dataesr/scientific-paragraphs-categorization huggingface dataset:
We also find that the inferred value of W max is generally robust against the choice of prior values of the same parameter, but depends on the data stream assimilated. LHF assimilation required high values of LAI that needed to be supported by ample soil moisture, and consequently inferred the highest value of W max. Assimilation of FAPAR, by contrast, yields values more consistent with observed rooting depth. Before the technique could be applied at larger scales, some issues regarding data bias and the simulated latent heat flux under water stress (i. e. during the dry season) need to be clarified. However, even before these issues are finally resolved, the structure of the posterior error covariance matrix can yield valuable information for the design of such observing systems. First of all, the parameters constrained by either assimilation experiment tend to be the same.
FAPAR assimilation further yields a positive uncertainty correlation between photosynthetic capacity and W max , but for the trees not the grasses. It appears that LHF contains more information on grass, and FAPAR on tree functions. The sign of the uncertainty correlation for W max against photosynthetic capacity also varies between FAPAR and LHF on the one hand, and the combined case on the other, where the correlation is negative. This indicates that the model operates in a different regime after the combined assimilation compared to the other two cases, where increases in W max and photosynthetic capacity have either opposing or concurrent effects on the agreement with the data streams assimilated. For the FAPAR assimilation, we find that posterior uncertainties of W max were correlated more strongly with other parameters than for LHF assimilation, with large correlations in particular for photosynthetic capacity of the seasonally green trees, their drought-phenology response and their stomata control.
This means that FAPAR resolves fewer processes than LHF. However, the combined case in general has fewer cases of simultaneous constraint of parameters than either of the cases with a single data stream, except for the correlation between W max and τ W for grasses (see above). It seems that the seasonal response of grasses is the dominant factor determining agreement with observations in this case. Further insights can be gained from considering changes in parameter values from prior to posterior. For the LHF assimilation case, where simulated FAPAR greatly overestimates satellite observations, it is the small τ W value for PFT2 of 10 days which is mainly responsible for the large LAI values. τ W has much higher posterior values of 170 days and 94 days for the other two cases, leading to a more pronounced LAI seasonality and also a lower overall LAI.
Conclusions and implications We present the first study that simultaneously assimilates latent heat fluxes as measured by the eddy covariance technique and satellite-derived FAPAR using variational data assimilation, here for a savanna site at Maun, Botswana. Simulated LHF and FAPAR show a reasonable seasonality for the case of assimilating the two data streams together. The optimization against both data streams leads to an average relative reduction in parameter uncertainty of more than 15 % for the 24 eco-hydrological parameters in CCDAS, compared to between 9 and 6 % for LHF and FAPAR assimilation alone. The important finding that FAPAR is able to constrain hydrological parameters is confirmed by a recent application of CCDAS at the global scale  (Kaminski et al. , 2012). The authors showed that by assimilating FAPAR, CCDAS was able to constrain not only hydrological parameters, but also estimates of the diagnostic quantities of soil moisture and evapotranspiration.
Similar to the present study, the constraint was improved when atmospheric CO 2 concentrations were assimilated simultaneously (in variance to but comparable to the EC fluxes used here). We thus further demonstrate the potential of multiple-data stream assimilation by a more detailed local case study. From our experience reported in this study, we conclude that the simultaneous assimilation of locally and globally available data streams is an ideal tool for the identification of biases between models and data that can help with the development of suitable bias models for global assimilation exercises  (Kaminski et al. , 2012). Locally available data can thus improve global-scale assimilation of continuous data streams of FAPAR from satellites, bridging local and global scales and thus furthering the goal of large-scale monitoring of such essential climate variables as root-zone soil moisture.
The approach of simultaneous assimilation of multi-data streams as presented here can be extended to include additional remote sensing products, for example using the surface soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) mission  (Kerr et al. , 2001). Despite of SMOS's lower spatial resolution (35-50 km), this would allow a rigorous assessment of the consistency of multiple data streams, as done here for FAPAR and LHF, but for data streams available with global coverage from remote sensing. More importantly, the combined assimilation of FAPAR data with surface soil moisture from SMOS in CCDAS would lead to a more complete description of the hydrological properties, due to the sensitivity of BETHY-simulated FAPAR to soil moisture in the entire root zone, not only in the nearsurface layer.
