dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA Our weather typing approach is based on daily winds at 850 hPa from the second version of the NCEP reanalyses  (Kanamitsu et al. , 2002) , for the years 1979-2013, during the 1 September to 30 April season, and within the window (15 • S-15 • N, 90 • E-160 • E). The 850 hPa wind field was shown in our previous work  (Moron et al. , 2010)  to provide a good description of monsoonal WTs over the MC, and we extend the dataset here to cover the whole MC archipelago, and the complete austral-summer monsoon season. In order to analyze WT relationships with convection, we employ interpolated daily outgoing longwave radiation (OLR) from the NOAA  (Liebmann and Smith, 1996)  dataset for the same space and time windows. We also use 3-hourly and daily estimated rainfall from TRMM 3b42  (Huffman et al.
, 2007; Chen et al. , 2013, version 7)  available on a 0. 25 • × 0. 25 • grid from January 1st, 1998, until the end of 2013. Units are converted from mm/hr to 1/10 mm/day and rainfall values are cube rooted to decrease skewness. Daily rainfall is available for only 15 complete seasons, from 1998/1999 on. All other atmospheric datasets have 242-day seasons, from 1 September to 30 April, over 34 years, with leap days removed, that is 242 days/year × 34 years = 8228 days.
The goal is to separate the impact of these drivers on the recurrence or persistence of the WTs from their impacts on the spatial patterns thereof. Lastly, we analyze the diurnal cycle of local-scale (0. 25 • ) rainfall to look at scale interactions and assess the ability of the WTs to capture any systematic impact on a shorter time scale, which is not explicitly resolved by the dynamical clustering of daily fields. The daily gridded wind data are first standardized using the long-term mean and standard deviation. The initial dimension of the dataset is then reduced by applying an EOF analysis that retains 75% of the total variance in the 26 leading PCs. The k-means method partitions iteratively the ensemble of 8228 days into k clusters in such a way as to minimize the sum of variances within clusters  (Diday and Simon, 1976).
Each coefficient β j can be interpreted as the increase of logodds of falling into k th WT vs. the (k * ) th one that would result from a one-unit increase in the j th predictor, while holding the other predictors constant. The accuracy of the model fit is independent of the WT chosen as the baseline. The maximum likelihood estimator β of β is calculated using the MATLAB function mnrfit. To estimate the model-based WT probability πi from β, the back transformation from Equation (  2 ) is given by πik = e (α + x i βk ) 1 + j = k * e (α + x i βj ) for k = k * , and (3) πik * = 1 1 + j = k * e (α + x i βj ) for k = k *. ( 4 ) The fit is estimated vs. a saturated model in which π was estimated independently for i = 1,. , N. The deviance G 2 for comparing any model to a saturated one is given by G 2 = 2 N i = 1 K k = 1 y ik log y ik πik.
WEATHER TYPES: ASSOCIATED LOCAL-SCALE RAINFALL ANOMALIES The analyses so far have focused on regional-scale anomalies. Downscaling to local-scale anomalies is now considered, using the 0. 25 • × 0. 25 • TRMM dataset  (Huffman et al. , 2007). We need to be cautious about these analyses since they refer to the second half of the period only and there are known biases in the TRMM remote-sensing data relative to in situ rain gages  (Dinku et al. , 2007). As before, we decompose the rainfall anomalies according to time scale. This provides a measure of how interannual and subseasonal rainfall variability is described by changes in frequency and amplitude of daily WTs. We first computed interannual and sub-seasonal anomalies in rainfall as in Figure  8 , after taking the cubic root of daily rainfall values to reduce skewness.
ACKNOWLEDGEMENT We thank  F. Kucharski ,  F. Molteni ,  J. Shukla , and  D. Straus  for their valuable comments and suggestions on the preliminary stages of this paper, during the school and workshop on "Weather regimes and weather types in the tropics and extratropics: Theory and application to prediction of weather and climate" held in October 2013 at the  International Centre for Theoretical Physics in Trieste. Three-hourly data from the TRMM Reanalysis-2 and OLR daily data were downloaded free of charge from the  Earth System Research Laboratory of NOAA's Physical Science Division  (http://www. esrl. noaa. gov/psd/data/gridded/). Michael Ghil  and  Andrew W. Robertson  were supported by  MURI  grant  N00014-12-1-0911  from the  Office of Naval Research , and  Andrew W. Robertson  also by the  International Fund for Agricultural Development.
