Gridded Data Research Articles

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

AbstractIn this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

Observed Global Changes in Sector‐Relevant Climate Extremes Indices—An Extension to HadEX3

AbstractGlobal gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector‐specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector‐relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude‐latitude grid for 1901–2018. We show changes in globally‐averaged time series of these indices in comparison with reanalysis products. Changes in temperature‐based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin < −2°C (TNltm2) are reducing, decreasing potential damage from frosts. Changes in heat wave characteristics show increases in the number, duration and intensity of these extreme events in most places. The gridded netCDF files and, where possible, the underlying station indices are available from https://www.metoffice.gov.uk/hadobs/hadex3 and https://www.climdex.org.

Spatiotemporal Variations in Upper‐Ocean Salinity Over the North Pacific in 2004–2021

AbstractInterannual variations in upper ocean salinity in the North Pacific Ocean (NP) were examined comprehensively over a period of 18 years (2004–2021) with gridded data sets considering vertical profiles of salinity. The salinity in nearly half of the basin varied in phase with a slight lag, and the rest of the basin exhibited the opposite phase on a decadal scale. The average salinity above 400 dbar prominently increased in the eastern and western mid‐latitude regions from approximately 2010 to the latter half of the 2010s, and decreased in the central subarctic and subtropics during the same period. This spatial pattern was similar to that of temperature change. While the freshening in the subtropics was remarkably above 100 dbar, salinity changes in the subarctic were large even in the subsurface. The first mode of the complex empirical orthogonal function analysis indicated the eastward (westward) propagation of salinity anomalies in the subarctic (subtropics), that is, a clockwise shift. Salinity budget analysis revealed that the changes in the 2010s were mainly caused by geostrophic advection in zonal regions around 40°N and 20°N, and the eastern NP. Precipitation also contributed to freshening in the subtropics, but not in the central subarctic. The meridional migration of salinity fronts in the subarctic and subtropics induced by wind stress, which is meridional contraction of the subtropical gyre, contributed to freshening in the central NP. It is unclear whether salinification in the eastern and western regions inevitably synchronizes and should be further examined.

Concurrent and dynamical interdependency of compound precipitation and wind speed extremes over India

Compound precipitation and wind speed extremes have the potential to cause significant socio-economic losses. Therefore, this study aims to investigate the association of compound precipitation and wind speed extremes in observed period (1981–2020) and future periods (2021–2060 (F1) and 2061–2100 (F2)) over India. In this study, we have used IMD precipitation and ERA5 windspeed gridded (0.250 × 0.250) data for the observed period. For future analysis, we have used CMIP6-based EC-Earth3 GCM (SSP2–4.5 and SSP5–8.5) and CORDEX CMIP5 RCM (RCP-4.5 and RCP-8.5) climate models for precipitation and wind speed outputs. We have analyzed the characteristics of precipitation and wind speed extremes using three approaches: empirical method, tail dependence analysis, and event coincidence analysis. The empirical analysis revealed that the proportion of the area with >240 Compound Precipitation and Wind speed Extremes (CPWE) on the same day is 2.8% in observed period. However, it increased to 22.22% in future period F2, particularly in the SSP5–8.5 scenario of the CMIP6 case, and this increase is observed in regions like Am, eastern parts of Aw and Cwa, Bwk, and Dsb. The tail dependence analysis revealed that the area with tail dependence >0.5 constitutes approximately 0.72% in the observed period across the study area. However, under climate change scenarios, this percentage varies between 2.45% to 3.66% and notably, the increase is observed in regions such as Am, Bwk, and Dsb. The results of precursor coincidence rate (pcr) shows a high value of 3.52% in the entire study area for the observed period. The future analysis reveals an increase of areas in between 3.18% to 5.14% for different climate change scenarios. Consequently, the trigger coincidence rate (tcr) results also revealed the occurrence of high tcr in the range of 0.52% to 5.79% across the study area within the temporal window. However, the tcr range increases to 2.17% to 23.32% during the monsoon season during the observed period with respect to the days in the temporal window. The overall results showed that the high values of tail dependence, pcr and tcr are concentrated in the Am, coastal areas of Aw and Cwa, as well as in the Bwk and Dsb regions. This will lead to increase the extreme precipitation driven by extreme wind speed in those regions consequently, the risk of flash floods, landslides and natural hazards may rise in the future. Overall, this study provides valuable insights into the spatial distribution of compound precipitation and wind speed extremes across the study area.

