To increase situational awareness and support evidence-based policymaking, we formulated a mathematical model for coronavirus disease transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a nonexponentially distributed incubation period, asymptomatic persons, and mild and severe forms of symptomatic disease. We used Bayesian inference to calibrate region-specific models for consistency with daily reports of confirmed cases in the 15 most populous metropolitan statistical areas in the United States. We also quantified uncertainty in parameter estimates and forecasts. This online learning approach enables early identification of new trends despite considerable variability in case reporting.

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.

SUMMARYWe present an overview of the Common Component Architecture (CCA) core specification and CCAFFEINE, a Sandia National Laboratories framework implementation compliant with the draft specification. CCAFFEINE stands for CCA Fast Framework Example In Need of Everything; that is, CCAFFEINE is fast, lightweight, and it aims to provide every 'framework service' by using external, portable components instead of integrating all services into a single, heavy framework core. By fast, we mean that the CCAFFEINE glue does not get between components in a way that slows down their interactions. We present the CCAFFEINE solutions to several fundamental problems in the application of component software approaches to the construction of single program multiple data (SPMD) applications. We demonstrate the integration of components from three organizations, two within Sandia and one at Oak Ridge National Laboratory. We outline some requirements for key enabling facilities needed for a successful component approach to SPMD application building.

Reynolds-Averaged Navier-Stokes (RANS) models are not very accurate for high Reynolds number, compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form error in the RANS model. In this work we pursue the hypothesis that RANS predictions could be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow. We formulate a Bayesian inverse problem to estimate 3 RANS parameters (C µ , C ǫ2 , C ǫ1) and use a Markov chain Monte Carlo (MCMC) method to develop a probability density function for them. The cost of MCMC is addressed by developing statistical surrogates for the RANS models. We find that only a subset of the (C µ , C ǫ2 , C ǫ1) space, R, supports realistic flow simulations. We use R as our prior belief when formulating the inverse problem. It is enforced with a classifier in our MCMC solution. We find that the calibrated parameters improve predictions of the entire flowfield substantially, compared to the nominal/literature values of (C µ , C ǫ2 , C ǫ1); further, this improvement is seen to hold for interactions at other Mach numbers and jet strengths for which we have experimental data to provide a comparison. We also quantify the residual error, which is an approximation of the model-form error; it is most easily measured in terms of turbulent stresses.

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models.We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive. 9Top: Results from the PPT performed using posterior distributions generated using both the correlated and uncorrelated models for the structural error, for US-ARM. The PPT tests were performed with 200 samples. The solid line is the median prediction, from the correlatederrors calibration; the dashed line is the corresponding prediction from the uncorrelatederror calibration. The error bars denote the inter-quartile range (IQR). The observations of log(LH) are plotted with symbols. The prediction with p de f is plotted with a dotted line. Lower left: VRH for both the calibrations, using blue for correlated-errors calibration and red for the other. The mauve sections denote the regions where the red and blue bars overlap. Lower right: Comparison of two realizations of predictions vis-à-vis the observations (in green). We plot the average prediction from the PPT, generated using correlated structural errors, in black. One realization of these predictions is plotted in blue; it shows the smooth variation in time that the observations show. The red plot shows a prediction generated using the uncorrelated structural error model. Compared to the seasonal variation in log(LH), the variation in predictions due to the two different structural error models is not very noticeable. 33 10 Posterior distributions of {F drai , log (Q dm ) , b, σ 2