Monte Carlo Way Research Articles

ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application

Abstract. This paper provides a description of ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer, or the exchange of gases, moisture, heat, and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow the assimilation of various streams of observations (fluxes, mixing ratios at multiple heights, etc.) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows the retrieval of parameters in an objective manner and enables the estimation of information that is difficult to obtain directly by observations, for example, free tropospheric mixing ratios or stomatal conductances. Furthermore, it allows the estimation of possible biases in observations. Modelling the carbon cycle at the ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple yet contains the core physics to simulate the essentials of a well-mixed boundary layer and of the land–atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of the best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, which is used for minimisation of this function. An implemented Monte Carlo way of running ICLASS can further help to handle non-linearity and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, in addition to tests for the full inverse modelling framework, and a successful example application for a grassland in the Netherlands.

Improving the Environmental Safety Risk Assessment in Construction Using Statistical Analysis Methods

The goal of the article is to assess risk for substantiating the economic and organizational efficiency of the construction in the context of ecologic safety. A quantitative risk estimation was made by means of the Monte Carlo way for negative and positive choices to avoid the ecological harm. The simulation algorithm imitated the distribution obtained from the ev-idence-based fit. The outcomes of a sensitivity investigation are also prepared to verify the suggestion. This risk analysis technique has a computer digital implementation. The simulation data outputs plainly demonstrate the alternative of the general norm of validation and the acceptance of the solution which is not harmful to the environment. In situations of uncertainty, the decision to select the optimistic flavor with high spending (to retain the reliableness of the technics) but less risk pretends to be a decisive factor in the eco-friendly protection strategies of the construction project.

Expectation Propagation-Based Sampling Decoding: Enhancement and Optimization

In this paper, the paradigm of expectation propagation (EP) algorithm in large-scale MIMO detection is extended by the sampling decoding in an Markov chain Monte Carlo way to boost the approximation of the target posterior distribution. The proposed EP-based sampling decoding scheme not only theoretically addresses the inherent convergence problem of EP, but also is able to achieve the near-optimal decoding performance with the increment of Markov moves. Specifically, the EP-based independent Metropolis-Hastings (MH) is proposed to guarantee the exponential convergence to the target posterior distribution, thus bridging the EP detector and the sampling decoding as a whole. Meanwhile, the output yielded by the EP detector also provides a good initial setup for the sampling decoding, which results in a better convergence performance in the approximation. To further improve the convergence performance and the decoding efficiency, the EP-based Gibbs sampling is given, where the choice of the standard deviation of the discrete Gaussian distribution in the Markov mixing is also studied for a better decoding performance. Moreover, we extend the proposed EP-based Gibbs sampling decoding to the soft-output decoding in MIMO bit-interleaved coded modulation (BICM) systems, which enjoys a flexible decoding trade-off between performance and complexity by the number of Markov moves.

Fuel management code system against experimental measurements of the CMRR reactor

The China Mianyang Research Reactor (CMRR), with a thermal power of 20 MW, is an important high performance research reactor operated in China. In this paper, we introduced the fuel management (FM) computation toolbox and confirmatory experiments performed at this reactor. The toolbox system utilizes two roadmaps, the deterministic strategy as the standard method and the Monte Carlo (MC) strategy as the reference way. Interface glue-code packages for both roadmaps are developed to link the existing kernel codes to perform the input-file generation, data manipulation, and output data processing for fuel cycle analysis. In particular, a new MC based coupling strategy is proposed called the “burnup related nuclides list” method which can perform the FM engineering problem with high accuracy and speed. For the aspect of confirmatory experiments, we emphasize on the methods and instruments, including the control rods (CRs) critical position, the CR worth curve, and the thermal neutron flux distribution. We have developed a reactivity meter to perform the CRs worth measurement using the inverse kinetics method, and employed the Dy-Al foils to perform the thermal neutron flux distribution measurements. All three sets of experiments are performed after each refueling. With these data accumulated over years, our FM toolbox system is profoundly validated for various combinations of fuel burnup and core arrangement. It can be concluded that our FM toolbox and strategies, both the deterministic and the MC way, are satisfactory for the FM mission of our reactor.

Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas

The Allen Brain Atlas of the adult mouse (ABA) consists of digitized expression profiles of thousands of genes in the mouse brain, co-registered to a common three-dimensional template (the Allen Reference Atlas).This brain-wide, genome-wide data set has triggered a renaissance in neuroanatomy. Its voxelized version (with cubic voxels of side 200 microns) is available for desktop computation in MATLAB. On the other hand, brain cells exhibit a great phenotypic diversity (in terms of size, shape and electrophysiological activity), which has inspired the names of some well-studied cell types, such as granule cells and medium spiny neurons. However, no exhaustive taxonomy of brain cell is available. A genetic classification of brain cells is being undertaken, and some cell types have been chraracterized by their transcriptome profiles. However, given a cell type characterized by its transcriptome, it is not clear where else in the brain similar cells can be found. The ABA can been used to solve this region-specificity problem in a data-driven way: rewriting the brain-wide expression profiles of all genes in the atlas as a sum of cell-type-specific transcriptome profiles is equivalent to solving a quadratic optimization problem at each voxel in the brain. However, the estimated brain-wide densities of 64 cell types published recently were based on one series of co-registered coronal in situ hybridization (ISH) images per gene, whereas the online ABA contains several image series per gene, including sagittal ones. In the presented work, we simulate the variability of cell-type densities in a Monte Carlo way by repeatedly drawing a random image series for each gene and solving the optimization problem. This yields error bars on the region-specificity of cell types.

A Unified Model for Moist Convective Boundary Layers Based on a Stochastic Eddy-Diffusivity/Mass-Flux Parameterization

Abstract A single-column model (SCM) is developed for representing moist convective boundary layers. The key component of the SCM is the parameterization of subgrid-scale vertical mixing, which is based on a stochastic eddy-diffusivity/mass-flux (EDMF) approach. In the EDMF framework, turbulent fluxes are calculated as a sum of the turbulent kinetic energy–based eddy-diffusivity component and a mass-flux component. The mass flux is modeled as a fixed number of steady-state plumes. The main challenge of the mass-flux model is to properly represent cumulus clouds, which are modeled as moist plumes. The solutions have to account for a realistic representation of condensation within the plumes and of lateral entrainment into the plumes. At the level of mean condensation within the updraft, the joint pdf of moist conserved variables and vertical velocity is used to estimate the proportion of dry and moist plumes and is sampled in a Monte Carlo way creating a predefined number of plumes. The lateral entrainment rate is modeled as a stochastic process resulting in a realistic decrease of the convective cloudiness with height above cloud base. In addition to the EDMF scheme, the following processes are included in the SCM: a pdf-based parameterization of subgrid-scale condensation, a simple longwave radiation, and one-dimensional dynamics. Note that in this approach there are two distinct pdfs, one representing the variability of updraft properties and the other one the variability of thermodynamic properties of the surrounding environment. The authors show that the model is able to capture the essential features of moist boundary layers, ranging from stratocumulus to shallow-cumulus regimes. Detailed comparisons, which include pdfs, profiles, and integrated budgets with the Barbados Oceanographic and Meteorological Experiment (BOMEX), Dynamics and Chemistry of Marine Stratocumulus (DYCOMS), and steady-state large-eddy simulation (LES) cases, are discussed to confirm the quality of the present approach.