Methods This systematic review will undertake an extensive search of literature to assess the impacts of different silvicultural interventions on timber production and the conservation value of natural tropical forests, and to further determine whether these outcomes can both be achieved by the same interventions and how different silvicultural interventions for combined sustained timber harvests and maintenance/conservation of other ecosystem services and biodiversity affect socio-economic outcomes. Data will be extracted for meta-analysis of at least a sub-set of the review questions. Findings are expected to help inform policy and develop evidence-based practice guidelines on the value (or otherwise) of silvicultural practices in tropical natural forests.
Keywords and phrases for searches in the main bibliographic databases (indicated by an asterisk in Table  2 ) were derived from the following PICOs listed below. The terms and search logic are shown in Table  1. We recognise the importance of studies published in languages other than English. The search strategy will therefore be applied (and documented carefully for peer review) to the following languages: English, French, Spanish, Portuguese, German, and Dutch, subject to time and resources. Where time and resources permit, and recognizing the potentially high cost of translating relevant studies, we will also search in: Bahasa Indonesia, Japanese, and Thai.
Search terms & Boolean logic Searches in CAB Abstracts, Web of Science and Scopus will be based on the logic A AND B AND C, where A-C are the sets of terms listed in Table  1. Within each set, alternative terms will be searched, using OR. CAB Abstracts, having a sub-database dedicated to forestry, will be searched using a modified strategy that makes use of CABICODES and descriptors from the CAB Thesaurus (Table  1  section D). The search was devised in consultation with the Editor of Forestry Abstracts, and the former editor of Forest Products Abstracts (one of the review authors). In our scoping studies a simpler search performed better than the strategy used for Scopus and Web of Science. A much simpler search strategy, based on that devised for CAB Abstracts, but excluding the CABICODES, will be used to search resources that cannot accommodate complex search strings.
Data synthesis and presentation A narrative synthesis of data from all studies included in the review will describe the quality of the results along with the study findings. Tables will be produced to summarise these results. Where studies report similar outcomes, meta-analysis may be possible. In these cases effect sizes will be standardised and weighted appropriately. Details of the quantitative analysis will emerge once full-text screening has yielded a set of studies that can be assessed for content and quality. Subgroup analysis of categories of studies will also be performed where sufficient studies report common sources of heterogeneity. In addition to publication bias (see below), sensitivity analysis will be carried out where possible following Brooks et al. [9]. Silvicultural effects will be presented visually in plots of mean effect sizes and variance.
Endnote a For the CAB Abstracts database, searching is limited to Title, Abstract and keyword fields using the field limiters shown in Table  1. Web of Science and Scopus have a default search mode that interrogates only those fields, which will be used. This eliminates papers that contain any of our keywords in, for example, journal titles, institution addresses, etc. KK110 is one of a suite of CABICODES, which are applied as additional indexing categories by abstractors.
g. ,  Behrendt et al. , 2011). It was also the launch site for radiosoundings. The purpose of this paper is to introduce the measurement performance of the UHOH RRL during HOPE and present highlights of the measurements. One highlight was the application of a novel switch for low-and high-background RRL. During the 18 intensive observation periods more than 200 h of measurements were collected. Measurements took place in the time between sunrise and sunset and were stopped in the case of rain or continuing dense cloud cover. In addition there were two night-long measurements. On one day, RHI (range-height indicator) scans were performed. During one day and one night, the lidar was pointed at a low elevation above the ground. Water vapor measurement started at the beginning of May; therefore only 100 h of water vapor data is available.
Detailed simulations were performed which showed that it is favorable to use another pair of filter center wavelengths in low-background conditions compared to cases with high solar background. The change between the settings can be made in a few minutes. Thus, it was possible to switch easily between the settings and acquire continuous time periods of data which included such changes. This paper is structured as follows: in Sect. 2 the new setup of the UHOH RRL is explained. Also the simulations for two  settings for the second rotational Raman channel are presented. Section 3 discusses the experimental results of the new system. Section 4 gives a short summary.
