Spatial Correlation Coefficient Research Articles

Robust Direction Estimation of Terrestrial Signal via Sparse Non-Uniform Array Reconfiguration under Perturbations

DOA (Direction of Arrival), as an important observation parameter for accurately locating the Signals of Opportunity (SOP), is vital for navigation in GNSS-challenged environments and can be effectively obtained through sparse arrays. In practical application, array perturbations affect the estimation accuracy and stability of DOA, thereby adversely affecting the positioning performance of SOP. Against this backdrop, we propose an approach to reconstruct non-uniform arrays under perturbation conditions, aiming to improve the robustness of DOA estimation in sparse arrays. Firstly, we theoretically derive the mathematical expressions of the Cramér–Rao Bound (CRB) and Spatial Correlation Coefficient (SCC) for the uniform linear array (ULA) with perturbation. Then, we minimize CRB as the objective function to mitigate the adverse effects of array perturbations on DOA estimation, and use SCC as a constraint to suppress sidelobes. By doing this, the non-uniform array reconstruction model is formulated as a high-order 0–1 optimization problem. To effectively solve this nonconvex model, we propose a polynomial-time algorithm, which can converge to the optimal approximate solution of the original model. Finally, through a series of simulation experiments utilizing frequency modulation (FM) signal as an example, the exceptional performance of this method in array reconstruction has been thoroughly validated. Experimental data show that the reconstructed non-uniform array excels in DOA estimation accuracy compared to other sparse arrays, making it particularly suitable for estimating the direction of terrestrial SOP in perturbed environments.

Evaluation of the Forecasting Performance of Supercooled Clouds for the Weather Modification Model of the Cloud and Precipitation Explicit Forecasting System

Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evaluation of the forecasting performance of the weather modification model named the Cloud and Precipitation Explicit Forecasting System (CPEFS) was conducted. The evaluation, based on selected forecast cases from 8 days in September and October 2018 initialized at 00 and 12 UTC every day, focused especially on the forecasting performance in supercooled clouds (vertical integrated supercooled liquid water, VISL > 0), including the comprehensive spatial distribution of cloud top temperature (CTT) and 3 h precipitation over 0.1 mm (R3 > 0.1). The results indicated that the forecasting performance for VISL > 0 was relatively good, with the Threat Score (TS) ranging from 0.46 to 0.67. The forecasts initialized at 12 UTC slightly outperformed the forecasts initialized at 00 UTC. Additionally, the corresponding spatial Anomaly Correlation Coefficient (ACC) of CTT between forecasts and observations was 0.23, and the TS for R3 > 0.1 reached as high as 0.87. For a mix of cold and warm cloud systems, there was a correlation between the forecasting performance of VISL > 0 and CTT. The trends in the TS for VISL > 0 and the ACC of CTT aligned with the forecast lead-time.

Experimental performance of a two-receivers and two-transmitters array X-ray communication system

In this work, we constructed a two-transmitter, two-receiver array based on grid-controlled modulated X-ray tubes and plastic-scintillator-coupled SiPM detectors to improve the communication rate of X-ray communication (XCOM) systems. The effects of various working parameters, and array dimensions on the bit-error-rate of the system were investigated. We derived the relationship between array dimensions and spatial correlation coefficient theoretically, and pointed out that a higher spatial correlation coefficient is the main reason affecting system performance experimentally. The array communication system achieves a communication data rate of 3.6 Mbps under a 12.5 cm laboratory atmospheric channel with a moderate spatial correlation coefficient of 0.5. The array XCOM system can effectively increase the data rate compared with previous work. This study can provide an experimental foundation and design reference for the establishment of a multiple-input, multiple-output array XCOM system.

Testing for spatial correlation under a complete bipartite network

This note shows that for a spatial regression with a weight matrix depicting a complete bipartite network, the Moran I test for zero spatial correlation is never rejected when the alternative is positive spatial correlation no matter how large the true value of the spatial correlation coefficient. In contrast, the null hypothesis of zero spatial correlation is always rejected (with probability one asymptotically) when the alternative is negative spatial correlation and the true value of the spatial correlation coefficient is near −1.

