For each histogram, the median NSB value is provided in a yellow frame, the standard deviation of the measure set in a red frame and finally the predicted NSB value for the considered location in a green frame. The prediction for each site has been obtained using a software simulation tool developed by DarkSkyLab (called Otus) and using as an input to the model the VIIRS-DNB radiance from the 2020 annual composite publicly available from the Earth Observation Group Web site. One can easily notice the significant spread of the histograms at most locations: on the 19 sites with a significant number of measures, 11 have a standard deviation greater or equal to 0.
Measures are stored locally within the Ninox into an SQLite database along with ephemeris data. Ninox exposes a Wi-Fi access point which can be used to connect to the system, retrieve data on a regular basis and manage and configure the system. Ninox maintains the current UTC time through an embedded RTC (real-time clock) and automatically acquires the geographical coordinates of the observing location through a GPS, which allows it to automatically start acquisitions at dusk and stop them at dawn. Ninox can therefore be seen as a standard Unihedron SQM with powerful automated acquisition and logging capabilities. Ninox has been designed to operate automatically during long periods of time (months or even years) so that gathered data can be used to perform a statistical analysis of the zenithal night sky brightness.
This capability is key to be able to produce the absolute indicator proposed in this article. All the measures used in the scope of this article have been gathered using Ninox automated photometer systems. In its current version, Ninox uses the standard weatherproof housing provided by Unihedron. This housing leads to NSB values roughly 0. 12 mag SQM /arcsec 2 darker than the actual sky luminance and a correction is systematically applied to all NSB measures. In order to facilitate the interpretation of Ninox data, DarkSkyLab has also developed a sophisticated software called Noxi to process the data recorded by Ninox systems and produce plots and indicators. The main features of the Noxi software are the following: • Own a graphical user interface to allow an easy interaction with the user when selecting options or specifying processes to be applied to NSB data; • Compile measures from one or several sites into an SQLite database with well defined attributes (date, time, location, NSB measure, ephemeris data, etc.
); • Allow the user to execute complex SQL queries against the compiled SQLite database to filter measures depending on the needs. SQL queries can include date ranges, Moon and Sun positions, time of the night, galactic and ecliptic latitude and longitude of the zenith, etc. ; • Produce various types of plots based on data filtered by the SQL query. These plots can be nightly NSB curves, date and NSB histograms, NSB density histograms, night stability diagrams, heatmaps, averaged nights, etc. ; • Produce statistics and indicators on NSB data sets such as the NSB Dispersion Ratio introduced here. All the results presented in this paper are produced by Noxi.
NSB density histograms. Statistical NSB data are often represented in the form of a density histogram, also called densitogram or jellyfish diagram  26. For a given site, all the NSB measurements over a large number of nights are reported into a single diagram with the time of the day in Coordinated Universal Time (UTC) on the horizontal axis and the NSB in magnitudes per square arcsecond (mag SQM /arcsec 2 ) on the vertical axis. Each pixel in the histogram represents the number of occurrences of a particular combination of UTC time and NSB value. The number of occurrences is coded with a specific color code depending on its value. Figure  2  shows an NSB density histogram recorded with a Ninox system at the Astrièves Observatory in the town of Gresseen-Vercors in France. Only the NSB measures performed when the Moon is below -5 • and the Sun below -8 • are considered when building density histograms.
The median absolute deviation is a statistical tool used to measure the variability of a data set, which is exactly what we try to achieve with the two NSB extensions above and below the nominal NSB. It is formally defined as MAD = median(|X i -X|) where X i in our case represents an NSB value and X is median(X i ). The median absolute deviation is a better choice than the usual standard deviation to measure the spread of NSB measures since the data does not follow a normal distribution. In order to make the determination of the NSB Dispersion Ratio stronger from a statistical standpoint, we use a bootstrapping with replacement resampling method on the set of night portions used to compute the indicator. Assuming we have N night portions at our disposal, we randomly select a sample of N items in this set of night portions knowing that a given item can appear multiple times in the sample (hence the bootstrapping with replacement).
e. the typical clear sky level, since it will be used to differentiate the NSB measures that go in each of the two sets to calculate the bright and dark dispersions. As we have seen earlier in the article, such a determination can be biased by natural light sources that raise or lower clear sky NSB at different times of the night. This can result into a "blurry" high density zone which makes the determination of the nominal NSB difficult or even impossible depending on the observation period. Based on the quantitative estimate of the different natural light sources presented above, the most important bias to address is the contribution of the Galactic plane. This contribution must be eliminated for all the NSB measures which are used to calculate the NDR indicator. In order to do that, Noxi, the Ninox processing software developed by DarkSkyLab, calculates for each NSB measure the corresponding Galactic plane and star fields contribution using the galactic coordinates of the zenith and integrating the combined flux of all stars in the field of view using the UCAC4 astrometry and photometry star catalogue.
