dataesr/scientific-paragraphs-categorization huggingface dataset:
SEARCH METHODOLOGY The methods used to search for binary merger signals and GWBs are largely the same as for the previous analysis described in  [30]. The one major change is the necessity to search the variably shaped, irregularly sized error regions provided by the IPN localizations. We begin by describing how that is done and then provide a brief review of the remainder of the analysis details, referring the reader to  [30]  for more details. Covering the error boxes-The analysis of the gravitational-wave data depends on the assumed sky direction to the GRB, since the data must be time-shifted according to the expected time-of-arrival at each detector. This is also weighted by the response of each GW detector to the assumed sky direction. Each GRB localization error box corresponds to a 3-σ region determined by the intersection of the IPN timing annuli, with a construction process described in detail in  [38].
However, most of these IPN error regions are larger than the directional resolution of the GW detector network. Thus, to maximize the likelihood of finding a gravitational-wave signal associated with the GRB, we perform a discrete search across the entire IPN error box by populating it with a grid of points and repeating the search at each of these points. The GW detectors have a timing resolution of ∼ 0. 5 ms, corresponding to a spatial resolution of a few degrees. Therefore, we chose search point separations of approximately 3. 6 degrees when only LIGO interferometers' data were used in the search and 1. 8 degrees when the more widely separated Virgo detector was also used. The probability distributions of search points over the error boxes were chosen Gaussian for long bursts (to assure that there are proportionally more points for those positions with larger probabilities of containing a signal) and uniform for short bursts (no assumptions made in terms of signal origin within the error box).
RESULTS A search for gravitational waves has been performed for a total of 223 GRBs. Of these, 27 were classified as short GRBs with a likely binary coalescence progenitor. The gravitational-wave data at the time of these GRBs has been searched for evidence of the coalescence waveforms. For 221 bursts, including 25 of the 27 short bursts we have performed a search for generic gravitational wave transients in the IPN error box around the time of the GRB. A full list of all GRBs analysed is available in the supplementary material, where we provide two tables listing the short and long GRBs analysed in this search. Modeled coalescing binary search results-For each GRB, we estimate the p-values of the gravitational wave candidate, by comparing with the background trials. The distribution of observed p-values is shown in Figure  1.
ASTROPHYSICAL INTERPRETATION Given that no significant event was found in our analyses, we place limits on GW emission based on both binary merger (for short GRBs) and generic gravitational wave burst (for all GRBs) signal models, and assess the potential of a similar search with secondgeneration gravitational-wave detectors around 2015-2020. Distance exclusion-For a given signal morphology, the gravitational wave analysis is efficient in recovering signals up to a certain distance limit that depends on the sensitivity of the detectors at the time of the search. We quote a 90% confidence level lower limit on the distance to each GRB progenitor: that is, the distance at which we recover 90% of simulated signals. The quoted exclusion distances are marginalized over systematic errors that are inherent in this analysis: errors introduced by the mismatch of a true GW signal and the waveforms used in the simulations  [9]  and amplitude errors from the calibration of the detector data  [47].
7, and that it detects short bursts with good efficiency up to a redshift of about 0. 45. The advanced LIGO and Virgo detectors are nearing completion. They are expected to start taking data in 2015 and reach their design sensitivity towards the end of the decade  [57]. We can use the results obtained here to extrapolate and predict what might be expected with the advanced detectors. The S5-6 and VSR1-3 runs comprised a total of around 21 months of two (or more) detector duty cycle. Over that period, the detectors' reach varied by approximately a factor of 4, from a 5 Mpc sensitive distance to NSNS sources for H2 in early S5 to 20 Mpc for H1 and L1 by the end of S6. Similarly, the current scenario  [57]  calls for FIG. 7. Cumulative redshift distribution F (R) exclusion from the analysis of 508 GRBs with the GW burst search.
With the recent advances in global soil moisture retrievals from satellites in the past decade, we are now in the position to study the related processes based on observations. Global surface soil moisture (SSM) datasets have been produced based on active and passive microwave satellite observations, including the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Scatterometer (ASCAT) SSM products  (Bartalis et al. , 2007; Kerr et al. , 2010; Njoku, Jackson, Lakshmi, Chan, & Nghiem, 2003; Owe, de Jeu, & Holmes, 2008). See also  Kerr (2007)  and  Wagner et al. (2007)  for a detailed review. SMOS is the first passive satellite specifically designed to measure SSM (and sea surface salinity) on a global scale  (Kerr et al. , 2010 (Kerr et al. , , 2012)). Since its launch in November 2009, SMOS has been recording brightness temperatures at L-Band (1.
