dataesr/scientific-paragraphs-categorization huggingface dataset:
The paper especially focuses on -AOD instead of mass concentrations to allow the assessment of atmospheric extinction (different from the air quality community), -dust aerosols allowing the evaluation of high solar potentials in Southern regions being dominated partly by large deserts (different from some parts of the air quality community), -hourly AOD to follow the need for at least hourly resolved irradiance time series for power plant simulations (instead of monthly means in the climate community), -daily AOD (instead of monthly means in the climate community) in order to quantify the value of hourly AOD modelling as the basis of daily mean generation, and compared to daily AOD assessments e. g. , from MODIS satellite data as suggested by some solar energy experts, -point-wise comparisons even for coarse resolution global model datasets reflecting the practice in the solar sector (instead of large area means used in several climate model validation studies), -comparing obviously different datasets as an hourly model run versus a monthly mean dataset.
This is motivated by the fact that using such datasets is the state-ofthe-art in many existing solar energy resource assessments and in order to quantify the impact created by using a higher resolved, but computationally expensive model, e. g. , in operational NWP centres. Section 2 describes all data used including the MATCH model version, the long-term dataset created and AERONET ground measurements. Section 3 discusses a suggested postprocessing with respect to overestimation in dust episodes. Section 4 provides overall validation results for daily and hourly mean AOD against the AERONET network and justifies the post-processing approach chosen, while Sect. 5 provides a station-wise and regionally resolved assessment. It also addresses the question of the need for hourly versus daily mean model output with respect to hourly observations.
MATCH model long-term dataset The Model of Atmospheric Transport and Chemistry (MATCH,  Collins et al. , 2001; Fillmore, 2005; Rasch et al. , 1997)  is a three-dimensional global transport model describing an external aerosol mixture of several aerosol types. Tropospheric sulphate, sea salt and carbonaceous aerosols are treated with a constant lognormal aerosol size distribution, while the dust model distinguishes 4 dust bin sizes from 0. 01-1, 1-10, 10-20, 20-50 µm effective particle radius and assumes a lognormal size distribution in each of the bins. Sea salt aerosols are treated only in the diagnosis mode without any transport. AOD is calculated separately for each aerosol type using optical extinction coefficients for each species following  Kiehl et al. (2000)  for sulphates and  Haywood and Ramaswamy (1998)  for sea salt and carbonaceous aerosols.
Dust optical properties are calculated from Mie theory using the refractive index as provided by  Patterson (1981). MATCH includes emission databases for seasonal sulphur emissions at 0 and 100 m height for 1990 and 2005, monthly mean surface di-methyl sulphate (DMS) emissions, monthly mean biomass burning black and organic carbon fluxes, monthly mean natural organic carbon fluxes from terpene emissions, fossil fuel black and organic carbon surface fluxes and an explicit dust mobilisation scheme (DEAD). Anthropogenic sulphur emissions are described in the Global Emissions Inventory Activity (GEIA) dataset  (Benkowitz et al. , 1996 ; http://www. geiacenter. org) with extensions made by  Smith et al. (2001)  providing country-level sulphur dioxide emissions from the US Environmental Protection Agency (USEPA), the European Environment Agency (EEA), an inventory for the former Soviet Union region by  Ryaboshapko et al.
(1996)  and Environment Canada. For Australia and New Zealand, emissions were taken from the 1985 GEIA 1B inventory  (Scholtz et al. , unpublished data). For the rest of Asia, Latin America, Africa and the Middle East, emissions are calculated from regional fuel consumption and fuel properties. Ship emissions are taken from IEA/OECD (1997), and industrial process emissions for developing regions follow the EDGAR database  (Olivier et al. , 1996). Biomass burning emissions from the EDGAR database are used, while emissions from traditional biomass combustion follow  Streets and Waldhoff (1998). A seasonal dependence of emission rates reflecting the fossil fuel consumption as a function of weather conditions is included. The biomass burning emission's seasonality is estimated using regional fire data.