It is not possible to cancel the contribution of the airglow due to its unpredictable nature, but since it only appears in rare occasions, it is not seen as a problem and is ignored. Regarding the contribution of the zodiacal light, it is considered as minimal at the zenith and it is also ignored. NDR = (N b • MAD b )/(N d • MAD d ) As an example, Fig. 9  shows on the left an NSB density histogram where the Galactic plane bias has not been corrected in the data, and on the right the same data but with the Galactic plane bias corrected. It is easy to see that in the latter the nominal NSB is much easier to determine, providing a more accurate reference level to calculate the NDR indicator. Once the Galactic plane bias has been corrected, the nominal NSB is determined as the highest density zone of the NSB histogram.
As a result, we obtain a set of residuals. The variance of all the residuals defines the NSS for the considered NSB dataset. Below a given value, the sky is considered as clear knowing that the NSS indicator has been calibrated on several NSB data sets for which the corresponding weather conditions are known; 2. A second heuristic will allow us to weight the Galactic bias correction to be applied to NSB measures according to their value. For non-polluted skies with high values of NSB, the full Galactic bias correction will be applied while below a certain NSB threshold (for instance 21 mag SQM /arcsec 2 which corresponds roughly to the brightest parts of the Milky Way) no correction will be applied. The NDR indicator is unitless since it is the ratio of two quantities with the same unit (mag SQM /arcsec 2 ).
For the data set presented in Fig. 9 , the NDR value which is obtained is 25 (which is justified by the fact that the bright extension in the density histogram is much higher and denser than the dark extension). This denotes a quite high level of light pollution despite the fact that the nominal NSB is at a level of 21. 6 mag SQM /arcsec 2. This highlights the fact that there is not always a strict correlation between the typical clear sky NSB obtained for a given site Figure  9. NSB density histograms of the same data set with no correction of the Galactic plane bias applied on the left and a full correction applied on the right. and its NDR indicator, i. e. the presence of clouds decreases the NSB more than we could have expected just by knowing the clear sky NSB. On that respect, the NDR ratio brings more information that the clear sky NSB alone.
The residual spread of NSB measures is due to changing atmospheric conditions at various time scales, but, for this particular contribution, we can expect a statistical compensation to eliminate a systematic associated bias. At the moment, the contribution of the Galactic plane and star fields is canceled into the NSB measures by calculating in the Noxi software the integrated flux of all the stars that belong to the field of view (using the UCAC4 star catalogue). However, this approach has proven some limitations, especially in the southern hemisphere where the Galactic center goes through the zenith and is particularly bright. A probable explanation for that lack of predictability is the fact that the Galactic plane contains diffuse sources such as nebulae which are not accounted for into the star catalogues and which actually cannot be ignored.
Acknowledgements This work was made possible thanks to the contribution of several private companies, institutions and amateur astronomy clubs in France, in particular  Deep Sky Chile in Atacama , the  Institut de Mécanique Céleste et de Calcul des Ephémérides (IMCCE) in Paris , the  Observatoire du Trièves in Gresse-en-Vercors, France, and l'Association Copernic 05 near Gap, France. We also want to thank  Sylvie Vauclair  for her review of the paper and advice about its publication. Finally, we want to thank the  Parc Naturel Régional de Millevaches-en-Limousin, France , for letting us using the results of their valuable night sky quality survey.
Study area and data Târgu Mureș (Marosvásárhely) is the main town in the district of Mureș (Maros) in the historical region of Transylvania, Romania (46°32'44''N 24°33'45''E) (Fig. 1 ). According to the latest Romanian census (Institutul Național de  Statistică, 2011) , it has a population of 134,290 inhabitants, an area of 49. 3 km 2 , making it the 16th largest city in the country. It extends along the two banks of river terraces of the River Mureș (Maros). At the country level, it is an important center of the chemical industry (Azomureș), and also of pharmaceutical, textile, wood, and food industries. The data used for calculating the surface temperature are remote sensing data from the MODIS and Landsat satellite sensors. Remote sensing technology makes LST measurements possible by satellites in different orbits, on a multi-temporal scale.
However, these satellites provide data in different spatial and temporal resolutions  (Maithani et al. , 2020; Jensen, 2015; Ndossi and Avdan, 2016). LST data from two different sensors (MODIS and Landsat) have used in this study. The reason for choosing this method is to find the balance between the spatial and temporal resolution of the available data. MODIS/MOD11A1 provides daily LST data with a spatial resolution of approximately 1 km. MODIS/MOD11A2, in turn, provides data of an average of 8 days with the same spatial resolution. Instead, Landsat sensors provide data with a much better spatial resolution (30 m). The Landsat 4-5 TIR band (band 6,10. 40 -12. 50 μm) obtains data at 120 m resolution, but it is resampled to 30 m. Landsat 7 band 6 (10. 40 -12. 50 μm) acquires data at 60 m resolution, then it is resampled to 30 m.
51 μm). In this study, we have used band 10, which is better in the calculation of current evapotranspiration, since it measures with high gain, while band 11 provides low gain  (Xu, 2015; Du et al. , 2015; Yu et al. , 2014; Xiao et al. , 2007; Rozenstein et al. , 2014; Sahana et al. , 2016). As we saw above, MODIS LST data has a lower spatial resolution (1 km) than Landsat (30 m). However, MODIS obtains daily data. Despite this, the MODIS LST calculation continues to improve the cloud pollution removal, the update of the look-up table coefficient (LUT) for the split-window algorithm, and the classification to obtain emissivity. Also, studies have tested with ground validation methods that MODIS LST data show dependable accuracy  (Eleftheriou et al. , 2018; Hulley et al. , 2012). On the other hand, the difficulty with using Landsat data is that they have a fairly long return period.
Besides, from March to May (spring), in the temperate zones, such as where Marosvásárhely is located, clouds are more frequent in the scenes. Therefore, the effective temporal resolution (i. e. , satellite images that can be used in the study period) can be greatly reduced. For this reason, we chose to use both MODIS LST and Landsat LST data to compare land surface temperature results. We have seen that the overall comparison results between 2020 and the long average of the years 2000-2019 of MODIS are corroborated with the results of Landsat and, knowing this, we have used the data of Landsat for the most in-depth analyses (hotspot-coldspot change detection analysis). The Landsat data used are presented in Table  1. In the case of Landsat data, the reference date is a scene from the emergency period, which is April 8, 2020.
Other satellite scenes have been searched in the previous years in the month of April with a maximum difference of 6 days to reduce the effect of the natural cycle of LST. Landsat's NIR and RED bands have been downloaded from the Collection 2 Level 2 database, where the atmospheric effects on products are corrected. This step is crucial, because if the surface emissivity is calculated from the normalized vegetation index, on NIR and RED bands must be corrected atmospheric effects  (Dissanayake et al. , 2019a (Dissanayake et al. , , 2019b;; Ranagalage et al. , 2018; Sekertekin and Bonafoni, 2020).
208 TIR bands have been downloaded from Level 1 products, because in this study, LST was calculated with the radiative transfer equation (RTE), and this equation requires that the TIR band must be in digital numbers (DN) (0-255). Vector data referring to the boundaries of different urban area categories and administrative boundaries are derived from the Urban Atlas data of the Copernicus Programme. Data processing and obtaining the results were carried out with open source softwares Google Earth Engine and QGIS.
LST extraction from MODIS data MODIS11A2 and MODIS11A1 LST data were created based on a generalized split-window algorithm under cloud-free conditions  (Wan, 2013; Duan et al. , 2019). The digital number (DN) values of MOD11A2 and MOD11A1 were calibrated to LST values in Kelvin scale by multiplying the DN with 0. 02. From the obtained results, 273. 15 were subtracted, thus the LST values in degrees Celsius were finally obtained. This whole process was done in Google Earth Engine using lines of JavaScript code. The monthly temperature anomaly was calculated with MOD11A2 data with the following equation  (Ceccato et al. , 2017) : 𝑟′ = 𝑟 -∑ 𝑟 , (1) where r'ij is the monthly anomaly, rij is the original monthly value, and the rest of the equation is the calculation of the monthly climatology, which is subtracted from the original monthly values.
LST extraction from Landsat data The authors have developed an automated processing model in QGIS Graphical Modeller for calculating the LST with Landsat satellite images. For Landsat 8, 7, and 5 products, three different models have been built, respectively. The difference between these models is that for Landsat 7 satellite images, a correction process of the gaps produced by the sensor's technique issue has been implemented in the model process, while for the other Landsat scene products, this step is not required (nor for Landsat 7 products before June 2003). In all models, cloud pixels have been excluded by identifying clouds with the BQA band of Level 1 by applying threshold values.
DN conversion to radiance Landsat 7 and 5 data were converted to radiance using the spectral radiance scale method: 𝐿 = * (𝑄𝑐𝑎𝑙 -𝑄𝑐𝑎𝑙𝑚𝑖𝑛) + 𝐿𝑀𝐼𝑁 , (5) while Landsat 8 data were converted to radiance using the gain and bias method: 𝐿 = 𝑀𝐿 * 𝑄𝑐𝑎𝑙 + 𝐴𝐿 , (6) where 𝑀𝐿 = , (7) 𝐴𝐿 = 𝐿𝑀𝐼𝑁 - * 𝑄𝑐𝑎𝑙𝑚𝑖𝑛. ( 8 ) In Eqs. (5-8), L λ is the spectral radiance in the sensor (satellite radiance), ML is the band-specific reset multiplicative factor from the metadata (RADIANCE_MULT_BAND_x, where x is the band number), AL is the bandspecific reset additive factor from the metadata (RADIANCE_MULT_BAND_x, where x is the band number), Qcal is the discretized and calibrated pixel values of the standard product (DN digital values), LMAX λ is the maximum spectral radiance corresponding to the band Qcalx, LMIN λ is the minimum spectral radiance corresponding to the band Qcalx, Qcalmax is the maximum pixel value depending on the radiometric resolution, Qcalmin is the minimum pixel value.
(  6 ) Declaration of results. (  7 ) Statement of conclusion. The t-statistic equation is as follows  (Brown and Melamed, 2012) : 𝑡 = √ , ( 12 ) where 𝑋 = ( ⋯ ) , (13) 𝑠 = ∑ ( ) , ( 14 ) where t is the dependent test statistic, ̅ is the mean difference,  is the standard deviation of the difference and n is the sample size. The two samples were obtained with the help of Raster Pixels to Points QGIS tool. Then the attribute tables were moved to Excel. Another paired samples t-test was performed using SPSS for the areas where LST decrease was identified. A mean comparison was made to find out if statistically there is a difference between the mean LST of the areas identified with LST decrease in the lockdown period of 2020 and the mean LST before 2020 (individual available LST data between 2003-2018) in the same period (March 16 -May 14).
For this reason, it has the greatest descent change (negative change) of LST. Because Landsat has a higher spatial resolution, we can see in more detail the areas with high and low temperatures and the areas that have changed (Fig. 3 ). Landsat results corroborate the MODIS results: the distribution of high and low values is different in 2020 than the long average of data existing between 2003-2018. Although we have higher values in 2020 in urban areas, their concentration has been reduced in some areas. However, in 2020 we can still observe existing heat islands (e. g. , areas of the Azomureș industrial estate). The Tudor district also stands out in the difference raster. Both MODIS and Landsat data show an increase in LST, in general in builtup areas. The trends in LST described below are also including the study period in ).
The difference in standard deviations between MODIS and Landsat shows that MODIS σ = 0. 12, Landsat σ = 0. 08. These differences between MODIS and Landsat are relevant to the LST trend, because they show how spatial resolution and acquisition methods of satellite images can influence the LST values obtained. Due to its higher spatial resolution, Landsat can better detect objects with higher or lower LST emissions. Differences between MODIS and Landsat are due to the different spatial resolution of the two sensors (Landsat LST offers more details about the territory and the objects) and the different modes of capturing the TIR and obtaining the LST. Fig. 4  shows the trend of LST between the years 2000-2020 with MODIS data in the built-up area. A downward trend can be observed between 2000-2005, however, after 2005, this becomes an upward trend of LST.
LST anomaly analysis Similarly to the LST temperature calculation, the LST anomaly with MODIS data was calculated for the study period between March 16 and May 14 for the years 2000-2020. These LST anomaly results for the different years are raster layers representing the LST anomaly within March 16 -May 14 in 2000-2020. For the years 2000-2019, an average resulting in a single LST anomaly average raster has been calculated. The 2020 anomaly is not included in this average, because the mean raster of 2000-2019 is subtracted from the 2020 raster, thus obtaining the LST anomaly difference. We calculated the monthly anomaly as the difference between the monthly climatology minus the average of months within the study period, i. e. , March-May. Then the rasters between 2000-2019 are added, and a long average comparing with the 2020 raster is calculated.
Coldspots are interpreted as a negative LST change and not as a downward trend. The 46 cases of coldspots are resulted from the difference between the 2020 raster and the average raster of 2003-2018. (These Landsat data do not include every year between 2000-2019, because no useful satellite image had found in these years just after applying the anti-COVID measures in the study period). This difference raster shows areas where compared to previous years, LST has decreased or increased in 2020. This raster layerwas used to identify statistically significant coldspots. Consequently, according to the difference between the 2003-2018 average and 2020, the 46 identified cases within the urban land class are specific areas that have transformed into coldspots. The percentage of the total size of the area that has shown a negative change, that is, it has become colder in the lockdown period of 2020 than during the same period of the previous years, is about 1% (0.