dataesr/scientific-paragraphs-categorization huggingface dataset:
Except for standard observations of the ECMWF MARS archive, new observations on the mesoscale can already be assimilated or are in preparation for assimilation. Of great interest are additional TD observations provided by passive profilers and lidar systems  [Wulfmeyer et al. , 2006; Grzeschik et al. , 2008]  or using GNSS STDs  [Zus et al. , 2011; Kawabata et al. , 2013]. Particularly, it is expected that the assimilation of TD profiles provided by ground-based networks will reduce the model imbalance at initial time and lead to improved, seamless forecasts from nowcasting to the medium range. Whereas WVDIAL, WVRL, and TRRL data can be directly assimilated, passive remote sensing systems still require the development of suitable model forward operators for the IR and the MWR, which should be strongly pursued.
This approach is valid, if both the first guess database and the observed radiances are bias free. Otherwise, any biases in the background, the observation, or the observation operator are linearly propagated through the variational retrieval. Additionally, situation-dependent biases can be introduced: 1. In case of a global background with related global covariance, any regional retrieval distant from the global mean will be biased toward the global mean. Similarly, seasonally varying biases can be introduced. 2. Observation operators consist of Fréchet derivatives of fast radiative transfer models. These are often truncated in not fully representing all variability in the radiance field due to precipitation, clouds, or aerosols. Correspondingly, biases will occur in situations where they are not properly detected.
Reviews of Geophysics 10. 1002/2014RG000476 1 km, while the MWR has only 2 DFS even though additional elevation angles at 10, 20, 30, and 42°are included in the retrieval. The additional information results in the AERI's 1 sigma uncertainty being smaller than the MWRs at all altitudes (Figures  9c  and 9d ). The AERI also does a better job capturing the structure of WV in the lowest 1 km (Figure  9b ); however, the MWR (using both 22-30 and 174-183 GHz channels) does a better job aloft. This is supported by the AERI having a smaller 1 sigma uncertainty than the MWR in the first kilometers but they are virtually the same above that (Figure  9d ). Furthermore, the DFS profiles (Figures  9e  and 9f ) demonstrate that the AERI's information content for T and WV drops off relatively quickly with height, with nearly 90% of its total information in the lowest 4 km, whereas the MWR still has a significant amount of information in its data above 4 km.
At the Geoforschungszentrum (GFZ) in Potsdam, Germany, the Earth Parameter and Orbit determination System (EPOS) software  [Gendt et al. , 2004]  is used to estimate ZTD and STD. Once the ZTD is estimated, ZHD is subtracted to obtain the zenith nonhydrostatic delay which is directly related to the IWV  [Bevis et al. , 1992]  by IWV ¼ Π T m ð ÞZWD: (13) Here Π depends on the atmospheric mean temperature T m that is calculated from surface meteorological measurements. Currently, data of ≈300 stations in Germany are analyzed hourly at GFZ, and ZTD and IWV products are available with a delay of 30 min. As shown in section 2. 1, IWV can easily be transformed to PW. The bias error in GNSS PW appears to be less than 1 mm with an overall error of less than 5% under moist, tropical conditions. This estimate is based on comparisons with Very Long Baseline Interferometry, radiosonde, MWR, and lidar data  [Li et al.
, 2003;  Reviews of Geophysics 10. 1002/2014RG000476  Gendt et al. , 2004; van Baelen et al. , 2005; Teke et al. , 2011; Ciesielski et al. , 2014; Leblanc et al. , 2011]. Hence, the IWV is recoverable with a random error of less than 1 mm and biases of less than 1 mm. This corresponds to a relative error in absolute humidity of 5% in a 2 km deep ABL with an absolute humidity of 10 g m À3 , if the error comes from the ABL WV estimate only. An example of an IWV map is shown in Figure  13. These maps are available with a temporal resolution of 15 min. The ZTD data are now assimilated operationally by several European NWP centers. Though they have some slight positive impact on the prediction skills, they are clearly of limited value as they do not contain information about horizontal (and vertical) refractivity gradients in the vicinity of a station.