, 1992; Sherlock et al. , 1999). (r) is a correction for the different atmospheric extinction at the two wavelengths λ RR and λ H 2 O : (r) = exp - r r 0 α λ RR , r dr exp - r r 0 α λ H 2 O , r dr. ( 8 ) To determine the calibration constant C, a collocated radiosounding can be used. It was found that a correction for the different extinction coefficients of the two wavelengths is negligible for ranges of up to a few kilometers because the difference is less than 0. 1 % up to 3 km. While the error analysis based on Poisson statistics determines the so-called shot noise errors, the total statistical error can be estimated with an analysis of the turbulent fluctuations in the atmosphere  (Lenschow et al. , 2000). For this, data with high temporal resolution are needed in order to resolve the timescale of these fluctuations.
At the beginning of the HOPE campaign, the elastic signal and the two Raman channels were recorded with this system. From early May on, the water vapor detection channel signal was implemented and its signal was recorded with the LICEL data acquisition instead of the elastic signal. The strong elastic signal was then stored with a transient recorder system (Compuscope 14100 card of GaGe Applied Inc. ) with resolutions of 3 m and 1 s, albeit only in analog mode. The data analysis contains the following steps. First, the photon-counting data are corrected for photomultiplier deadtime effects. A dead time of 4. 8 ns was determined by comparing the analog with the photon-counting signal and used  for the correction. It has to be noted that the dead time not only is influenced by the photomultiplier alone but also depends on the data acquisition system.
Then, the signals are background-corrected and smoothed in range and time with gliding average lengths that depend on the noise conditions and application. For example, for turbulence analysis  (Behrendt et al. , 2014)  high temporal resolution of 10 s is needed, while for studies of the temperature gradient a low statistical error is essential. How the statistical error is affected by the averaging can be seen in Eq. (  5 ). Temperature and other data products are then determined with the ana-log and photon-counting signals separately and merged afterwards if needed. We found that the alternative approach of first merging the analog and photon-counting signals and then deriving the data products in a second step with the merged signals  (Newsom et al. , 2009)  results in unstable temperature calibration functions because the merging procedure produces small erroneous fluctuations which influence the temperature measurements critically.
To determine the statistical uncertainties of the data, the analog data are scaled to the photon-counting signals in order to attribute virtual count rates to the analog data  (Whiteman et al. , 2006). It turned out that this scaling results in accurate shot noise error estimates also for the analog data. In  Behrendt et al. (2014)  it is shown that the total statistical error depends mainly on the noise error. It is therefore possible to obtain a good error estimation by calculating the shot noise error through Poisson statistics. From the combination of the two temperature-dependent signals, a temperature-independent molecular reference signal can also be obtained. The weighted sum of the signals with a weighting factor that depends on the system characteristics  (Behrendt et al. , 2002)  can be used as a reference signal for the calculation of the water vapor MR, particle backscatter coefficient β, and extinction coefficient α (see also Fig.
2 km for the H setting. S = 1 was reached at 4. 3 and 6. 6 km. As expected, the setting L shows smaller errors for low altitudes and is favorable up to 7 km. The simulated error for high background is shown in Fig. 11. The absolute error values are in this case higher than in the case of low background. Below 1 km, S is less than 1, which results in a better performance of the low-background setting than the highbackground setting. Above 1 km, S is larger than 1 and the Figure  10. Comparison of the statistical error of the temperature measurements with both settings, T HBS and T LBS , at lowbackground conditions. The profiles were derived from data collected over about 20 min (55 000 laser shots) and with 105 m gliding average. Above 7 km altitude, the high-background configuration shows smaller errors due to the lower atmospheric temperatures at these heights and also the decrease in signal-to-noise ratio.
