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Abstract During the winter of 2020/21 an ensemble of FV3-LAM forecasts was produced over the contiguous United States for the Winter Weather Experiment using five physics suites. These forecasts are evaluated with the goal of optimizing physics parameterizations within the future operational Rapid Refresh Forecast System (RRFS) in the Unified Forecast System (UFS) realm and for selecting suitable physics suites for a multiphysics RRFS ensemble. The five physics suites have different combinations of land surface models (LSMs), planetary boundary layer (PBL) parameterizations, and surface layer schemes, chosen from those used in current and possible future operational systems and likely to be supported in the operational UFS. Full-season evaluation reveals a persistent near-surface cold bias in the U.S. Northeast from one suite and a nighttime warm bias in the southern Great Plains in another suite, while other suites have smaller biases. A representative case is chosen to diagnose the cause for each of these biases using sensitivity simulations with different physics combinations or modified parameters and verified with additional mesonet observations. The cold bias in the Northeast is attributed to aspects of the Noah-MP LSM over snow cover, where Noah-MP simulates lower soil water content, and thus lower thermal conductivity than other LSMs, leading to less upward ground heat flux during nighttime and consequently lower surface temperature. The nighttime warm bias found in the southern Great Plains is attributed to overestimation of vertical mixing in the K -profile-based eddy-diffusivity mass-flux (K-EDMF) PBL scheme and insufficient land–atmospheric coupling from the GFS surface layer scheme over short vegetation. A few key parameters driving these systematic biases are identified.
When parallel algorithms for simulation were introduced in the 1970s, their development and use interested only experts in parallel computation. This circumstance changed as multi-core processors became commonplace, putting a parallel computer into the hands of every modeler. A natural outcome is growing interest in parallel simulation among persons not intimately familiar with parallel computing. At the same time, parallel simulation tools continue to be developed with the implicit assumption that the modeler is knowledgeable about parallel programming. The unintended consequence is a rapidly growing number of users of parallel simulation tools that are unlikely to recognize when the interaction of race conditions, partitioning strategies, and simultaneous action in their simulation models make results non-reproducible, thereby calling into question the validity of conclusions drawn from the simulation data. Here, we illustrate the potential dangers of exposing parallel algorithms to users who are not experts in parallel computation with example models constructed using existing parallel simulation tools. By doing so, we hope to refocus tool developers on usability, even if this new focus incurs loss of some performance.
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The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.
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Analysis of microbial communities by high-throughput pyrosequencing of SSU rRNA gene PCR amplicons has transformed microbial ecology research and led to the observation that many communities contain a diverse assortment of rare taxa–a phenomenon termed the Rare Biosphere. Multiple studies have investigated the effect of pyrosequencing read quality on operational taxonomic unit (OTU) richness for contrived communities, yet there is limited information on the fidelity of community structure estimates obtained through this approach. Given that PCR biases are widely recognized, and further unknown biases may arise from the sequencing process itself, a priori assumptions about the neutrality of the data generation process are at best unvalidated. Furthermore, post-sequencing quality control algorithms have not been explicitly evaluated for the accuracy of recovered representative sequences and its impact on downstream analyses, reducing useful discussion on pyrosequencing reads to their diversity and abundances. Here we report on community structures and sequences recovered for in vitro-simulated communities consisting of twenty 16S rRNA gene clones tiered at known proportions. PCR amplicon libraries of the V3–V4 and V6 hypervariable regions from the in vitro-simulated communities were sequenced using the Roche 454 GS FLX Titanium platform. Commonly used quality control protocols resulted in the formation of OTUs with >1% abundance composed entirely of erroneous sequences, while over-aggressive clustering approaches obfuscated real, expected OTUs. The pyrosequencing process itself did not appear to impose significant biases on overall community structure estimates, although the detection limit for rare taxa may be affected by PCR amplicon size and quality control approach employed. Meanwhile, PCR biases associated with the initial amplicon generation may impose greater distortions in the observed community structure.
Safeguarding uranium enrichment facilities is a serious concern for the International Atomic Energy Agency (IAEA). Safeguards methods have changed over the years, most recently switching to an improved safeguards model that calls for new technologies to help keep up with the increasing size and complexity of today’s gas centrifuge enrichment plants (GCEPs). One of the primary goals of the IAEA is to detect the production of uranium at levels greater than those an enrichment facility may have declared. In order to accomplish this goal, new enrichment monitors need to be as accurate as possible.
The expression for the overlap, Equation (1) in the published article at Nuclear Fusion 51 (2011) 023003, was incorrect and should be replaced.
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This document reviews the sources of uncertainty, standards, and the UQ development plan for the MAMA software. Our intention is to allow users to generate quantification data with uncertainty based on the contributing back end sources. We do not intend to add in front end uncertainty into the software itself, due to the numerous different instruments that could be used, and the enormous variation in factors that would add to that uncertainty among different users, samples, and sample prep routines. We could not possibly or reasonably address them all. We will however, add in the uncertainty component from the image scale bar, which must be provided by the user as an input to the software, and could be used to incorporate any front end uncertainty. (Note: the detailing of these front end uncertainties is beyond the scope of this software effort.) The verification testing we have done generates uncertainties that are fundamentally pixel based. Given the uncertainty in the scale bar, we can then propagate the uncertainty and convert to uncertainty in all calculated results in microns (or whatever unit the scale bar is in.) Users can then add any additional instrumentation and sample prep uncertainty to the resulting output as appropriate for the data calculation and analysis being performed.
Presenting a systematic study of the standard analysis of rod-pinch radiographs for obtaining quantitative measurements of areal mass densities, and making suggestions for improving the methodology of obtaining quantitative information from radiographed objects.
This informal report presents the measurement data and information to document the performance of the advanced Precision Data Technology, Inc. (PDT) sealed cell boron-10 plate neutron detector that makes use of the advanced coating materials and procedures. In 2015, PDT changed the boron coating materials and application procedures to significantly increase the efficiency of their basic corrugated plate detector performance. A prototype sealed cell unit was supplied to LANL for testing and comparison with prior detector cells. Also, LANL had reference detector slabs from the original neutron collar (UNCL) and the new Antech UNCL with the removable 3He tubes. The comparison data is presented in this report.
In this short report, we give an analysis of the extrapolated thermal conductivity of UO2 from earlier molecular dynamics (MD) simulations [1]. Because almost all material properties are functions of temperature, e.g. fission gas release, the fuel thermal conductivity is the most important parameter from a model sensitivity perspective [2]. Thus, it is useful to perform such analysis.