Seasonal SO x emissions are provided in a 1 • × 1 • resolution for the reference year 1990 and projected into the future years  2005, 2020, 2035, 2050, 2065, 2080 and 2095. Emissions at the surface (0 m height) from low-temperature burning are distinguished from 100 m injection height's sulphur as typical for factory or ship sources with high-temperature combustion. The emission database is interpolated linearly towards the actual date. Oceanic DMS concentrations are described in a monthly 1 × 1 • climatology derived by  Kettle et al. (1999). Black and organic carbon emission inventories are monthly climatologies based on  Liousse et al. (1996)  for biomass burning aerosols and  Penner et al. (1993)  for fossil fuel burning sources. The production of natural organics is assumed as 10 %  (Collins et al.
The MATCH/DLR v4 dlr1. 0 version has been obtained from NCAR (National Centre for Atmospheric Research) and modified for data handling purposes mainly. It is not the purpose of this work to improve the modelling inside MATCH, but to take it as an example of a state-of-theart global model and assess it from the solar sector's pointof-view. Additionally, it has been adapted to provide AOD output at other wavelengths than the standard 550 nm and Angstroem coefficients. To cover the period of the Meteosat satellite observations a long-term dataset has been generated: This dataset consists of hourly values for the period 1 January 1983 to 31 December 2009. It uses the NCEP/NCAR reanalysis (http://rda. ucar. edu/datasets/ds090. 0/) with a T62L28 resolution input resulting in an approx. 1. 9 • reduced Gaussian grid.
1 ). For hourly resolved comparisons a minimum of 480 observation hours (40 days times 12 h) is required, reducing the number of stations in hourly validations to 55. Hourly resolved MATCH/DLR output between 01:00 and 24:00 UTC is averaged to obtain daily means. Overall a number of 9899 coincidences is analysed in the daily mean dataset and 64 795 coincidences are found in the hourly dataset.
Post-processing of MATCH/DLR A sudden and strong increase of MATCH/DLR AOD is sometimes found in the dust aerosol component motivating the hypothesis of an overestimation in such events. Typically, AERONET observations are missing in these situations. Visual interpretation in Meteosat Second Generation's SEVIRI (Spinning Enhanced Visible and Infrared Imager) colour composite imagery reveals that such cases are characterised typically by a cloud system with additional dust uptake. AERONET typically cannot distinguish clouds from dust in such mixed cloud/dust cases and observations are removed from the level 2 database in the cloud clearing scheme. Therefore, AERONET generally can neither support nor reject the hypothesis of overestimating peaks in MATCH/DLR in most of these cases. Additionally, one has to be aware about the tendency in AERONET data to flag strong dust events as cloudy and to remove this data.
Large overestimations with MATCH/DLR AOD values above 1. 0 can also be corrected by the post-processing. Both the correction of underestimated small AOD and overestimated large AOD values supports the hypothesis, that the dust uptake in MATCH/DLR is too strong in the mixed cloud/aerosol cases. Nevertheless, there are a number of cases with large AERONET/AOD values above 1. 0 which are not found in the MATCH/DLR dataset and the post-processing scheme also over-corrects some daily mean AOD values larger than 1. 0 which are found around the bisecting line in the daily mean statistics. Both bias and RMSE decrease from 0. 04 and 0. 26 (without post-processing) to 0. 02 and 0. 23 for daily means and from 0. 05 and 0. 32 to 0. 03 and 0. 28 for hourly means if the postprocessing is used. In the overall statistics of daily means the post-processing effect is only small.
This is reasonable, as these strong dust peaks occur only in a small number compared to the overall number of coincidences. Additionally, they are also frequently hidden through the automatic cloudclearing in AERONET or to the overlap of a both partially cloudy and aerosol situations resulting in no coincidences with AERONET in these cases. Nevertheless, if stations are evaluated separately, a significant improvement can be seen for several stations (Fig. 5 ). Figure  5  and several following figures present target plots following ideas as introduced by Jolliff et al. (  2009 ) and currently being widely adapted in the air quality community. It sets bias and centred root-mean-square-errors (CRMSE) in relation to each other. The CRMSE is the standard-deviation of the differences between the reference and the modelled AOD dataset.
Only Capo Verde (station no. 37) has a large positive bias of 0. 19 resulting from overestimations of AOD observations around 0. 5. Especially Djougou, Ilorin and Banizoumbou have strong negative biases of -0. 31, -0. 31 and -0. 11, respectively. This results from a general underestimation of AERONET observations above 0. 7 at these stations. This holds also for the dust cases selected by the Dubovik criterion at these stations. This remarkable and general difference between the African and the Arabian stations indicates that strong dust overestimations in the MATCH/DLR dataset seem to be a regional issue for the Arabian Peninsula and the Middle East. This may point towards the erodibility databases as the most relevant factor for improvement instead of a general error in modelling e. g. , of the dry deposition or the dust mobilisation.
Hourly mean AOD values As there is a need for a globally available database, the spatial resolution cannot be reduced to a 10 or 50 km horizontal grid due to computational constraints. Nevertheless, an increase in the temporal resolution from monthly means to daily or hourly values is requested by solar energy experts. There has been the attempt to provide higher resolved aerosol information based on daily satellite observations e. g. , from MODIS or MISR. A combination of satellite instruments can provide at its best an estimate of the daily mean generated from two or more cloud-free observations, but even this is not possible in many regions of the world. Based on the variability seen in AERONET measurements hourly resolved aerosol information has been required as well, but it is not known if nowadays aerosol modelling is capable to provide sufficient accuracy for an hourly dataset.
Comparison against an aerosol climatology used as state-of-the-art Currently, the SOLEMI database  (Schillings et al. , 2004)  for long-term global and direct irradiance time series is based on the GACP (NASA/GISS,  Tegen et al. , 1997)    tative datasets applicable for all calendar years. Obviously, this temporal and spatial resolution is rather coarse. Using a global scale MATCH/DLR with a daily or even hourly temporal resolution and with a spatial resolution of 1. 9 degrees holds the potential to improve this situation. Therefore, a comparison between validation results against AERONET measurements is performed for the MATCH/DLR daily mean and the GACP dataset. The GACP dataset shows large underestimations with biases up to -0. 5 in Central Africa (light green), which are smaller in the MATCH/DLR dataset (Fig.
9 ). Negative biases in Venise and ISDGM CNR (Italy) are corrected by MATCH/DLR. In the Arabian Peninsula (blue), strong negative biases tend to become strong positive biases and the amplitude changes from being too small in the GACP climatology to being too large in the MATCH/DLR. Small negative biases between 0 and -0. 1 remain small, but become positive between 0 and 0. 1 at several stations. And finally, there are two stations Al Khaznah and Mezaira which have a negligible bias in GACP and get a large bias in the MATCH/DLR dataset. Overall, the performance on the Ara-bian Peninsula for both datasets needs improvement. Northern African stations (orange-red) show a similar behaviour. Izana and Lampedusa are getting a large positive bias in the MATCH/DLR dataset while it is close to zero in the GACP.
On the other hand, Blida, Cairo, Nes Ziona, and Saada are better within MATCH/DLR. The major difference is found in the correlation coefficients which are much larger for the MATCH/DLR dataset for most stations with exception of some stations at the Arabian Peninsula (Fig. 10 ). Exceptions are the stations Dhadnah, Hamim, Maarco, Mussafa, Sir Bu Nuair, SMART and SMART POL. They are all located on the Arabian Peninsula close to dust sources. For Dhadnah, Hamim and Mussafa, several dust events are well met, but others are strongly under-or overestimated in time series of daily values. Maarco, Sir Bu Nuair, SMART, and SMART POL are having less than 60 coincidence days in the year 2004 and being close to dust sources, the results are driven by a small number of events only. Nevertheless, also here some events are well met, while others are over-or underestimated.
Summary and conclusions This paper focuses on the accuracy assessment of daily and hourly resolved state-of-the-art global aerosol modelling with respect to solar energy needs. It uses the MATCH/DLR model version v4 dlr1. 0 for this purpose and provides an overview on aerosol physics and chemistry, the emission databases and the transport scheme implemented in this model. A dataset for 1983 to 2009 in hourly resolution on a 1. 9 • reduced Gaussian grid has been created as auxiliary dataset for long-term satellite-based solar irradiance databases in order to replace monthly mean databases on a 4 × 5 • grid used so far. This dataset is validated against AERONET data in Europe, Africa and the Middle East which implies a validation in clear-sky conditions. This also fulfils the needs of solar energy for accurate AOD in clearsky situations, while aerosol loads in cloudy conditions are not interesting for this purpose.
