dataesr/scientific-paragraphs-categorization huggingface dataset:
Near real-time service The main input data are again GRACE Level-1B data for the recovery of the Earth's gravity field but this time individual ACs derive in their NRT mode rapid gravity products for flood and drought alerting with latencies down to 5 d. The increased temporal resolution from typically 1 month to 1 d is achieved by using sophisticated regularization techniques to compensate for the loss of spatial resolution. GFZ converts the measured intersatellite tracking data by means of spacecraft velocities into daily differences of the dynamic forcing acting on the twin satellites that are then reduced by forces stemming from geophysical background models and non-conservative origin. The reduced data are then projected by means of functions of geopotential gradient differences to Earth gravity potential variations at ground level.
GNSS reprocessing Besides ultraprecise K-Band intersatellite ranging, the GRACE satellites, as well as all other dedicated gravity missions, are equipped with onboard dual-frequency Global Positioning System (GPS) receivers to allow for precise GPS high-low satelliteto-satellite tracking (GPS hl-SST). In case of the GRACE and the recently launched GRACE-FO mission the analysis of GPS data is thus an inherent part of the Level-1B data processing to 3 http://www. zki. dlr. de infer monthly gravity field solutions (e. g. Beutler et al. 2010). For this purpose high-quality information on GPS satellite orbits and GPS satellite clock corrections is necessary. State-of-the-art products are routinely provided by the International GNSS Service (IGS; cf. Dow et al. 2009 ). The operational IGS products are, however, continuously improved by taking the latest developments into account, for example, updates in the underlying reference frame  (Altamimi et al.
(2018). This includes post-processing of the GRACE data, processing of three different GNSS data sets used for the validation, as well as the computation of performance evaluation indicators, which are degree and cumulative degree WRMS reduction measures. For detailed information regarding these aspects we refer to  Chen et al. (2018). Here we only mention some slight differences with respect to  Chen et al. (2018). For the GRACE data postprocessing we add back SLR-derived degree 1 coefficients from  Sośnica et al. (2015)  and we restore the AOD1B atmospheric and oceanic dealiasing products from the EGSIEM combined dealiasing products (see Section 4. 1). Regarding the three GNSS station time-series, we use only those station time-series that do not suffer from any data gap within the 2-yr period of our EGSIEM gravity field solutions.
Since data pre-processing, outlier detection and gravity field computation in both approaches differ significantly, two suitably independent solutions are produced on a daily basis. GFZ converts the measured intersatellite range-rates and rangeaccelerations by means of spacecraft velocities derived from precise orbit determination (POD) into differences of the dynamic forcing acting on the twin satellites. These derived dynamic observations can be reduced by forces stemming from geophysical background models, representing the lithosphere density contrast (static gravity field), third body attractions, ocean tides, atmospheric and oceanic non-tidal mass variations (AOD1B) and forces of non-conservative (mainly drag and solar radiation pressure) origin. The reduced data is then projected by means of functions of geopotential gradient differences to Earth gravity potential variations at ground level and expressed in equivalent water layer thickness.
An operational test run of the NRT service was foreseen in the final phase of the EGSIEM project between 2017 April and September. However, the deteriorating health of GRACE-B posed a serious challenge in GRACE data processing and is the limiting factor in the quality of the gravity field solutions. Following a battery cell failure on 2016 October 25, the accelerometer aboard GRACE-B was turned off. Its data stream has subsequently been replaced with a 'transplant' data product, where the accelerometer measurements of GRACE-A are shifted in time and rotated to substitute the missing measurements on GRACE-B. Furthermore, to shed load on the GRACE batteries following the cell failure, intersatellite K-Band ranging (KBR) data were only collected in orbit segments where the satellites were fully exposed to the sun.