Disaster impact prediction in the power grid using artificial intelligence based on Texas synthetic grid data replication

Power grids are endangered yearly by disasters that could well affect our livelihood. This paper investigates an AI (Artificial Intelligence) solution to understand better and ultimately predict disasters using the data from previously observed disasters and artificially generated data. The data is fed to the AI in the form of resiliency calculations, which are handled by another sub-program and then go through stages of linear regression models, normal curving models, and decision tree models. Afterward, the main body assigns a value percentile to each of the predictions and reaches a final prediction that is reliable, fast, and accurate. The AI then reviews its decision and checks it with the actual data to see if the forecast is accurate and refines itself according to the newly gathered data. This Algorithm has massive potential implications for decision-making while facing a disaster. It can also help test out different approaches to the problems in a safe environment before the actual procedure initiates, saving time and reducing costs. This paper is an extension to this lab’s effort to simulate a real-life disaster and grid via disaster data and multi-layer analysis to predict the steps of disaster and the grid’s responses. The raw data (generator’s generations, load’s amount, lines current, etc.) is refined through the resiliency equations introduced by this lab to be evaluated via the algorithm.

Anticipating a risky future: long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open-source basis globally, and implement machine learning models tailored for time series analysis, i.e., based on long short-term memory (LSTM) networks, for prediction of future time steps. Specifically, we harvest WorldPop population data and teach LSTM and convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms, which are derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and random forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data; the most accurate prediction obtained with an LSTM equipped with a bidirectional learning scheme features a root-mean-squared error of 3.63 people per 100 × 100 m grid cell while maintaining an excellent model fit (R2= 0.995). We deploy this model for anticipation of population along a 3-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 cm s−2, the population is anticipated to experience growth of almost 30 % over the forecasted time span, which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area is anticipated to grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.

AgERA5 representation of seasonal mean and extreme temperatures in the Northern Cape, South Africa

Over regions with sparse observation networks, including South Africa’s Northern Cape Province, gridded data sets represent valuable supplementary data sources enabling spatially detailed climate investigations. Their performance is, however, influenced by regional characteristics, thus a performance assessment should be a prerequisite for any regional application. Through a pairwise comparison with eight point-based temperature records, we evaluated the AgERA5 data sets representation of mean summer (November–March; Tms) and winter (May–September; Tmw) temperatures and respective seasonal heatwave and coldwave characteristics across the Northern Cape for 1980–2020. Correlations ranging from 0.48 to 0.92 for Tms and from 0.38 to 0.94 for Tmw reflect relatively strong, but varying, temporal correspondence between the AgERA5 data and stations. Low biases, averaging −0.08 (0.17) °C and ranging from -0.79 to 2.10 (-0.40 to 1.47) °C for Tms (Tmw) were evident. Biases for the heatwave (coldwave) magnitudes were low, averaging -0.38 (0.19) °C2, and ranging from -1.55 to 1.47 (-2.05 to 2.91) °C2. Biases for the heatwave (coldwave) frequency were also low, but typically overestimated, averaging 1.19 (0.73) days, and ranging from -1.33 to 5.60 (-1.61 to 3.39) days. Biases for the heatwave (coldwave) number were low and typically overestimated, averaging 0.27 (0.08) events, and ranging from -0.28 to 1.40 (-0.39 to 0.39) events. Despite some stations depicting consistently poor performance, the study results support further application of the AgERA5 product for spatiotemporal analyses of mean and extreme temperatures across the Northern Cape, provided limitations are adequately acknowledged. Further application of the fine-resolution AgERA5 product will greatly inform impact-based studies exploring mean and extreme temperature influences over the Northern Cape Province.

Analysis of recent trends and spatiotemporal changes of droughts over Serbia using high-resolution gridded data

The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E-OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia.

Emissions and Atmospheric Dry and Wet Deposition of Trace Metals from Natural and Anthropogenic Sources in Mainland China

Trace metals from natural and anthropogenic sources impact the atmospheric environment and enter the soil through dry and wet atmospheric deposition, ultimately affecting human health. In this study, we established an emission inventory of Pb, As, Cr, and Cd in East Asia (80° E–140° E, 15° N–50° N) for the year 2017, including dust and anthropogenic sources from both land and marine. We modified the Community Multiscale Air Quality (CMAQ) model to provide gridded data on concentrations, as well as dry and wet atmospheric deposition fluxes of metals, with a focus on mainland China. The emissions of Pb, As, Cr, and Cd in East Asia were 19,253, 3415, 3332, and 9379 tons, respectively, in 2017, with 55%, 69%, 25%, and 58% distributed in the fine mode. The spatial distribution of atmospheric concentrations and dry deposition of trace metals was similar to that of emissions, while the spatial distribution of precipitation-related wet deposition was further east and greater in the south than in the north. In mainland China, the average bulk-deposition fluxes of Pb, As, Cr, and Cd were 1036.5, 170.3, 465.9, and 185.0 μg·m−2·year−1, respectively. Our study provides gridded data on trace metals in mainland China, which can be used for assessing air quality, human exposure risks, and metal inputs to soils.

