Model Configuration Research Articles

The Cycle 46 Configuration of the HARMONIE-AROME Forecast Model

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included.

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Research on Data Feature Selection of Sharing Platform Based on Particle Swarm Optimization Algorithm

Abstract. This paper is based on the optimization of machine learning models such as SVM (Support Vector Machine) and LightGBM through the application of PSO (Particle Swarm Optimization) and combines shared platform recovery data to provide training material for machine models. The goal is to accelerate the parameter configuration of machine learning models and enhance the rigor of feature selection through the application of algorithmic models. The experimental results show that PSO performs well in optimizing parameters for machine learning models like SVM and LightGBM, achieving high levels in evaluation metrics such as accuracy, recall, precision, and F1 score. Based on this, a framework is proposed for embedding the PSO algorithm into a shared platform architecture, including layers for data collection and preprocessing, algorithm integration and optimization, decision support and service, and feedback and optimization. This framework allows for precise predictions of user behavior, market demand, and other factors, achieving automated scheduling of machine model parameters.

Updates to NOAA’s Unified Forecast System’s Cumulus Convection Parameterization Scheme between GFSv16 and GFSv17

Abstract This study outlines the updates made to cumulus convection parameterizations between Global Forecast System, version 16 (GFSv16), and the forthcoming GFSv17 which will be the first global forecast application to become operational under the Unified Forecast System infrastructure. The updates, addressing known systematic errors and biases, incorporate innovations such as stochasticity, three-dimensional subgrid organizational effects, and a prognostic closure evolution. The changes are shown to improve tropical temperature/humidity biases, convective available potential energy (CAPE) forecasts, CONUS precipitation, and tropical variability. By examining individual updates’ impact on temperature, humidity profiles, and precipitation power spectra, we find that the new prognostic closure and stricter precipitation evaporation criteria alleviate a dry and cold bias in the tropical boundary layer and enhance precipitation variability in the tropics. Stricter triggering criteria also allow for more CAPE buildup, particularly over the tropics. The cumulus convection updates have a modest impact on precipitation skill scores over CONUS, but overall, there is an improvement when comparing GFSv16 and the latest GFSv17 prototype configurations, in particularly for larger thresholds. The study also highlights the challenges in developing convection parameterizations suitable for both coupled and uncoupled model configurations. Evaluation of the MJO shows varying responses to cumulus convection changes depending on whether the model is coupled with a dynamic ocean model.

법적 판단에서의 증거평가 차원: 다차원 척도법 및 펴기의 적용

The purpose of this study was to explore the dimensions that laypersons use to evaluate evidence presented in criminal cases and to determine whether the dimensions differ according to legal decision using rankings of the importance of evidence. Multidimensional scaling was performed using the proximity matrix generated from the entire data set, and it was found that laypeople are likely to evaluate evidence according to the legal party from which it was presented (Dimension 1), while Dimension 2 may reflect the sequence of events or the objectivity of the evidence itself. The multidimensional scaling configurations for the guilty and not guilty decision were similar to the configuration of the total data, but the clustering of the evidence and the meaning of the dimensions tended to be clearer in the guilty decision configuration. When comparing vector model configurations of multidimensional unfolding using ranking data, no evidence or dimension was observed to be particularly important to those who were pro-defendant, but a majority of those who were pro-prosecution people valued the defense's evidence. Although this study is exploratory and has limited generalizability to laypersons' evidence evaluation dimensions, it is expected to provide essential knowledge for systemic understanding of the jury decision-making process.

Study of recovery factor and CO(2) storage behavior in a reservoir using numerical simulation

In this study, the behavior of the recovery factor during the displacement and storage phases of carbon dioxide (CO2) in an Ecuadorian reservoir was investigated by numerical simulation. Continuous injection and CO2 WAG methods were used. The methodology included field data collection, creation of a static reservoir model, configuration of the PVT model with CMG software, construction of a dynamic model in GEM-CMG based on rock-fluid properties, and evaluation of CO2 injection scenarios. After a screening analysis, the optimal scenario was identified and the performance of the recovery factor over time was studied. The results indicated that, during the 8 years of simulation, the recovery factor with continuous injection was 28%, while with the WAG method it reached 31%. It was concluded that WAG was the most efficient method. Regarding storage, it was observed that continuous injection was able to store 83% of the CO2 in the reservoir.

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model’s performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.

Detecting phase transitions based on siamese neural network

Machine learning has been widely applied in physics research. Although unsupervised learning can extract the critical points of phase transitions, the percolation model remains a challenge. Unsupervised learning using the raw configurations of the percolation model fails to capture the critical points. To capture the configuration characteristics of the percolation model, this paper proposes using the maximum cluster as input to the neural network. It is well understood that the order parameter of the percolation model is not simply the particle density, but rather the probability that a given site or bond belongs to the percolating cluster. Additionally, we introduce the use of a Siamese Neural Network (SNN) to detect percolation phase transitions. Unlike unsupervised dimensionality reduction methods or supervised binary classification outputs, the SNN produces a scalar output referred to as similarity. By combining the maximum cluster and the SNN, we not only successfully extract the critical value of the percolation model, but also calculate the correlation exponent via data collapse. We believe that the SNN has great potential in handling phase transition classification problems and can serve as a reference for studying other phase transition systems.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Use of geoprocessing in the simulation of mining tailings dam rupture

The study of hypothetical dam rupture aims to understand the flow through the valley downstream of the volume released by a tailing dam rupture to seek preventive actions in the event of a real rupture. In this research, the open and free program for simulating dam ruptures, called HEC-RAS (Hydrologic Engineering Center-River Analysis System), software developed by the US Army Corps of Engineers Hydrologic Engineering Center is used for simulation dam ruptures. A hydrodynamic simulation of the rupture of dam B1, in Brumadinho, Minas Gerais, Brazil, was applied, is developed for this test area. Six simulation scenarios are defined, for the configuration of the hydrodynamic model of the valley downstream of the B1 dam, where all the input data identical besides the DTMs that are configured from a rescaling process of an original DTM obtained from a LiDAR (Light Detection and Ranging) survey, ranging the spatial resolution from 1 m to 25 m. Each of these DTMs is introduced into the simulation processes in HEC-RAS. A direct comparation with data obtained from the real rupture event is made to validate the results of these simulation scenarios. The flooded areas obtained in the model were assessed using the performance indicator F that considers the under and overestimated areas. The other results are subjected to a descriptive statistical analysis through the coefficient of variation (Cv) to verify the discrepancies between the values ​​for each scenario. Form the the results, it is concluded that the scenarios presented very similar performance, in terms of the rupture of the studied dam, with the velocity being the variable most sensitive to the spatial resolution change, without presenting a very discrepant response in relation to the other metrics analyzed.

Predictive soil mapping using random forest models: Applications in pH and soil organic matter assessment

Digital Soil Mapping (DSM) presents a highly scalable and efficient alternative to traditional soil analysis, which is typically limited by its labor-intensive processes, time constraints and low spatial resolution. By utilizing advanced computational techniques such as machine learning and remote sensing, DSM overcomes these limitations and improves the accuracy, efficiency and scalability of soil property assessments. This study, conducted across Tamil Nadu, India, applied DSM and Random Forest (RF) models to predict 2 key soil properties: pH and Soil Organic Matter (SOM). We employed Conditioned Latin Hypercube Sampling (cLHS) for optimized sampling point selection and utilized the Boruta algorithm to identify the most relevant covariates for accurate modeling. The RF models were fine-tuned using a comprehensive grid search, with the optimal configuration spanning from 500 to 2000 trees (ntree) and mtry from 1 to 11. The best-performing model was found with 2000 trees and mtry set to 1 yielding superior prediction for SOM and pH with Root Mean Square Error (RMSE) values of 0.71 and 0.60 respectively, showcasing a high level of predictive accuracy. Our findings emphasize the critical role that remote sensing indices play in predicting SOM, while pH was influenced by both terrain features and remote sensing data. In comparison to previous studies, this research offers novel improvements in both sampling optimization and model configuration, leading to enhanced predictive performance. These results hold significant potential for sustainable land-use planning, agricultural productivity and environmental management.

HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition

Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the second stage, temporal features are extracted using a transformer encoder. In the final stage, the features are mapped using a deep-learning network. The proposed HCTC method is investigated using the benchmark RadioML database and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method achieves a better performance in modulation signal classification. Additionally, the performance of the proposed method is evaluated when applied to different batch sizes and model configurations. Finally, open issues in modulation recognition research are addressed, and future research perspectives are discussed.

Construction 4.0 implementation for performance improvement: an innovation management perspective

Purpose The emergence of Construction 4.0 technologies provides an impetus for radical change and rejuvenates the interest of stakeholders in addressing long-standing performance issues in the construction sector. However, construction firms struggle to implement Construction 4.0 technologies for performance measurement and improvement. Therefore, the purpose of this research is to develop a conceptual model of innovation management for implementing Construction 4.0 that guides and facilitates the strategic transformation of construction firms. Design/methodology/approach A conceptual model of innovation management is presented, and the findings are synthesised based on a literature review, 20 semi-structured interviews, two focus group discussions, three workshops, expert consultation and observations on three digitally-enabled projects. Data were inductively analysed using thematic analysis. Findings The analysis of empirical data revealed: (i) Four scenarios that could lead the industry to different futures, based on the extent of research and development, and the extent of integration/collaboration; (ii) Construction 4.0 capability stages for a sustained implementation route; (iii) Possible business model configurations derived from servitisation strategies; and (iv) Skills management challenges for organisations. Research limitations/implications First, the empirical data was only collected in the UK with its unique industry context, which may limit the applicability of the results. Second, most of the research data comes from the private sector, without the views of public sector organisations. Third, the model needs to be further validated with specific data-driven use cases to address productivity and sustainability issues. Practical implications Successful Construction 4.0 transformation requires a concerted effort of stakeholders, including those in the supply chain, technology companies, innovation networks and government. Although a stakeholder’s action would depend on others’ actions, each stakeholder should undertake action that can influence the factors within their control (such as the extent of collaboration and investment) and the outcomes. Originality/value The conceptual model brings together and establishes the relationships between the scenarios, Construction 4.0 capability stages, business models and skills management. It provides the first step that guides the fuzzy front-end of Construction 4.0 implementation, underpins the transformation to the desired future and builds long-term innovation capabilities.

Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Validation measurement of vehicle pass-by models for dynamic urban environments

Simulation and auralization of urban traffic noise can help city planners and residents to improve existing urban areas or plan new attractive living spaces while enabling discussions about shared auditory experiences, regardless of expertise. Evaluating street noise in the context of urban areas requires careful consideration of their urban surroundings, such as landscapes, buildings, and other structures. Obtaining measurements to validate simulations may be difficult given the variety and complexity of sounds within an urban setting. Recreating the real urban scenario on a test track, e.g., including building facades and exact source and receiver positions, helps validate simulations but also poses challenges due to varying road surfaces, weather conditions, and disturbances such as industrial or fly-over sounds. This research represents a method of bringing a measurement set-up consisting of source (e.g., vehicle), receiver (e.g., pedestrian), and surfaces (e.g., walls) to the more controlled environment of a hemi-anechoic chamber. The method is intended for the validation of previous vehicle pass-by measurements by utilizing sine sweeps with walls parked at varying distances. These environments aim to deliver more insight into the relevance of diffracted sound for configuration of urban sound simulation models.

Verification of frequency-dependent impedance boundaries for the Finite-Volume Time-Domain scheme

Numerical simulation methods are widely used to approximate solutions to the wave-equation, taking into account the initial condition ('initial sources') and boundary conditions ('wall materials'). Trustworthy simulations are essential; a primary step is to ensure the correctness of the implementation through a process known as code verification. In this work, we do code verification by studying the convergence of the known finite-volume time-domain (FVTD) method with a frequency-dependent boundary condition modeled as an inductance/resistance/capacitance (LRC) termination. Due to the difficulties of obtaining an analytical solution for frequency-dependent impedance boundaries, we thoroughly derive and implement The Method of Manufactured Solutions tailored to the wave-equation inside a rectangular room for uniform frequency- dependent wall impedance. Asymptotic estimates are found empirically based on a convergence study compared against the manufactured ground truth solution. We investigate the impact of the frequency-dependent boundaries on the convergence affected by the total sum of numerical errors and validate the theoretical properties of the FVTD scheme for various LRC model configurations. The method should also serve as a useful resource for others to verify the correctness of their code implementations.

Improving Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model

AbstractDeficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDL's 0.25° nominal horizontal grid spacing OM4 (uncoupled) configuration of the MOM6 ocean model, with various modifications that target biases in the original model. We test several enhancements to the existing mixing schemes and evaluate them against observational constraints from Tropical Atmosphere Ocean moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its surface boundary layer mixing scheme, and can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing, leading to improved diurnal deep‐cycle variability, a more realistic time‐mean tropical thermocline structure, and a better Pacific Equatorial Undercurrent.

Decadal prediction centers prepare for a major volcanic eruption

Abstract The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

A 45-Year Updating Wind and Wave Hindcast Over the Oman Sea and the Arabian Sea

This study focuses on developing a comprehensive and reliable wind and wave hindcast for the Oman Sea and Arabian Sea, spanning a significant 45-year period (1979-2024). The objective is to capture the intricate wind and wave climate of the region, characterized by distinct monsoon cycles and occasional tropical cyclones. The availability of extensive wave data over four decades would be an irreplaceable tool for researchers and engineers, enabling improved accuracy in extreme value analysis, sediment transport studies, and wave-induced current simulations. Despite limited field observations, all available measured data within the region were collected, analyzed for configuration of the models and utilized for model calibration and validation. The hindcast data was generated using the Weather Research and Forecasting (WRF) model for winds and WAVEWATCH III (WW3) model for waves, and evaluated against observational and measured wind and wave parameters. A comprehensive statistical analysis of the hindcast model's performance reveals adequate agreement with measured data, as evidenced by root mean square errors (RMSEs) of 1.23m/s for wind speeds and 0.37m for significant wave heights in average. These results underscore the model's reliability for research and engineering applications, particularly in the Arabian Sea, with a focus on the northern coastlines of the Oman Sea. The specific model configuration employed in this study holds significant potential for future investigations in the northern Indian Ocean, offering a valuable tool for understanding and predicting climatical conditions in this region.