dataesr/scientific-paragraphs-categorization huggingface dataset:
Context and needs of the numerical atmospheric composition prediction community Numerical atmospheric aerosol prediction (NAAP) is still an activity in its infancy, born largely from the global climate and air quality communities. It is a sub-component of the larger and far more mature field of NWP, and as such, it is reasonable to expect that NAAP will follow the overall architecture and best practices set up by the NWP community. This includes, in particular, best practices in using and setting requirements for observational data. Just as there are requirements for radiosonde releases and weather station data transmission, one would expect similar considerations for parameters such as PM 10 (total mass of particles with an aerodynamic diameter of less than 10 µm), PM 2. 5 (total mass of particles with an aerodynamic diameter of less than 2.
5 µm), and other key parameters such as aerosol optical depth (AOD), extinction coefficient, mass concentrations of individual chemical components, and light scattering and absorption coefficients. To a large degree this type of data is already being collected in many countries around the world and intercalibration procedures are in place in existing surface networks. This said, it is acknowledged that even within the typical WMO meteorological feeds, there are differences in reporting practices among countries, longstanding biases in instrumentation deployed, and challenges to modernization (e. g. , in commercial radiosonde products;  Ingleby et al. , 2016). There are, however, a number of additional unique challenges facing the NAAP community that should be addressed and integrated into the development of relevant global aerosol data streams.
They are not meant to introduce more rigidity but rather should be interpreted for awareness and practicality. Given the early state of the field and diversity in development approaches and customer requirements at aerosol prediction centers, the community requires flexibility as it finds its way. Regardless of data type, whether in situ or from remote sensing, there are three guiding principles that should be considered. 1. Data should be easily accessible, publicly available, reasonably well documented, and, for baseline quantities, encoded into a similar format. Currently data distribution is diffuse and potential users have difficulty maintaining and evaluating global-scale data outside of the largest and most consistent networks (for example the NASA Aerosol Robotic Network (AERONET) sun photometer dataset;  Holben et al.
This statement of guidance is often called gap analysis as it shows whether the current observing system is suitable for the given application and what is needed in the future observing system in order for it to meet the requirements set out by the user community. To facilitate this process, the WMO maintains an online database on user requirements and observing system capabilities called Observing Systems Capability Analysis and Review Tool (OSCAR). Details on the RRR are provided in  Eyre et al. (2013)  and references therein. Recently, the WMO GAW set up an ad hoc Task Team on Observational Requirements and Satellite Measurements as regards Atmospheric Composition and Related Physical Parameters (TT-ObsReq, http://www. wmo. int/pages/prog/arep/ gaw/TaskTeamObsReq. html, last access: 12 July 2018) to review the user requirements specifically for atmospheric composition.
, 2003). Each relies on different dynamical cores, advection solvers, and aerosol microphysics schemes that necessarily generate a large degree of diversity among the various models (see for example  Ginoux, 2003). The range of horizontal and vertical resolutions across the models is also very diverse, as is inline versus offline architecture. In general, increasing resolution does not necessarily mean better model skills as it may request new tuning of parameters of subscale processes (e. g. , orographic gravity wave drag), as well as larger ensemble runs due to high variability. While all centers are pursuing data assimilation, four have multiple species data assimilation capabilities (namely ECMWF, FNMOC/NRL, GMAO, and JMA), while the Met Office has a dust-only system with data assimilation.
aemet. es/, last access: 16 July 2018;  Terradellas, 2016). Both initiatives have demonstrated that simply collecting different forecasts in a single database and generating web pages with common plotting conventions is an effective tool for developers to assess and improve their forecasting systems. Use of ensemble forecast techniques is especially relevant for situations associated with unstable weather patterns, or in extreme conditions. Ensemble approaches are also known to have more skills at longer ranges (> 3 days) for which the probabilistic approach provides more reliable information than a single model run due to the model error increasing over time. Moreover, an exhaustive comparison of different models with each other and against multi-model products as well as observations can reveal weaknesses of individual models and provide an assessment of model uncertainties in simulating the aerosol cycle.
Finally, the dust-focused satellite data should be complemented by improved spaceborne assessments of soil moisture, vegetation cover (green and brown), and soil mineralogy to better characterize varying conditions in source regions  (Kergoat et al. , 2017). For soil mineralogy, airborne and spaceborne spectroscopic mapping (such as DLR En-MAP and upcoming NASA-EMIT missions) provides a new resource to determine the relative abundance of the key dust source minerals with sufficient detail and coverage, but this resource has been virtually unexplored in the context of dust modeling.
g. , primary organic matter, POM; and black carbon, BC). Sulfates, nitrates, other inorganics, secondary organic aerosol (SOA), and BC are supplemented by emissions of key gases important for secondary aerosol particle production (e. g. , SO 2 , NO x , ammonia, isoprene, alkenes, aromatics, terpenes). These inventories are the result of large-scale land classification maps, fuel inventories, and transportation corridor databases. Individual source classifications vary by study author but often include power production, heavy industry/smelting, biofuels, mobile sources, road dust, agricultural field emissions, agricultural-domestic stack and burn piles, and plant emissions of species such as isoprene and terpenes. We classify larger open biomass burning, including agriculture field burning, as distinct.