Acknowledgements. This study was supported by the  Postdoctoral Fellowships for Research Abroad, Japan Society for the Promotion of Science , the  Ministry of Education, Science, Culture, Sports and Technology of Japan , and the  QUEST  programme funded by the  Natural Environment Research Council ; UK funding for this work was partly provided by the  European Commission  through the project  GEOCARBON  ( FP7-283080 ). Edited by:  J. Pongratz.
To set up z DT at time t, the potential vorticity pressure of 2 PVU is used, referring to the study by  Thouret et al. (2006)  with MOZAIC data when selecting only the cruise part of flights to document the upper troposphere-lower stratosphere. The potential vorticity pressures provided by the operational European Centre for Medium-Range Weather Forecast (ECMWF) analyses (T213) are interpolated for the specific aircraft positions and available with a 150 m vertical resolution on the MOZAIC database  (Thouret et al. , 2006; Zbinden et al. , 2006). The methodology to assess the pure tropospheric profiles, explained just below, is illustrated in Fig. 1  with three typical cases selected over Germany ((a) 8 December 1994, (b) 10 May 2000) and Japan ((c) 16 August 1995). When z DT < z top , and only in this case, MOZAIC samples the entire troposphere (Fig.
Our PTP(X, z, t) derivation can be summarized by the following three typical cases, where z = [z 0 , z DT ] at time t, PTP(X, z, t) = MP(X, z, t) = TP(X, z, t), (1) PTP(X, z, t) = TP(X, z , t) + TP(X, z s , s), (2) PTP(X, z, t) = TP(X, z , t) + TP(X, z s , s) + Mfit(X, z f , s), (3) and using the equation (1) -if z DT < z top , in case (a), Fig. 1a ; (2) -if z top < z DT < z s , in case (b), Fig. 1b  with z = [z 0 , z top ] and z s = [z top , z DT ]; (3 Table  2  provides the percentage of profiles corresponding to the 3 cases encountered at the 11 sites. We found more than 50 % of the profiles of Uaemi and USsouth belong to case (c), while less than 8 % for Europe. Note that the database encompasses domestic flights which connect two close airports, such as Abu Dhabi and Dubai (∼ 150 km apart) or Dallas and Houston (∼ 400 km apart).
We noted a greater O 3 bias in winter, when the tropopause is low (2-3 DU over Germany, > 5 DU over Japan). Therefore, the O 3 difference is probably mostly explained by stratospheric air contamination but is also explained by clear-sky conditions or the satellite overpass time capturing the daily maximum. All three factors can enhance the satellite response. We point out the need for accurate discrimination between the stratospheric and tropospheric reservoirs, and indeed the monthly tropopause is an unappropriated criterion. This point is crucial, merits consideration and must be clarified within the framework of tropospheric trends and radiative transfer studies. These fully defined and pure tropospheric products will be available in the MOZAIC/IAGOS database to the scientific community.
Acknowledgements. The authors gratefully acknowledge  F. Karcher , who is deeply involved in the MOZAIC/IAGOS programme. Acknowledgements are addressed to the  European Commission  for their strong support as well as to Airbus and the airlines (Lufthansa, Austrian,  Air France ) who have carried the MOZAIC equipment and performed its maintenance free of charge since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Météo-France , and  Forschungszentrum Jülich  ( FZJ, Jülich, Germany ), and the database is supported by  ETHER  ( CNES  and  INSU-CNRS ). We also thank the  German Weather Service (Meteorological Observatory at Hohenpeissenberg) , the  National Space Development Agency of Japan  and  NASA (Wallops Island Flight Facility)  for providing their data through the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC)  operated by  Environment Canada, Toronto ,  Ontario ,  Canada , under the auspices of the  World Meteorological Organization.
The SBE37SMP probe measured temperature, conductivity and pressure every 3 min. Depth, potential temperature, practical salinity and potential density anomaly were calculated based on the equation of state of seawater EOS-80  (Fofonoff and Millard, 1983). The SBE 37 SMP sensors were calibrated using a pre-cruise calibration, yielding an initial accuracy of 0. 002 • C on temperature and 0. 005 on salinity. The standard deviation on ADCP current speed and direction measure-ments was 1. 3 cm s -1 and 2 • , respectively. All raw time series were checked to remove spurious data points. Seapoint turbidimeters were programmed to measure water turbidity in Formazin Turbidity Units (FTU) every 20 s in auto-gain mode. FTU values were transformed into suspended sediment concentration (SSC, mg l -1 ) using the calibration by  Guillén et al.
