Linear Scaling Method Research Articles

Improved Gaussian basis sets for norm-conserving 4f-in-core pseudopotentials of trivalent lanthanides (Ln = Ce-Lu).

The first-principles quantum chemical computations often scale as Nk (N = basis sets; k = 1-4 for linear scaling, Hartree-Fock or density functional theory methods), which makes the development of accurate pseudopotentials and efficient basis sets necessary ingredients in modeling of heavy elements such as lanthanides and actinides. Recently, we have developed 4f-in-core norm-conserving pseudopotentials and associated basis sets for the trivalent lanthanides [Lu et al., J. Chem. Theory Comput. 19, 82-96 (2023)]. In the present paper, we present a unified approach to optimize high-quality Gaussian basis sets for modeling and simulations of condensed-phase systems. The newly generated basis sets not only capture the low total energy and fairly reasonable condition number of overlap matrix of lanthanide-containing systems, but also exhibit good transferability and reproducibility. These advantages ensure the accuracy of the basis sets while avoiding linear dependency concern of atom-centered basis sets. The performance of the basis sets is further illustrated in lanthanide molecular and condensed-phase systems by using Gaussian-plane wave density functional approach of CP2K. These new basis sets can be of particular interest to model structurally complicated lanthanide molecules, clusters, solutions, and solid systems.

The Quality of Embedding Charges Is Critical for Convergence of Many-Body Expansions When BSSE Is Absent.

There is conflicting evidence in the literature concerning the benefits of charge embedding on the convergence of many-body expansions (MBEs). Using a systematic series of water and ion-water clusters of varying size, this study indicates that the effects of charge embedding can be masked by basis set superposition error (BSSE). When BSSE is removed, this study demonstrates that charge embedding can significantly accelerate MBE convergence, where the electrostatically embedded two-body method, EE-MBE(2), can often yield accuracy close to the four-body method, MBE(4). Contrary to previous studies on smaller systems, this work shows that the performance of EE-MBE is highly sensitive to the charge model, with the best performance obtained when the natural population analysis (NPA) charge model is used and generated at the same level of theory used in the subsystem and supersystem calculations. It was demonstrated that the "3c" composite method, PBEh-3c, yields NPA atomic charges that are in excellent agreement with those obtained from supersystem density functional theory calculations. The linear-scaling X-Polarization method provides a more general approach to estimating these supersystem QM atomic charges, but its performance depends on how the fragments are defined.

Validation of full-length radiograph based musculoskeletal modeling method to estimate medial and lateral knee contact forces

BackgroundAnatomical parameters of the pelvis, femur, and tibia derived from the full-length radiograph can be used to create a more accurate musculoskeletal model compared to marker-based linear scaling method. However, whether this model leads to more accurate estimations of medial knee contact force (MCF) and lateral knee contact force (LCF) than marker-based linear scaling method is still unknown. Research questionThis main purpose of this study was to determine whether musculoskeletal model generated from full-length radiograph improves the estimations of MCF and LCF. MethodsAn open-source dataset including marker trajectories, ground reaction forces, in vivo knee contact forces, and full-length radiograph was used to evaluate the accuracy of full-length radiograph musculoskeletal modeling method. Subject-specific musculoskeletal models were created using anatomical parameters derived from the full-length radiograph or marker-based linear scaling methods. MCF and LCF were estimated using musculoskeletal simulations of normal walking trails. The accuracy of modeling methods was determined by comparing the estimated and in vivo measured MCF and LCF. ResultsCompared to the marker-based linear scaling approach, the full-length radiograph musculoskeletal modeling method exhibited decreases of 38.3 % and 41.3 % in root mean square error for MCF and LCF respectively, as well as reductions of 50.0 % and 49.3 % in mean peak errors for MCF and LCF respectively. SignificanceThe full-length radiograph musculoskeletal modeling method provides a more accurate way to estimate MCF and LCF compared to the traditional maker-based linear scaling approach, which may contribute to understand the initiation, progression, and treatment of OA.

New approach to researching the development level of the creatosphere in human-centered economy in Russia

Objective: to develop conceptual provisions of human-centered economy by forming a new approach to the study of the creatosphere as a constituent element of the said economic system in order to develop its management. Methods: analysis, synthesis, content analysis, scheme of contradiction, linear scaling method, minimax normalization. Results: the characteristic of the creatosphere of the human-centered economy is revealed from the viewpoint of satisfying the individual’s needs in creativity; the creatosphere structure is analyzed as an element covering all spheres of the economic system under consideration; the model of homeostasis of the human-centered economy is developed through identifying its elements-opposites (on the one hand – natural and material spheres, services, social sphere, infosphere, digital and cognitive spheres, on the other hand – creatosphere), as well as the model of the basic resource (flow of ideas) and regulator (human needs); the predetermining role of the creatosphere in the development of the said economic system is outlined; the contradictions are revealed at the level of human-centered economy, the resolution of which contributes to the development of an individual and the economic system as a whole. Scientific novelty: it consists in the development of a methodology for assessing the creatosphere and testing it on the Russian subjects. Unlike those available in the literature, the methodology allows comprehensively assessing the creatosphere development as an element that affects the human-centered economy as a whole. The novelty consists also in defining a vector in the creatosphere management based on the formed approach to its research through the upscaling of best regional practices. Practical significance: the results of comprehension of the human-centered economy creatosphere through the scheme of contradictions may serve as a basis for further research in order to build a scientifically sound theory of the said economic system and to form directions for resolving the identified contradictions. The study results are the basis to further define the development level of the Russian human-centered economy in order to develop measures for its effective management.

The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments

AbstractCharacterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.

Evaluating changes in flood frequency due to climate change in the Western Cape, South Africa

This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods.

The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach

Potential Evapotranspiration (PET) is an important variable for monitoring daily agricultural activity as well as meteorological drought. Therefore, it is necessary to investigate the influence of different options of the physical dynamical downscaling and boundary layer schemes on the simulated PET. Using the RegCM4 regional climate model, four simulations were conducted (two for each case) in the period 1997 to 2017. In all simulations, the RegCM4 was configured with 25 km resolution and downscaled by the ERA-Interim reanalysis dataset. To ensure a reliable estimation of the PET, a calibrated version of the Hargreaves-Samani equation was adopted. A high-resolution product of the ERA5 was used as the observational dataset. Results showed that the simulated PET is insensitive either to the dynamical downscaling or the boundary layer options. Concerning the annual climatological cycle, the RegCM4’s performance varies with month and location. Quantitatively, a root mean square error lies between 1 mm and 1.6 mm day-1, the Nash-Sutcliffe efficiency between 0.2 and 0.6, and the coefficient of determination between 0.5 and 0.75. Additionally, the Linear Scaling (LS) method showed its added value in the evaluation/validation periods. In conclusion, the RegCM4 can be used to develop a regional PET map of Egypt using the LS either in the present climate or under different future scenarios.

Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent

The precipitation over the Indonesian Maritime Continent (IMC) is one of the most challenging atmospheric parameters to predict accurately. The Weather Research Forecasting (WRF) model used in this study produces overestimated predictions of precipitation intensity compared to satellite data. Therefore, it is necessary to modify the model output to minimize the bias between predictions and satellite observations. The modification includes adjusting the dynamic model parameters and settings to better reflect atmospheric conditions in the IMC region, applying bias correction techniques through a linear scaling method, aligning the monthly average of the model's output with observational data, and conducting statistical analysis in several areas within the IMC. This procedure has significantly reduced the biases and is considered acceptable for each satellite area. Based on the results of the statistical analysis and by applying the precipitation threshold criteria, the accuracy of the predictions in each observation area is quite good, ranging from 0.59 to 1.0. Precipitation with a threshold of 50 mm/day or higher exhibits good accuracy, with a minimum value of 0.59 for RI and a maximum value of 0.97 for RIII and RV. On the other hand, precipitation with a threshold of 20 mm/day or higher demonstrates very good accuracy, with values of 0.97 for RI and 1.00 for RIII to RVI.

Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines.

Molecular NMR shieldings, J-couplings, and magnetizabilities from numeric atom-centered orbital based density-functional calculations

This paper reports and benchmarks a new implementation of nuclear magnetic resonance shieldings, magnetizabilities, and J-couplings for molecules within semilocal density functional theory, based on numeric atom-centered orbital (NAO) basis sets. NAO basis sets are attractive for the calculation of these nuclear magnetic resonance (NMR) parameters because NAOs provide accurate atomic orbital representations especially near the nucleus, enabling high-quality results at modest computational cost. Moreover, NAOs are readily adaptable for linear scaling methods, enabling efficient calculations of large systems. The paper has five main parts: (1) It reviews the formalism of density functional calculations of NMR parameters in one comprehensive text to make the mathematical background available in a self-contained way. (2) The paper quantifies the attainable precision of NAO basis sets for shieldings in comparison to specialized Gaussian basis sets, showing similar performance for similar basis set size. (3) The paper quantifies the precision of calculated magnetizabilities, where the NAO basis sets appear to outperform several established Gaussian basis sets of similar size. (4) The paper quantifies the precision of computed J-couplings, for which a group of customized NAO basis sets achieves precision of ∼Hz for smaller basis set sizes than some established Gaussian basis sets. (5) The paper demonstrates that the implementation is applicable to systems beyond 1000 atoms in size.

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very well with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, the delta change method seems to have with no biases for maximum temperature, but increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Spatiotemporal assessment of rainfall and drought projection for integrated dam management in Benut River Basin, Malaysia under CMIP6 scenarios

This study assesses the influence of climate change on rainfall and drought in the Benut River Basin (BRB) to support the integrated management of Machap dam. The study seeks to reshape dam safety components in the near future through a technical focus group discussion (FGD) with key stakeholders. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset for the immediate future (2020–2040) under various scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), historical Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall datasets (1981 to 2021) are employed. A multi-filtration process ranks twenty-three Global Climate Models (GCMs) using seven relative importance metrics (RIMs). This work's novelty lies in a focused GCM evaluation through individual contributions quantified with RIMs, providing a refined ranking that address the limitations of traditional metrics. Then, the Compromise Programming Index (CPI) aggregates the ranked models, with the top subset selected through Jenks Optimised Classification (JOC), identifying ACCESS-ESM1–5 and CMCC-ESM2 as top models. The selected GCMs were bias-corrected using the linear scaling method and validated before ensembled, effectively replicating the probability distribution function (PDF) of CHIRPS data, as confirmed by a Taylor diagram indicating the closest agreement. The study reveals a generally wetter tendency in the northernmost area of Machap dam under all SSPs, with 5–9 % maximum increasing percentages. Conversely, downstream areas anticipate decreasing rainfall. June (6–22 %) and July (7–22 %) project higher rainfall, indicating increased water availability in a relatively dry period, while December (3–11 %) suggests increasing flood risk. The South West monsoon (SWM) anticipates lower rainfall in early March (3–7 %) and more severe dry periods in August (8–11 %). Future drought occurrences are projected homogeneously across scenarios, with drought months ranging from 15 % to 19 % of the 252 months under all severity classifications. In particular, the study enhances myDAMS by proposing continuous development of the guideline under diverse scenario for Machap dam's overall management and sustainability. Projected scenarios, coupled with outcomes from FGD, can inform basin-scale dam management, aiding risk mitigation for critical dam infrastructure.

Climate change and hydropower resilience in Nepal: an integrated modeling approach in the Madi River Basin

ABSTRACT Responding to Nepal's hydropower sector's vulnerability to climate change, this study investigates its impact on energy security, focusing on the Madi River Basin's river systems. This study conducted a rigorous analysis of the basin's historical and future hydroclimatic trends, using the linear scaling method to correct inherent biases in 13 GCMs, resulting in the selection of 6 BCMs with above-satisfactory performance. Future projections reveal an increase in annual precipitation with a higher increment in SSP585 by the end of the century, and a temperature rise ranging from 1.8 to 3.5 °C from the baseline in the far future under moderate- to high-emission scenarios. These hydroclimatic projections are then forced into the calibrated soil and water assessment tool (SWAT) model with very good performance (both R2 and NSE greater than 0.8). The streamflow projection demonstrates an overall increasing trend, marked by significant flow reduction in early months and pronounced monsoon peaks. The analysis of three distinct hydropower projects reveals unique challenges and opportunities, underscoring the heterogeneous nature of projects and the need for location-specific planning and strategic management. This study provides crucial insights for sustainable development in renewable energy, laying the foundation for future research and policy in similar hydroclimatic settings.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Thermal error compensation in length inspection of large-scale aircraft spars in nonuniform temperature environments

As the primary load-bearing structures, large-scale aircraft spars are subject to the influence of nonuniform temperatures in field conditions, resulting in thermal errors during on-site length inspections. These errors require accurate compensation. Conventional linear scaling methods, based on the assumption of uniform temperatures and a retrieved coefficient of thermal expansion (CTE), suffer from low accuracy in nonuniform temperature environments. To address this issue, this paper proposed a thermal error compensation method that incorporates CTE recalibration. First, we established a predictive model for thermal errors in nonuniform temperature fields through differential thermoelastic analysis. Secondly, a recalibration technique for the sensitive parameter, CTE, was introduced to enhance the prediction accuracy. Finally, leveraging the equivalent form of the model, an uncertainty assessment approach tailored for nonuniform temperature conditions was formulated for the compensated lengths. Numerical and case studies of a nearly 1 m long simplified wing spar were implemented to validate the performance of the thermal error compensation method. The results indicate that the presented method improved accuracy by over 57.6% in terms of RMSE compared to the linear scaling method using average temperatures, which would provide a viable approach to achieve a more accurate on-site length inspection of aircraft components.

Lapse rate-adjusted bias correction for CMIP6 GCM precipitation data: An application to the Monsoon Asia Region.

Bias correction (BC) of General Circulation Models (GCMs) variables is a common practice when it is being used for climate impact assessment studies at regional scales. The present study proposes a bias correction method (LR-Reg) that first adjusts the original GCM precipitation for local lapse rate corrections and later bias corrects the lapse rate-adjusted GCMs precipitation data with linear regression coefficients. We evaluated LR-Reg BC method in comparison to Linear Scaling (LS) and Quantile Mapping (QMap) BC methods, and NASA's downscaled NEX data for Monsoon Asia region. This study used Coupled Model Intercomparison Project Phase 6 (CMIP6)-based MIROC6 GCM precipitation with historical and projected shared socio-economic pathways (SSP) scenarios (SSP245 and SSP585) datasets. The BC comparison results show that the relative percentage reduction in mean absolute error (MAE) values of LR-Reg over LS-BC was up to 10-30% while this relative reduction in MAE values of LR-Reg was 30-50% over QMap-BC and 75-100% over NASA's NEX-data. The future projected precipitation over Monsoon Asia during dry season shows more decreased precipitation by up to 100% mostly in the south Asia while during wet season shows more increased precipitation by up to 50% mostly in the northeastern China and in the Himalayan belts with respect to the baseline condition (1970-2005). The results on the average precipitation per 0.25 degree increase in latitude analysis shows that the maximums of average monsoon precipitation during baseline period occur at 0 and 25 degree latitudes while the projected monsoon precipitation during both SSP scenarios occurs at 10 and 20 degree latitudes which clearly shows an inward shift in the latitude axis for the projected precipitation in the Monsoon Asia.

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1–5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021–2060 and far-future period 2061–2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3–7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

21st Century climate change projections of temperature and precipitation in Central Kashmir Valley under RCP 4.5 and RCP 8.5

Regional climate models (RCMs) give more reliable results for a regional impact study of climate change, but they still have a significant bias that has to be corrected before they can be utilised in climate change research. In this study, two methods for local bias correction of Tmax, Tmin and precipitation data at monthly scales, namely the modified difference approach (MDA) and the linear scaling (LS) method, were applied and validated to minimize the bias between the modelled (HAD GEM2-ES-GCM) and observed climate data in Central Kashmir Valley. Tmax, Tmin and precipitation correction functions generated using the LS method on a monthly time scale were shown to be excellent than MDA for bias correction of weather data to make it close to observed data in both scenarios (RCP 4.5 & 8.5). Comparison between two scenarios was done to determine the climate change extent in Central Kashmir Valley using LS method. The past 30 years observed average temperature and precipitation was 14.17 °C and 734.06 mm, respectively considered as a baseline for comparison purpose. Annual Taverage (°C) showed increase in all the three time slices and maximum increase by 3.09 and 5.72 °C during far future (FF) (2071-2095) under RCP 4.5 & 8.5, respectively. Whereas, the results of average annual precipitation also showed increase in future scenario and maximum increase by 29.25 mm (3.98%) during mid future (2046-2070) and 215.98 mm (29.42%) during end future (2071-2095), under RCP 4.5 & 8.5 respectively. It was concluded that under RCP 8.5 scenario climate change was quite significant than RCP 4.5.

Ab initio electronic structure calculations based on numerical atomic orbitals: Basic fomalisms and recent progresses

AbstractThe numerical atomic orbital (NAO) basis sets offer a computationally efficient option for electronic structure calculations, as they require fewer basis functions compared with other types of basis sets. Moreover, their strict localization allows for easy combination with current linear scaling methods, enabling efficient calculation of large physical systems. In recent years, NAO bases have become increasingly popular in modern electronic structure codes. This article provides a review of the ab initio electronic structure calculations using NAO bases. We begin by introducing basic formalisms of the NAO‐based electronic structure method, including NAO base set generation, self‐consistent calculations, force, and stress calculations. We will then discuss some recent advances in the methods based on the NAO bases, such as real‐time dependent density functional theory (rt‐TDDFT), efficient implementation of hybrid functionals, and other advanced electronic structure methods. Finally, we introduce the ab initio tight‐binding model, which can be generated directly after the self‐consistent calculations. The model allows for efficient calculation of electronic structures, and the associated topological, and optical properties of the systems.This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Computational Materials Science