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There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

International audience; NIDM-Results provides a harmonised representation for fMRI results reporting using Semantic Web technologies (Maumet et al. 2016). While those technologies are particularly well suited for aggregation across complex datasets, using them can be costly in terms of initial development time to generate and read the corresponding serialisations. While the technology is machine accessible, it can be difficult to comprehend by humans. This hinders adoption by scientific communities and by software developers used to more-lightweight data-exchange formats, such as JSON. JSON-LD: a JSON representation for semantic graphs (“JSON-LD 1.1” n.d.) was created to address this limitation and recent extensions to the specification allow creating JSON-LD documents that are structured more similar to simple JSON. This representation is simultaneously readable by a large number of JSON-based applications and by Semantic Web tools. Here we review our work on building a JSON-LD representation for NIDM-Results data and exposing it to Datalad (Halchenko et al. 2018), a data-management tool suitable for neuroimaging datasets with built-in support for metadata extraction and search.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the ‘last mile’ implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Two schemes for modeling condensation in warm nonprecipitating clouds are compared. The first one is the efficient bulk condensation scheme where cloudy volumes are always at saturation and cloud water evaporates instantaneously to maintain saturation. The second one is the comprehensive bin condensation scheme that predicts the evolution of the cloud droplet spectrum and allows sub- and supersaturations in cloudy volumes. The emphasis is on the impact of the two schemes on cloud dynamics. Theoretical considerations show that the bulk condensation scheme provides more buoyancy than the bin scheme, but the effect is small, with the potential density temperature difference around 0.1 K for 1% supersaturation. The 1D advection–condensation tests document the high-vertical-resolution requirement for the bin scheme to resolve the cloud-base supersaturation maximum and CCN activation, which is difficult to employ in 3D cloud simulations. Simulations of shallow convection cloud fields are executed applying bulk and bin schemes, with the mean droplet concentrations in the bin scheme covering a wide range, from about 5 to over 4000 cm−3. Simulations employ the microphysical piggybacking methodology to extract impacts with high confidence. They show that the differences in cloud fields simulated with bulk and bin schemes come not from small differences in the condensation but from more significant differences in the evaporation of cloud water near cloud edges as a result of entrainment and mixing with the environment. The latter makes the impact of cloud microphysics on simulated macroscopic cloud field properties even more difficult to assess because of highly uncertain subgrid-scale parameterizations.

This paper accompanies the first release of libmpdata++, a C++ library implementing the multi-dimensional positive-definite advection transport algorithm (MPDATA) on regular structured grid. The library offers basic numerical solvers for systems of generalised transport equations. The solvers are forward-in-time, conservative and non-linearly stable. The libmpdata++ library covers the basic second-order-accurate formulation of MPDATA, its third-order variant, the infinite-gauge option for variable-sign fields and a flux-corrected transport extension to guarantee non-oscillatory solutions. The library is equipped with a non-symmetric variational elliptic solver for implicit evaluation of pressure gradient terms. All solvers offer parallelisation through domain decomposition using shared-memory parallelisation. The paper describes the library programming interface, and serves as a user guide. Supported options are illustrated with benchmarks discussed in the MPDATA literature. Benchmark descriptions include code snippets as well as quantitative representations of simulation results. Examples of applications include homogeneous transport in one, two and three dimensions in Cartesian and spherical domains; a shallow-water system compared with analytical solution (originally derived for a 2-D case); and a buoyant convection problem in an incompressible Boussinesq fluid with interfacial instability. All the examples are implemented out of the library tree. Regardless of the differences in the problem dimensionality, right-hand-side terms, boundary conditions and parallelisation approach, all the examples use the same unmodified library, which is a key goal of libmpdata++ design. The design, based on the principle of separation of concerns, prioritises the user and developer productivity. The libmpdata++ library is implemented in C++, making use of the Blitz++ multi-dimensional array containers, and is released as free/libre and open-source software.