Long-term mooring line at the canyon head In addition to the two high-resolution, short-lived mooring deployments, another mooring line was present at the CCC head (42 • 23. 15 N, 3 • 19. 53 E; 300 m depth; Fig. 1b ), as part of a long-term monitoring programme active since autumn 2004. This line was equipped with a near-bottom set of instruments comprising an Aanderaa RCM9 current meter (5 mab; sampling rate 30 min), an AQUA logger 520 Seapoint turbidimeter (6 mab; sampling rate 2 min) and a Technicap PPS3/3 sediment trap (25 mab; sampling interval 15 days). Data from this line will be used to weigh the relevance of water and particle fluxes along the southern canyon flank against those on the canyon head (CH), and also as a reference to put the main study period (3-21 March 2011) into a broader perspective.
Current data were rotated when necessary to meet the main canyon axis direction (∼ 130 • ). Reported positive (negative) values are considered up-canyon (down-canyon). SSC obtained from turbidimeters and current speeds (ADCP cells at SF1 and SF2; single-point current meter at CH) measured at similar depths were combined to obtain the suspended sediment flux (i. e. the horizontal transport of particles in suspension pulled by the water flow) in units of mg m -2 s -1. This approach could be applied only to the depth levels where simultaneous turbidity and current measurements were available (75 and 115 mab at lines SF1 and SF2 and 5 mab at line CH).
Assessment of mooring line dynamics The dynamics of the mooring lines were assessed using the Mooring Design and Dynamics (MDD) software  (Dewey, 1999)  and validated by comparing the modelled deepening of the line to the pressure sensor located in the ADCP units. The simulation of the behaviour of the ADCP at 160 mab and the sediment trap at 40 mab for different observed speeds compared well with the observed deepening of the line (Fig. 2 ). Deepening and tilting increased markedly under current speeds surpassing 40 cm s -1 (an approximate 30 % of current records were above that threshold at both lines). According to the MDD simulations, during the maximum current speeds (90 cm s -1 ) recorded at SF2, the sediment trap originally at 40 mab deepened to only a few metres above the seafloor and its inclination was nearly horizontal.
Hydrological vertical profiling and ship-based ADCP data Vertical profiling of water column properties was conducted intensively during the CASCADE cruise, covering a wide set of coastal, shelf, slope and open-sea stations. The conductivity-temperature-depth (CTD) profiles used in this work are restricted to the CCC area and comprise a highresolution temporal sampling at a single station between the two temporary mooring lines during the period of the storm, and two cross-canyon transects after the storm (see Fig. 1b  for station locations). CTD casts were carried out using a Seabird 911Plus CTD probe equipped with a SBE 32 Carousel water sampler. Thirteen data channels (pressure, dual temperature and conductivity with pump, dissolved oxygen, light attenuation, turbidity, fluorescence, dissolved oxygen, photosynthetically active radiation, surface photosynthetically active radiation, colored dissolved organic matter, and altimetry) were measured at a rate of 24 Hz.
Temperature and salinity were corrected using pre-and post-cruise calibrations, yielding a precision of 0. 01 • C on temperature and 0. 003 on salinity. The casts were conducted from the surface to a few metres above the seafloor whenever possible. A low-pass filter was used to compensate for the different time response of the sensors and to remove outliers. A ship-roll and minimum probe velocity filter (< 0. 10 m s -1 ) was applied to each cast to disallow pressure slowdowns and reversals. After filtering, the downcast portion of each cast was pressure-averaged and sequenced into 1 db pressure intervals. The 1 db-averaged pressure, temperature and conductivity data were used to compute depth, potential temperature, salinity and potential density anomaly. Temperature is ITS-90 and salinity is PSS-78.