, 2012; Wigneron et al. , 2007) , that is, multi-angular observations are used to simultaneously retrieve SSM (directly quantified in m 3 /m 3 ) and the vegetation optical depth at nadir (τ-NAD) based on a standard iterative minimization approach of a cost function  (Wigneron, Waldteufel, Chanzy, Calvet, & Kerr, 2000). SMOSL3 ascending retrievals were selected in this study as they have generally been proven to be more accurate than SMOSL3 descending retrievals  (Alyaari et al. , 2014; Al-Yaari et al. , 2014). The SMOSL3 datasets provide flags that can be used to screen out questionable SSM retrievals  (Jacquette et al. , 2010; Kerr, Jacquette, et al. , 2013) , in particular because of radio-frequency interferences (see Section 2. 2 for more details). It should be noted that, in the present study, we used the latest version available at CATDS.
ASCAT ASCAT is a real-aperture radar instrument that operates at C-band (5. 255 GHz) on-board the Metop satellite since 2006, which crosses the equator at 21:30 LST for the ascending overpass and at 09:30 LST for the descending overpass. In this study, we used SSM products generated with the WARP5. 5 software provided by TU-Wien, which is the latest version of the algorithm used to produce this SSM dataset. As for SMOSL3, we only considered here morning overpasses, as previous findings indicated that the ascending ASCAT overpass retrievals are less accurate than the descending (i. e. , morning) ones (e. g. ,  Brocca et al. , 2010). ASCAT SSM data are provided in terms of degree of saturation, that is, in relative units ranging between 0 (dry) and 100 (saturated). These extremes correspond, respectively, to the lowest and highest values of the observed backscatter over the first few centimeters of soil (b 3 cm).
RFI originates, for example, from satellite transmissions, aircraft communications, radar, or TV radio-links and contaminates the passive microwave emissions from Earth  (Njoku, Ashcroft, Chan, & Li, 2005; Oliva et al. , 2012). Fig. 1  shows the global spatial pattern of the probability of RFI occurrence in the SMOS observations, presented as average of the probability of RFI occurrences during the period  (2010) (2011) (2012). In the present study, RFI effects were filtered out, using RFI flags provided in the SMOSL3 product. SMOSL3 SSM values were excluded if one of the following conditions was fulfilled (i) DQX N 0. 06, (ii) DQX is equal to fill value, or (iii) percentage of radio frequency interference (RFI_Per) N 30%. ASCAT, SMOSL3, and the reference MERRA-Land dataset are distributed on different grids and formats.
In this study, a nearest neighbor approach (e. g. ,  Draper, Mahfouf, Calvet, Martin, & Wagner, 2011; Rüdiger et al. , 2009)  was used to re-project all the datasets onto a regular 0. 25°× 0. 25°grid. Finally, all the three SSM datasets were screened, applying additional static masks, to remove grid cells with (i) steep mountainous terrain, based on a topographic complexity flag (provided with the ASCAT data) greater than 10%  (Draper et al. , 2012), (ii)  open water, identified as having a wetland fraction (provided with the ASCAT data) greater than 5%, and (iii) frozen soil conditions, identified as having soil temperatures (top layer) below 276 K, obtained from MERRA-Land. It should be noted that all the statistical indicators were computed only when all the three SSM data were available from the different datasets and therefore the number of ASCAT and SMOSL3 SSM data used in the time series are identical, which is illustrated in Fig.
Comparison using triple collocation error model The triple collocation error model (TCE) is a powerful statistical tool to estimate the RMSD of a set of at least three linearly related data sources with uncorrelated errors. Stoffelen (1998)  introduced the TCE model to evaluate wind vector datasets derived from a model, buoy measurements and scatterometer observations. Scipal, Holmes, de Jeu,  Naeimi, and Wagner (2008)  later used the TCE to evaluate SSM datasets derived from models and satellites. Then, other authors (e. g. ,  Dorigo et al. , 2010; Draper et al. , 2013; Loew & Schlenz, 2011; Miralles, Crow, & Cosh, 2010; Parinussa et al. , 2011 ) also used the TCE method to characterize the errors of SSM derived from models and remote sensing. In this study, TCE was applied to the ASCAT, SMOS, and MERRA-Land SSM products, and specifically to their long-term anomalies, using 2010-2012 time series centered on its mean.