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The fifth-generation ECMWF Reanalysis – Land (ERA5-Land) soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to absorb the spatial-structure information of the observed data well and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between the model surface soil moisture and observation increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining these with the model physical processes, the image assimilation system can also gradually improve the spatial structure of soil moisture content at a depth of 7–28 cm, with the spatial correlation coefficient between the model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 d. The results of this study adequately demonstrate the application potential of image assimilation system in short-term climate prediction.

Sensitivity analysis of cumulus and microphysics schemes in the WRF model in simulating Extreme Rainfall Events over the hilly terrain of Nagaland

A comprehensive study is carried out to investigate the sensitivity of various combinations between cumulus (CU) and microphysics (MP) schemes using the Weather Research and Forecasting (WRF) model for simulating Extreme Rainfall Events (EREs) over the hilly terrain of Nagaland (25.1 oN – 27.2 oN; 93.2 oE– 95.3 °E). The study explores fifty-four distinct simulation combinations resulting from six CU and nine MP schemes. The simulated rainfall is compared with ground-based rain gauge network and Global Precipitation Measurement (GPM) satellite observations. The investigation focuses primarily on finding the optimal CU-MP scheme combination based on spatiotemporal precision achieved by the identified schemes. The C14 (New Simplified Arakawa Schubert) cumulus scheme is found to be the optimum individual CU scheme. Similarly, the M14 (WDM5) microphysics scheme performs as the optimum individual MP scheme. The study highlights the significance of evaluating the performance of combined schemes over individual ones. Among the fifty-four combinations, sensitivity analysis identified six top-performing combinations, out of which the C11-M8 (Multiscale Kain Fritsch - New Thompson) combination stands out to be optimum with a robust correlation coefficient (CC) of 0.75, lower standard deviation (STD) of 0.76 mm, and RMSD of 0.51 mm. A variation in lead/lag time is found to vary from (−) 3.2 to (+) 5.0 h, with a median value of (+) 1.3 h, suggesting that, on average, the model tends to predict the occurrence of peak rainfall events slightly earlier than observed one. The absolute difference in latitudes (longitudes) of peak rainfall variation ranges from 0.42o - 0.8o (0 o - 0.51o) with a median value of 0.66o (0.37o), indicating that the model could better represent the longitudinal peak rainfall positions with notable accuracy. The spatial correlation coefficient (SCC) ranges between 0.31 and 0.68 with a median value of 0.50. From the overall analysis, it is also found that double moment MP schemes (New Thompson, Morrison (2 M), WDM5, WDM6) consistently perform better than the single moment MP schemes (Kessler, Lin et al., WSM3, WSM5, WSM6), whether in combination with CU schemes or individually. Spatiotemporal variations observed across different cases underscore the pivotal role of accurate topography representation in complex terrains, emphasizing the significance of terrain data in shaping rainfall patterns.

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.

On the Size Discrepancies between Datasets from China Meteorological Administration and Joint Typhoon Warning Center for the Northwestern Pacific Tropical Cyclones

This study analyzes the Northwestern Pacific tropical cyclone (TC) size difference between the China Meteorological Administration (CMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. The TC size is defined by the near-surface 34-knot wind radius (R34). Although there is a high correlation (correlation coefficient of 0.71) between CMA and JTWC R34 values, significant discrepancies are still found between them. The JTWC tends to report larger R34 values than the CMA for large-sized TCs, while the trend is reversed for compact TCs. Despite spatial distribution discrepancies, both datasets exhibit significant similarity (spatial correlation coefficient of 0.61), particularly in latitudinal distribution; higher R34 values are observed near 25° N. An investigation of key parameters affecting R34 estimations shows that the discrepancies in R34 values between the two agencies’ estimates of TC size are primarily influenced by the size itself and latitude. There is a high correlation between R34 difference and R34 values, with a high correlation of up to 0.58 with the JTWC’s R34 values. There is also a significant correlation between R34 difference and latitude, with a correlation coefficient of 0.26 in both the CMA and JTWC datasets. Case studies of Typhoons “Danas” and “Maysak” confirm distinct characteristics in R34 estimations during different development stages, with the JTWC capturing TC intensification better, while the CMA underestimates TC size during rapid growth phases. During the weakening stage of the TC, both agencies accurately estimate the R34 values. These findings contribute valuable insights into the discrepancies and characteristics of R34 datasets, informing the selection and utilization of data for typhoon research and forecasting.