, 2011; Repasky et al. , 2013; Wagner et al. , 2013a]  demonstrated that these challenges with respect to laser performance can be overcome. Current properties of advanced laser transmitters for ground-based WVDIAL are causing systematic errors of < 1%  [Wagner et al. , 2011 [Wagner et al. , , 2013a;; Metzendorf et al. , 2015]. Spectroscopic errors are specified in the new HITRAN2012 database and are of the order 2%. Errors due to the temperature and pressure dependence of σ WV are < 2% as well  [Rothman et al. , 2003 [Rothman et al. , , 2005;; Späth et al. , 2014]. Using an end-to-end simulator, the combined system bias can be estimated  [Di Girolamo et al. , 2004b , 2004c , 2008; ESA, 2004]. An example is presented in Figure  22. A vertically pointing WVDIAL was simulated operating in U.
What requirements are needed for a future ground-based network of WV and T observing systems in order to produce a significant impact? This is outlined in an overview of the most important applications of observing systems in Earth system sciences, which can be separated in four areas: (1) Monitoring, (2) verification and calibration, (3) DA, and (4) process studies. The requirements identified are (1) compact setup in combination with low cost for purchase and maintenance, (2) capability of operating these systems in networks, (3) possibility of routine operation in different climate regions, (4) simple data analysis with well-defined error estimates, (5) low bias (<2-5% systematic error in WV and < 0. 5 K in T), and (6) profiling capability with high temporal (10 min) and spatial resolution (a few 100 m) in the ABL up to about 3-4 km with a noise error of < 10%.
However, due to the lower bias of active remote sensing, its characterization and separation of the precision may be more meaningful. In passive remote sensing, an analysis error covariance matrix is provided as well but the resulting RMS error can contain a bias, which depends on the choice of the first guess database and the meteorological conditions. Extensive research is still necessary with respect to the comparison of the performance of passive and active remote sensing systems in different climate regions and under different meteorological conditions and their synergy. It is extremely urgent to develop and to apply new observational TD profiling capabilities. Considering the costs for setup, operation, and maintenance, ground-based networks with high density and large coverage are most promising to accelerate our knowledge of energy and water cycles from regional to global scales.
The RMS error of a passive remote sensing retrieval can be approximated by the analysis error covariance matrix of the variational retrieval  [Rodgers, 2000]. This assumes both a Gaussian error distribution of the first guess database as well as of the noise error of the measured radiances and the radiative transfer forward operator. The noise error is given by the shape of the probabilistic density function (PDF) of the measurement process (see Figure  A1 ). The width of the PDF is often estimated by the noise error standard deviation. The estimation of the noise error covariance matrix is not only essential for retrieval characterizations but also for DA applications. In contrast to the bias, the noise error can be reduced by improving the SNR of the measured signals. For active remote sensing, the noise error can be estimated theoretically, e.
This is possible with special, low-noise in situ sensors and active remote sensing systems. Usually a bias statistics is derived using a large set of comparisons under different meteorological conditions. In this case Δ ¼ q ref À q m ¼ B ref À B m þ ε ref À ε m ≅ B ref À B m ≈ À B m ; if B ref j j << B m j j: (A4) This demonstrates the importance of the development of reference standards for TD profiling. By a statistical analysis of the intercomparison data also the RMS error ffiffiffiffiffi σ 2 Δ p can be estimated: Reviews of Geophysics 10. 1002/2014RG000476 σ 2 Δ ≡ 1 N X i Δ i ð Þ 2 ¼ 1 N X i B ref;i À B m;i þ ε ref;i À ε m;i À Á 2 σ 2 Δ ¼ 1 N X i B ref;i À B m;i À Á 2 þ 2 B ref;i À B m;i À Á ε ref;i À ε m;i À Á þ ε ref;i À ε m;i À Á 2 h i σ 2 Δ ≈ B ref À B m À Á 2 þ σ 2 ref þ σ 2 m ≈ B m 2 þ σ 2 m ; if B ref j j; σ ref << B m j j; σ m : (A5) The last expression holds, if the noise errors of the reference and the measurement are uncorrelated, which should generally be the case.