The profiles of temperature, potential temperature, temperature gradient, and water vapor mixing ratio and their corresponding statistical uncertainties measured on 19 May 2013 between 13:00 and 13:30 UTC are shown in Fig. 15. This time period near local noon was chosen because the highest background values during the 25 h measurement period were found here; these examples thus illustrate the lower limit of the measurement performance, with all other periods having smaller statistical uncertainties. For the temperature profiles an average of 167 raw data profiles was used, and a gliding height average of 105 m was applied. A pressure profile measured by the radiosonde started at 13:00 UTC was used to calculate potential temperature. In the profiles of temperature, potential temperature, and temperature gradient one can see the characteristics of a well-mixed boundary layer up to about 1100 m.
Regional/global climate models combinations AMMA-ENSEMBLES is an international collaborative model intercomparison experiment that provides a set of RCM simulations results covering most of the African continent at a resolution of 50 km  (Paeth and Diederich, 2011a,b). The RCMs are driven by either the ERA-INTERIM reanalysis, or by global climate models outputs simulated under the SRES A1B emission scenarios  (Paeth and Diederich, 2011a,b). The data sets are available for free in the online database (http://ensemblesrt3. dmi. dk/). Four different RCM/GCM combinations (listed in Table  2 ) are selected from the AMMA-ENSEMBLES database to include three different RCMs (HIRHAM, REMO and RCA) driven by two different GCMs (HADCM3 and ECHAM5)  (Paeth and Diederich, 2011a,b). All RCMs are run under the SRES A1B scenarios.
Conclusion This study presents the first evaluation of the projected changes in precipitation in Senegal with RCMs and two downscaling techniques. The daily data observed at six stations between 1950 and 2007 as well as four RCM simulations driven by two different GCMs are taken into account to evaluate the impact of future climate changes under the emission of greenhouse gases scenario A1B. GEV distributions are fitted to the precipitation extremes observed and projected to provide an analysis based on quantiles for different return periods. Results show that the two techniques generally agree on the direction of the change when applied to the outputs of same climate model, but sometimes lead to very different projections of the direction and magnitude of the change in extreme precipitations.
The recent evolutions in civil UAV ease of use led the University of La Rochelle to conduct an UAV program around its own potential costal application. An application program involving La Rochelle University and the District of Oleron Island began in January 2015 and lasted through July of 2015. The aims were to choose 9 study areas and survey them during the winter season. The studies concerned surveying the dikes and coastal sand dunes of Oleron Island. During each flight, an action sport camera fixed on the UAV's brushless gimbal took a series of 150 pictures. After processing the photographs and using a 3D reconstruction plugin via Photoscan, we were able to export high-resolution ortho-imagery, DSM and 3D models. After applying GIS treatment to these images, volumetric evolutions between flights were revealed through a DDVM (Difference of Digital volumetric Model), in order to study sand movements on coastal sand dunes.
In association with this program, the GEMAPI regulation (GEMAPI In French, flooding prevention and aquatic environments management) will give the municipalities the authority of managing and surveying their dikes. As a consequence, the university decided to lead a common action to survey the dikes and coastal sand dunes of the Oleron Island. For 6 months, (from January 2015 to July 2015), more than 40 flights were led on oversea areas Island. This application enabled us to answer different questions: 1 -Can the coastal UAV application be interesting for coastal researches? 2 -Can the coastal UAV application be precise enough to measure sand and dike movements? 3 -Can this application be more efficient than traditional methods? 4 -Can the coastal UAV application be less expensive than traditional methods? The 3D reconstructions were lead using the softwares "Agisoft Photoscan" and "Esri ArcGis".
After those two steps, the set of pictures is provided to the Agisoft Photoscan software for 3D reconstruction. Before the 3D reconstruction process, a specific task is realized for applying masks onto backlight, and establishing UAV landing skids. The first 3D reconstruction process, i. e. the relative alignment of cameras, is always realized on the entire part of images (high quality settings). The other processes, dense cloud, mesh and texture, are realized only in low quality shots to help the georeferencing processing and save time. After recognizing and marking all GCPs, the post-processed GPS data information is imported. The project (geo-referenced) is then completely rebuilt with higher spatial resolution for the second time. Generally, the overall process of 3D reconstruction does not exceed 40 minutes with high quality settings.
