dataesr/scientific-paragraphs-categorization huggingface dataset:
The GOME-2 and OMI inversions decrease the fire emission by factors of 2 and 2. 5, respectively (Fig. 12 ). Even stronger decreases (factor of 3) are found over northern Bolivia and central Amazonia (Fig. 9 ). These emission reductions are supported by comparison with CO columns observed by IASI  (George et al. , 2009). The use of fire emissions from GFEDv3 leads to strongly overestimated CO columns in comparison to IASI observations in August 2010 (Fig. 15 ), reaching almost a factor of 2 over western Amazonia. Significant improvement in the model-data match is achieved when the emission reduction inferred by the OMI-based inversion is implemented and applied not only to NMVOCs but also to other compounds including CO. The GFEDv3 emissions of CO in 2010 were also found to be substantially overestimated, by a factor of ∼ 1.
12 ). Isoprene fluxes over Amazonia derived by GOME-2 and OMI inversions are equal to 92. 5 and 73. 7 Tg, respectively. These re 25 and 7 % lower than the prior and in good agreement with previous studies using satellite HCHO observations from the SCIAMACHY instrument  (Stavrakou et al. , 2009b). The seasonal variation of the posterior fluxes is consistent with the a priori inventory, except during the transitional wet-to-dry period (April-June) with both GOME-2 and OMI satellite data sets pointing to a significant flux decrease of ca. 25 % (Fig. 13 ). This behavior confirms previous comparisons using GOME HCHO observations suggesting that factors other than the temperature influence the observed variability  (Barkley et al. , 2008) , such as the growth of new leaves causing a temporary shutdown of the emissions  (Barkley et al.
Indochina The northern part of the Indochina Peninsula (primarily Myanmar, also Assam in India and parts of Thailand) faces intense forest fires during the dry season, as very well seen in the GOME-2 and OMI HCHO time series, with values reaching 15 ×10 15 molec cm -2 in March, about 3 times higher than in the wet season (Fig. 7 ). Both the GOME-2 and OMI data point to substantial but very contrasting updates in the pyrogenic fluxes during the fire season (March, Fig. 9 ): flux reductions by a factor of 2-5 over Myanmar and surrounding forested areas, and flux increases by a factor of almost 2 (or more in the case of OMI) over the southeastern part of the peninsula, which includes Cambodia, southern Laos and southern Vietnam. In the OMI-DE inversion assuming doubled errors on the a pri- ori fluxes, the updates are even more pronounced and reach a factor of 4 over parts of Vietnam and Laos.
The annual emissions in the entire region are decreased by 15 and 26 % according to the OMI and GOME-2 inversions, respectively. As illustrated in Fig. 16 , the optimization leads to a substantial improvement of the model performance, although the HCHO columns remain significantly underestimated (by up to 20 %) in the southern part of the peninsula (e. g. , Cambodia), most likely due to a too strong underestimation of the GFEDv3 emissions used as a priori in the model. The emission drop over Myanmar and the need for higher emissions in the southeast are partly confirmed by comparisons with IASI CO columns. Indeed, as seen in the lower panel of Fig. 16 , modeled CO simulations using biomass burning fluxes optimized using OMI data (i. e. , reduced by ca. 26 % in March relative to GFEDv3) display a better agreement with the observed CO columns, despite an underestimation by ∼ 10 % over most of the peninsula.
43 TgC in March in the region 100-108 • E, 10-18 • N, i. e. , a factor of 10 higher than in the GFEDv3 inventory (4. 3 TgC). The differences between GFEDv3 and FINN are most likely due to inherent differences in the proxy variables used in the respective emission models, burned area in the case of GFEDv3, and active fire counts in the FINN model, both retrieved from MODIS satellite data. The two inventories provide, however, very similar estimates for the more forested, northwestern part of Indochina (19-27 • N, 97-100 • E): 105 TgC in GFEDv3, 124 TgC in FINNv1. 5. Considerable cloudiness during the rainy season (May-October) causes gaps in the OMI HCHO time series, due to the exclusion of scenes with ≥ 40 % cloud cover. The GOME-2 data series are comparatively less affected by this issue, due to the diurnal precipitation and cloudiness patterns during the monsoon season.