Assessing the suitability of CFSR data for SWAT model hydrologic simulation of Kunthipuzha river basin, Kerala, India

Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.

Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala

The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.

Agro-climatic zone-wise drought hazards in Karnataka under historical and future climate scenarios

This study performed the spatio-temporal analysis of drought hazards across the agro-climatic zones (ACZs) of Karnataka under historical and future climate scenarios. The India Meteorological Department’s high-resolution gridded data for1989-2019 was used for historical drought occurrence analysis. Coordinated Regional Climate Downscaling Experiment ensemble data of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were used for analysing future drought hazards in the near (2031-2060) and end term (2061-2099) periods. The standardised precipitation index (SPI) was used to calculate the frequency of droughts at different accumulation periods of 1, 3, 6, 9, and 12 months. Subsequently, the ACZ-wise drought hazard index (DHI) was calculated and mapped geospatially using ArcGIS. The results indicated that moderate drought events have the highest frequencies of occurrence, followed by severe and extreme drought events for all accumulation periods. During 1989-2019, 54.8%, 28.3% and 16.7% of droughts were moderate, severe, and extreme, respectively. An increase of 2.6% and 2.4% in the frequency of moderate droughts is projected under the RCP4.5 and 8.5 scenarios, respectively, in the end term. Under both historical and future climate scenarios, a high frequency of extreme droughts was observed in the long accumulation periods (SPI-9 and SPI-12), whereas the frequency of moderate droughts was observed to be high in the short accumulation periods (SPI-1 and SPI-3). Under the historical scenario, the frequency of droughts in the extreme category was high in the southern transition, central dry, and north eastern dry zones, severe category in the northern dry, southern transition, and coastal zones, and moderate category in the north transition, hill, and southern dry zones. Among the 30 districts of Karnataka, Chitradurga, Udupi, Tumakuru, Ballari, Koppala, Raichuruand Gadaga districts have very high DHI. This study sheds light on the potential consequences of climate change on drought scenarios in the Karnataka state’s agro-climate zones and urges for zone specific drought adaptation and mitigation strategies to strengthen the State resilience.

Extreme springs in Switzerland since 1763 in climate and phenological indices

Abstract. Historical sources report manifold on hazardous past climate and weather events that had considerable impacts on society. Studying changes in the occurrence or mechanisms behind such events is, however, hampered by a lack of spatially and temporally complete weather data. In particular, the spring season has received less attention in comparison to summer and winter but is nevertheless relevant, since weather conditions in spring can delay vegetation and create substantial damage due to late-frost events. For Switzerland, we created a daily high-resolution (1 × 1 km2) reconstruction of temperature and precipitation fields from 1763 to 1960 that forms, together with present-day meteorological fields, a 258-year-long gridded data set. With this data set, we study changes in long-term climate and historical weather events based on climate and phenological indices focusing on the spring season. Climate and phenological indices show few changes in the mean during the first 200 years compared to the most recent period from 1991 to 2020, where climate change signals clearly emerged in many indices. We evaluate the climate and phenological indices for three cases of extreme spring weather conditions: an unusually warm spring, two late-frost events, and three cold springs. Warm springs are much more frequent in the 21st century, but a very warm and early spring also occurred in 1862. Spring temperatures, however, do not agree on how anomalously warm the spring was when comparing the Swiss temperature reconstruction with reanalyses that extend back to 1868. The three springs of 1785, 1837, and 1853 were particularly cold, with historical sources reporting, for example, prolonged lake freezing and abundant snowfall. Whereas the springs of 1837 and 1853 were characterized by cold and wet conditions, in the spring of 1785 wet days were below average, and frost days reached an all-time maximum, in particular in the Swiss Plateau, indicating inversion conditions. Such conditions are in line with a high occurrence of northeasterly and high-pressure weather types and historical sources describing Bise conditions, a regional wind in the Alpine area related to inversions. Studying such historical events is valuable, since similar atmospheric conditions can lead to cold springs affecting vegetation growth and agricultural production.

Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning

ABSTRACT Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.

Estimation of Permafrost Ground Ice to 10 m Depth on the Qinghai‐Tibet Plateau

ABSTRACTPermafrost ground ice melting could alter hydrological processes in cold regions by releasing water. Currently, there is a lack of gridded data of ground ice from the Qinghai‐Tibet Plateau (QTP). Using 664 borehole sample records, we applied a random forest (RF) method to predict the ground ice content of permafrost between 2 and 10 m depth in three layers (2–3, 3–5, and 5–10 m) at a spatial resolution of 1 km. The RF predictions demonstrated an R2 value exceeding 0.80 for all three layers with a negligible positive overestimation (0.98%–1.85%). The ground ice content of the first layer (2–3 m) can be predicted primarily using climate variables, but the contribution of terrain and soil variables increases as the depth increases. The total water storage of ground ice across the QTP permafrost (2–10 m depth) is approximately 3330.0 km3, with 403.5 km3 in the 2–3 m layer, 857.2 km3 in the 3–5 m layer, and 2069.3 km3 in the 5–10 m layer. This study generated for the first time a gridded dataset of the shallow permafrost ground ice content across the entire QTP which can be used to improve simulations of hydrological processes in the permafrost regions.