Ortho-imagery verifications The geo-referencing control of the ortho-image is achieved by considering the original GCPs (figure  3 ), marked with the differential GPS. The original points are displayed over the ortho-image (figure  4 ) by GIS software. The maximum tolerance distance requested by the program between each GPS point and the centre of the target is 7 cm. If the points are greater than 7 cm apart from each other, the whole Photoscan geo-referencing process is reiterated. Total volumetric evolution can then be easily obtained by a sum function using statistic tools of the GIS software. Other statistics are also available: the minimum value (Min), the maximum value (Max), the mean value (Mean), the number of cells in each polygon (Count), and the range of values (Range). All those results give precise statistics of evolutions and uncertainties for analysis.
, 2010). Details of the MOZART version used in the MACC global products can be found in  Kinnison et al. (2007)  and  Stein et al. (2011 Stein et al. ( , 2012)). In our simulation, the IFS and the MOZART model run in parallel and exchange several two-and three-dimensional fields every model hour using the Ocean Atmosphere Sea Ice Soil version 4 (OASIS4) coupling software  (Valcke and Redler, 2006) , thereby producing three-dimensional IFS fields for O 3 , CO, SO 2 , NO x , HCHO, sea salt aerosol, desert dust, black carbon, organic matter, and total aerosol. The IFS provides meteorological data to MOZART. Data assimilation and transport of the MACC species takes place in the IFS, while the whole chemical reaction system is calculated in the MOZART model. The MACC_osuite (operational suite) is the global nearreal-time MACC model production run for aerosol and reactive gases.
Here, we have investigated only the MACC analysis. In contrast to the reanalysis, the MACC_osuite is a near-real-time run, which implies that it is only run once in near-real-time and may thus contain inconsistencies in, e. g. , the assimilated data. The MACC_osuite was based on the IFS cycle CY36R1 with IFS model resolution of approximately 100 km by 100 km at 60 levels (T159L60) from September 2009 to July 2012. The gas-phase chemistry module in this cycle is based on MOZART version 3. 0  (Kinnison et al. , 2007). The model has been upgraded, following updates of the ECMWF meteorological model and MACCspecific updates, i. e. in chemical data assimilation and with respect to the chemical model itself. Thus, from July 2012 onwards, the MACC_osuite has run with a change of the meteorological model to a new IFS cycle (version CY37R3), with an IFS model resolution of approximately 80 km at 60 levels (T255L60) and an upgrade of the MOZART version 3.
Emission inventories and assimilated data sets In the MACC_osuite, anthropogenic emissions are based on emissions from the EU project REanalysis of the TRopospheric chemical composition Over (RETRO) the past 40 years merged with updated emissions for East Asia from the Regional Emission inventory in ASia (REAS) inventory  (Schultz et al. , 2007)  -in the following referred to as RETRO-REAS. The horizontal resolution is 0. 5 • in latitude and longitude and it contains a monthly temporal resolution. Biogenic emissions are taken from Global Emissions InitiAtive (GEIA), fire emissions are based on a climatology derived from Global Fire Emissions Database version 2 (GFEDv2;  van der Werf et al. , 2006)  until April 2010, when fire emissions change to global fire assimilation system (GFAS) emissions  (Kaiser et al.
0  (Kaiser et al. , 2012) , for both gas-phase and aerosol. In the MACC_osuite, the initial conditions for some of the chemical species are provided by data assimilation of atmospheric composition observations from satellites (see  Benedetti et al. , 2008; Inness et al. , 2009 Inness et al. , , 2013;; Massart et al. , 2014). Table  1  lists the assimilated data products. From September 2009 to June 2012, O 3 total columns from the microwave limb sounder (MLS) and solar backscatter ultraviolet (SBUV-2) instruments are assimilated, as well as ozone monitoring instrument (OMI) and SCIAMACHY total columns (the latter only until March 2012, when the European Space Agency lost contact with the ENVIronmental SATellite -ENVISAT). The CO total columns are assimilated from the Infrared Atmospheric Sounding Interferometer (IASI) sensor and aerosol total optical depth is assimilated from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument.