dataesr/scientific-paragraphs-categorization huggingface dataset:
The 3-10day synoptic time scale (white bars) corresponds to about 50% of the observed intraseasonal variance. All models reproduce this amount at an accuracy of 610%, indicating that they are likely able to simulate AEW-like variability  (Ruti and Dell'Aquila 2010). However, even though convection can be organized at the synoptic or intraseasonal scales, most of summer rainfall over West Africa is provided by a very few heavily precipitating mesoscale convective systems  (Mathon et al. 2002). As a consequence, precipitation is highly intermittent from day to day and has very little persistence over the Sahel. Consistent with FIG. 16. Autocorrelation of (a) JAS and (b) December-February (DJF) 1-90-day filtered precipitation at a 1-day lag for the GPCP daily dataset. Only grid points where the mean precipitation was.
1 mm day 21 were considered. (c) Autocorrelation of 1-90-day filtered precipitation at a 1-day lag for GPCP and CMIP5 models averaged over the domain (58-158N, 108W-108E). Autocorrelation was computed for each JAS season for 1997-2008 for  GPCP and 1979-2008  for CMIP5 model, and then averaged over all years. CMIP5 models and the GPCP dataset were regridded on the NOAA OLR grid before any computations. the fact that models have difficulties to represent such convective systems [see also section 5b(1)], none of them captures the very high-frequency (1-3 days, gray bars) proportion of almost 30%. Even the high-resolution GFDL-HIRAM-C180 and -C360 runs reach only 20% in this band. This notion of persistence can be quantitatively characterized by the autocorrelation function of precipitation. Using it,  Lin et al.
2009). South of the ITCZ, the contribution of propagating systems decreases to 50%  (Fink et al. 2002; Depraetere et al. 2009) , and a more common late afternoon peak arises. This bimodal structure observed here is consistent with the secondary nighttime peak emerging from global datasets  (Yang et al. 2008 ). To our knowledge, no model explicitly includes a proper representation of propagating mesoscale convective systems such as squall lines, so that they are not expected to capture the diurnal cycle of precipitation over Niamey. Indeed, Fig. 17  illustrates that the precipitation distribution of the models is qualitatively very similar between the two sites. The variations between the two sites are related to a more seldom occurrence of rain events at higher latitudes. This accounts for the differences in the distribution of the two IPSL models.
Summary and conclusions In this paper, we present a comprehensive analysis of the representation of the West African monsoon in the recently available CMIP5 simulations of both presentday and future climates. The model behavior over the Sahel region is examined across a range of time scales, going from climate change projections and multidecadal and interannual variability to the intraseasonal and diurnal fluctuations. A specific emphasis is put on the use of a comprehensive set of observational data now available (in particular AMMA and satellite data) to evaluate the WAM representation across those scales. CMIP5 climate change projections in surface air temperature and precipitation are found to be very similar to those of CMIP3. A robust tendency to warming over the Sahel is noticed (about 4 K on average in the RCP8.
Although encouraging progresses have been achieved, many systematic and robust biases of the coupled and atmospheric models have not improved from CMIP3 to CMIP5. This weakens our confidence in climate projection over West Africa, and even beyond over remote regions such as the Pacific (e. g. ,  Ding et al. 2012). A large program aiming to address these systematic biases needs to be designed by the research community, under the umbrella of international programs. The observational datasets, acquired with AMMA and more recent programs such as Fennec  (Washington et al. 2012) , should be a backbone of these efforts. The results of the present study point to the need to separate as much as possible the issues related to slow and fast physical processes. Many systematic errors appear rapidly and could be addressed with numerous shortduration numerical experiments based on observed case studies and high-resolution modeling results.
Acknowledgments. Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from  France , the  United Kingdom , the  United States , and  Africa , and by a  European Union program. Detailed information on scientific coordination and funding is available on the AMMA International web site http://www. amma-international. org. We wish particularly to thank the principal investigators who make the various AMMA-CATCH groundbased measurements data sets available to the AMMA data base:  S. Galle ,  C. Lloyd ,  B. Cappelaere ,  F. Timouk , and  L. Kergoat  for the surface radiation measurements, and  T. Vischel ,  M. Gosset ,  G. Quantin , and  E. Mougin  for the rain gauge network measurements. The PIRATA buoy measurements were provided by  TAO  Project Office of  NOAA/PMEL.
Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, is expected to be launched in late 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution, in order to gain new insights into the Earth system by improving our understanding of the Earth's interior and environment. In order to derive advanced models of the geomagnetic field (and other higher-level data products) it is necessary to take explicit advantage of the constellation aspect of Swarm. The Swarm SCARF (Satellite Constellation Application and Research Facility) has been established with the goal of deriving Level-2 products by combination of data from the three satellites, and of the various instruments.
g. the magnetic field measurements taken by each of the three Swarm satellites. These Level-1b data, as well as the higher-Level Swarm data products described in this paper, will be distributed by ESRIN (Frascati/I). Swarm will simultaneously obtain a space-time characterisation of both the internal field sources in the Earth and the ionospheric-magnetospheric current systems. The research objectives assigned to the mission are: (a) studies of core dynamics, geodynamo processes, and core-mantle interaction; (b) mapping of the lithospheric magnetisation and its geological interpretation; (c) determination of the 3-D electrical conductivity of the mantle; and (d) investigation of electric currents flowing in the magnetosphere and ionosphere. A challenging part, however, is the separation of the var-ious sources (in the core, lithosphere, ionosphere, magnetosphere etc.
However, if explicit advantage is taken of the constellation, there is some potential for model improvement better than √ N. A constellation of three satellites can do more than three single satellites, and therefore a (smart) combination of data from all three satellites, and of the various instruments, allows for taking full advantage of the Swarm constellation. Analysis of the Swarm data will greatly improve existing and provide new models of the near-Earth magnetic field of high resolution and authenticity compared to what is possible with singlesatellite missions like Ørsted  (Olsen, 2007)  and CHAMP  (Reigber et al. , 2005). In recognition of the large effort needed to extract the various types of scientific information from the complex set of observations a group of institutions and organisations have joined the SMART consortium (Swarm Magnetic and Atmospheric Research Team).
The purpose of the consortium is to contribute to the optimal science return from the Swarm mission by a coordinated effort to exploit the constellation aspects of this unique mission. This effort is obviously in accordance with ESA's aim of providing the scientific community with the best possible products from the Swarm mission, and it was decided to establish a Swarm SCARF (Satellite Constellation Application and Research Facility), with the purpose of deriving commonly used scientific models and quantities, the so-called Level-2 products and make them available to the scientific community at large. Advanced Swarm-derived models of the geomagnetic field and other Level-2 data products are determined from the Level-1b data and auxiliary (i. e. non-Swarm) data and provide the prospect of investigating hitherto undetected features of the Earth's interior.
SCARF (sometimes also called "Level-2 Processing System", L2PS) comprises in its present form a joint effort between the six European partners: DTU (Lyngby/DK), TU Delft (Delft/NL), BGS (Edinburgh/GB), ETH (Zürich/CH), GFZ (Potsdam/D) and IPGP (Paris/F) with contributions from CUP (Prague/CZ), NOAA (Boulder/USA) and GSFC/NASA (Greenbelt/USA). The team behind SCARF has designed and implemented algorithms to derive advanced models of the geomagnetic field describing sources in the core, lithosphere, ionosphere and magnetosphere, models of the electrical conductivity of Earth's mantle and time series of thermospheric wind and density at the positions of the Swarm satellites. These models, which are state-of-the-art implementations of current knowledge, are intended to facilitate and increase the use of the Swarm data by a much wider community than the one represented in the SMART consortium itself.