Africa Over Africa, the annual pyrogenic source, amounting to 40. 7 TgVOC in the a priori, is reduced to 26. 2 and 32. 6 TgVOC in the GOME-2 and OMI inversions, respectively (Table  3 ). A smaller reduction is also inferred for isoprene fluxes, estimated at 76. 6 Tg (GOME-2) and 75. 2 Tg (OMI), within 10 % of the a priori value (81. 6 Tg). These estimates are in line with recently reported isoprene fluxes over Africa based on the NASA data set of OMI HCHO columns (77 TgC or 87 Tg isoprene compared to 116 Tg isoprene in the a priori reported fluxes,  Marais et al. , 2012). The spatial distribution of the emission updates is displayed in Figs. 9, 10 and 11. African fire occurrence peaks in central Africa (e. g. , the Democratic Republic of the Congo or DRC) in early June (Fig. 7 ) and lasts until August with the end of the dry season.
7 ), in good agreement with our previous study using SCIAMACHY HCHO columns  (Stavrakou et al. , 2009b). Only small changes are inferred for isoprene emissions in Northern Africa, with 11 % (GOME-2) and 16 % (OMI) decrease compared to the a priori emissions, as illustrated in Fig. 13. In Southern Africa, biogenic fluxes are highest in January and lowest in July, while the fire season starts in May and peaks in September (Figs. 12 and 13). Both GOME-2 and OMI inversions infer only small isoprene flux updates (Table 3). Regarding fire emissions, in contrast with GOME-2 data suggesting a ca. 30 % flux reduction (to 17. 6 from 25. 8 TgVOC), the OMI-based estimate lies within 10 % of the a priori, due to a compensation of flux decreases north of approximately 15 • S, and flux increases in the southernmost part of the continent, south of ca.
The satellite, on the other hand, although in qualitative agreement with the above, suggests higher ratios of 13:30 to 09:30 LT columns than the model at mid-latitudes, whereas no clear pattern emerges in tropical regions (Fig. 7 ). Nevertheless, these discrepancies in terms of morning/afternoon ratios are most often small in comparison with the model-data differences in the HCHO columns themselves. As a consequence, the emission fluxes inferred from both GOME-2 and OMI inversions are found to be generally very consistent. They both suggest a strong decrease of the global biomass burning source, by about 35 %. The decrease is mostly concentrated in the Tropics, e. g. , over Amazonia (factor of > 2 reduction), over equatorial Africa and over Myanmar and the surrounding regions (factor of 2-5 reduction in March).
These updates are confirmed by comparing CO columns predicted by the model using the biomass burning emissions estimated by the OMI inversion with IASI CO columns. The results are also consistent with a recent study using MOPITT CO columns  (Bloom et al. , 2015). The seasonal profile of the emissions is generally well preserved by the inversions, except for a significant enhancement near the end of the dry season, in particular over Southern Africa (in October) but also Amazonia (in September) and Indochina (in April). Both satellite data sets point to strong enhancements of agricultural fire fluxes in the North China Plain in June (factor of almost 2) and in the southern part of Indochina, compared to the a priori estimate. Very good agreement between the inversion results is found for isoprene fluxes, with global annual fluxes reduced by 9 % (GOME-2) and 13 % (OMI) compared to the a priori 363 Tg.
Acknowledgements. This research was supported by the  Belgian Science Policy Office  through the  PRODEX projects ACROSAT  and  IASI. Flow  ( 2014-2015 ) and by the  European Space Agency (ESA)  through the  GlobEmission  project (2011)(2012)(2013)(2014)(2015)(2016). P. -F. Coheur is senior research associate with FRS-FNRS. The authors would like to thank the two anonymous reviewers for their careful reading and constructive criticism. Edited by:  C. H. Song  www. atmos-chem-phys. net/15/11861/2015/Atmos. Chem. Phys. , 15, 11861-11884, 2015.