Simulation Performance of Temperature and Precipitation in the Yangtze River by Different Cumulus and Land Surface Schemes in RegCM4

To improve the simulation performance of the RegCM4 model in climate simulations over the Yangtze River Basin (YRB), it is essential to determine the optimal cumulus convection and land surface process schemes from the numerous physical parameterization options within RegCM4. In this study, we selected five cumulus convection schemes (Kuo, Grell, Emanuel, Tiedtke, and Kain–Fritsch) and three land surface process schemes (BATS, CLM3.5, and CLM4.5) to configure 72 mixed schemes. Four years of short-term simulations (1990–1993) with a horizontal resolution of 50 km were conducted using ERA-Interim as the initial and boundary conditions for the 72 schemes. The climate simulation performance of all schemes in the YRB was comprehensively evaluated using a multi-criteria scoring approach. The results indicate that among the selected cumulus convection schemes, the Kain–Fritsch scheme, applied to both ocean and land, demonstrates optimal performance in simulating precipitation over the YRB, with spatial correlation coefficients between simulated and observed annual precipitation around 0.3. Compared to the Community Land Models (CLM3.5 and CLM4.5), BATS exhibits superior capabilities in reproducing the temperature features of the region, with spatial correlation coefficients between simulated and observed values typically exceeding 0.99 and standard deviations within 1.25 °C. Under the optimal KF scheme, the simulated soil moisture in the YRB using CLMs is notably drier, ranging from −7.79 to −8.39 kg/m2, compared to that achieved with BATS. The findings provide a localized reference for the parameterization schemes of RegCM4 in the YRB.

Impacts of physical parameterization scheme of snow and soil properties over the Tibetan Plateau on the simulation of summer rainfall in China

Accurate representation of soil and snow state variables in the land surface model is crucial for good simulation results. In order to investigate the potential impacts of the overestimated snow cover over the Tibetan Plateau (TP) on precipitation over China, we designed comparative experiments over TP with and without updated soil texture data, soil hydrological parameters, and the fresh snow density scheme to address this issue with the Weather Research and Forecasting Model (WRF). The results show that snow cover fraction (SCF) and surface albedo in winter on the TP were overestimated in the WRF control experiment (CTL), with albedo bias exceeding 0.4 in the western TP. The SCF in the WRF improved experiment (WRFIM) is about 8% lower than that of the CTL, and the bias is decreased to 0.2–0.3 in the interior of the TP. The thermal and dynamic effects of the TP in winter have a significant impact on the weather and climate of East Asia during summer. Thus, the simulated summer rainfall bias is significantly reduced by WRFIM in North China, and the mid-lower reaches of the Yangtze River, along with the spatial correlation coefficient between the observed-based and WRF-simulated rainfall increased from 0.76 in CTL to 0.88 in WRFIM. The WRFIM-simulated reductions in snow cover and albedo cause a decrease in upward shortwave radiation and eventually increase net surface radiation. The horizontal temperature gradient of the atmosphere is increased in the WRFIM compared to the CTL, and the increased temperature in the lower and middle layers accommodates more water vapor, which is not conducive to precipitation formation. Correspondingly, the Western Pacific Subtropical High at 500 hPa in the middle troposphere weakened. The low-level anticyclonic circulation at 850 hPa also weakened to the east, affecting water vapor transport in eastern China. It leads to a decrease in the water vapor transported northward by southeasterly from the western Pacific Ocean and a decrease in the water vapor flux reaching North China. This work indicates that accurate characterization of soil properties and snow cover on the TP is crucial for improving the simulation bias of summer rainfall in China.

Deep learning-based forecasting model for chlorophyll-a response to tropical cyclones in the Western North Pacific

Tropical cyclones cause increases in sea surface chlorophyll-a concentration, which is important for studying variations in the regional marine environment. Precisely forecasting the variations of sea surface chlorophyll-a concentration induced by tropical cyclones remains a challenge. In this research, a bidirectional long short-term memory (BiLSTM) neural network deep learning model was applied to predict the variations of sea surface chlorophyll-a concentration induced by typhoons in the Western North Pacific (WNP). Typhoons occurring between 2011 and 2020 were used as training cases and those from 2021 to 2022 as forecasting and testing cases. The input variables of the deep learning model include the sea surface wind at 10 meters (U10 and V10), sea surface temperature anomaly (SSTA), and sea surface chlorophyll-a concentration. The output variable was the chlorophyll-a concentration one day after the passage of the typhoon. Data from the previous 7 days were used to predict the chlorophyll-a concentration one day after the typhoon's passage, and the rolling forecast method was employed to predict chlorophyll-a concentration in the following 7 days. To assess the impact of input variables on the model's forecasting performance, ablation experiments were conducted. The results showed that when using U10, V10, and chlorophyll-a from the previous seven days as inputs, the model exhibited the best overall forecasting performance. Taking Typhoons Chanthu, In-fa, and Malou as examples, the root mean square error (RMSE) for the forecast results are 0.0143 mg · m−3, 0.0087 mg · m−3, and 0.0030 mg · m−3, the mean absolute errors (MAE) are 0.0072 mg · m−3, 0.0074 mg · m−3, and 0.0025 mg · m−3, and the spatial anomaly correlation coefficients (ACC) are 0.9968, 0.9775, and 0.9721, respectively. The results reveal that the most accurate forecasting performance was observed during the mid-phase of the moderate-intensity Typhoon Muifa, with RMSE, MAE, and ACC values of 0.0040 mg · m−3, 0.0032 mg · m−3, and 0.9894, respectively. The BiLSTM neural network model had the best forecasting performance for typhoons of moderate intensity and during the mid-term phase. This is because moderate-intensity typhoons or the mature phase of any typhoon tend to have relatively stable and more predictable paths, resulting in better predictions of chlorophyll-a concentrations. In future work, we intend to extend our training and forecasting to typhoons of various intensities, aiming to further refine and enhance predictive performance.

Improvement of Albedo and Snow-Cover Simulation during Snow Events over the Tibetan Plateau

Abstract The snow albedo is a vital component of land–atmosphere coupling models. It plays a critical role in regulating land surface energy exchange by controlling incoming solar radiation absorbed by the land surface and influencing the timing and rate of snowmelt. Accurate snow albedo simulation is essential to obtain surface energy balance and snow-cover estimates. Here, the simulation of albedo and snow cover using the Weather Research and Forecasting Model and an improved snow albedo scheme is verified against satellite-retrieved products during and immediately following eight snowfall events over the Tibetan Plateau. The improved model successfully characterizes the spatial pattern and inverted U-shaped temporal pattern of albedo over the entire Tibetan Plateau. This is attributed to the local optimization of snow-age parameters and explicit consideration of snow depth in the improved scheme. Compared with the previous model, the model proposed herein greatly decreases the overestimated albedo (by 0.13–0.27), yielding a bias range of ±0.08, mean relative bias decrease of 70%, and significant increase in the spatial correlation coefficient of 0.03–0.39 (mean: 0.13). The significant improvements of albedo estimates appear in deep snow-covered regions, largely attributed to parameter optimization related to snow albedo decay, while less improvements appear over the shallow snow-covered regions. Accurate reproduction of the spatiotemporal variation in albedo alleviated snow-cover overestimation by small amounts. For snow-cover estimates, the improved model consistently decreases the false-alarm rate by 0.03, and increases the overall accuracy and equitable threat score by 0.04 and 0.03, respectively. Moreover, the improved scheme shows an equivalent improvement of albedo estimates at both 1- and 5-km grid spacing over the eastern Tibetan Plateau; this is also true for snow-cover estimates. Significance Statement Snow albedo schemes in widely used numerical weather prediction models show notable shortcomings in complex mountainous regions, hindering accurate surface energy balance and snow-cover prediction. The purpose of this study is to better understand the role of snow albedo on snow-cover estimates and reveal the application potential of an improved snow albedo scheme across the Tibetan Plateau. This is important because snow albedo influences the timing and rate of snowmelt, and in turn snow-cover estimates, through regulating the surface energy budget. Our results highlight the strong application potential of our improved scheme in reducing snow simulation errors, confirm the importance of snow depth on snow albedo, and provide a new perspective for improving the accuracy of snow forecast over the topographically high Tibetan Plateau.