The work performed by SCARF is a major extension on the "End-To-End" mission simulation that has been performed during Phase A of the Swarm mission, the results of which have been published in a special issue  (Vol. 58 No. 4, 2006)  of Earth, Planets and Space (cf. Olsen et al. , 2006  for an overview). The present paper describes the Swarm input data products (Level-1b and auxiliary data) used by SCARF, the various processing chains of SCARF, and the Level-2 output data products determined by SCARF and distributed by ESA through the PDGS (Payload Data Ground Segment) at ESRIN. The content of the paper is as follows: Section 2 summarizes the various Level-1b data, with emphasis on the 1 Hz time series of the magnetic field observations. Section 3 describes the various processing chains and resulting Level-2 data products.
Swarm Level-1b Products The Level-1b products of the Swarm mission contain time-series of quality-screened, calibrated, and corrected measurements given in physical, SI units in geo-localized reference frames. Level-1b products are provided individually for each of the three satellites Swarm A, Swarm B, and Swarm C on a daily basis, i. e. each product contains all available data of that day from 00:00 until (but not including) 24:00 UT time. An example illustrating the filenaming convention of Swarm Level-1b 1 Hz magnetic product MAGx LR from 23. February 2013 is given in Fig. 1. Table  1  lists the various Level-1b data products. Ephemeris Products are data obtained by the GPS receiver, the accelerometer and the star tracker; the Magnetic Products contain measurements taken by the two magnetometers (augmented with position and attitude measured by the star tracker), while the Plasma Products contain data obtained by the Thermal Ion Imager and the Langmuir probe.
Electromagnetic transfer functions (C-responses) are also provided (Level-2 products MCR 1DM 2 and MCR 3DM 2 ). 3. 2 Level-2 products related to acceleration, orbit determination and thermospheric wind and density Precise reduced-dynamic and kinematic orbit solutions for the three Swarm satellites are provided in the Level-2 products SP3xCOM 2 and SP3xKIN 2. As part of this POD processing chain also the accelerometer calibration parameters (product ACCxCAL 2 ) as well as nongravitational accelerations (ACCxPOD 2 ) are determined. Time series of thermospheric winds and density at the location of each of the three Swarm satellites are provided in the Level-2 product DNSxWND 2. Details of the processing scheme are given in  Visser et al. (2013). The data flow of the two chains that result in these Level-2 products is shown in Fig.
(2013). The processing schemes resulting in time series of Field-Aligned Currents (FAC) as provided in FACxTMS 2F (single satellite solution), resp. FAC TMS 2F (obtained by combining data from Swarm A and B) are described in  Ritter et al. (2013). Dayside Eastward Equatorial Electric Field (EEF) values are derived for each equatorial crossing of each satellite (x = A, B, or C) and are provided in the product EEFxTMS 2F. More details on that chain are given in  Alken et al. (2013).
Swarm Processing Time-line and Data Availability The processing of Swarm data into Level-1b and Level-2 products is generally performed as soon as the appropriate input data are available. As baseline, data from the Swarm satellites themselves are downlinked on a daily basis (a more frequent downlink, in particular for making Swarm data more suitable for space-weather applications, is under consideration), however, the various processing steps may require certain auxiliary data inputs and/or a significant time-span of Swarm input data in order to produce high quality output products. Consequently the Swarm data processing time-line is as follows: • Three days (72 hours) after downlink Swarm Level-1b data are processed All estimated models are subject to an evaluation andwhen parallel models are available-cross-comparisons which will be documented in the Swarm Level-2 Validation Products (Myy VAL 2 ) with a processing time of up to one month.
The data incorporated in the HITRAN database for 14 NH 3 are analyzed using a comprehensive and consistent set of quantum numbers, empirical lower energy levels and the BYTe variationally calculated line list as reference points. Labelings are checked to ensure that they obey both the usual selection rules and the HITRAN labeling formalisms; the problems identified are corrected where possible. Further assignments are brought into question by combination difference (CD) checking of implied upper energy levels. The CD analysis yields an 89% complete 14 NH 3 energy level list up to 6610 cm -1 and J ¼21, selfconsistent to 0. 1 cm -1. In combination with the 1723 previously unassigned and unlabeled lines in HITRAN a total of 2529 problem lines were identified for re-analysis. The compiled energy level list was used to label and assign the set of problem transitions, resulting in a total of 249 new assignments and a further 368 new labelings.
