Resolution Simulations Research Articles

Towards a high resolution simulation framework for building integrated photovoltaics under partial shading in urban environments

This paper presents a framework for modeling the performance of photovoltaic (PV) systems, which is intended to be used in the design, optimization, and deployment of PV systems in urban environments. Urban PV and building integrated PVs (BIPV) are a crucial component of the transition to a low-carbon society. However, existing frameworks for simulating PV systems accurately characterize PV under partial shading, leading to performance gaps with systems deployed in environments such as cities where shading is more common than in large open fields. In this paper, we extend a framework from literature for modeling parametric BIPV arrays using a power model that characterizes IV-curves at the PV cell. We describe the framework and perform a validation against a measured rooftop PV array dataset. We then evaluate the extended framework to other PV modeling and simulation frameworks found in the literature through the simulation of a simplified facade under various levels of shading. Results show that the extended framework is necessary to account for partial shading, but there may be use cases when lower resolution frameworks may be just as accurate. Amongst the frameworks a range of predicted power outputs under the shading conditions was observed; from 6% to 846% with a mean of 162% from the other frameworks relative to the proposed framework. The range is due to the extensive set of conditions tested, where it was observed that in conditions where shading is present but sparse, there is a larger range of predicted output from the various frameworks. Whereas in conditions where shading coverage is high this range is lower. Lastly, we demonstrate the use of the framework on three arrays. Again it was observed that homogeneous shading conditions may not require the level of resolution provided by the proposed framework, while heterogeneous could benefit the most from the approach. This indicates, and is discussed in the paper, that modelers should consider the shading conditions present when deciding on which framework to apply.

Flight Conflict Resolution Simulation Study Based on the Improved Fruit Fly Optimization Algorithm

Flight Conflict Resolution Simulation Study Based on the Improved Fruit Fly Optimization Algorithm

Grade control drillhole spacing and mining selectivity determination using high resolution simulations applied on distinctly heterogeneous open pit mines

The grade control drillhole spacing and mining selectivity decisions are typically made using the resource model estimated from exploration and infill drilling data. Once production starts, large quantities of grade control data are collected to delineate ore and waste boundaries for ore control. In this work, a grade control drillhole spacing and mining selectivity optimization workflow is presented which allows the practitioner to use site specific knowledge to determine the most profitable ore control mining scenario. The densely gridded production data is used to simulate a ground truth block model from which scenarios of variable drillhole spacing and selectivity are evaluated against their corresponding costs to determine the maximum profit scenario. Eleven mining scenarios are evaluated using year production data from three distinctly heterogeneous mine with drillhole spacing and mining selectivity varying from 3 × 3 × 3 m (27 m3) to 30 × 30 × 15 m (13,500 m3). The profit differences from the optimum scenario varied by millions of dollars (1–8%) against the next best case depending on the heterogeneity of the deposit. Practitioners could apply this workflow to inform grade control drillhole spacing and mining selectivity decisions for different domains within the mine or multiple pits especially if distinctly heterogeneous volumes exist.

Brightest Cluster Galaxy Offsets in Cold Dark Matter

The distribution of offsets between the brightest cluster galaxies of galaxy clusters and the centroid of their dark matter distributions is a promising probe of the underlying dark matter physics. In particular, since this distribution is sensitive to the shape of the potential in galaxy cluster cores, it constitutes a test of dark matter self-interaction on the largest mass scales in the universe. We examine these offsets in three suites of modern cosmological simulations; IllustrisTNG, MillenniumTNG and BAHAMAS. For clusters above , we examine the dependence of the offset distribution on gravitational softening length, the method used to identify centroids, redshift, mass, baryonic physics, and establish the stability of our results with respect to various nuisance parameter choices. We find that offsets are overwhelmingly measured to be smaller than the minimum converged length scale in each simulation, with a median offset of in the highest resolution simulation considered, TNG300-1, which uses a gravitational softening length of . We also find that centroids identified via source extraction on smoothed dark matter and stellar particle data are consistent with the potential minimum, but that observationally relevant methods sensitive to cluster strong gravitational lensing scales, or those using the the “light traces mass” approach, in this context meaning gas is used as a tracer for the potential, can overestimate offsets by factors of ∼10 and ∼30, respectively. This has the potential to reduce tensions with existing offset measurements which have served as evidence for a nonzero dark matter self-interaction cross section.

Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery

Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images used in this study are Hyperion 2014 and PRISMA 2021. The method involves creating different levels of spectral resolution simulations from hyperspectral images to detect vegetation changes. Meanwhile, the vegetation change Clustering method employs unsupervised (k-means) techniques. The cluster results can indicate vegetation changes such as vegetation degradation, vegetation, devegetation, or no change, though they currently have low accuracy. The highest accuracy is by Simulated RapidEye image simulations, is 33.5%. The low accuracy results attributed insufficient preprocessing, particularly in topographic correction. Additionally, this research indicates that the spectral resolution levels do not have a significant impact on vegetation change detection, as the differences in change classes at each level are very small.

On the suitability of a convolutional neural network based RCM-emulator for fine spatio-temporal precipitation

High resolution regional climate models (RCM) are necessary to capture local precipitation but are too expensive to fully explore the uncertainties associated with future projections. To resolve the large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to learn the relationship between low-resolution predictors and high resolution surface variables. When trained the emulator can be applied to any low resolution simulation to produce ensembles of high resolution emulated simulations. This study assesses the suitability of applying the RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire precipitation distribution over any grid point. Under a perfect conditions framework, the resulting emulator shows striking ability to reproduce the RCM original series with an excellent spatio-temporal correlation. In particular, a very good behaviour is obtained for the two tails of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates consistent precipitation objects even if the highest frequency details are missed. The emulator quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces the correct high resolution spatial structure and heavy precipitation intensity. Nevertheless, further work is needed to establish a relevant evaluation framework for GCM applications.

From nutrients to fish: Impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework

The ocean sustains ecosystems that are essential for human livelihood and habitability of the planet. The ocean holds an enormous amount of carbon, and serves as a critical source of nutrition for human societies worldwide. Climate variability and change impact marine biogeochemistry and ecosystems. Thus, having state-of-the-art simulations of the ocean, which include marine biogeochemistry and ecosystems, is critical for understanding the role of climate variability and change on the ocean biosphere. Here we present a novel global eddy-resolving (0.1° horizontal resolution) simulation of the ocean and sea ice, including ocean biogeochemistry, performed with the Community Earth System Model (CESM). The simulation is forced by the atmospheric dataset based on the Japanese Reanalysis (JRA-55) product over the 1958–2021 period. We present a novel configuration of the CESM marine ecosystem model in this simulation which includes two zooplankton classes: microzooplankton and mesozooplankton. This novel planktonic food web structure facilitates “offline” coupling with the Fisheries Size and Functional Type (FEISTY) model. FEISTY is a size- and trait-based model of fish functional types contributing to fisheries. We present an evaluation of the ocean biogeochemistry, marine ecosystem (including fish types), and sea ice in this high resolution simulation compared to available observations and a corresponding low resolution (nominal 1°) simulation. Our analysis offers insights into environmental controls on trophodynamics within the ocean. We find that this high resolution simulation provides a realistic reconstruction of nutrients, oxygen, sea ice, plankton and fish distributions over the global ocean. On global and large regional scales the high resolution simulation is comparable to the standard 1° simulation, but on smaller scales, explicitly resolving the mesoscale dynamics is shown to be important for accurately capturing trophodynamic structuring, especially in coastal ecosystems. We show that fine-scale ocean features leave imprints on ocean ecosystems, from plankton to fish, from the tropics to polar regions. This simulation also offers insights on ocean acidification over the past 64 years, as well as how large-scale climate variations may impact upper trophic levels. The data generated by the simulations are publicly available and will be a fruitful community resource for a large variety of oceanographic science questions.

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

AbstractEstimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.

Resolving Weather Fronts Increases the Large‐Scale Circulation Response to Gulf Stream SST Anomalies in Variable‐Resolution CESM2 Simulations

AbstractCanonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs.

