Big Earth Data最新文献

{"title":"An interactive tool for visualization and prediction of solar radiation and photovoltaic generation in Colombia","authors":"Gabriel Narváez, L. F. Giraldo, M. Bressan, C. A. Guillén, María A. Pabón, Nicolás Díaz, Manuel Felipe Porras, B. Medina, Fernando Jiménez, Guillermo Jiménez-Estévez, A. Pantoja, Corinne Alonso","doi":"10.1080/20964471.2023.2185920","DOIUrl":"https://doi.org/10.1080/20964471.2023.2185920","url":null,"abstract":"ABSTRACT This paper presents the building process of an interactive instrument called the Colombian Solar Atlas able to easily visualize meteorological data but also assess the current and future potentials of solar photovoltaic generation throughout the whole territory of Colombia, South America. This new tool is based on two different meteorological databases. The first one is done with historical data extracted from satellite imagery information, and the other one corresponds to data issues from regional-scale climate change projection models. The satellite database was validated with different in-situ measurements. The Colombian Solar Atlas uses basic and advanced photovoltaic generation models to estimate the generation of a custom solar installation. With this tool, a user selects a point on the map and can have directly pertinent information to search for an optimal location with a spatial resolution of 4 km2. This tool is the first open interactive online tool particularly adapted to study the photovoltaic power potential in Colombia, considering the country’s needs and native language.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1 1","pages":"904 - 929"},"PeriodicalIF":4.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79809799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning","authors":"Yanxing Hu, T. Che, L. Dai, Yu Zhu, Lin Xiao, Jie Deng, Xin Li","doi":"10.1080/20964471.2023.2177435","DOIUrl":"https://doi.org/10.1080/20964471.2023.2177435","url":null,"abstract":". A h igh-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth product and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these 20 problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR-2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporated geolocation (latitude and longitude), and topographic data (elevation), which were used 25 as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time period. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indexes of the fused (best original) dataset yielded a coefficient of determination R 2 of 0.81 (0.23), Root Mean 30 Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31%) between the fused snow depth and in situ observations was distributed from -5 cm to 5 cm depths. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision under snow depths of less than 100 cm with a relatively homogeneous surrounding environment. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher 35 precision, with R 2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment and snow disaster and hazard prevention. The new fused snow depth dataset is freely available from the National Plateau Data Center (TPDC) and can be downloaded at 40 https://dx.doi.org/10.11888/Snow.tpdc.271701 (Che et al","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"7 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84318465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvements in rainfall estimation over Bangkok, Thailand by merging satellite, radar, and gauge rainfall datasets with the geostatistical method","authors":"P. Wetchayont, C. Ekkawatpanit, Sunsern Rueangrit, Jittawat Manduang","doi":"10.1080/20964471.2023.2171581","DOIUrl":"https://doi.org/10.1080/20964471.2023.2171581","url":null,"abstract":"ABSTRACT Bangkok is located in a low land area, and floods frequently occur from rainfall, river discharge, and tides. High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models. The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources: an infrared channel from the Himawari-8 satellite, rain gauges, and ground-based radar observations. This study applied cloud classification and bias correction using rain gauges to discriminate these errors. The bias factors were interpolated using the ordinary kriging (OK) method to fill in the areas of estimated rainfall where no rain gauge was available. The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination. The merged algorithm was then adopted to produce hourly merged rainfall products (GSR). Compared to the initial estimated product, the GSR is significantly more accurate. The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use. Furthermore, these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"151 1","pages":"251 - 275"},"PeriodicalIF":4.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83307933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive and up-to-date web-based interactive 3D emergency response and visualization system using Cesium Digital Earth: taking landslide disaster as an example","authors":"Zhiyuan Yang, Jing Li, J. Hyyppä, J. Gong, Jingbin Liu, Banghui Yang","doi":"10.1080/20964471.2023.2172823","DOIUrl":"https://doi.org/10.1080/20964471.2023.2172823","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82375844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data","authors":"S. Karozis, I. Klampanos, A. Sfetsos, D. Vlachogiannis","doi":"10.1080/20964471.2023.2172820","DOIUrl":"https://doi.org/10.1080/20964471.2023.2172820","url":null,"abstract":"ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"3 1 1","pages":"231 - 250"},"PeriodicalIF":4.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82229391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios","authors":"Y. Zhuang, Jingyong Zhang","doi":"10.1080/20964471.2022.2161218","DOIUrl":"https://doi.org/10.1080/20964471.2022.2161218","url":null,"abstract":"ABSTRACT Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems. In this study, we focus on the major Belt and Road Initiative (BRI) regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C, 2°C, and 3°C above pre-industrial levels under the Representative Concentration Pathway (RCP) 8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848° (~5 km) globally from EnviDat. The temperature variables include the daily maximum (Tmax), minimum (Tmin) and average (Tmp) surface air temperatures, and the diurnal temperature range (DTR). We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986–2005. The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986–2005. Furthermore, we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C, 2°C, and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean. Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions, and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions. The dataset is available at https://doi.org/10.57760/sciencedb.01850.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"17 1","pages":"375 - 397"},"PeriodicalIF":4.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76436878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of ozone pollution and analysis of meteorological factors in the Yangtze River Delta","authors":"Yufen Cao, Yuanhao Qu, Jinghui Ma","doi":"10.1080/20964471.2022.2157093","DOIUrl":"https://doi.org/10.1080/20964471.2022.2157093","url":null,"abstract":"ABSTRACT Serious regional ozone (O3) pollution often plagues the Yangtze River Delta (YRD). The formation mechanism of these regional pollution events, including the meteorological and emission factors leading to these pollution events and how to affect the distribution of O3, still needs further research and exploration. In this study, we first define the standard of O3 regional pollution in the YRD, and then select 248 regional pollution cases from 2015 to 2020 according to the defined standard. For the pollution cases in pollution months (May and June), PCT (principal component analysis in T-mode) classification method is used to classify the ozone concentration distribution in YRD area. The regional distribution of the O3 concentrations in the YRD is divided into five types, and the overall type (Type 1) accounts for 15%, which is related to the control of YRD area by high-pressure center. Under the control of high pressure, the weather is sunny with the high temperature, and this weather condition is favorable for ozone generation and intercity transmission, causing extensive pollution. The double center type (Type 2) accounts for 8%. This type of YRD is controlled by the front of the high pressure (the high-pressure center is located in North China), and the weather in the middle and north is conducive to the generation and transmission of O3. Inland type (Type 3) accounts for 24%. The main body of this type of high pressure is located in Mongolia. The easterly wind in YRD area is conducive to the inland transmission of O3 precursors. The northern coastal type (Type 4) accounts for 44%. This type of YRD area is mainly controlled by the weak pressure field. The weather in the northern coastal area is sunny and the solar radiation for a long time is conducive to the formation of O3. The southern coastal type (Type 5) accounts for 10%, the solar radiation is strong in the southern region mainly under the influence of the post-offshore high pressure. This study provides new insights into the relationship between O3 pollution distribution types and atmospheric circulation in YRD area, and reveals the difference of potential meteorological impacts of different O3 pollution distribution types.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"50 1","pages":"318 - 337"},"PeriodicalIF":4.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78277337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018","authors":"Rongrong Zhang, Virgílio A. Bento, Junyu Qi, Feng Xu, Jianjun Wu, Jianxiu Qiu, Jianwei Li, Wei Shui, Qianfeng Wang","doi":"10.1080/20964471.2022.2148331","DOIUrl":"https://doi.org/10.1080/20964471.2022.2148331","url":null,"abstract":"ABSTRACT Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1 1","pages":"860 - 885"},"PeriodicalIF":4.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85566454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data","authors":"Siqin Wang, Ruomei Wang, Xiao Huang, Zhenlong Li, S. Bao","doi":"10.1080/20964471.2022.2163130","DOIUrl":"https://doi.org/10.1080/20964471.2022.2163130","url":null,"abstract":"ABSTRACT COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"36 1","pages":"37 - 58"},"PeriodicalIF":4.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88790868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"City Digital Twins: their maturity level and differentiation from 3D city models","authors":"Homa Masoumi, S. Shirowzhan, Paria Eskandarpour, C. Pettit","doi":"10.1080/20964471.2022.2160156","DOIUrl":"https://doi.org/10.1080/20964471.2022.2160156","url":null,"abstract":"ABSTRACT The emerging field of City Digital Twins has advanced in recent years with the help of digital infrastructure and technologies connected to the Internet of Things (IoT). However, the evolution of this field has been so fast that a gap has opened in relation to systematic reviews of the relevant literature and the maturation of City Digital Twins on an urban scale. Our work bridges this gap by highlighting maturity in the field. We conducted a systematic literature review with bibliometric and content analysis of 41 selected papers published in Web of Science and Scopus databases, covering five areas: data types and sources, case studies, applied technologies and methods, maturity spectrum, and applications. Based on maturity indicators, the majority of the reviewed studies (90%) were at initial to medium stages of maturity (up to element 3), most of them focused on 3D modelling, monitoring and visualisation. However, digital twins cannot be limited to 3D models, monitoring and visualisation, for they can be developed to include two-directional interactions between humans and computers. Such a high level of maturity, which was not found in the reviewed studies, requires advanced technologies and methods such as cloud computing, artificial intelligence, BIM and GIS. We also found that further studies are essential if the field is to handle the complex urban challenges of multidisciplinary digital twins . While City Digital Twins extend by definition beyond mere 3D city modelling, some studies involving 3D city models still refer to their subjects as City Digital Twins. Among the research gaps we identified, we’d like to highlight the need for near-real-time data analytics algorithms, which could furnish City Digital Twins with big data insights. Other opportunities include public participation capabilities to increase social collaboration, integrating BIM and GIS technologies and improving storage and computation infrastructure.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"623 1","pages":"1 - 36"},"PeriodicalIF":4.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77214454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}