However, the actual ammonia emission budget remains uncertain, perhaps by up to 50%  [4]. In particular its relatively short atmospheric lifetime and the large variability in its emissions means that any attempt to monitor ammonia concentrations should use high spatial resolution. There have been a number of attempts to use spectroscopy to monitor atmospheric ammonia concentrations Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/jqsrt from space, such as the measurements reported from the IASI/MetOp  [5] , MIPAS  [6]  and TES  [7]  satellite experiments. These remote sensing applications are heavily dependent on the availability of reliable laboratory data for ammonia. However, the available laboratory spectroscopic data are not entirely satisfactory. For example, a very recent intercomparison of eleven ammonia measurement techniques  [8]  identified the quality of gas phase standards as one of the three significant issues affecting the precision of the measurements.
Ammonia is also known to be present in both gas giants  [9]  and cool brown dwarfs  [10, 11]  where it provides an important source of opacity. Its presence has also been predicted in exoplanets  [12, 13] , but is yet to be detected. The room temperature spectroscopy of ammonia is encapsulated in the HIgh resolution TRANsmission molecular absorption database (HITRAN)  [14]. Recent editions of this database incorporate 29 084 predominantly experimental transition frequencies and intensities for two isotopologues. In most case labelings are also given using the normal mode convention for vibrational states and the usual rigid-rotor convention for rotational states. These data come from the 10 sources  [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]. However, some of the data in HITRAN has not changed since the 1986 edition and recent experimental  [25] [26] [27] [28] [29]  and theoretical studies of the spectrum of ammonia  [30] [31] [32] [33] [34]  suggest problems with this compilation.
In particular, a number of new measurements  [25] [26] [27] [28]  identified problems with line intensities in the ν 2 band of the HITRAN data and Chu et al. [29]  report new accurate intensities for the 2ν 2 -ν 2 band which raise further issues. Whilst usually considered of secondary importance to the positions and intensities, correct assignment of upper and lower energy levels to observed transitions is imperative in the previously mentioned applications due to the Boltzmann factor in the transition intensity expression  [35]. Hence, complete and accurate assignment of energy levels to experimental spectra is vital. However, this is not found to be uniformly true in HITRAN, for which several assignment issues have been identified. To achieve the goal of improved assignment it is usual, although not strictly necessary, to adopt some approximate set of quantum numbers to label the energy levels in some systematic manner.
Labels, in either local modes or the ordinary normal mode quantum numbers, must be estimated via expansion of the wave-function. However, although such labels may be approximate in either convention, the calculations employ only rigorous quantum numbers and automatically account for all resonances. Experience has shown that use of empirically corrected frequencies with ab initio dipole moments is an effective method of predicting reliable transition intensities  [40, 41]. In this paper we review the current contents of the HITRAN database. The only lines we propose deleting from the HITRAN data are those 51 lines belonging to 2ν 2 -ν 2 we consider to be unreliable (see below). Section 8 describes the addition of many more new lines produced using a combination of empirical energy levels and BYTe intensities in the 2ν 2 -ν 2 , ν 2 þ ν 4 -ν 4 , ν 4 -ν 2 , ν 4 -ν 4 and 2ν 2 -2ν 2 hot bands, which leads to a final line list with an increased total size of 40 224 lines.
HITRAN makes use of only a single signed l¼l 3 +l 4 value. This gives rise to problems in the ν 3 þ ν 4 band where the translation between these two systems is not well defined for l¼ 0. States with l ¼0 have two symmetry levels, A′ 1 and A′ 2 respectively (see above). Furthermore HITRAN uses a l ¼-1, 0, +1 labeling for this band rather than l ¼-2, 0, +2. We now turn to the actual line data in HITRAN. First it was found that a total of 933 lines of 3ν 2 =ν 2 þ ν 4 in the 2150-3100 cm -1 region taken from Kleiner et al. [18] , and ν 3 þ ν 4 =ν 1 þ ν 4 in the 4790-5295 cm -1 region taken from Brown and Margolis  [19] , had conflicting assignment of inversion symmetry labels. This label is over-specified in the format adopted by HITRAN and the problem occurs when the rotational and vibrational quantum labels carry conflicting inversion symmetry labels.