Materials and methods We arrayed microcosms of heterotrophic media along a 15-km transect beneath the understory of a tropical coastal forest on the Yucatan Peninsula, Mexico (climate data available here: http://www. worldclim. org/). We added 100 ml of autoclaved, filter-sterilized nutrient broth (Difco Laboratories, Detroit, MI, USA) containing 10 mg ml -1 cycloheximide (Sigma-Aldrich, St Louis, MO, USA) as a eukaryotic inhibitor to sterile Mason jars covered with a fine (1 mm mesh) screen. To investigate the role of stochasticity via dispersal, duplicate microcosms (Mason jars) were arrayed along the transect in locations that were at least 1 km apart (Supplementary Figure  1A ). At 3, 19, 23. 5, 27. 5, 43. 5, 51. 5 , 67 and 99 h, microcosms were sampled (4 ml) after stirring the media with a sterile pipet.
Half of each sample was used to measure optical density at 595 nm while the other half was preserved at -20 °C for DNA extraction using the UltraClean Microbial DNA (MO BIO Laboratories, Carlsbad, CA, USA) kit. We amplified 16 S rRNA genes for pyrosequencing using the primers and conditions described in  Fierer et al. (2008)  and  Knelman et al. (2012) ; none of the samples taken at 3 h yielded PCR products that were visible on an agarose gel and these samples were thus discarded from the study. As well, the sterility of microcosm 9B (all time points) and of sample 8B at time point 5 were compromised and these samples were discarded from future analyses. Pyrosequencing was performed by Engencore (University of South Carolina). Pyrosequencing data were screened using the QIIME toolkit  (Caporaso et al.
These analyses were conducted in QIIME using the PERMANOVA function and PERMDISP function (which in turn uses the betadisper function of the vegan package in R). We calculated the nearest taxon and net relatedness indices (NTI and NRI, respectively  (Webb, 2000) ) for each time point using the ses. mpd and the ses. mntd functions in the picante package in R and multiplying values by -1  (Kembel et al. , 2010). For this analysis, we used the filter_tree command in QIIME to trim the gg_13_5 Greengenes tree to contain only OTUs that were observed in this study. We used ANOVA and Kruskal-Wallis post hoc tests (pgirmess package) in R to determine differences in community aggregated trait values for rRNA copy number, NTI and NRI across time. Finally, we generated frequencyabundance plots for successional time points 23.
5 h and onwards (19 h showed nondistinguishable patterns from the 23. 5 h time point) and implemented the  Sloan et al. (2006)  neutral community model to determine if frequency-abundance patterns were indicative of neutral assembly. We also examined three additional data sets relating to primary and secondary successional sequences: a salt marsh sediment (Dini-Andreote et al. , 2015), a recently deglaciated soil from the Toklat glacier in Alaska, and a burned forest soil from the Four Mile Canyon Fire outside of Boulder, CO  (Ferrenberg et al. , 2013). Soils from the Toklat glacier were sampled in July of 2014. Samples were collected from frost-heave mounds along the glacial chronosequence by collecting the top 2 cm of material. Samples were frozen upon removal from the field and shipped to the University of Colorado at Boulder and kept at -80 °C until processing.
Statistical differences between successional categories were examined using nonparametric Kruskal-Wallis ANOVA (pgirmess package). The Toklat glacier forefield data set was divided into the unvegetated and vegetated chronosequence for this categorical analysis and a linear regression model on the natural log transformed response variable was also performed. All sequence data have been archived and are publically available at the doi:10. 6084/m9. figshare. 1556152.
DNA microarrays Microarray analyses of Drosophila larvae were performed as described previously  [40]. Microarray hybridization and array scanning were performed at IGBMC Strasbourg. Data obtained from microarray experiments are available at the European Bioinformatics Institute (http:// www. ebi. ac. uk/arrayexpress/) under the accession numbers E-MTAB-3842 and E-MEXP-2125.
Acknowledgments We thank  Zsuzsanna Ujfaludi ,  Gabriella Pankotai-Bodo  and  Gabor Papai  for helpful discussions and revisions. We thank  P. Nakatani  and  J. Kadonaga  for providing antibodies raised against  Drosophila TFIID  subunits. We also thank  Adrienn Csebella-Bakota  for maintaining Drosophila strains.
E-MEXP-2125. This research was supported by the  European Union  and the  State of Hungary , cofinanced by the  European Social Fund  in the framework of  TÁMOP 4. 2. 4. A/2-11-1-2012-0001  " National Excellence Program " and  HURO/1101/173/ 2. 2. 1   Hungary-Romania Cross-Border Co -operation Program 2007-2013. The grants provided for the financial support for the reagents used in this study.