Towards integration of LOTOS-EUROS high resolution simulations and heterogenous low-cost sensor observations

Official air quality monitoring networks are often scarce and unevenly spatially distributed. In recent years, the use of low-cost sensors next to official networks is increasing. These additional networks provide measurements of high spatial and temporal resolution and potentially reveal patterns and emissions sources that are hard to detect with conventional methods. In this work, the data assimilation method implemented around the LOTOS-EUROS chemistry transport model is employed to assimilate measurements from heterogenous low-cost sensor networks around the city of Eindhoven in the Netherlands in November 2021. Three data assimilation experiments are performed and evaluated against a free run of the model. In the first one, measurements from the low-cost Innovatief Lucht Meetsysteem (ILM) network are exploited. In the second one, the citizen science network SamenMeten is used and in the third one, a combination of both datasets is applied. In the assimilation experiments at a domain around the city of Eindhoven, it is shown to be essential to use boundary conditions from an assimilation on a larger domain to account for the variability in pollution that originates from sources outside the domain of interest. Such an improvement in boundary conditions counts for a decrease in the initial free run negative biases of 45% for PM10 and 23% for PM2.5 in the city of Eindhoven. The assimilation of low-cost measurements in the region after the correction of the boundary conditions decreases the absolute PM10 biases in the 3 independent official stations over the city of Eindhoven further from −4.4 μg m−3 to about 0.8 μg m−3 averaged over the three experiments. Also, the correlation coefficient is increased from 0.75 to 0.89 and the normalized root mean square is decreased from 0.47 to 0.25. We conclude that the improved boundary conditions and assimilation of observations from dense low-cost networks are able to improve the LOTOS-EUROS simulations at urban scale.

Emission signatures from sub-parsec post-Newtonian binaries embedded in circumbinary discs

We studied the dynamical evolution of quasi-circular, equal-mass massive black hole binaries embedded in circumbinary discs from separations of ∼100 Rg down to the merger, following the post merger evolution. The binary orbit evolves owing to the presence of the gaseous disc and the addition of post-Newtonian (PN) corrections up to the 2.5 PN order, therefore including the dissipative gravitational wave back reaction. We investigated two cases of relatively cold and warm circumbinary discs, with aspect ratios of H/R = 0.03, 0.1, respectively, employing 3D hyper-Lagrangian resolution simulations with the GIZMO-MFM code. We extracted spectral energy distributions and light curves in different frequency bands (i.e. X-ray, optical, and UV) from the simulations. We find a clear two orders of magnitude drop in the X-ray flux right before merger if the disc is warm, while we identify a significant increase in the UV flux regardless of the disc temperature. The optical flux shows clear distinctive modulations on the binary orbital period and on the cavity edge period, regardless of the disc temperature. We find that the presence of a cold disc can accelerate the coalescence of the binary by up to 130 s over the last five days of inspiral, implying a phase shift accumulation of about 0.14 radians compared to the binary evolution in vacuum. These differences are triggered by the presence of the gaseous disc and might have implications on the waveforms that can be detected in principle. We discuss the implications that these distinctive signatures might have for existing and upcoming time domain surveys and for multi-messenger astronomy.

Synthesis and in-depth structure determination of a novel metastable high-pressure CrTe3 phase

This study reports the synthesis and crystal structure determination of a novel CrTe3 phase using various experimental and theoretical methods. The average stoichiometry and local phase separation of this quenched high-pressure phase were characterized by ex situ synchrotron powder X-ray diffraction and total scattering. Several structural models were obtained using simulated annealing, but all suffered from an imperfect Rietveld refinement, especially at higher diffraction angles. Finally, a novel stoichiometrically correct crystal structure model was proposed on the basis of electron diffraction data and refined against powder diffraction data using the Rietveld method. Scanning electron microscopy–energy-dispersive X-ray spectrometry (EDX) measurements verified the targeted 1:3 (Cr:Te) average stoichiometry for the starting compound and for the quenched high-pressure phase within experimental errors. Scanning transmission electron microscopy (STEM)–EDX was used to examine minute variations of the Cr-to-Te ratio at the nanoscale. Precession electron diffraction (PED) experiments were applied for the nanoscale structure analysis of the quenched high-pressure phase. The proposed monoclinic model from PED experiments provided an improved fit to the X-ray patterns, especially after introducing atomic anisotropic displacement parameters and partial occupancy of Cr atoms. Atomic resolution STEM and simulations were conducted to identify variations in the Cr-atom site-occupancy factor. No significant variations were observed experimentally for several zone axes. The magnetic properties of the novel CrTe3 phase were investigated through temperature- and field-dependent magnetization measurements. In order to understand these properties, auxiliary theoretical investigations have been performed by first-principles electronic structure calculations and Monte Carlo simulations. The obtained results allow the observed magnetization behavior to be interpreted as the consequence of competition between the applied magnetic field and the Cr–Cr exchange interactions, leading to a decrease of the magnetization towards T = 0 K typical for antiferromagnetic systems, as well as a field-induced enhanced magnetization around the critical temperature due to the high magnetic susceptibility in this region.