Since 2011 the satellites were subject to regular position switching maneuvers to conserve fuel on GRACE-B. These events are correlated with the β angle between the orbit plane and the Earth-Sun direction and have a periodicity of 161 d. When β is smaller than approximately 15 • , the intersatellite link was switched off and the KBR data are completely missing. Following this procedure, intersatellite observations between the GRACE spacecraft were once more available since 2017 March 17. To alleviate the accelerometer transplant process, the pitch angle of both spacecraft relative to the line of sight was increased from zero to approximately one degree (Himanshu Save, Gerhard Kruizinga, personal communication 2017) on March 30 (Fig. 8 , left-hand panel). The resulting increased KBR antenna phase centre correction (PCC) magnitude (Fig.
8 , right-hand panel) causes errors in spacecraft attitude more prominently propagating into the gravity field solutions and manifest in east-west striping patterns. KBR data collection was steadily increased from sunlight-only orbital segments (approximately 40 per cent reduction of observation count) to full revolutions. On May 2 the accelerometer on GRACE-B was switched on again for 22 d at the expense of the amount of KBR data collected during this period. Nevertheless during this time span the nominal set of GRACE science data was available. The operational phase of the NRT-Service started on April 1 with both GFZ and TUG producing daily GRACE solutions in an automated manner until 2017 June 29, when the instruments have been switched-off again. In fact these were the last gravity measurements of GRACE and the mission was officially declared ended on 2017 October 27.
5 he problems with KBR antenna PCCs described above impacted the quality of the TUG and GFZ models in a different manner. While GFZ relies fully on JPL's PCCs provided within the Level-1B data records, TUG calculates their own correction using a smoothed attitude product based on a combination of star camera observations with angular accelerations  (Klinger et al. 2014). This combination acts as a low pass filter and reduces high frequency noise in the derived PCC's compared to the Level-1B data set. The discrepancies between the solutions become obvious when looking at daily total water storage maps (Fig. 9 ) and derived flood indices (Fig. 10 ) within the operational test run (e. g. 2017 June 5) and a daily solution at 'normal' satellite conditions (e. g. 2008 June 5). In terms of difference RMS over the continents, TUG and GFZ solutions differ approximately 2.
However, it is now known that the real structure of an RNA is not always the structure of minimum free energy but often a structure close to it. Hence, being able to generate sub-optimal structures is an important feature for the RNA complex prediction problem. Moreover, RNAs can have several structures, as the riboswitches, forming complexes with other RNAs  [21]  or other molecules to regulate the gene expression. Sub-optimal structures are, especially, used together with SHAPE data to elaborate a conformational ensemble  [22]  that helps to determine the different states of RNAs or RNA complexes. Sub-optimal structures are also used to identify homologous ncRNAs in bacteria  [23]. To our knowledge, only NUPACK provides sub-optimal structures. Finally, among the state of the art, only NanoFolder, the NUPACK package and MultiRNAFold are available.
NanoFolder is available on a web server and the NUPACK package as well as MultiRNAFold are available as sources. Here, we propose an original approach and a tool for RNA complex prediction including pseudoknots and crossing interactions. Our approach takes advantage of the numerous tools dedicated to RNA secondary structure prediction as well as RNA-RNA interaction prediction. Indeed, an RNA complex can be considered as a set of structured RNAs interacting with each other, where the secondary structure of each RNA can impact the interactions and vice versa. For each RNA and for each pair of RNAs, several possible secondary structures can be predicted. The prediction of an RNA complex can therefore be viewed as the best combination among those different predictions that achieves the minimum free energy.
RCPred: implementation of BLS-CMWCP for RNA complex prediction The BLS heuristic proposed in  [32]  has a set of parameters to modulate the strength of the perturbation (L 0 and L Max ), the maximum number of non-improving solutions before a strong perturbation is performed (T), the coefficients for accepting non-improving solutions (α s and α r ), the coefficient for tabu tenure (φ) and the probability for applying directed perturbations (P 0 ). Some parameters were fixed in the BLS heuristic and we used them as such in BLS-CMWCP. We determined the other parameters by performing experiments on a dataset of 30 graphs derived from RNA complexes. We then chose the following parameters: L 0 = 0. 1 * |V |, L Max = 0. 1 * |V |, T = 10, α s = 0. 5, α r = 0. 5, φ = 7 and P 0 = 1. The stop condition for the local search occurs either when the optimum clique is found or the maximum number of iterations is reached.