Acknowledgements We are grateful to the  International Foundation for High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG)  for making it possible for us to do our measurements at Jungfraujoch, and to  Mark Rollog  for the analyses of stable isotopes ratios in snow water. We thank  MeteoSwiss  for providing data on meteorological conditions at the observatory Jungfraujoch, and  Dr. Stephan Henne  ( EMPA ) for highresolution particle transport simulations (FLEXPART) which enabled us to identify the main source regions of air masses. The work reported here was supported by the  Swiss National Science Foundation (SNF)  through the grant  200021_140228. Measurements of total solid particles were performed by  Paul Scherrer Institute  in the framework of the  Global Atmosphere Watch (GAW)  programme funded by  MeteoSwiss  with further support provided by the European  FP7  project  BACCHUS  (grant agreement no.
Atmospheric Administration NOAA database (http://www. nodc. noaa. gov/OC5/indprod. html), grouped per season, and used to calculate α V/L. Vapour source regions were derived from source sensitivity plots calculated with a Lagrangian particle dispersion model and made available online by Stephan Henne at the Swiss Federal Laboratories for Materials Science and Technology (EMPA), Dübendorf, Switzerland (individual results are available on the webpage http://lagrange. empa. ch/FLEXPART_browser/). The surface average temperatures of source areas in the North Atlantic and the Mediterranean Sea were derived from a National Oceanic and.
Analysis of organic carbon and nitrogen Freeze-dried sediment samples were homogenized by grinding, and then the weighed aliquots were acidified by adding 2 mL of 1 M HCl to every 100 mg of sample. The acidified samples were dried at >60 C under a stream of filtered air, then mixed with 1 mL Milli-Q water and freeze-dried again. Samples were weighed again to account for the change of weight during the acid treatment. Aliquots of approximately 20 mg were weighed into 5 Â 8 mm tin capsules for the measurement of total organic carbon (TOC), total nitrogen The C/N ratios were determined as mol/mol ratios, which transformed from the %TOC and %TN weight data obtained as part of the stable isotope analyses. Replicate measurements of a certified reference material (Low Organic Content Soil, Elemental Micro-Analysis, UK) indicated analytical errors of 0.
Some of these geophysical products have been merged with geostationary data to provide, for instance, daily accumulation of rainfall all over the intertropical region. These products compare favorably with references from ground based or space-borne observation systems. The contribution of the mission unique orbit to its scientific objectives is investigated. Preliminary studies indicate a positive impact on both, humidity Numerical Weather Prediction forecasts thanks to the assimilation of SAPHIR Level 1 data, and on the rainfall estimation derived from the Global Precipitation Mission constellation. After a long commissioning phase, most of the data and the geophysical products suite are validated and readily available for further scientific investigation by the international community.
The MT mission also serves operational objectives through real time delivery of the SAPHIR radiometer data via EUMETSAT to the numerical weather prediction centers. The MT mission complements and reinforces the existing fleet of research and meteorological satellites with a close connection to various agencies operating other radiation budget instruments  (Loeb et al. , 2009)  as well as the participants to the Global Precipitation Measurement constellation  (Hou et al. , 2014)  of which MT is an official member. This paper aims at reviewing the Megha-Tropiques mission and its status after 3 years in orbit with emphasis on the French scientific activities. It is organized as follows. Section The Mission and Its Performance introduces the satellite and details the payloads and the orbit as well as the geophysical products derived from Megha-Tropiques, alone, and in combination with other satellite measurements.
Instantaneous rainfall The Bayesian Retrieval Algorithm Including Neural Networks (BRAIN) retrieval  (Viltard et al. , 2006; Kirstetter et al. , 2013)  is a Bayesian approach that extracts from the multi spectral microwave observations a representative rain profile obtained from a database. The microwave brightness temperature profiles database is constructed primarily from observations of the Precipitation Radar on board the Tropical Rainfall Measuring Mission and radiative transfer simulations. The microphysical assumptions of the radiative transfer computations have been recently revisited using dedicated airborne and radar campaigns  (Fontaine et al. , 2014; Martini et al. , in press ). In the framework of the MT mission, the BRAIN retrieval is applied on all the platforms participating in the Global Precipitation Mission  (Hou et al.