Direct prediction for oceanic mesoscale eddy geospatial distribution through prior statistical deep learning

Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on two-dimensional gridded data suffer from error accumulation issues due to their indirect prediction approach, which involves first predicting the future environmental field and then identifying the ME patterns within it. Additionally, these models ignore the potential influence of prior knowledge related to ME on prediction outcomes. To address these issues, this paper proposes a direct prediction DL network called ES-ConvGRU, which incorporates historical prior statistics. First, we conduct an analysis of ME data from the Northwest Pacific region, extracting prior statistical knowledge such as interannual variations and seasonal characteristics. Second, mining patterns for the spatiotemporal features of both ME sequence and sea surface environmental field sequence are established. Furthermore, a nonlinear approach for mapping prior statistical knowledge and a multi-step autoregressive prediction model are designed, facilitating the integration of prior knowledge with multidimensional spatiotemporal features for ME prediction. Finally, the performance and effectiveness of ES-ConvGRU are evaluated. It achieves an average pixel accuracy of 93.21%, a mean intersection over union of 81.48%, and a frequency-weighted intersection over union of 87.55% for the 7-day distribution prediction. The results show that ES-ConvGRU exhibits notable advantages compared to the existing indirect and direct prediction methods.

Research on data-driven model for power grid fault diagnosis fusing topological quantification information

In increasingly complex power grid operation scenarios and fault modes, rapid and accurate fault identification is highly important for improving the reliability of power systems. Faced with the massive amount of available power grid data, the rapid development of artificial intelligence technology provides a powerful tool for power grid fault diagnosis. However, existing data-driven diagnosis methods lack quantitative power grid topology change representations, cannot integrate power grid fault topology with alarm information, and have limited effectiveness in terms of diagnosing complex faults. To address these issues, a data-driven power grid fault diagnosis model that integrates topological quantitative features is proposed. By studying the changes in the topological connection relationships of equipment before and after a power grid fault and the topological connectivity in a power failure zone, based on the basic topological network indicators in graph theory, a representation of the quantitative features of the power grid fault topology is achieved, and these quantitative features and the alarm information are integrated to construct a data-driven power grid fault diagnosis model. The model uses the light gradient boosting machine algorithm to determine the types of complex faults and accurately identify faulty equipment, addressing the lack of model diagnosis effects considered in previous studies. Finally, the accuracy and effectiveness of the model are verified by using simulated fault cases.

The application of the internet of things technology in the smart grid

The development of electric power and the energy industry is an important guarantee for national economic development. With the explosion of population and the development of science and technology, the power system needs to be more intelligent and networked to facilitate management and optimization. In recent years, the Internet of Things (IoT) technology has continued to develop through the combination of Internet technology and sensor technology, and the fast network transmission speed as well as the miniaturization and refinement of sensing can help the construction of the smart grid. This paper analyzes the application of IoT technology in the smart grid, including different types of smart grid architecture and the practical application of the IoT technology in the whole process of power transmission. Besides, the paper also discusses different application links and network levels. At present, the IoT technology has been applied to the smart grid information collection, transmission, and processing process, participating in information management in the power station, power transmission, substation, and other links. However, due to the complexity of processing the massive data of the power grid and the multiple network connections during the transmission process, ensuring the security of information and networks has become a challenge. While realizing high-speed communication, it is necessary to ensure the security of users as well as the information communication and networks.

Numerical simulation of rime ice accretion on airfoil using rigid sphere model

A 2D/3D numerical model for rime ice shape prediction is developed by combining the rigid sphere model (RSM) and computational fluid dynamics (CFD), and its characteristics are investigated. In the RSM, the ice accretion is modeled based on first principles with little physical modeling. The ice particle on an airfoil is described as a rigid sphere, and the ice shape is represented as a collection of rigid spheres. We use a non-body-fitted hierarchical Cartesian grid-based CFD solver so that complex ice shape represented by RSM can be handled. The tree data of the hierarchical Cartesian grid is also used for fast search of droplet positions. In addition, multiple ice particles are modeled as one large particle to reduce the number of trajectory calculations. The sensitivity of several parameters in the calculation is studied in 2D. Subsequently, 3D ice accretion analysis on NACA0012 airfoil using the RSM is performed for the first time. The simulation results are compared with those of the wind-tunnel experiments conducted at NASA. Although the RSM is a simple ice accretion model, reliable results are obtained in both 2D and 3D calculations.

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.