Several real-time smoke forecasting products exist and are related to satellite-based active fire hot spot or burn area databases. The most established global aerosol forecasts are represented in the International Cooperative for Aerosol Prediction (ICAP). Four models include dedicated smoke treatment: CAMS (ECMWF and partners), MASINGAR (MRI-JMA), GEOS-5 (NASA), and NAAPS (U. S. Navy), for which the first two use emissions from the Global Fire Assimilation System (GFAS;  Kaiser et al. , 2012; Di Giuseppe et al. , 2017 , 2018) , the second from a similar Quick Fire Emissions Dataset (QFED;  Darmenov and da Silva, 2013)  based on fire radiative power (FRP), and the last from the hotspot-based Fire Locating and Modeling of Burning Emissions (FLAMBE) system  (Reid et al. , 2009a). Currently most models scale biomass burning emissions to reach acceptable values of biomass burning aerosol optical thickness close to observations (MODIS or AERONET).
, 2010; Martin et al. , 2012; Sofiev et al. , 2012). In some extreme cases, fire emissions can even reach the upper troposphere-lower stratosphere region  (Fromm et al. , 2006). The height in the atmosphere at which this occurs is often referred to as the injection height. An observational dataset of injection heights exists through the MISR Plume Height Project (MPHP;  Nelson et al. , 2013) , based on a combination of MISR smoke aerosol and MODIS thermal anomaly products. This dataset has recently been updated and extended to produce the MPHP2 dataset. These observations have been very useful in calibrating and/or evaluating global biomass burning emissions injection height datasets  (Sofiev et al. , 2013). Satellite products that can provide, in NRT, an estimate of this injection height would greatly help in accurately forecasting large biomass burning events.
It should be noted that improving the forecast of precipitation remains a major challenge for the NWP. Inaccuracies of the precipitation forecast directly influence the quality of the aerosol forecasts. Improving the surface information can be achieved by better linking NAAP to advanced land-surface modeling and by updating to the most recent land-use datasets. Observations of wet deposition fluxes are available from acid deposition networks. These observations could be used to evaluate wet deposition of soluble aerosols such as sulfate, nitrates, and ammonia. For this purpose, the observations need to be made available in a timely manner and at a temporal resolution suited for NAAP evaluation, which is often higher than the frequency (i. e. , annual means) required for impact monitoring. Finally, the observed wet deposition fluxes are often strongly influenced by local processes, which makes it necessary to filter the observation in such a way that they are representative of the scale resolved by the NAAP models.
In particular they reviewed the current state of global measurements (ground based and satellite), chemical transport modeling (global and hemispheric), and measurement-model fusion and mapping techniques (see GAW Report 234, at https:// www. wmo. int/pages/prog/arep/gaw/documents, last access: 18 July 2018). 4 Data assimilation for aerosol prediction 4. 1 General concepts Data assimilation systems have become an important component of aerosol prediction for both gas-and particulate-phase species. Several global and regional models currently provide analysis of gases and/or aerosol particles. Currently five centers routinely assimilate aerosol data into their models, ECMWF CAMS, FNMOC-NRL, GMAO, JMA, and the UK Met Office. As an example among others, the CAMS system incorporates retrieved observations of ozone, CO, SO 2 , NO 2 , and AOD in its analysis in order to provide initial conditions for the prediction of these species.
, 2010) , providing many NRT composition, weather, and surface products at ∼ 3 h latency (such as AIRS, MISR, MLS, MODIS, MOPPIT, or OMI). Expedited products (∼ 24 h) are also available, for example, from the CALIPSO program. Future aerosol-related lidar missions such as EarthCARE and Aeolus, are now establishing best-effort NRT data delivery, following the example of LANCE expedited products. This has also been made possible by the fruitful collaboration between modeling commu-nity and data provider, in an effort to make an optimal use of the resources and provide the best service to the end users. As discussed previously, most aerosol assimilation systems at the moment rely on products such as AOD, rather than raw measurements such as satellite radiances. However, the tendency in the future will likely be towards the use of satellite radiances, either raw or aggregated and possibly cloud-cleared, for consistency with the current approach in NWP.
g. , due to the similarity in surface properties or viewing geometries). Additionally, other information is deemed necessary for the correct assimilation of the observations, such as averaging kernels for chemical species. Moreover, retrieval errors should be required to stay below a certain threshold in order to make the cut for assimilation. 2. Biases should be quantified and, where possible, corrected before data provision for assimilation  (Zhang and Reid, 2006). Even sophisticated assimilation systems with online bias correction struggle with aerosol observations as there is limited redundancy at the moment and no single satellite sensor can be used as an absolute reference as they all suffer from biases. Ground-based lidars and sun photometers are currently being investigated to provide a bias-free anchoring for satellite data or as a dataset to be assimilated in their own right  (Rubin et al.