Top of the atmosphere radiation flux While precipitation and humidity can be compared to in-situ or to ground-based data for evaluation purposes, it is not the case for the TOA radiative fluxes. Similarly to the radiance validation procedure (Section The Mission and Its Performance), the instantaneous fluxes in both the shortwave and the longwave domains have been compared using the collocated CERES and ScaRaB data sets. The overall performances of the SCARAB derived flux is scene dependent with slightly better comparison scores over land and desert than over ocean  (Raberanto et al. , 2015). An example is provided in Table  4  that indicates that the instantaneous fluxes are characterized by an average Root Mean Square error (RMS) of 10. 5 Wm -2 for SW and 3 Wm -2 for LW within the initial specifications (RMS ∼10 Wm -2 ).
Level-4 Products The level-4 products derived from the GPM and Megha-Tropiques payloads rely on the use of geostationary data from the fleet of operational meteorological satellites. A significant effort has been dedicated to control the quality of the GEO observations  (Szantai et al. , 2011)  to insure the best usage of the meteorological satellites. Similarly, an in-depth evaluation of a cloud classification technique that provides contextual information of the interpretation of MT data, using the A-TRAIN observations has been finalized  (Sèze et al. , 2015).
The current implementation works with a 5 • × 5 day training dataset which has been identified as a good compromise for stability and accuracy for such a constellation  (Chambon et al. , 2012b). A 30 min resolution for the IR imagery is used to construct the daily rainfall accumulation. The evaluation of TAPEER daily estimates is based on comparisons with various rainfall products that include ground rain gauges, and/or satellite information as well as a systematic use of the operational and research rain gauges networks. The uncertainty information is also used in the satellite product validation step  (Chambon et al. , 2012a). Figure  8  presents a map of the number of rainy days (defined as above 1 mm per day) detected by TAPEER and by the GPCP v2. 2 estimate  (Huffman et al. , 2001)  during the boreal summer 2012 period.
Impact of the Madras Data on Daily Accumulation Rainfall Estimation The estimation of the improvements, due to MADRAS to the GPM constellation for daily accumulation rainfall estimation is not an obvious task. Synthetic computations indicate that a significant positive impact is expected from a low inclination satellite but adding sun synchronous observations does not change much the rainfall estimates after a given amount of satellite have been integrated in the constellation  (Chambon et al. , 2012b). To be useful to the multiplatform algorithm, the MADRAS radiometer should bring in a significant amount of rainy pixels to the merging process. The quantity of useful pixels depends on: (i) the quality and availability of the level 1 observations, (ii) the phasing between the MT sampling   (Desbois et al.
Conclusions The Megha-Tropiques mission is operating since October, 2011. A long commissioning phase resulted in significant delays in the performance assessment of both the instruments and the geophysical products. After 3 years, it is seen that the instrumentation is performing within the specifications and the SAPHIR and SCARAB instruments are continuously acquiring measurements nominally. The level 1 data are readily available. Furthermore, the real time stream of the SAPHIR data has been operational since May 2014 by EUMETSAT though EUMetCast. MADRAS stopped data acquisition on January 26th, 2013; the MADRAS data are in an intermediate version and are still under a restricted data policy. Efforts are directed toward making a final version widely available in a short term future. A suite of geophysical products derived from MT and the GPM constellation platforms has been finalized and evaluated.
Acknowledgments The authors are grateful to  Dr Michel Desbois  who triggered and led the  Megha-Tropiques mission  from its inception up to 2007. We also acknowledge our late colleague  Dr Michel Viollier  for his significant contribution to the SCARAB scientific developments. Drs F. Aires ,  M. Capderou ,  L. Eymard ,  T. Fiolleau ,  I. Jobard ,  S. Kacimi ,  P. E. Kirstetter ,  A. Lifermann ,  A. Martini ,  D. Renaut ,  G. Sèze , and  A. Szantai  are all thanked for their useful contribution to the success of the scientific exploitation at various stages of its development. A large suite of short-term contract programmers considerably helped us to finalize the retrievals algorithms and their contributions to the mission are thankfully acknowledged. The authors further acknowledge  Ms N.
Coastal regions are a resource for societies while being under severe pressure from a variety of factors. They also show a large diversity of optical characteristics, and the potential to optically classify these waters and distinguish similarities between regions is a fruitful application for satellite ocean color. Recognizing the specificities and complexity of coastal waters in terms of optical properties, a training data set is assembled for coastal regions and marginal seas using full resolution SeaWiFS global remote sensing reflectance R RS data that maximize the geographic coverage and seasonal sampling of the domain. An unsupervised clustering technique is operated on the training data set to derive a set of 16 classes that cover conditions from very turbid to oligotrophic. When applied to a global seven-year SeaWiFS data set, this set of optical water types allows an efficient classification of coastal regions, marginal seas and large inland water bodies.