This annotation confirmed the organism as Bacillus subtilis with 100% identity. 4,233 genes were predicted, of which 4,144 have a known function, including several encoding for proteins involved in riboflavin synthesis and transport. The genome sequence of this Bacillus subtilis strain will be useful for further characterization of its GM nature and in developing a specific method for its detection in food and feed additives by enforcement laboratories. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. JYFL00000000. The version described in this paper is version JYFL01000000.
However large data sets do not exist for this quantity, so we focus instead on the fire radiative power (FRP), a remotely-sensed variable strongly linked with the fire intensity. The general framework of this study is the estimation of fire size and intensity of individual fires in the Mediterranean Basin using parametric statistical methods. Several studies focusing on the estimation of fire size exist, proposing to derive this quantity based on meteorological and geographical covariates. Their authors mainly use statistical learning techniques in order to give a quantitative or qualitative insight on fire size  (Alonso-Betanzos et al. , 2003; Cortez and Morais, 2007; Sakr et al. , 2011). In some cases this analysis is extrapolated to future weather in the context of climate change  (Amatulli et al.
g. Pereira et al. , 2005, Fig. 2 ). Winter and early spring fires can also occur in Portugal and the Balkans (e. g. Moriondo et al. , 2006 , Table  1 ). However, we focus our analysis on the summer period to avoid seasonal changes in the driving factors, especially at the scale of the Mediterranean basin. Such a generalization of our approach is left for future work. There are 5821 and 4840 wildfires in our two BA data sets and 24 273 wildfires in our FRP data set. The FRP is retrieved by using measured radiance of the 4 and 11 µm channels at nadir. Other spectral bands are used for assessing cloud masking, glint, bright surface and other sources of false alarms and disturbances. FRP is provided at 1 km resolution by the MOD14 product. BA is retrieved from the observed changes in land cover.
The incertitude can be as large as 5 days and is caused by several factors such as cloud cover impairment of remote sensing and lack of detection of wildfires at the beginning of their development. As we deal with statistics on a large number of such wildfires, the uncertainty is reduced. Additionally, since the time period of study is mostly cloud-free, the uncertainty on the day of detection should be low  (Giglio et al. , 2010). This is confirmed by the strong link between fire and synoptic weather dynamics observed using the same methods in  Her-nandez et al. (2015b). In the following sections we will call these BA and FRP data sets BA M and FRP respectively. We also use the EFFIS Rapid Damage Assessment system, provided by the JRC of the European Commission  (European Commission, 2010).
This set is built using 250 m MODIS images. A first step of automated classification is used to isolate fire events and a post-processing using human visualization of the burnt scar is performed. A cross-analysis using the active fire MODIS product, land-cover data sets as well as fire event news collected in the EFFIS News module is finally done to ensure a low number of misclassifications (http: //forest. jrc. ec. europa. eu/effis/). The system records burnt areas of approximately 40 ha and larger  (Sedano et al. , 2013). It also contains smaller wildfires, but is less complete below this 40 ha threshold. The JRC provided the data for the 2006-2012 time period. We call this BA data set BA E in the following. A 3-D (latitude, longitude and time) connected component algorithm is used to determine what are the distinct fire events in the BA M data set.
Meteorological covariates Our weather database was built upon the ERA-Interim reanalysis of the European Center for Medium-range Weather Forecast (ECMWF)  (Dee et al. , 2011). The horizontal resolution of the reanalysis does not allow the derivation of the small-scale weather conditions in the immediate vicinity of the fire. To link the weather data to the fire data, we take the ERA-Interim grid point nearest from the detected fire event. We then associate to this event the weather recorded at 12:00 UTC the day of first detection. We extract the following meteorological covariates: -T 2 (in K): the 2 m air temperature anomaly, the difference between the 12:00 UTC 2 m air temperature and its climatological daily mean; -WS 10 (in m s -1 ): the 10 m wind speed; -N precip (in days): the anomaly with respect to the climatology of the number of days when precipitations ≥ 0.