This occurs also on the Arabian Peninsula, but is not observed at the Central African stations -these tend to underestimate at some locations. Case studies show that these overestimations result from cloud systems with dust uptake. The regional dependency of overestimations and the lack of such effects in other regions like Central Africa point towards deficiencies in the underlying erodibility databases. A post processing is empirically developed based on 2003 data to detect and eliminate these peak-like overestimations automatically. Suspicious dust events are removed if a strong temporal gradient in the dust component from day to day above 0. 7 is observed or if the AOD value is above 1. 0 with at least a 95 % dust contribution. Flagged values are replaced by linearly interpolated values and finally, a 3 days low pass filter is applied.
Extreme outliers are filtered out by this empirical procedure, but it has to be noted that dust events are replaced by low aerosol conditions. From the perspective of the solar sector being mainly interested in accurate values after such a cloudy period this is justified, but it is not appropriate for aerosol research especially in any dust mobilisation or aerosol-cloud interaction studies. In the Mediterranean, several stations in dust regions show a positive impact in cloud-free situations as validated with AERONET measurements, while other stations are less influenced. On the Arabian Peninsula and in Northern Africa, a positive impact can be observed at all stations. Frequently, AERONET data is missing due to the cloud-clearing scheme on those days with eliminated peaks. It can be speculated, that these peaks are realistic dust events in mixed dust/cloud conditions leading to a removal of the AERONET measurement by its own cloud detection.
Such problems are well known for AERONET measurements and dust validation studies and restrict the assessment of the exact amplitude of the dust peak itself in MATCH. Nevertheless, the remaining data after such events suggest that the peak amplitude is typically modelled too high or that the deposition after the event is modelled to low. In terms of RMSE the comparison of daily mean vs. hourly resolved modelling from nowadays state-of-the-art global modelling does not justify the use of an hourly resolved aerosol auxiliary dataset for solar energy applications. Nevertheless, it is assumed that more accurate daily means are obtained if the underlying dataset has a higher temporal resolution. This finding might change in future as a result of improvements of global aerosol modelling e. g. , for dust parameterisations, emission databases and data assimilation efforts currently on-going.
The MATCH model is evaluated also against the GACP database as the currently used aerosol climatology in many solar databases. Especially, the correlation coefficient of daily mean MATCH/DLR is significantly higher than for the GACP dataset quantifying the expected value higher resolved aerosol data. Correlation coefficients above 0. 7 are reached frequently while the use of a climatology results in much lower or even negative correlation coefficients at most stations. Nevertheless, it has to be noted that even if correlation coefficients are clearly increased by using a daily resolved dataset, they are still far from being sufficient. For many stations a linear correlation between modelled and observed values cannot be assumed -resulting in values of the correlation around 0. 4 and at its maximum around 0.
7. Some stations even show correlation coefficients below 0. 1 or even negative. It has to be noted that the solar energy community requires bias-free information together with a correct frequency distribution of hourly AOD in order to derive proper frequency distributions of irradiances. The accuracy of the individual hourly AOD value is less important in assessments of historical irradiance data for power plant site selection and design as long as the frequency distribution is met successfully. Therefore, any increased modelling capability of the observed variance is welcome. The MATCH/DLR dataset is currently under implementation for the SOLEMI irradiance processor which is a part of the European Commission's Global Monitoring for Environment and Security (GMES) programme. Finally, it can be concluded that several regions are modelled with a sufficient accuracy, while further investigations on dust emission schemes are recommended to increase the data quality on the Arabian Peninsula and for some Northern African stations.