A drawback of using the SeaWiFS MLAC data is that data acquisition has been uneven in space and time following the operation of receiving ground stations. The focus of this work is on coastal regions and marginal seas. To isolate the part of the global ocean that responds to that vague definition, a set of arbitrary criteria was applied to define the domain of study. First, grid points were excluded if the shortest distance to the coast was larger than 200 km or if the bottom depth was deeper than 4000 m (9000 m for the region along the western coasts of South and Central America). Bathymetry is defined according to the General Bathymetric Chart of the Oceans (GEBCO) 1-minute gridded data set. Finally, some marginal seas, parts of which were excluded by these criteria, were restored to their full extent in the domain of analysis, e.
The selected name does not necessarily reflect the entire geographic domain usually associated with the region but only the coastal/shelf part considered for the analysis. The SeaWiFS data were processed for the interval 1998-2004 (7 years) which was a period of unrestricted distribution of LAC data by NASA for research purposes. Fig. 2  illustrates the total number of days with valid data that went into the analysis. The average number is 283 days over the 7-year period (standard deviation, s. d. , 221 days) for the entire domain. This relatively small amount of days is explained by the use of MLAC imagery as explained above but is sufficient to conduct a global study. This coverage appears highly variable, having a maximum of 767 days for the Mediterranean Sea (MEDI) and a minimum of 6 days for the Laptev Sea (LAPT).
Statistics Once the class membership p was calculated for each day and bin, the normalized membership p ⁎ was expressed for class ic as: p Ã ic ð Þ ¼ p ic ð Þ X NC ic¼1 p ic ð Þ: ð5Þ The classification scheme does not impose a sum of memberships equal to 1. For instance, a spectrum associated with optical conditions not well represented in the training data set could have low class membership for all classes. It is therefore important to illustrate the distribution of total memberships to assess the degree to which the classification scheme appropriately covers the optical variability of the considered water body. For each bin, the number of days when different thresholds of total memberships were reached was also recorded. Normalized memberships were used when comparing and combining data from different days or locations.
e. , ±4 with respect to the reference case), in order to test the impact of a coarser and finer classification, respectively. The same classification processing was then repeated on the SeaWiFS data with the case of 12 and 20 classes: the total class membership has global averages equal to 1. 01 (s. d. , 0. 25) and 1. 05 (s. d. , 0. 28), respectively, and shows similar spatial variations as the reference case (not shown). The total class membership thus appears relatively stable with respect to the number of classes, at least in the considered interval. The issue of the number of classes is further discussed in Section 4. 2. Maps of average class membership were created for each class (see supplementary material), while the class most frequently selected as the class of maximum membership (dominant class) is shown in Fig.
Optical classification and remote sensing A first element of discussion is about the potential and limitations of remote sensing for optical classification. For global applications, the potential allowed by remote sensing since 1997 in terms of spatial, temporal and spectral resolution is that of SeaWiFS-like missions, and the optical variability that can be detected is tied to the spatial resolution of the sensor. For this study, a grid with a resolution of 1/48th-degree (approximately 2. 3 km) was selected using LAC SeaWiFS data. This is to our knowledge the first time LAC SeaWiFS data are used systematically at global scale, albeit for coastal applications (see  Mélin, Steinich, Gobron, Pinty and Verstraete, 2002 , for terrestrial vegetation science) and this resolution is at least twice that of standard global products.
Acknowledgments The authors thank the  Ocean Biology Processing Group of NASA  for the distribution of SeaWiFS data. The  British Oceanographic Data Centre  is acknowledged for the GEBCO data set. This research has been funded by the  GlobCoast  project (www. foresea. fr/globcoast)which is funded by the  French Agence Nationale de la Recherche  ( ANR 2011 BS56 018 01 ). The  GlobCoast  project is affiliated to the LOICZ and AQUIMER. The study also contributes to the  Climate Change Initiative of the European Space Agency. Finally , a reviewer is thanked for useful comments.