The choice of the function gives more weight to the quality of the fit for the right tail of the distribution. If F (x) and Fn (x) were to have different asymptotic behaviours for large values of x the AD2R criterion would be very large. The minimization of the AD2R criterion then has the theoretical advantage of making a better fitting of the distribution for larger values of the selected variable. All the AD2R values found for each distribution and data set are available in Table  1. Computations were done in R (R Core Team, 2013) using the "fitdistrplus" package  (Delignette-Muller and Dutang, 2015). We see that for the BA data sets there are two distributions selected, Gamma and GEV. We will continue using only the GEV distribution since the difference seen for the BA M data set between these two distribution is very small (AD2R values of 20.
3 for the GEV distribution and 20. 0 for the Gamma distribution), whereas for the BA E data set the difference is much larger (AD2R values of 3. 45 for the GEV distribution and 23. 6 for the Gamma distribution). For FRP the Gamma distribution is selected. Surprisingly the Exponential distribution fits the BA data sets poorly. This could be due to the absence of the < 25 ha wildfires in our BA M and BA E data sets, whereas they are taken into account in  Schoenberg et al. (2003). Figure  2  shows the normalized histograms and modelled densities of BA and FRP with accompanying QQ-plots for all considered data sets. The QQ-plots were computed using the car package  (Fox and Weisberg, 2011). For values of BA smaller than 40 ha, the QQ-plots depart from the 95 %-level confidence intervals. Conversely, the QQ-plots are within the confidence intervals for larger values.
weather conditions  (Hernandez et al. , 2015a) , having an accurate fit of the high values of BA and FRP is enough for our modelling framework. Caution should therefore be taken when trying to interpret these distributions for low values of BA. For FRP, the QQ-plot remains within the 95 %-level confidence intervals for all values. Besides the AD2R criterion, Fig. 2  shows that the GEV and Gamma models fit the data accurately and can be considered suited for our model. In the following, we will take the strong hypothesis that the observations coming from the BA and FRP data sets have respectively GEV and Gamma distributions conditionally to the weather. This hypothesis was tested on large subsets of the data sets corresponding to particularly favourable or un-favourable weather conditions. We take as favourable conditions T 2 ≥ 5 K and WS 10 ≥ 6 m s -1 and as unfavourable conditions T 2 ≤ 0 and WS 10 ≤ 3 m s -1.
Methodology The general framework of our methodology is the parametric estimation of the conditional probability density function of BA or FRP with respect to T 2 , WS 10 and N precip. In other words we seek f Y |X (y) = f Y (y|X = x) with y the fire variable, X the meteorological covariates and x a specific value taken by the covariates. We made the hypothesis in the previous section that f log 10 (BA/25) ∼ GEV(µ, σ , ξ ) and f log 10 (FRP/4) ∼ Gamma(α, β) for all subsets of our data sets. Therefore to approximate the values of the parameters of these distributions we need to compute the distribution of y near the point X = x. To do so we choose to retain the 10 % of our data sets nearest of the point X = x and to estimate the parameters of the distribution by minimizing the AD2R criterion.
The fraction of nearest neighbours was chosen to be sufficient to estimate a distribution function. The calculation of these nearest neighbours was done in R using the FNN package  (Beygelzimer et al. , 2013). It must be noted that due to the curse of dimensionality taking a larger number of covariates would lead to a very large inaccuracy on x  (Hastie et al. , 2009, pp. 22-23). In order to tackle this issue we select only three covariates for our density estimation. The choice of these covariates was done using Fig. 1. We wish to retain covariates that cover a broad range of temporal variability and for which BA and FRP exhibit strong significant variability. We therefore choose to take X = ( T 2 , WS 10 , N precip ) for all data sets. For computation purposes we choose not to estimate f Y |X at each possible value of x.
However the two modes are hard to distinguish statistically because of the low number of points in our BA data sets (5821 for BA M and 4840 for BA E ). The difference of results between the BA M and BA E probabilities is due to the BA E data set spanning over the 2006-2012 time period, therefore missing the 2003 and 2005 megafire events which are present in the BA M data set  (San-Miguel-Ayanz et al. , 2013). Regarding fire intensity, FRP is a growing function of WS 10 , T 2 and a decreasing function of N precip , which is significant at the 90 % confidence level. The variability linked to T 2 and WS 10 is discussed in  Hernandez et al. (2015a)  and found back on this figure. Because we use a meteorological covariate depending on past weather ( N precip ), a seasonal preconditioning of high fire risk can be assessed.