Spatial resolution and sampling The requirement on spatial resolution of the observations needed for assimilation is quite relaxed due to the fact that current global assimilation for operational aerosol prediction cannot afford to run very high-resolution analysis. For this reason, even data with coarse spatial resolution (100 km) can be beneficial. However, in most cases, current satellitebased sensors have a much better spatial resolution down to a few kilometers for passive sensors and a few hundred meters for active sensors (depending on the application). Spatial sampling is possibly more important than resolution for assimilation. It has been shown that assimilation of an instrument with large spatial sampling (wide swath) such as MODIS is more beneficial than assimilation of highly accurate measurements from a passive sensor with a narrow swath.
Temporal resolution The issue of temporal resolution is similar to that of spatial resolution. In principle highly temporally resolved data are beneficial to the analysis, particularly because they provide information on the diurnal aerosol variability. However, issues connected to large data volume may arise. This is particularly true for datasets coming from geostationary satellites, which now provide data with a temporal resolution of 10-15 min. In some cases, such data have to be heavily thinned or averaged  (Saide et al. , 2014). This is obviously only a technical limitation that might not be applicable across the range of assimilation systems. For example, the new generation of Japanese geostationary satellites, Himawari 8-9  (Bessho et al. , 2016)  provide excellent data that have been demonstrated to be of use for data assimilation  (Yumimoto et al.
This can be particularly challenging due to the paucity of radiosondes and wind profilers in this region as well as the limitations of atmospheric motion vectors (AMVs) over non-cloudy regions. To this end, the wind observations which will be collected by the Doppler wind lidar AL-ADIN onboard the Aeolus satellite, to be launched in August 2018, will be a welcome addition. 3. Surface temperature and dew point help to better constrain soil moisture in the top soil layer in most data assimilation systems. This can be particularly important for dust from semiarid areas like the Sahel as well as East Asian semiarid areas, where seasonal soil moisture and vegetation can be a major factor for uncertainties.
Resilience Several data sources are needed to ensure resilience of the system and a wealth of observation-based information. Currently most centers rely on satellite data for the analysis of aerosol particles. The next generation of satellite measurements are designed to provide more information on the horizontal and vertical distribution of atmospheric particulate, but current efforts often focus on trace gases, while aerosol products are often considered secondary. It is important to consider that some satellites currently providing vital information for aerosol assimilation are coming to the end of their lifetime (for example MODIS). It will therefore be crucial that there are concerted efforts to replace such instruments and insure continuation of data provision and long-term consistency of the records.
In the case of MODIS, VIIRS data play exactly that role. For spaceborne lidar instruments such as CALIOP, however, the situation is less clear as, at the moment, there is no single mission aimed at replacing it when the CALIPSO satellite will be decommissioned. The same is true for advanced multi-angle imaging spectroradiometers such as the MISR instrument. Frequent instrumental changes may cause problems for data uptake and recalibration of bias corrections, impinging as well on the quality of the forecast products. Efforts are also under way to use ground-based and aircraft measurements.
In the present section, we will focus on the evaluation conducted as part of the implementation of an operational forecast. For operational purposes, it is important that these observations are delivered timely and on a regular basis, to ensure the possibility of a routine evaluation. As pointed out in Sect. 2, in addition to aerosol measurements it is also important to include meteorological and chemical observations in the model evaluation process to complement and understand the resulting aerosol predicted fields. Moreover, taking into account that there are operational forecasting systems with data assimilation, it is important to include independent observational datasets (not used during the data assimilation process) in the model evaluation. Referring to the requirements of observations as outlined in the introduction (i.
e. , ease of access and consistency, uncertainty, and speed of delivery), globally consistent and available datasets such as for AOD from AERONET or NASA satellites by default currently drive the evaluation process and consequently model development. AERONET's ability to provide high accuracy of fine-and coarse-mode AOD data over the globe with typical preliminary data availability within 6-24 h makes it a favored metric variable  (Sessions et al. , 2015). Likewise, the maturity, coverage, speed, and ease of access of MODIS aerosol retrievals makes MODIS AOD retrievals the dominant satellite verification product (as discussed in Sect. 4 favored for data assimilation as well). This dominance of AOD to some degree is because of the exclusion of perhaps more applicable baseline variables not meeting the noted observational requirement, such as PM 2.
5 , and PM 1 ). They also provide an evaluation of the aerosol size distribution on surface level. For the atmospheric column, the AE (which provides aerosol size information) and the separation of AOD into fine-mode and coarse-mode contributions can be used to evaluate the aerosol size distribution. Additionally, since datasets of weather surface records have better spatial and temporal coverage, observations of horizontal visibility included in meteorological reports are used as an alternative way to monitor aerosol events in NRT and to qualitatively evaluate the aerosol forecasts. In addition, key meteorological variables such as surface winds (linked to emissions of natural aerosols) and precipitation (linked to wet deposition) should be considered.
Conclusions Numerical atmospheric aerosol prediction is at a crossroads. It has experienced quick progress in recent years due to the availability of aerosol models, aerosol satellite observations, data assimilation techniques, and the know-how of numerical weather prediction. This paper takes stock of past achievements and reflects on how further progress can be made with a focus on user requirements for aerosol measurements in the context of operational prediction. Requirements are discussed in relation to modeling, assimilation, and evaluation and concern resolution, sampling, accuracy, and timeliness of the observations. However, it was felt that no hardline requirements can be set up in terms of goal, threshold, and breakthrough values given the relative youth of NAAP. Rather, this study aims at developing the needs of a new community and establishing scientific criteria based on which those values can be defined at a later stage.
At this moment, there is a more pressing need to recognize that measurements of aerosol particle properties are not only a "nice-to-have" element in operational and observational ground-based research networks and spaceborne platforms, but they are instead an important and necessary part of the Global Observing System. Further improvements to NAAP will likely follow several directions, including better representation of aerosol processes. This will require pitching the right level of complexity (especially in terms of chemical speciation) and obtaining the best possible meteorological information from NWP and the relevant aerosol measurements to calibrate and evaluate aerosol parametrizations. improved data assimilation, in terms of both technique and choice of aerosol variables to be assimilated.
Key questions for the future are whether there is a benefit to move from assimilating AOD to assimilating clear-sky radiances in the shortwave spectrum and how to make the best possible use of vertical profiles from lidar observations. better aerosol data fueled by a stronger integration of NAAP with aerosol data providers and clear presentation of user requirements. NAAP ought to better consider the issue of aerosol speciation and aerosol size distribution in aerosol modeling and data assimilation and verification. Concerning aerosol requirements, we recommend the following stepwise approach. The community should start with a better quantification of requirements for total mass, chemical speciation, and size distribution at the surface with the aim to improve emissions and boundary layer processes.
As found in T. melanosporum, glycoside hydrolase 6 (GH6) and GH7 are absent from the T. borchii genome, confirming that ectomycorrhizal species have a reduced content of plant cell wall-degrading enzymes  (5, 8). The haploid genome of T. borchii Tbo3840 exhibits the mating type locus MAT1-2-1 with HMG box (protein identification [ID] 1092840); the other mating type locus was not detected, as expected for a heterothallic species  (6, 17). Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number NESQ00000000. The version described in this paper is version NESQ01000000. All of the sequences are also available at the JGI-DOE Mycocosm portal (13) (http://genome. jgi. doe. gov/Tubbor1).
ACKNOWLEDGMENTS The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , was supported by the  Office of Science of the U. S. Department of Energy  under contract  DE-AC02-05CH11231. The  UMR1136  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE ).
15 The LAI measures were carried out in the sites previously installed in 1984 and monitored till 1994 by the International Livestock Centre for Africa (ILCA) and by the Institut d'Economie Rurale (IER, Bamako)  (Hiernaux et al. , 1984; Hiernaux et al. , 2009a; 2009b) , and reactivated by the AMMA-CATCH observing system during the AMMA project  (Redelsperger et al. , 2006). These 1 km x 1 km sites were chosen within large and relatively homogeneous areas, to sample the main vegetation types and canopies encountered within the super-site: herbaceous canopies on sandy soils, seasonally inundated Acacia open 20 forest and its understory herb layer in a clayed-loamy plain, and a seasonally inundated herbaceous canopy in a clayed loamy plain. In addition, a few data were acquired at an erosion surface site and in a crop millet field, for comparison purposes.
At the Acacia forest site, the camera was mounted on 5 a levelled tripod, and orientated upward at a height of approximately 0. 8 m above the forest floor and maintained horizontal thanks to bubble-levels. Prior to the field campaigns, the camera was calibrated using the method described in http://www. avignon. inra. fr/can_eye, to compute the optical centre of the camera-fisheye lens system  (Demarez et al. , 2008). The collected hemispherical pictures were analysed using the image processing software CAN-EYE V5. 0 which is dedicated to the classification of green and non 10 green vegetation elements, and to series of photographs  (Baret and Weiss, 2004). The CAN-EYE software provides LAI estimates from gap fraction measurements derived from the hemispherical photographs (Figure  2b, 2d ), and has proved to be efficient to derive the vegetation variables of herbaceous  (Demarez et al.