Density was calculated based on the equation of state of seawater EOS80  (Fofonoff and Millard, 1983). Water turbidity, measured in FTUs by a Seapoint sensor, was transformed into suspended sediment concentration (SSC) following the calibration by  Guillén et al. (2000). Current speed data from a ship-based ADCP (RDI-Ocean Surveyor, 38 kHz) profiling 0-700 m in 16 m-wide cells were also used. The ADCP data were processed with the Common Oceanographic Data Access System (CODAS) software  (Firing et al. , 1995) , in agreement with the best practices suggested by the Go-Ship Group  (Firing and Hummon, 2010). Bottom-track calibration was made to determine precisely transducer orientation relative to the heading sensor, and then the data were corrected for possible problems in the navigation recordings and bad ADCP bins/profiles.
7, 8). Sediment traps recorded downward mass fluxes one order of magnitude higher in the southern canyon flank than at CH during the main period of eastern storms. Given the wide difference in sampling intervals between traps deployed in the south canyon flank (35 h) and in the canyon head (15 days), a better comparison can be achieved by integrating the available data for the ensemble of the collecting period (4-19 March). The integrated downward mass flux thus obtained mounts up to 51. 2 g m -2 d -1 and 55. 4 g m -2 d -1 at SF1 and SF2, respectively, whereas it amounts to an estimated value of only 8. 5 g m -2 d -1 in the canyon head for the same period. These numbers and comparisons must, however, be taken with caution because the high current speeds and the strong oscillations experienced by the mooring lines during the periods of maximum flux (see Fig.
Acknowledgements. We are grateful to the crew and officials of  R/V l'Atalante  and to all the scientific and technical staff involved in the CASCADE cruise. This work was funded by the  HERMIONE  Project ( FP7-ENV-2008-1-226354 ) under the  European Commission's Seventh Framework Programme , the  French programme MERMEX  under the MISTRALS framework, the  GRACCIE-CONSOLIDER  project ( CSD2007-00067 ) and the  Spanish  project  DOS MARES  ( CTM2010-21810-C03-01 ). University of Barcelona researchers benefitted from grant  2009 SGR 1305  by  Generalitat de Catalunya  to excellence research groups. L. Houpert acknowledges the support of the  Direction Générale de l'Armement  (supervisor:  Elisabeth Gibert-Brunet ). J. Martín  was funded through a  JAE-DOC  contract within the  programme "Junta para la Ampliación de Estudios ", granted by  Consejo Superior de Investigaciones Científicas (Spain)  and co-financed by the  European Social Fund.
Nevertheless, we expect this model to provide a first estimation of solid precipitation and snow depth in this region, as it is based on an optimised cloud microphysics scheme and a detailed surface snow scheme. After the introduction exposed in Sect. 1, we present in Sect. 2 the RCM and the experimental setup that we used to perform our simulation. In Sect. 3, we describe the observational datasets used for the model validation. In Sect. 4, we compare the atmospheric circulation described in the reanalyses with those modelled with the RCM. In Sect. 5, we focus on the ability of our model to simulate precipitation during the monsoon and the extra-monsoon periods. In Sect. 6, we validate the simulated snow cover using satellite products for the snow cover extent, which are clearly more reliable than the observations of precipitation.
Modelled and observed precipitation Figure  3  shows the monsoon 2001 total precipitation simulated with MAR (panel a), and deduced from the ERA-INTERIM (panel c), TRMM (panel e) and APHRODITE (panel g) datasets (see their description in Table  1 ). Generally, monsoon precipitation is more intense in the south and in the east of the Himalaya. Such a pattern is related to the moisture flux shown in Fig. 1a. Monsoon precipitation exceeds averaged values of 500 mm month -1 over Bangladesh and Arunachal Pradesh. During the same period, precipitation is very low, ranging from 10 to 50 mm month -1 over most parts of the Tibetan Plateau. Monsoon precipitation reaches also very high values in some regions of the Himalayan mountain range, with values locally exceeding 500 mm month -1. We found however very strong differences between the different estimations of precipitation shown in Fig.
Yatagai et al. (2012)  explained that the rain gauge network used to construct APHRODITE data is denser in the Indian plain and in the foothills of the Himalaya than in areas of high altitude. Therefore, we estimate that the "dry bias" in the MAR simulation is real, whereas the "humid bias" may not be an artefact of our model, due to the uncertainty of the APHRODITE network in these regions. This point is corroborated by the fact that commonly used rain gauges are well known to strongly underestimate solid precipitation  (Goodison et al. , 1998). As discussed in Sect. 1, TRMM data has also been found to underestimate precipitation over mountainous areas. This remotely sensed observation shows also precipitation rates lower in the foothill of the Himalaya than those measured with the APHRODITE network (Fig.