The estimated SSM anomalies at time t from product i (denoted θ i (t) in the following) are linked to the unknown true SSM θ(t) by a multiplicative bias term β i together with an error ε i : θ 1 t ð Þ ¼ β 1 :θ t ð Þ þ ε 1 ð6Þ θ 2 t ð Þ ¼ β 2 :θ t ð Þ þ ε 2 ð7Þ θ 3 t ð Þ ¼ β 3 :θ t ð Þ þ ε 3 ð8Þ Note that since centered time series (anomaly from Eq. (  5 ) without normalization) are used here, a constant bias term is not needed in Eqs. (  6 )-(  8 ). One of the datasets has to be defined as the reference dataset, namely MERRA-Land in this study, with β 1 = 1. The other two datasets can then be calibrated using θ i ⁎ = θ i /β i and ε i ⁎ = ε i /β i in Eqs. (  6 )-(  8 ) to obtain: θ 1 Ã ¼ θ þ ε 1 Ã ð9Þ θ 2 Ã ¼ θ þ ε 2 Ã ð10Þ θ 3 Ã ¼ θ þ ε 3 Ã ð11Þ where θ 2 ⁎ and θ 3 ⁎ are the rescaled measurements, and ε 2 ⁎ and ε 3 ⁎ are the rescaled random errors (see, e.
g. ,  Draper et al. , 2013). By pairwise subtraction of Eqs. (  9 )-(  11 ) and subsequent averaging over the crossmultiplied differences, we obtain: ε 1 2Ã ¼ b θ 1 Ã -θ 2 Ã À Á θ 1 Ã -θ 3 Ã À Á N ð12Þ ε 2 2Ã ¼ b θ 1 Ã -θ 2 Ã À Á θ 2 Ã -θ 3 Ã À Á N ð13Þ ε 3 2Ã ¼ b θ 1 Ã -θ 3 Ã À Á θ 2 Ã -θ 3 Ã À Á N ð14Þ where b N is the long-term mean, and the square root of the estimated ε i 2⁎ are the triple collocation errors estimates. The above derivation, and hence the validity of the TCE analysis, is based on the assumptions that the errors ε i of the three datasets are uncorrelated, and that the three datasets can be linearly modeled as in Eqs. (  6 )-(  8 )  (Dorigo et al. , 2010; Janssen, Abdalla, Hersbach, & Bidlot, 2007; Scipal, Dorigo, & de Jeu, 2010). Because the three datasets are largely independent, TCE can be expected to perform well, but any residual error cross-correlations among the datasets would result in biased error estimates  (Yilmaz & Crow, 2012).
Spatial Analysis of SSM retrievals at the global scale Fig. 4  shows global maps of the time series correlation coefficient R for original SSM values and monthly anomalies (with only significant correlations, i. e. , p b 0. 05), the ubRMSD, and the bias (Section 2. 3). In these maps, SMOSL3 (right panels) and ASCAT (left panels) were Fig. 3. Global distribution of the long term mean leaf area index (LAI)  (Dirmeyer et al. , 2006). evaluated against the MERRA-Land reference dataset at each pixel over the 05/2010-12/2012 period. In general, the metrics for SMOSL3 and ASCAT show a similar spatial correspondence with the MERRA-Land SSM over most of the globe. Fig. 4a  and b  shows that strong correlations (R is generally greater than ~0. 5) between the global remotely sensed and the reference SSM products are found in the transition zones between wet and dry climates (e.
g. , Sahel), in the Great Plains (USA), western Europe, Australia, India, Kazakhstan, and south-eastern Brazil. This can be explained by the strong seasonal annual cycle of SSM in these regions  (Koster et al. , 2004). Conversely, remotely sensed datasets exhibited weak correlations (R is generally less than 0. 15) against the reference in arid regions due to the small range of natural variation in the SSM values. The correlations can even be negative between the ASCAT and MERRA-Land data pairs in some arid sites (e. g. , Saudi Arabia and North Africa; Fig. 4a ). Low correlations for both SMOSL3 and ASCAT in high latitude regions can also be seen in Fig. 4a  and b , where the R values drop below 0. 20. Time series correlation values (R) computed for seasonal anomalies, as described in Section 2.