Datasets and RCPred inputs In the following experiments, we use a dataset composed of 90 non-redundant RNA complexes. The dataset is extracted from the database RNA STRAND  [43]  that gathers 4,666 secondary structures of single and multistrand RNAs. All the recovered complexes are experimentally validated by NMR or X-ray and are not composed of modified nucleotides. We are interested here in complexes longer than 20 nucleotides and smaller than 1000 nucleotides. Because of the complexity in time of the tools from the state of the art, we exclude the complexes longer than 1000 nucleotides in order to be able to generate several secondary structures and interactions. The dataset of the benchmark is available on the EvryRNA platform. For each RNA, we generated the secondary structures using three tools from the literature for RNA secondary structure prediction, namely BiokoP  [24] , pKiss  [26]  and RNAsubopt (from the ViennaRNA package  [6] ).
Comparison with the state of the art We then compare RCPred with NanoFolder, NUPACK and MultiRNAFold. We report the F 1 -score results of these tools on our dataset in Fig. 5. We execute RCPred 5 times and, for each execution, we recover the maximum F 1 -score solution of the 10 first solutions returned for each complex. We then compute the mean of these recovered solutions. For NUPACK, we report the maximum F 1 -scores among the 10 first solutions returned. Note that we could not test NanoFolder (which is usable through a web server) on the longest complexes (of size greater than 550 nucleotides), because of the size limitation of the web server. Also we encounter some difficulties with the tool MultiRNAFold on some complexes. As we can see on Fig. 5 , RCPred obtains better F 1 -scores than the other tools in most cases.
With the sub-optimal solutions, we can guarantee that at least a predicted structure is close to the referenced one. For almost all complexes smaller than 120 nucleotides, RCPred is able to find accurate predictions with F 1 -scores higher than 80%. For longer complexes, RCPred, as well as the other tools, becomes less effective with a maximum F 1 -score around 70%. Similar results and findings are obtained with sensitivity, PPV, and MCC statistics (see Additional file 1). We summarize these results by reporting the average on all the dataset in Table  1. As NanoFolder and MultiR-NAFold were not able to give results for some complexes, their means are done only on the complexes that were successfully predicted. The results show that in average RCPred is able to predict complexes more accurately than NanoFolder, NUPACK and MultiRNAFold.
For RCPred and NUPACK, the maximum F 1 -scores among the 10 first returned solutions are taken for each complex to compute the mean. The corresponding means of sensitivity, PPV and MCC are given. For RCPred, the mean is computed over 5 executions. Bold text indicates the higher scores Moreover, a model cannot capture all the subtleties of the minimum free energy computation of an RNA complex of more than two strands. Sub-optimal solutions are then needed to allow to cover the discrepancies of the model. Among the existing tools, only the tool from the NUPACK package can generate sub-optimal solutions. We test RCPred on a large dataset composed of 90 RNA complexes of various lengths, including or not pseudoknots. We show that RCPred is able to predict accurately RNA complex secondary structures and gives competitive results compared to NanoFolder, NUPACK and MultiR-NAFold.
Each returned RNA complex has a global free energy resulting from the sum of the free energies of the secondary structures and of the interactions composing it. A perspective could be to improve the global free energy computation of the complexes (by adapting for example the calculation method used in RNAeval from ViennaRNA package  [6] ) and reorder them accordingly. The time execution of RCPred varies between 0. 05 s in average on 5 executions for the smallest complex and 16. 7 seconds for the longest. We are currently working on optimizing the time execution. In RNA complexes, the tertiary interactions are numerous and have an important role in the stabilization of the global structure. There exist databases gathering 3D motifs appearing in single RNA structures, like the Rna3Dmotif database  [44].
F 1 -Fig. 3 F 13 Fig. 3 F 1 -score results of RCPred. Results are shown for the 10 first solutions returned by RCPred in average on 5 executions on our dataset. The secondary structure inputs are generated by BiokoP, pKiss and RNAsubopt and the interaction inputs are generated by RNAsubopt. The complex lengths are indicated in nucleotides in the superior axis.