3f ). Concerning precipitation in the most eastern part of Himalaya and Tibetan Plateau, both APHRODITE and MAR simulation show lower precipitation rates than TRMM and ERA-INTERIM data in this region. Once again, we have to be very careful when estimating which of these products is the closest to reality, considering their large uncertainties. Figure  4  shows the total precipitation from October 2001 to May 2002 simulated with MAR (panel a), and estimated from the ERA-INTERIM (panel c), from TRMM (panel e) and from the APHRODITE network (panel g). In general, this period is characterised by very low precipitation rates over the Tibetan Plateau and the Indian Plain, ranging between 0 and 50 mm month -1 over these regions. During this period of the year, precipitation is higher over the Himalaya, in particular in the western and the eastern part of the mountain range, where it can reach several hundreds of mm month -1 depending on the dataset.
Snow cover estimation from satellites and models Using remote-sensing data, it is possible to evaluate the Snow Cover Extent (SCE) from 1960 to present day. For example, the Interactive Multisensor Snow and Ice Mapping System (IMS) data is available for the whole Northern Hemisphere, and is distributed by the NOAA (NOAA, NSIDC, 2004). Recent satellite imagery has been found to be a very useful tool to describe SCE  (Derksen and Brown, 2012)  and is therefore widely used to validate models (e. g. Brutel-Vuilmet et al. , 2013). Immerzeel et al. (2009)  and  Bookhagen and Burbank (2010)  have suggested hydrological applications using remote sensing over the Himalaya. However, Snow Water Equivalent (SWE) cannot be estimated accurately with satellite data, in particular over mountainous regions  (Takala et al.
, 2011). Therefore, SWE remains a variable quite difficult to estimate at global and regional scales. Until today, local observations are the only way to accurately measure SWE. Therefore, it is quite difficult to estimate SWE at large scales, as it can be spatially highly variable, in particular  in regions with a complex topography. In the Himalaya, the maximum of SCE occurs at the beginning of spring (Bookhagen and Burbank, 2010). Figure  5  shows the SWE equivalent in April 2001 estimated from the ERA-INTERIM (Fig. 5a ), analysed from the Canadian Meteorological Center (CMC,  Brasnett, 1999)  and modelled with MAR (Fig. 5c ). For validation purposes, we show in Fig. 5d  the SCE observed with MODIS satellite in April 2001 with regions of SCE higher than 50 % in yellow to red. To check the ability of a model to simulate the snow cover, we expect it to successfully simulate snow where MODIS data shows SCE values higher than 50 %.
Elsewhere, the major part of the surface is free of snow, and a model that does not consider sub grid snow fraction is not supposed to simulate presence of snow. As mentioned previously, MODIS, CMC and ERA-INTERIM products are described more in detail in Table  1. 5b  and d ). Snow cover appears to be better represented in the MAR simulation (Fig. 5c ) than in the ERA-INTERIM and in the CMC estimations. Overall, our model simulates a snow cover that is in agreement with MODIS data in April 2001. The snow cover duration modelled with MAR is also comparable with those measured by the IMS satellite data A strong bias in the MAR simulation is visible in the eastern part of Himalaya, where the snow cover duration is largely underestimated (Figs. 5c  and 6 ). Part of this bias is certainly explained by an underestimation of snowfall.
Even with a quite realistic pattern of snow cover in central and western Himalaya, the simulated snow cover duration is relatively over-estimated in comparison with the IMS observation, which may be induced by too strong levels of snowfall in the MAR simulation. Applying RCMs in this region,  Dimri and Niyogi (2012)  and  Dimri et al. (2013)  have found also high precipitation rates in this region in comparison with the gridded estimation of the APHRODITE network. The high levels of snow cover extent observed during the end of the winter in this region are necessarily induced by snowfall occurring in fall or in winter. Therefore, in agreement with  Dimri and Niyogi (2013)  and  Dimri et al. (2013) , we also suggest that APHRODITE data underestimate precipitation in this region. Finally, the real precipitation rates in these regions are likely higher than the APHRODITE estimation and lower than those simulated with MAR.