Acoustic scattering modeling and sound field characteristics of rough seafloor in shallow sea

Acoustic scattering is an important part of ocean acoustics, and the acoustic scattering caused by the unevenness of the seafloor surface is one of the reasons for the fluctuation of acoustic propagation in the ocean. In order to solve the acoustic scattering problem of sea bottom surface roughness, normal wave theory is used to model the acoustic field. To simplify the problem, Lambert’s law is used to establish the seafloor rough scattering model in horizontal layered shallow sea waveguides, and the scattering field is assumed to be isotropic in the horizontal direction. Based on this model, the amplitude distribution and the phase distribution of the scattered sound pressure are obtained, and the intensity of the scattered sound field and its spatial correlation coefficient are simulated numerically. The prediction of the scattered sound field under rough interface conditions is realized, and the variation of the spatial characteristics of the scattered sound field with the roughness of the seafloor is revealed. The results show that when Lambert’s law is used to describe the rough interface acoustic scattering and when the seafloor roughness is smaller than the wavelength, the spatial correlation coefficient of the scattered sound field at two different positions in space has a change rule of periodic oscillation attenuation with the increase of spatial distance, and in the vertical direction, the oscillation period is larger and the attenuation is slower. When the roughness increases, the oscillation amplitude of the horizontal and the vertical correlation coefficient gradually increase, the oscillation period of the horizontal correlation coefficient gradually decreases, and the vertical correlation coefficient no longer attenuates in the direction near the seafloor, which is the result of the weakening of the seafloor acoustic scattering. The model theory in this paper can also be extended to the acoustic scattering modeling of rough sea surface. For the case of non-horizontal seabed, the scattered sound field of the rough interface in the waveguide can be obtained by using coupled normal wave or adiabatic normal wave theory.

Spatial analysis of care for patients undergoing dialysis therapy in the state of Minas Gerais, Brazil, between 2015 and 2019.

To analyze the spatial flow of care for patients undergoing dialysis therapy in the health regions of the State of Minas Gerais. Ecological study whose population was patients undergoing dialysis therapy in public, philanthropic institutions or whose treatment was paid for by the Unified Health System in private clinics in partnership, in the State of Minas Gerais. Patients were grouped by health region of residence. The proportions of patients who underwent dialysis were calculated, as well as enrollment on the kidney transplant list in their own region of residence or outside it. Person correlations of these proportions with socioeconomic and care indicators of the health regions were estimated. Spatial exploratory techniques estimated general (Moran's I) and local (LISA) spatial correlation coefficients. Regions with higher GDP had a higher number of nephrologists and a higher proportion of registrations in the region of residence. A cluster of regions with low GDP was identified further to the northeast of the State (also with lower nephrologist ratio values), a cluster with a high proportion of those registered on the transplant list in the center of the State, and a cluster with a low proportion of dialysis in the same region of residence further southeast. Regional disparities were evident in relation to the proportion of patients registered on the waiting list for kidney transplantation, the proportion of patients undergoing dialysis in the same region of residence and the proportion of patients registered on the waiting list for kidney transplantation in the same region of residence. residence.

Noise-Induced Defects in Honeycomb Lattice Structure: A Phase-Field Crystal Study

One of the classes of the kinetic phase-field model in the form of the two-mode hyperbolic phase-field crystal model (modified PFC model) is used for the study of the noise effect of the crystalline structure. Special attention is paid to the origin of the defect’s microstructure in the crystalline honeycomb lattice due to induced colored noise. It shows that the noise–time correlation coefficient τζ, comparable to the diffusion time, enhances the grain boundary mobilities. Instead, a small spatial correlation coefficient, λζ, close to the first lattice parameter of the honeycomb crystal, stabilizes the structure. The finite non-zero value of the relaxation time τ for the atomic flux significantly slows the local relaxation of the fluctuated field and leads to the grains’ fragmentation and formation of the disordered phases. The obtained results are applicable to the hexagonal atomic structures and, in particular, to honeycomb crystals, such as boron nitride, in which the lattice defects might be simulated through the induced colored noise.

Evaluation of CMIP6 model performance in simulating the PDO and its future change

This study comprehensively assesses the model performance in simulating the variation of the Pacific Decadal Oscillation (PDO) in the winter time (November to March) during the historical period of 1870–2014 using 40 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its future change is also investigated under four Shared Socioeconomic Pathway scenarios on the basis of 17 CMIP6 models. Results show that CMIP6 models can reasonably reproduce the observed sea surface temperature anomaly mode associated with the PDO, with high spatial correlation coefficients of above 0.7. However, CMIP6 models generally perform poorly in simulating the variability of the PDO, with low correlation coefficients for most models, except FGOALS-g3 (0.57), GISS-E2-1-H (0.42), and NESM3 (0.37), which present a relatively greater correlation. The multi-model ensemble (MME) outperforms the individual models and can capture the dominant periodicity of the PDO (∼50 years), but it fails to capture the periodicity of ∼20 years. Additionally, the MME result underestimates the variability of the PDO. In response to future warming, the PDO is expected to transfer from a negative to positive phase around the 2050s. The 50-year periodicity of the PDO is projected to decrease compared to the historical period.摘要本文基于40个CMIP6模式输出结果, 系统评估了模式对历史时期太平洋年代际振荡(PDO)的模拟性能, 并利用其中17个模式的4种共享社会经济路径情景数据预估了PDO未来可能变化趋势. 结果表明, CMIP6模式能够合理再现PDO相关海表温度异常的空间模态, 但模式模拟PDO位相演变的能力普遍较弱. 多模式集合能够合理再现PDO的50年左右周期, 但无法模拟出其20年左右周期, 并且低估了PDO的变化幅度. 在未来变暖情景下, PDO可能在2050左右出现由负位相向正位相的转变, 同时其50年左右周期也将明显缩短.

A coherence-improved and mass-balanced inflow turbulence generation method for large eddy simulation

Realistic inflow turbulence is crucial in large eddy simulations for obtaining accurate results. In this paper, an improved inflow turbulence generation method is proposed, called the coherence-improved and mass-balanced random flow generation (CMRFG) method. First, a study of the effect of the inlet mass-balanced condition is presented. Subsequently, the proposed method is explicitly deduced to satisfy arbitrary spatial coherence functions and the mass-balanced condition, so the generated turbulent fields effectively preserve the original turbulence characteristics, eliminating the need for additional inlet mass flux corrections. Meanwhile, the novel method retains the advantages of the previous method, satisfying arbitrary turbulence intensities, temporal spectra, temporal correlations, and spatial correlation coefficients. Additionally, the proposed method also ensures compliance with the divergence-free condition and Taylor's frozen hypothesis, which are essential for accurate flow field development. Finally, large eddy simulations of both homogeneous isotropic and anisotropic turbulent fields demonstrate that the turbulent fields generated by CMRFG closely align with experimental results and exhibit no artificial pressure fluctuations within the computational domain.

Analysis and application of spatio-temporal correlation characteristics of measured ocean ambient noise

Gain in sonar signal processing is closely linked to the spatio-temporal coherence characteristics of the ocean ambient noise field. The gain relies on the spatio-temporal coherence coefficient of the signal and noise, with the aim of maximizing the former and minimizing the latter. Currently, there has been ample research on the spatial characteristics of the noise field, while studies on the temporal coherence characteristics of single-point noise fields are limited. In this paper, a temporal and spatial coherence model of wind-induced noise is established based on the normal mode theory, and the impact of bandwidth and spectral structure on the time coherence coefficient of wind-induced noise is analyzed.To measure the environmental noise in 103 m of shallow water in the South China Sea, a 64-element vertical array was utilized. The results demonstrate that the measured vertical spatial correlation coefficient aligns closely with the theoretical findings when wind-induced noise is predominant. The measured temporal correlation coefficient of narrowband noise exhibits a cosine function with gradual attenuation of the envelope, and the correlation radius remains independent of frequency. As the bandwidth increases, the measured broadband noise temporal correlation radius gradually decreases. Wind speed has minimal influence on the temporal and spatial correlation coefficients of wind-induced noise. In the presence of interferences such as ships and pulses, the measured temporal and spatial correlation coefficients of wind-induced noise exhibit larger values in certain intervals.Lastly, leveraging the characteristic that the temporal coherence radius of noise is significantly smaller than that of the line spectrum signal, a single-hydrophone cross-spectrum method is proposed to process the line spectrum signal. This approach effectively enhances the signal-to-noise ratio of the line spectrum.

Assessment of NEX-GDDP-CMIP6 Downscale Data in Simulating Extreme Precipitation over the Huai River Basin

This study aimed to assess the performance of 35 global climate models included in NEX-GDDP-CMIP6, derived from downscaling CMIP6 data to high spatial (25 km) and temporal (daily) resolutions, in reproducing extreme precipitation events over the Huai River Basin. Eight widely used extreme precipitation indices were employed to quantitatively describe the models’ capability of simulation. Results indicate that the majority of models can reasonably capture trends, with UKESM1-0-LL performing the best among all considered models. All models demonstrate high accuracy in simulating climatological means, especially for the total precipitation (PRCPTOT), displaying a spatial correlation coefficient exceeding 0.8 when compared to the observed data. NorESM2-MM and MRI-ESM2-0 can accurately simulate the frequency and intensity of extreme precipitation, respectively. In general, UKESM1-0-LL, CESM2, MIROC6, MRI-ESM2-0, CMCC-CM2-SR5, and MPI-ESM-2-LR exhibit superior simulation capabilities in terms of capturing both the trends and climatology of extreme precipitation. The aforementioned findings provide guidance for future studies on the regional impacts of climate change using NEX model data, and therefore hold great importance in comprehending the regional impacts of, and the adaptability to, climate change, as well as the development of adaptation strategies.

Evaluation of CMIP6 model skills in simulating tropical climate extremes over Malawi, Southern Africa

Malawi, a developing country in southeast Africa, is one of the most vulnerable countries to climate change and associated impacts. Availability of observed data to inform our knowledge on climate change is however, a key challenge and has led to relatively little research in the subject. Alternative climate data products, such as the Global Climate Models (GCMs) phase6 of Coupled Model Intercomparison Project (CMIP6), accords the chance to bridge this knowledge gap. These products however, need some validation against observed data to ascertain their level of performance. This study therefore, evaluates the ability of nineteen CMIP6 models in simulating both annual and seasonal temperature and precipitation over Malawi from 1980 to 2014. Observed Model performance metrics such as bias, root mean square error (RMSE), spatial correlation coefficient, standard deviation and Percentage Bias (PBIAS) were employed to assess the ability of the individual models. Our quantitative analysis shows that most of the models could simulate both temperature and precipitation over the study area, with correlation coefficient values of over 0.70, RMSE values between 0.9 and 2.0 and PBIAS of ≤\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le$$\\end{document} 10%. The results are suggesting better performance of CMIP6 than those reported in previous studies over the study domain using CMIP3 and CMIP5 model datasets. Of all the nineteen models evaluated in this study, no single model performed best compared to observed dataset, because the models are varying in performance from season to season. Hence, climate end users are advised to use simulations of temperature and precipitation over the study area from CMIP6 models with care for decision making on the mitigation and adaptation of climate change.