Early warning systems (EWS) for river flooding are strategic tools for effective disaster risk management in many world regions. When driven by ensemble Numerical Weather Predictions (NWP), flood EWS can provide skillful streamflow forecasts beyond the monthly time scale in large river basins. Yet, effective flood detection is challenged by accurate estimation of warning thresholds that identify specific hazard levels along the entire river network and forecast horizon. This research describes a novel approach to estimate warning thresholds which retain statistical consistency with the operational forecasts at all lead times. The procedure is developed in the context of the Global Flood Awareness System (GloFAS). A 21-year forecast-consistent dataset is used to derive thresholds with global coverage and forecast range up to six weeks.
GloFAS is designed to forecast large scale river flooding worldwide by detecting river reaches where predicted streamflow has significant probability to exceed model-consistent warning thresholds in a 30-day forecasting horizon  (Alfieri et al. , 2013). The estimation of suitable exceedance thresholds is a key task in flood early warning, where alerts are determined by the ratio between streamflow estimates and reference thresholds. Hence, model consistency is achieved through the use of the same hydrological model and meteorological product to derive both streamflow forecasts and the reanalysis dataset used to derive the thresholds. Accurate representation of the true river conditions along the river network is a long standing challenge in the global hydrological community  (Bierkens et al.
, 2015; Nijssen et al. , 2001 ). Yet, several examples have shown that skillful early warning systems based on threshold exceedance analysis favor the concept of model consistency rather than a mere pursuit of the true absolute hydrological states  (Pappenberger et al. , 2015b; Raynaud et al. , 2015; Reed et al. , 2007). In GloFAS, the estimation of warning thresholds is determined by the global meteorological datasets used to reproduce a long term streamflow simulation, and in turn derive threshold values for selected occurrence probabilities. The current operational setup stems from the outcomes of the work by  Hirpa et al. (2016). The key question in  Hirpa et al. (2016)  is whether flood thresholds derived from a reforecast climatology, hence favoring the consistence between thresholds and daily operational forecasts, yield more skillful prediction of severe flood events compared to using thresholds derived from a reanalysis product, hence more focused on reproducing the true state of the atmosphere.
To this end, the authors produced a continuous atmospheric forcing by concatenating data from the first days of a weather reforecasts dataset issued twice per week over 20 years, and found a marginal improvement in flood detection skills. The debate around the best choice of flood thresholds has been recently reopened, the main reasons being summarized as follow: -ERA5, the latest generation global atmospheric reanalysis product by ECMWF, has become available in 2018  (Hersbach et al. , 2018). Key features compared to the previous version ERA-Interim  (Dee et al. , 2011)  include 10 years' worth of advances in NWP, higher spatial resolution, improvements in the ingested observations, and a near-real time updating service (2-5 days latency) known as ERA5T, which makes it particularly appealing for the daily updating of the global hydrological conditions used to initialize GloFAS streamflow forecasts.
2. Assessing the variability of flood thresholds through the forecast range. 3. Assessing whether the approach based on time-invariant flood thresholds is fit for purpose or whether range-dependent thresholds should be adopted instead through the forecast horizon. In this article we present a new approach for deriving GloFAS flood thresholds over different weekly forecast horizons, based on an atmospheric reforecasts dataset, and we compare them with thresholds based on a global hydrological reanalysis forced by ERA5, hence consistent with the initial hydrological conditions.
ERA5 ERA5  (Hersbach et al. , 2018)  is the latest climate reanalysis dataset produced by ECMWF through the Copernicus Climate Change Service (C3S). ERA5 data extending from 1979 to the present became available at the end of 2018, while the second phase, extending back to 1950, is planned for release by autumn 2019. ERA5 is based on the Integrated Forecasting System (IFS) Cycle 41r2 which was operational in 2016. Therefore, ERA5 benefits from a decade of developments in model physics, numerics and data assimilation, compared to the ERA-Interim IFS Cycle 31r2, operational in 2006. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim, ERA5 has a number of innovative features, including hourly output, and an uncertainty estimate. ECMWF forecasts from ERA5 analysis show a gain of up to one day in skill with respect to ERA-Interim  (Haiden et al.