3, are shown in Fig. 4c  and d. The global spatial patterns are again relatively similar for both SMOSL3 and ASCAT, with a slightly better ability of SMOSL3 to capture the short-term SSM variability of the reference than ASCAT in Central America and Australia, while ASCAT was found to be slightly better in Europe, India, and parts of China. For both datasets, rather high correlation values (R N 0. 5) with the reference were found in eastern Australia, southern South Africa, Western Europe, and Central America, whereas low values were found in the northern Arabian Peninsula, North Africa, and tundra regions. Fig. 4e -h shows a similar distribution of ubRMSD and bias values for both SMOSL3 and ASCAT products. The ubRMSD values show a clear spatial distribution: low ubRMSD and bias values were found in deserts (e.
However, the values of the biases are quite different: in comparison with the MERRA-Land SSM values, higher SSM values can be noted for ASCAT, especially in the boreal regions, whereas lower SSM values can be noted for SMOSL3. The positive bias, found mainly at high latitude regions, in the ASCAT retrievals which is associated to wetter months (i. e. summer periods) can be partially explained by errors in the FAO database used to convert the ASCAT degree of saturation to volumetric water content where values for a few pixels in the northern hemisphere exceed 0. 6 m 3 /m 3. Fig. 5a  and b compares the areas where SMOSL3 correlates better with the reference than ASCAT (red), and where ASCAT correlates better with the reference than SMOSL3 (green). Looking at original datasets, it can be seen that better correlations with MERRA-Land were obtained with ASCAT over regions with high to moderate vegetation density and in regions where there is a strong seasonality in the SSM variability (e.
2), so there is no-data at latitudes above 55°N in the winter time. The main difference between the three HDs is a difference in mean, with higher SSMs according to MERRA-Land. This is consistent with the negative biases of the remotely sensed SSM products with respect to the MERRA-Land reference shown in Fig. 4g-h. Moreover, Fig. 7  reveals a common periodical behavior with time and latitude: the lowest values are comprised in two "parallel" sinusoidal bands around the equator reaching the minima around April. Hence, ASCAT and SMOSL3 capture the SSM seasonal variations in the inter-tropical area as simulated by MERRA-Land. The meridional shift of the Intertropical Convergence Zone (ITCZ) is well detected by all three datasets, but MERRA-Land presents higher seasonal cycle variations. The main differences in the SSM distribution are found in the Northern Hemisphere particularly related to the increase of SSM values during the summer period.
Also,  Leroux et al. (2011)  did not exclude SSM data measured during the commissioning phase which might have increased the error for the SMOS dataset. Moreover, the way to handle data filtering using flags such as the data quality index and RFI percentage may be different in both studies. As shown in Fig. 8a  and b , the error estimates for both products are lowest in arid regions (e. g. , Arabian Peninsula, Central Australia, and Egypt) due to low amounts of precipitation received leading to a low temporal variability of SSM in these regions. Higher TCE errors were found for both SMOSL3 and ASCAT over India and over locations near the Equator (e. g. , South Sudan, Zambia) where MERRA-Land is much less reliable due to the paucity of precipitation gauges, particularly over most of the African continent.
Summary of the results This study investigated the consistency of two microwave-based SSM products with respect to a reference SSM product, namely the MERRA-Land SSM product, derived from the MERRA reanalysis, for the period 05/2010-12/2012 at the global scale. The two remote sensing products are (i) the SMOSL3 SSM product, which is a microwave-based product derived from L-band passive brightness temperature measurements developed and supported by the CATDS, and (ii) the ASCAT SSM product, which is a microwave-based product derived from C-band active backscatter measurements, developed and supported by TU-Wien. The analysis of the original data shows, in general, a good correspondence between the SMOSL3 and ASCAT derived SSM products with the MERRA-Land reference. For instance, SMOSL3 and ASCAT successfully captured the spatio-temporal dynamics of the MERRA-Land SSM product, as seen in the correlation analyses, in the transition zones between wet and dry climates (e.
g. , Great Plains of North America, Sahel), Eastern Australia, and South-eastern regions of Brazil. It is worth noting that the regions of good agreement between SMOSL3, ASCAT, and MERRA-Land are also regions of strong coupling between soil moisture and precipitation as demonstrated by  Koster et al. (2004). SMOSL3 and ASCAT exhibited weak correlations with the MERRA-Land reference data in tundra and arid regions (e. g. , Sahara, Arabian Peninsula, and central Australia). ASCAT even exhibited negative correlations over some of the dry deserts (e. g. , Sahara). These low correlations may be explained by the small range of variation in the SSM values in these dry regions which corresponds roughly to the remotely sensed retrieval accuracy (~0. 04 m 3 /m 3 ,  Kerr et al. , 2001). Issues with the ASCAT SSM retrievals in dry regions may be explained by (i) systematic errors in the retrieval algorithm due to different scattering mechanisms in dry soils  (Wagner et al.