Acknowledgements. Our thanks for the provision of ground measurements go to  Ana Maria Silva ,  Anatoli Chaikovsky ,  Andrew Clive Banks ,  Arnon Karnieli ,  Bertil Hakansson ,  Brent Holben ,  Christoph Kleefeld ,  Christophe Francois ,  Didier Tanré ,  Gian Paolo Gobbi ,  Gilbert Sappe ,  Giuseppe Zibordi ,  Hammad Ben-Chekroun ,  Jean-Francois Leon ,  Jens Boesenberg ,  Kevin Ruddick ,  Luigi Alberotanza ,  Maria Rita Perrone ,  Natalia Chubarova ,  Olavi Kärner ,  Paulo Artaxo ,  Peter Koepke ,  Philippe Goloub ,  Rachel T. Pinker ,  Roland Doerffer ,  Serge Despiau ,  Sergio Pugnaghi ,  Spyros Rapsomanikis , and  Victoria E. Cachorro Revilla  for their AERONET measurements. This work has been partly supported by the national projects SESK (Standardisation of energy yield assessments for solar thermal power plants) and Solar-Med-Atlas funded by the  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety in Germany  (contracts 032 50 84 A and  10 1 130 ) and extended during the  EU Seventh Research Framework Programme 's project  MACC (Monitoring Atmospheric Composition and Climate)  under contract  218793.
Microarray hybridization, data acquisition, and analysis Immunoprecipitated DNA and whole-cell DNA were amplified, labeled and hybridized to Agilent 44 k yeast whole genome oligonucleotide arrays as described  [33]. Microarray images were read using an Axon 4000B scanner and analyzed using the GenePix Pro 6. 0 software (Axon Instruments). Files were converted to text files and analyzed using the R software. The signal intensities of profiles were normalized, by dividing all values by the mean of the lowest 10% ratio probes of the array (decile normalization, as described  [24] ). In this way, the 10% lowest values fall below 1, so that everything below and around this value can be interpreted as background. The resulting normalized data were next denoised and smoothed, as described before  [23].
Raw data from  [33] ,  [3]  and  [24]  were reanalyzed as described before  [23]. Peaks were identified after denoising and smoothing with a 2 kb window (except for the data by  [24] , where a 300 bp window was used), and compared as described  [23]. In the set1D Zip3-Flag 6 and 7 hr ChIP-chip assays, a very high signal was obtained, and we adjusted the threshold to 5 to obtain a number of Zip3 peaks comparable to that of the other experiments. High Zip3 DSB sites were DSB sites that coincide with a Zip3 peak the signal intensity of which differed by less than 50 ranks from that of the DSB site; Low Zip3 DSB sites were DSB sites either not bound by Zip3 or that coincide with a Zip3 peak the signal intensity of which was at least 100 ranks lower than that of the DSB site. For the chromosome coordinates, we used the Saccharomyces Genome Database features (http://downloads.
Non-overlapping notches of two boxes are indicative that the two medians are statistically different. p value indicates the result of a Wilcoxon test between the two DSB populations. The rad50S and dmc1D DSB datasets are from [3]. Red1 binding data are from [24]. (C) Analysis of the indicated features at ''High rad50S'' or ''Low rad50S'' DSB sites (see details in the text). Boxplots as in (B). p value indicates the result of a Wilcoxon test between the two DSB populations. The rad50S and dmc1D DSB datasets are from [3]. Red1 binding data are from [24]. (D) Model for the role of the Low Zip3 DSB. Two alternatives are proposed for the function of the Low Zip3 DSB. On the left panel, Low Zip3 DSBs are used for homolog pairing, before the High Zip3 DSBs are chosen for crossover. On the right panel, High Zip3 DSB are formed first and are the ones designated for crossover, the Low Zip3 occur later and may be used in case not enough early DSB engaged in crossover (see Discussion).
Plant material The panel used in this study was composed of 168 traditional and improved japonica accessions (Table  S1 ). The accessions in the panel were mainly tropical accessions, with a few temperate accessions included for reference purposes. Two additional accessions, IR64, an improved indica variety, and Azucena, a traditional japonica variety, which are known to have contrasting root systems  [34] , were used as controls. Seeds of the accessions were obtained either from the Centre de Ressources Biologiques Tropicales de Montpellier or from the International Rice Research Institute (IRRI) gene bank (accession numbers in Table  S1 ). For each accession, the seeds were produced by single seed descent over two generations in a Cirad Montpellier greenhouse to ensure that the samples were homogeneous.