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One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the t...
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One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the two types of perturbations in the ECMWF operational ensemble prediction system. First, the similarity between perturbations generated using initial-time singular vectors (SVs) and analyses from the ensemble data assimilation (EDA) system is assessed. Results show that the EDAperturbations are less localized geographically and have a better coverage of the Tropics. EDA perturbations have also smaller scales than SV-based perturbations, and have a less evident upshear vertical tilt, which explains why they grow less with forecast time. Then, the use of EDA-based perturbations in the ECMWF ensemble prediction system is studied. Results indicate that if used alone, EDA-based perturbations lead to an under-dispersive and less skilful ensemble then the one based on initial-timeSVs only. Combining the EDA and the initial-time SVs gives a system with a better agreement between ensemble spread and the error of the ensemble mean, a smaller ensemble-mean error and more skilful probabilistic forecasts than the current operational system based on initial-time and evolved SVs.
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摘要 :
One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the t...
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One of the crucial aspects of the design of an ensemble prediction system is the definition of the ensemble of initial states. This work investigates the use of singular vectors, an ensemble of analyses, and a combination of the two types of perturbations in the ECMWF operational ensemble prediction system. First, the similarity between perturbations generated using initial-time singular vectors (SVs) and analyses from the ensemble data assimilation (EDA) system is assessed. Results show that the EDAperturbations are less localized geographically and have a better coverage of the Tropics. EDA perturbations have also smaller scales than SV-based perturbations, and have a less evident upshear vertical tilt, which explains why they grow less with forecast time.
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In November 2000 the resolution of the forecast model in the operational European Centre for Medium-Range Weather Forecasts Ensemble Prediction System was increased from a 120 km truncation scale (EPS) to an 80 km truncation scale...
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In November 2000 the resolution of the forecast model in the operational European Centre for Medium-Range Weather Forecasts Ensemble Prediction System was increased from a 120 km truncation scale (EPS) to an 80 km truncation scale (High-resolution EPS or HEPS). The HEPS performance is compared with that of EPS and with different flavours of poor-man's ensembles. Average results based on Brier skill scores and the potential economic value of probabilistic predictions for 57 winter and 30 summer cases indicate that the new HEPS system is about 12 hours more skilful than the old EPS. Averages over 39 winter cases indicate that HEPS forecasts perform better than five-centre ensemble forecasts. Results also show that if forecasts are transformed into parametrized Gaussian distribution functions centred on the bias-corrected ensemble mean and with re-scaled standard deviation, HEPS-based parametrized forecasts outperform all other configurations. Diagnostics based on parametrized forecast probabilitiesindicate that the different impact on the probabilistic or deterministic forecast skill is related to the fact that HEPS better represents the daily variation in the uncertainty of the atmosphere, and is not simply a reflection of improved mean bias orof a better level of spread.
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The European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VAREPS) is a system designed to provide skilful predictions of small-scale, severe-weather events in the early forecast...
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The European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VAREPS) is a system designed to provide skilful predictions of small-scale, severe-weather events in the early forecast range, and accuratelarge-scale forecast guidance in the extended forecast range (say beyond forecast day 7). In this work, first the rationale behind VAREPS is presented, and then the performance of VAREPS with a truncation at forecast day 7, i.e. T_L399L40(dO-7) and T_L255L40(d7-15), is discussed and compared to the performance of two constant resolution systems, a T_L255L40 and a T_L319L40 (this latter one requires similar computing resources to VAREPS). Average results based on up to 111 cases indicate that VAREPS hasa higher forecast-time-integrated skill, and it provides better forecasts in the early forecast range without losing accuracy in the long forecast range. In the early forecast range, the differences in forecast performance can be very large and responsible for substantial improvements in the prediction of weather variables such as surface wind, significant wave height and total precipitation, as was shown in two case-studies. Average results have also shown that the VAREPS extension to 15 days (the oldEPS system was run operationally only up to forecast day 10) will provide users with some skilful extended-range forecasts.
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The function of intrinsically disordered proteins may be interpreted in terms of their structural ensembles. The article by Schwalbe and colleagues in this issue of Structure combines NMR and SAXS constraints to generate structura...
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The function of intrinsically disordered proteins may be interpreted in terms of their structural ensembles. The article by Schwalbe and colleagues in this issue of Structure combines NMR and SAXS constraints to generate structural ensembles that unveil important functional and pathological features.
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TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts. This report discusses some prelim...
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TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts. This report discusses some preliminary results from predictability studies based on the ensemble data exchanged within TIGGE, and available at the TIGGE archive centres. In the first part of this work, the key characteristics of the eight ensemble systems available in the TIGGE database at the time of writing (December 2007) are compared, and the strengths and weaknesses of each system are highlighted. Then, issues related to the generation of multi-model/multi-analysis ensemble products are discussed, and some preliminary results on the potential value ofcombining different ensembles to generate medium-range products with a grand multi-model/multi-analysis global ensemble are presented. One of the key results documented in this work is the large difference between the performance of the single ensembles
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TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts. This report discusses some prelim...
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TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts. This report discusses some preliminary results from predictability studies based on the ensemble data exchanged within TIGGE, and available at the TIGGE archive centres. In the first part of this work, the key characteristics of the eight ensemble systems available in the TIGGE database at the time of writing (December 2007) are compared, and the strengths and weaknesses of each system are highlighted. Then, issues related to the generation of multi-model/multi-analysis ensemble products are discussed, and some preliminary results on the potential value ofcombining different ensembles to generate medium-range products with a grand multi-model/multi-analysis global ensemble are presented.
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In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictabilit...
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In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2-3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.
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Since the extreme summer of 2003 the importance of early drought warning has become increasingly recognized even in water-rich countries such as Switzerland. Spring 2011 illustrated drought conditions in Switzerland again, which a...
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Since the extreme summer of 2003 the importance of early drought warning has become increasingly recognized even in water-rich countries such as Switzerland. Spring 2011 illustrated drought conditions in Switzerland again, which are expected to become more frequent in the future. Two fundamental questions related to drought early warning are: (I) How long before a hydrological drought occurs can it be predicted? (2) How long are initial conditions important for streamflow simulations? To address these questions, we assessed the relative importance of the current hydrological state and weather during the prediction period. Ensemble streamflow prediction (ESP) and reverse ESP (ESPrev) experiments were performed with the conceptual catchment model, HBV, for 21 Swiss catchments. The relative importance of the initial hydrological state and weather during the prediction period was evaluated by comparing the simulations of both experiments to a common reference simulation. To further distinguish between effects of weather and catchment properties, a catchment relaxation time was calculated using temporally constant average meteorological input. The relative importance of the initial conditions varied with the start of the simulation. The maximum detectable influences of initial conditions ranged from 50 days to at least a year. Drier initial conditions of soil moisture and groundwater as well as more initial snow resulted in longer influences of initial conditions. The catchment relaxation varied seasonally for higher elevation catchments, but remained constant for lower catchments, which indicates the importance of snow for streamflow predictability. Longer persistence seemed to also stem from larger groundwater storages in mountainous catchments, which may motivate a reconsideration of the sensitivity of these catchments to low flows in a changing climate. (C) 2014 Elsevier B.V. All rights reserved.
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NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses u...
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NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. As in the case of assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3 '- and 5 '-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.
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