This paper presents an approach to locally predict homogeneity of the subgrid-scale turbulent mixing in large-eddy simulation of shallow clouds applying double-moment warm-rain microphysics. The homogeneity of subgrid-scale mixing refers to the partitioning of the cloud water evaporation due to parameterized entrainment between changes of the mean droplet radius and changes of the mean droplet concentration. Homogeneous and extremely inhomogeneous mixing represent two limits of possible scenarios, where the droplet concentration and the mean droplet radius remains unchanged during the microphysical adjustment, respectively. To predict the subgrid-scale mixing scenario, the double-moment microphysics scheme is merged with the approach to delay droplet evaporation resulting from entrainment. Details of the new scheme and its application in the Barbados Oceanographic and Meteorological Experiment (BOMEX) shallow convection case are discussed. The simulated homogeneity of mixing varies significantly inside small convective clouds, from close to homogeneous to close to extremely inhomogeneous. The mean mixing characteristics become more homogeneous with height, reflecting increases of the mean droplet size and the mean turbulence intensity, both favoring homogeneous mixing. Model results are consistent with microphysical effects of entrainment and mixing deduced from field observations. Mixing close to homogeneous is predicted in volumes with the highest liquid water content (LWC) and strongest updraft at a given height, whereas mixing in strongly diluted volumes is typically close to extremely inhomogeneous. The simulated homogeneity of mixing has a small impact on mean microphysical characteristics. This result agrees with the previous study applying prescribed mixing scenarios and can be explained by the high humidity of the clear air involved in the subgrid-scale mixing.

This paper discusses aircraft observations and large-eddy simulation (LES) of the 15 May 2008, North Sea boundary-layer clouds from the EUCAARI-IMPACT field campaign. These clouds were advected from the north-east by the prevailing lower-tropspheric winds, and featured stratocumulus-over-cumulus cloud formations. Almost-solid stratocumulus deck in the upper part of the relatively deep weakly decoupled marine boundary layer overlaid a field of small cumuli with a cloud fraction of ~10%. The two cloud formations featured distinct microphysical characteristics that were in general agreement with numerous past observations of strongly-diluted shallow cumuli on the one hand and solid marine boundary-layer stratocumulus on the other. Macrophysical and microphysical cloud properties were reproduced well by the double-moment warm-rain microphysics large-eddy simulation. A novel feature of the model is its capability to locally predict homogeneity of the subgrid-scale mixing between the cloud and its cloud-free environment. In the double-moment warm-rain microphysics scheme, the homogeneity is controlled by a single parameter α, that ranges from 0 to 1 and limiting values representing the homogeneous and the extremely inhomogeneous mixing scenarios, respectively. Parameter α depends on the characteristic time scales of the droplet evaporation and of the turbulent homogenization. In the model, these scales are derived locally based on the subgrid-scale turbulent kinetic energy, spatial scale of cloudy filaments, the mean cloud droplet radius, and the humidity of the cloud-free air entrained into the cloud. Simulated mixing is on average quite inhomogeneous, with the mean parameter α around 0.7 across the entire depth of the cloud field, but with local variations across almost the entire range, especially near the base and the top of the cloud field.

Results of four Lagrangian stratocumulus-to-shallow-cumulus transition cases as obtained from six different large-eddy simulation models are presented. The model output is remarkably consistent in terms of the representation of the evolution of the mean state, which is characterized by a stratocumulus cloud layer that rises with time and that warms and dries relative to the subcloud layer. Also, the effect of the diurnal insolation on cloud-top entrainment and the moisture flux at the top of the subcloud layer are consistently captured by the models. For some cases, the models diverge in terms of the liquid water path (LWP) during nighttime, which can be explained from the difference in the sign of the buoyancy flux at cloud base. If the subcloud buoyancy fluxes are positive, turbulence sustains a vertically well-mixed layer, causing a cloud layer that is relatively cold and moist and consequently has a high LWP. After some simulation time, all cases exhibit subcloud-layer dynamics that appear to be similar to those of the dry convective boundary layer. The humidity flux from the subcloud layer toward the stratocumulus cloud layer, which is one of the major sources of stratocumulus cloud liquid water, is larger during the night than during the day. The sensible heat flux becomes constant in time, whereas the latent heat flux tends to increase during the transition. These findings are explained from a budget analysis of the subcloud layer.