, 2018) , which is reflected not only in average weather variables but also with regard to large-scale weather patterns such as tropical cyclones. In this work we used daily maps of precipitation, minimum, mean and maximum surface air temperature, mean sea level pressure, incoming solar radiation, wind speed and relative humidity extracted from ERA5 for 32 complete years between 1986 and 2017. Some variables were then processed to produce estimates of potential evapotranspiration using the Penman-Monteith equation. This represents one of the main dynamic input variables of the hydrological model Lisflood, together with mean surface air temperature and precipitation. ERA5 data was used to model the daily hydrological states over 1986-2017.
Atmospheric reforecasts ECMWF reforecasts are global scale forecast runs that use the same Integrated Forecasting System (IFS) model version as the real-time ensemble forecasts (ECMWF-ENS), for the past 20 years. Similar to the operational forecasts, reforecasts have horizontal resolutions of 18 km for up to 15-day lead time and 36 km for longer forecast lead times up to 46-day. In this work we used the unperturbed ensemble member (i. e. , the control run) of 6-week reforecasts with daily resolution, initialized once per week in the entire period of availability, coupled with the last year of operational forecasts. The collected dataset thus includes 1218 sets of reforecasts with global coverage, spanning 21 years between 21/11/1996 and 25/12/2017. More in details, each of the 21 years of data is composed by 58 weekly reforecasts starting in mid-November of the previous year, so that each day of each year is ultimately simulated in 6 different reforecasts with lead time between 1 and 6 weeks.
Methods We investigated differences in the statistics between ERA5 and the reforecasts dataset at all lead times, and their resulting hydrological simulations. The analysis is split in two steps, first focusing on the precipitation statistics and then on the flood warning thresholds. With regard to precipitation, we analyzed the mean annual maxima of daily precipitation (Pmax) and the mean annual precipitation (MAP), including spatial patterns of the differences between the two datasets. Due to their non-parametric nature, precipitation statistics are key diagnostic indicators that anticipate and explain possible differences between the corresponding hydrological model outputs. In this regard, Pmax is representative of extreme precipitation and is generally well correlated to floods (e. g.
,  Guillot and Duband, 1967) , while MAP is an indicator of long term bias between the considered datasets. The second part of the analysis is performed on flood thresholds based on ERA5 versus those based on reforecasts. To this end, we first ran a 32-year global hydrological simulation forced with ERA5 and extracted discharge time series at all points of the river network. Then, 1,218 sets of 6-week reforecasts, initialized once per week between 21/  11/1996 and 25/12/2017, were used as input to run as many hydrological simulations, taking initial conditions from the corresponding date in the ERA5-based run. Such model runs forced by reforecasts include 58 different 6-week simulations per year, starting on the 21 November of the previous year, so that each day of each year is ultimately modeled in six different simulations, with forecast range spanning between 1 and 6 weeks.
We applied Extreme Value Distribution (EVD) fitting to the annual maximum discharges extracted from the ERA5-based dataset as well as from the six datasets stemming from reforecasts, after reordering the data by weekly lead time. The first two years of the ERA5-based dataset were discarded to guarantee sufficient model warm-up, leaving 30 years of data for the EVD fitting, while in the case of reforecasts all 21 years of data were used, given that meaningful initial conditions were provided by the ERA5-based run. Such 9-year difference (i. e. , 30 versus 21 years) in the length of the two datasets may produce discontinuities in the related flood thresholds, in case of substantial differences in the statistics of the extremes in those 9 years when reforecasts are not available. In view of the direct implications of this research with the operational runs of GloFAS we have  opted to accept this potential discontinuities, for the following reasons: • While the ERA5 dataset is continuously extended in near-real time and therefore keeps growing in size, ECMWF reforecasts are regenerated every week using the latest IFS model version (which is updated more than once per year), yet only for the most recent 20 years.