Whilst the 2ν 2 frequency data of Cottaz et al. [20]  are accurate up to about 10 -4 cm -1 , the 2ν 2 -ν 2 data  [15]  are quoted with accuracy greater than 1 cm -1. Furthermore, the 2ν 2 -ν 2 data are only referenced to the HITRAN 1986 database  [15]  and it was not therefore verifiable by comparison with an original source. As a result upper energy levels were derived from the 2ν 2 band where possible, and 2ν 2 -ν 2 lines from HITRAN 1986 not consistent with these values to within 1 cm -1 were not used in the analysis below. However, upper levels belonging to only 2ν 2 -ν 2 transitions were retained at this point. Since all other CD upper levels occurred in bunches, it was possible to resolve all remaining problems by a judicious choice from two distinct sets of energy levels in each case.
[32]  to unassigned HITRAN data are also found by our work. These were in general agreement with our assignments although our BYTe derived labels differ in some cases. Out of these lines 18 (associated with five upper levels) required significant relaxation of the error margin used to achieve CD assignment. In some cases upper labels derived from BYTe also differed from Huang et al. 's labels in the manner previously discussed. 5. Intensity ratios for the ν 2 band at 296 K: comparison of BYTe  [34]  and new measurements  [25] [26] [27] [28]  with HITRAN  [15]. Lines are grouped by symbol according to lower state symmetry (a/s). In general HITRAN intensities are found to be too weak for transitions from a symmetry lower levels. Dashed lines indicate upper and lower limits according to a HITRAN uncertainty estimate of 15%.
Intensity comparison Comparisons of HITRAN intensities with BYTe and recent experimental values were carried out using quoted experimental errors and HITRAN error estimates. In general agreement was within these given errors, with three notable exceptions. Firstly, the microwave and ν 2 far infrared data  [24]  were found to be very noisy for absolute intensities weaker than about 10 -24 cm -1 /(molecule cm -2 ) at 296 K. Furthermore, intensities from these sources were also systematically shifted, in some case by more than the estimated HITRAN uncertainty (15%) as seen in Fig. 5. The structure visible in general corresponds to transitions with a/s lower level inversion symmetry respectively, for the relatively weaker and stronger bunches of lines. As further validation, ν 2 intensities were compared with the recent and more accurate experimental intensities available for the ν 2 band due to Fabian and Yamada  [26] , Guinet et al.
Finally, as a measure of completeness, band strengths for each of the vibrational bands included in HITRAN were compared with BYTe calculations for both previous and updated HITRAN data sets. The values are compared in Table  7 , alongside the number of transitions included in each summation. For BYTe the band strengths included all lines with relative intensity stronger than 1 Â 10 -12 times the strongest line (5. 516 Â 10 -19 cm -1 /(molecule cm -2 )), in the 0-8000 cm -1 range. It is important to note that since these band strengths depend on vibrational labeling, interacting bands and bands for which BYTe labeling is considered unreliable may be better treated as a combined summation. It is easy to use Table  7  to determine the regions where HITRAN is lacking. In particular the 2ν 2 -ν 2 and 2ν 4 -ν 2 bands appear to lack significant intensity.
Due to a lack of upper levels this was not necessarily possible for all lines. The number of lines produced and the band strengths for the updated line lists are included in Table  7. In the case of 2ν 2 -ν 2 the 10 experimental intensities of Chu et al. [29]  were used to further improve the line list. The result was used to replace the HITRAN data whilst retaining previous HITRAN values for the air broadening, self-broadening, pressure shift, and temperature dependence values. This led to removal of 51 HITRAN lines which are considered unreliable. There were also 1416 lines new to HITRAN, for which, self-broadening, pressure shift, and temperature dependence data were not available. A comparison of the resulting line list and the previous HITRAN data are seen in Fig. 7. As discussed above the new intensities are seen to be approximately twice the strength of the previous HITRAN intensities.