, 2013)  and (ii) changes in small-scale surface roughness, produced by wind-blown sand  (Frison & Mougin, 1996). Anomaly time series correlations show, in general, similar spatial patterns compared to the correlations found using original datasets, but with lower R values, especially in the transition zones. The global scale analysis of the bias and ubRMSD also confirmed these results. However, opposite patterns were generally obtained in terms of bias: ASCAT is generally wetter than MERRA-Land (positive bias), while SMOSL3 is generally drier than MERRA-Land (negative bias). Additional insights were provided by the Hovmöller diagrams, which visualize the time changes in SSM as a function of latitude. It is found that even though strong correlations are found between all three products at global scale, the spatio-temporal patterns shown in the HD may be quite different for SMOSL3, ASCAT and MERRA-Land in some latitudinal bands.
Discussion These results may appear as surprising because microwave sensors should be more efficient to sense through moderate vegetation at Lband than at C-band (Al-Yaari et al. , 2014): with increasing frequency (i) scattering and attenuation effects by vegetation elements (leaves, stems, trunks, branches, fruits, etc. ) increase and (ii) the sampling depth in soil decreases. However, in this study, SMOS and ASCAT differ not only in terms of frequency but also in terms of microwave technology: SMOS is a radiometer (i. e. a passive microwave system), while ASCAT is a scatterometer (i. e. an active microwave system). Previous studies comparing SSM retrievals from radiometer and scatterometer systems  (Brocca et al. , 2011; Rüdiger et al. , 2009 ) also found that SSM products retrieved from scatterometer data were less impacted by vegetation than those retrieved from radiometers data.
However, the gauge density is very sparse in Africa and at high latitudes. In these regions in particular, a lack of consistency between the remote sensing products and MERRA-Land SSM does not necessarily imply poor performance by the remote sensing estimates. Other factors that determine the skill of MERRA-Land soil moisture include the radiation forcing as well as the land model physics and associated model parameters, whose quality is similarly variable across the globe. Looking ahead, improvements in the retrieval algorithms as well as in the LSM data can be expected. For the SMOSL3 product, this includes enhancements especially in terms of RFI filtering and dry bias correction. For ASCAT, the issues found over arid regions are currently under investigation. Finally, the next version of the MERRA reanalysis is currently in production and features improved precipitation forcing, the single most critical input to SSM estimates from models.
Acknowledgments This research work was funded by the  TOSCA (Terre Océan Surfaces Continentales et Atmosphère) CNES program  and the  Islamic Development Bank (IDB). Gabrielle De Lannoy  and  Rolf Reichle  were supported by the  NASA   Soil Moisture Active Passive mission. The authors acknowledge CATDS for the SMOSL3 dataset (http://catds. ifremer. fr)and the  Vienna University of Technology  ( TU-WIEN ) for the ASCAT product. SMOS team at  CESBIO  is thanked for fruitful discussions. Mr Christophe Moisy  provided valuable technical assistance.
5 cm -1. Because of the differing spectral windows, the study can only be indicative for some of the future missions. We prepared three data sets: GOSAT spectra collocated with TCCON observations, GOSAT spectra of 2 entire years over Europe, and a global ensemble of synthetic spectra. To degrade the spectral resolution of GOSAT measurements we convolved the spectra with a broad Gaussian or sinc function -equivalent to assuming a shorter optical path length for the GOSAT interferometer. No noise was added to the GOSAT spectra, i. e. we concentrate on the effect of the reduced spectral resolution and stick to the GOSAT signal-tonoise ratio (SNR). The question of whether a reduced retrieval performance at lower resolution can be compensated by a higher SNR can only be answered with synthetic data.
We will briefly address this issue when we discuss the data set of simulated spectra. The differences between retrieved greenhouse gas columns from GOSAT spectra and collocated TCCON observations served as our estimate for the absolute accuracy of GOSAT retrievals. The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier transform spectrometers, established in 2004  (Wunch et al. , 2011a). Its goal is to remotely measure column abundances of CO 2 , CO, CH 4 , N 2 O and other gases that absorb in the near-infrared (SWIR). Currently, there are 18 operational observation sites affiliated with TCCON. TC-CON measurements are used to validate column abundances of greenhouse gases retrieved from satellite measurements  (Butz et al. , 2011; Morino et al. , 2011; Parker et al.