Methods and results of an automatic analysis of a complete sample of Swift-XRT observations of GRBs

We present a homogeneous X-ray analysis of all 318 gamma-ray bursts detected by the X-ray telescope (XRT) on the Swift satellite up to 2008 July 23; this represents the largest sample of X-ray GRB data published to date. In Sections 2–3 , we detail the methods which the Swift -XRT team has developed to produce the enhanced positions, light curves, hardness ratios and spectra presented in this paper. Software using these methods continues to create such products for all new GRBs observed by the Swift -XRT. We also detail web-based tools allowing users to create these products for any object observed by the XRT, not just GRBs. In Sections 4–6 , we present the results of our analysis of GRBs, including probability distribution functions of the temporal and spectral properties of the sample. We demonstrate evidence for a consistent underlying behaviour which can produce a range of light-curve morphologies, and attempt to interpret this behaviour in the framework of external forward shock emission. We find several difficulties, in particular that reconciliation of our data with the forward shock model requires energy injection to continue for days to weeks.     

Gamma ray burst studies with THESEUS

Gamma-ray Bursts (GRBs) are the most powerful transients in the Universe, over–shining for a few seconds all other γ-ray sky sources. Their emission is produced within narrowly collimated relativistic jets launched after the core–collapse of massive stars or the merger of compact binaries. THESEUS will open a new window for the use of GRBs as cosmological tools by securing a statistically significant sample of high-z GRBs, as well as by providing a large number of GRBs at low–intermediate redshifts extending the current samples to low luminosities. The wide energy band and unprecedented sensitivity of the Soft X-ray Imager (SXI) and X-Gamma rays Imaging Spectrometer (XGIS) instruments provide us a new route to unveil the nature of the prompt emission. For the first time, a full characterisation of the prompt emission spectrum from 0.3 keV to 10 MeV with unprecedented large count statistics will be possible revealing the signatures of synchrotron emission. SXI spectra, extending down to 0.3 keV, will constrain the local metal absorption and, for the brightest events, the progenitors’ ejecta composition. Investigation of the nature of the internal energy dissipation mechanisms will be obtained through the systematic study with XGIS of the sub-second variability unexplored so far over such a wide energy range. THESEUS will follow the spectral evolution of the prompt emission down to the soft X–ray band during the early steep decay and through the plateau phase with the unique ability of extending above 10 keV the spectral study of these early afterglow emission phases.

     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z _H), differential reflectivity (Z _DR), co-polar correlation coefficient (ρ _HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R ² and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R ² and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.     

Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This causes additional frictional drag resisting the transmission of axial load (resulting from surface slack‐off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may become ‘locked‐up’ making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set‐up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial load and radial clearance can help avoid lock‐up conditions and costly failure of the tubular. In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross‐validation of the Back Propagation Neural Network (BPNN) algorithm, a two‐hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd.     

Young star clusters in the Large Magellanic Cloud: NGC 1805 and 1818

We present colour–magnitude diagrams for two rich (≈10⁴ M_⊙) Large Magellanic Cloud star clusters with ages ≈10⁷ yr, constructed from optical and near-infrared data obtained with the Hubble Space Telescope . These data are part of an HST project to study LMC clusters with a range of ages. In this paper we investigate the massive star content of the young clusters, and determine the cluster ages and metallicities, paying particular attention to Be-star and blue-straggler populations and evidence of age spreads. We compare our data with detailed stellar-population simulations to investigate the turn-off structure of ≈25 Myr stellar systems, highlighting the complexity of the blue-straggler phenomenon.