This paper discusses an extension of the approach proposed previously to represent the delay of cloud water evaporation and buoyancy reversal due to the cloud–environment mixing in bulk microphysics large-eddy simulation of clouds. In the original approach, an additional equation for the mean spatial scale of cloudy filaments was introduced to represent the progress toward microscale homogenization of a volume undergoing turbulent cloud–environment mixing, with the evaporation of cloud water allowed only when the filament scale approached the Kolmogorov microscale. Here, it is shown through a posteriori analysis of model simulations that one should also predict the volume fraction of the cloudy air that was diagnosed in the original approach. The resulting model of turbulent mixing and homogenization, referred to as the λ–β model, is applied in a series of shallow convection simulations using various spatial resolutions and compared to the traditional bulk model. This work represents an intermediate step in the development of a modeling framework to simulate characteristics of microphysical transformations during entrainment and subgrid-scale turbulent mixing.

This paper accompanies first release of libmpdata++, a C++ library implementing the Multidimensional Positive-Definite Advection Transport Algorithm (MPDATA). The library offers basic numerical solvers for systems of generalised transport equations. The solvers are forward-in-time, conservative and non-linearly stable. The libmpdata++ library covers the basic second-order-accurate formulation of MPDATA, its third-order variant, the infinite-gauge option for variable-sign fields and a flux-corrected transport extension to guarantee non-oscillatory solutions. The library is equipped with a non-symmetric variational elliptic solver for implicit evaluation of pressure gradient terms. All solvers offer parallelisation through domain decomposition using shared-memory parallelisation. The paper describes the library programming interface, and serves as a user guide. Supported options are illustrated with benchmarks discussed in the MPDATA literature. Benchmark descriptions include code snippets as well as quantitative representations of simulation results. Examples of applications include: homogeneous transport in one, two and three dimensions in Cartesian and spherical domains; shallow-water system compared with analytical solution (originally derived for a 2D case); and a buoyant convection problem in an incompressible Boussinesq fluid with interfacial instability. All the examples are implemented out of the library tree. Regardless of the differences in the problem dimensionality, right-hand-side terms, boundary conditions and parallelisation approach, all the examples use the same unmodified library, which is a key goal of libmpdata++ design. The libmpdata++ library is implemented in C++, making use of the Blitz++ multi-dimensioanl array containers, and is released as free/libre and open-source software., Among the changes are: new chapter 5.4, updated presentation in chapter 6.3 and editorial corrections

This paper presents computational approach to locally predict the homogeneity of subgrid-scale turbulent mixing between a cloud and its environment in large-eddy simulation of warm (ice-free) shallow convective clouds applying a double-moment bulk microphysics scheme. The term homogenity of mixing refers to the change of the mean droplet size associated with evaporation of cloud water due to entrainment. The two contrasting limits are the homogeneous mixing, where the radius of all droplets is reduced and the concentration does not change during microscale homogenization, and the extremely inhomogeneous mixing, where the microscale homogenization leads to complete evaporation of some droplets and does not affect the rest. The novel approach is applied to simulations of shallow convective cloud field. The results show that locally the homogeneity of mixing can vary significantly because of the spatial variability of the intensity of turbulence and the mean droplet radius. On average, however, the mixing becomes more homogeneous with height because of higher turbulence intensities and larger droplet sizes aloft.

The neutralino sector of the U(1) extended SUSY is presented and some collider and cosmology-related phenomenology discussed.