Correcting for the overabundance of low-mass quiescent galaxies in semi-analytic models

ABSTRACT We compare the l-galaxies semi-analytic model to deep observational data from the UKIDSS Ultra Deep Survey (UDS) across the redshift range 0.5 < z < 3. We find that the overabundance of low-mass, passive galaxies at high redshifts in the model can be attributed solely to the properties of ‘orphan’ galaxies, i.e. satellite galaxies where the simulation has lost track of the host dark matter sub-halo. We implement a simple model that boosts the star formation rates in orphan galaxies by matching them to non-orphaned satellite galaxies at a similar evolutionary stage. This straightforward change largely addresses the discrepancy in the low-mass passive fraction across all redshifts. We find that the orphan problem is somewhat alleviated by higher resolution simulations, but the preservation of a larger gas reservoir in orphans is still required to produce a better fit to the observed space density of low-mass passive galaxies. Our findings are also robust to the precise definition of the passive galaxy population. In general, considering the vastly different prescriptions used for orphans in semi-analytic models, we recommend that they are analysed separately from the resolved satellite galaxy population, particularly with JWST observations reigniting interest in the low-mass regime in which they dominate.

High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions

High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Heavy black hole seed formation in high-z atomic cooling halos

Context. Halos with masses in excess of the atomic limit are believed to be ideal environments in which to form heavy black hole seeds with masses above 103 M⊙. In cases where the H2 fraction is suppressed, this is expected to lead to reduced fragmentation of the gas and the generation of a top-heavy initial mass function. In extreme cases this can result in the formation of massive black hole seeds. Resolving the initial fragmentation scale and the resulting protostellar masses has, until now, not been robustly tested. Aims. We run zoom-in simulations of atomically cooled halos in which the formation of H2 is suppressed to assess whether they can truly resist fragmentation at high densities and tilt the initial mass function towards a more top-heavy form and the formation of massive black hole seeds. Methods. Cosmological simulations were performed with the moving mesh code AREPO, using a primordial chemistry network until z ∼ 11. Three haloes with masses in excess of the atomic cooling mass were then selected for detailed examination via zoom-ins. A series of zoom-in simulations, with varying levels of maximum spatial resolution, captured the resulting fragmentation and formation of metal-free stars using the sink particle technique. The highest resolution simulations resolved densities up to 10−6 g cm−3 (1018 cm−3) and captured a further 100 yr of fragmentation behaviour at the centre of the halo. Lower resolution simulations were then used to model the future accretion behaviour of the sinks over longer timescales. Results. Our simulations show intense fragmentation in the central region of the halos, leading to a large number of near-solar mass protostars. Even in the presence of a super-critical Lyman-Werner radiation field (JLW > 105J21), H2 continues to form within the inner ∼2000 au of the halo. Despite the increased fragmentation, the halos produce a protostellar mass spectrum that peaks at higher masses relative to standard Population III star-forming halos. The most massive protostars have accretion rates of 10−3–10−1 M⊙ yr−1 after the first 100 years of evolution, while the total mass of the central region grows at 1 M⊙ yr−1. Lower resolution zoom-ins show that the total mass of the system continues to accrete at ∼1 M⊙ yr−1 for at least 104 yr, although how this mass is distributed amongst the rapidly growing number of protostars is unclear. However, assuming that a fraction of stars can continue to accrete rapidly, the formation of a sub-population of stars with masses in excess of 103 M⊙ is likely in these halos. In the most optimistic case, we predict the formation of heavy black hole seeds with masses in excess of 104 M⊙, assuming an accretion behaviour in line with expectations from super-competitive accretion and/or frequent mergers with secondary protostars.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Accelerating high order discontinuous Galerkin solvers using neural networks: Wall bounded flows

High order solvers are accurate but computationally expensive as they require small time steps to advance the solution in time. In this work we include a corrective forcing to a low order solution to improve the accuracy while advancing in time with larger time steps, and achieve fast computations. The work uses a discontinuous Galerkin framework, where the polynomial order, inside each mesh element, can be varied to provide low or high accuracy. The corrective forcing is included for each high order Gauss nodal point in the mesh. This work is a continuation of [1, 2], where we extend the methodology to wall bounded flows. Namely, we adapt the methodology to a turbulent channel at Reτ = 182. In this case, we use three neural networks to correct different regions of the flow, which are distinguished by their y+ distance to the wall. The methodology is able to correct the low resolution simulation to attain flow statistics that are comparable to high order simulations. We include comparisons for the mean, Reynolds stresses and shear stress on the wall. We achieve good predictions using the corrected low order solution, in mean velocity and its corresponded fluctuations, as well as the shear stress on the wall.

Seeing and turbulence profile simulations over complex terrain at the Thai National Observatory using a chemistry-coupled regional forecasting model

Abstract This study utilized advanced numerical simulations with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to predict anticipated astronomical seeing conditions at the Thai National Observatory (TNO). The study evaluated the effects of both gas-phase and aerosol-phase chemical processes in the Earth’s atmosphere, along with the impact of spatial and temporal resolution on model performance. These simulations were validated against measurements from the Differential Image Motion Monitor (DIMM) and the Slope Detection and Ranging (SLODAR) technique. Due to the inherent temporal variability of the DIMM observations, a 24-h moving average window was applied to both DIMM data and WRF-Chem model outputs. This reduced the percentage root-mean-square error in the comparison between the two data sets from 23 per cent to 11 per cent and increased the correlation coefficient from 0.21 to 0.59. Chemistry played a minor role during the study period, contributing 3.49 per cent to astronomical seeing. However, it did affect the model’s accuracy. Additionally, the study revealed that higher spatial and temporal resolution simulations did not necessarily improve the model’s accuracy. When compared to SLODAR observations of the refractive index structure constant (Cn2dh), the simulations captured altitude variations within ±25 per cent above 5 km and 25–50 per cent below 5 km. Dome seeing also played a role, contributing to around 90 per cent or more in the lowest altitude layer. The results emphasized the significance of seeing predictions in providing valuable insights into complex atmospheric phenomena and how to mitigate the effects of atmospheric turbulence on telescopes.

Using Bright Point Shapes to Constrain Wave Heating of the Solar Corona: Predictions for DKIST

Magnetic bright points on the solar photosphere mark the footpoints of kilogauss magnetic flux tubes extending toward the corona. Convective buffeting of these tubes is believed to excite magnetohydrodynamic waves, which can propagate to the corona and deposit heat there. Measuring wave excitation via bright point motion can thus constrain coronal and heliospheric models, and this has been done extensively with centroid tracking, which can estimate kink-mode wave excitation. DKIST is the first telescope to provide well-resolved observations of bright points, allowing shape and size measurements to probe the excitation of other wave modes that have been difficult, if not impossible, to study to date. In this work, we demonstrate a method of automatic bright point tracking that robustly identifies the shapes of bright points, and we develop a technique for interpreting measured bright point shape changes as the driving of a range of thin-tube wave modes. We demonstrate these techniques on a MURaM simulation of DKIST-like resolution. These initial results suggest that modes other than the long-studied kink mode could increase the total available energy budget for wave heating by 50%. Pending observational verification as well as modeling of the propagation and dissipation of these additional wave modes, this could represent a significant increase in the potency of wave-turbulence heating models.