Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemic...Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.展开更多
[Objective]In response to the issue of insufficient integrity in hourly routine meteorological element data files,this paper aims to improve the availability and reliability of data files,and provide high-quality data...[Objective]In response to the issue of insufficient integrity in hourly routine meteorological element data files,this paper aims to improve the availability and reliability of data files,and provide high-quality data file support for meteorological forecasting and services.[Method]In this paper,an efficient and accurate method for data file quality control and fusion processing is developed.By locating the missing measurement time,data are extracted from the"AWZ.db"database and the minute routine meteorological element data file,and merged into the hourly routine meteorological element data file.[Result]Data processing efficiency and accuracy are significantly improved,and the problem of incomplete hourly routine meteorological element data files is solved.At the same time,it emphasizes the importance of ensuring the accuracy of the files used and carefully checking and verifying the fusion results,and proposes strategies to improve data quality.[Conclusion]This method provides convenience for observation personnel and effectively improves the integrity and accuracy of data files.In the future,it is expected to provide more reliable data support for meteorological forecasting and services.展开更多
With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,...With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,it is very important to improve the efficiency of decision-making by quickly obtaining accurate meteorological observation data.However,the traditional method of meteorological data collection and transmission has a large delay in data acquisition due to the conversion of public network and internal network,which affects the timeliness of emergency decision-making.This paper proposes a solution based on the Internet of things platform combined with MQTT protocol,which aims to realize the efficient and reliable real-time collection and transmission of meteorological data,shorten the data acquisition time,improve the emergency response speed,and meet the needs of temporary observation.展开更多
In this paper,the latest progress,major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed.First,the latest three FengYun(FY)meteorological satelli...In this paper,the latest progress,major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed.First,the latest three FengYun(FY)meteorological satellites(FY-2H,FY-3D,and FY-4A)and their primary objectives are introduced Second,the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated,including the latest geostationary(FY-2/4)and polar-orbit(FY-3)satellites.Third,the radiometric calibration techniques and accuracies of reflective solar bands,thermal infrared bands,and passive microwave bands for FY meteorological satellites are discussed.It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods,including the vicarious China radiance calibration site calibration,pseudo invariant calibration site calibration,deep convective clouds calibration,and lunar calibration.Fourth,recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length.The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models.Lastly,the latest progress in radiative transfer,absorption and scattering calculations for satellite remote sensing is summarized,and some important research using a new radiative transfer model are illustrated.展开更多
Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment a...Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.展开更多
Taibus County, Inner Mongolia, China, lies in a farming-pastoral ecotone, where severe wind erosion and various aeolian sand hazards are prevalent and fixed and semi-fixed sand dunes occur frequently. This study was c...Taibus County, Inner Mongolia, China, lies in a farming-pastoral ecotone, where severe wind erosion and various aeolian sand hazards are prevalent and fixed and semi-fixed sand dunes occur frequently. This study was conducted to investigate the relationships between sand transportation rate and wind speed for the fixed and semi-fixed sand dunes based on field measurements. The annual quantity of soil erosion by wind was estimated using meteorological wind data. The results indicated that the sand transportation rate in Taibus County in 2000 was 57.38 kg cm-1 year-1 for the semi-fixed dunes and 4.56 kg cm-1 year-1 for the fixed dunes. The total duration of erosive winds covered 12.5% of the time of the year, and spring posed the highest potential of sand transportation. Wind with low speed (≤ 17 m s-1) and high frequency plays a dominant role in sand transportation, while strong wind (≥ 17 m s-1) with low frequency significantly enhanced the sand transportation. Erosive wind speed, directions, and frequency were three crucial dynamic factors influencing sand hazards in the farming-pastoral ecotone. The dominant factors intensifying sand and dust storms in Taibus County might be related to the favorable wind condition in combination with the durable drought, which led to land desertification and vegetation degradation.展开更多
Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available ...Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available for climate analysis in Pakistan and India, the present study emphases on an extensive quality control and homogenization of daily maximum temperature, minimum temperature and precipitation data in the Jhelum River basin, Pakistan and India. A combination of different quality control methods and relative homogeneity tests were applied to achieve the objective of the study. To check the improvement after homogenization, correlation coefficients between the test and reference series calculated before and after the homogenization process were compared with each other. It was found that about 0.59%, 0.78% and 0.023% of the total data values are detected as outliers in maximum temperature, minimum temperature and precipitation data, respectively. About 32% of maximum temperature, 50% of minimum temperature and 7% of precipitation time series were inhomogeneous, in the Jhelum River basin. After the quality control and homogenization, 1% to 11% improvement was observed in the infected climate variables. This study concludes that precipitation daily time series are fairly homogeneous, except two stations （Naran and Gulmarg）, and of a good quality. However, maximum and minimum temperature datasets require an extensive quality control and homogeneity check before using them into climate analysis in the Jhelum River basin.展开更多
Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DF...Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.展开更多
The spatial pattern of meteorological factors cannot be accurately simulated by using observations from meteorological stations(OMS) that are distributed sparsely in complex terrain. It is expected that the spatial-te...The spatial pattern of meteorological factors cannot be accurately simulated by using observations from meteorological stations(OMS) that are distributed sparsely in complex terrain. It is expected that the spatial-temporal characteristics of drought in regions with complex terrain can be better represented by meteorological data with the high spatial-temporal resolution and accuracy. In this study, Standard Precipitation Evapotranspiration Index(SPEI) calculated with meteorological factors extracted from ITPCAS(China Meteorological Forcing Dataset produced by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences) was applied to identify the spatial-temporal characteristics of drought in Shaanxi Province of China, during the period of 1979–2016. Drought areas detected by SPEI calculated with data from ITPCAS(SPEI-ITPCAS) on the seasonal scale were validated by historical drought records from the Chinese Meteorological Disaster Canon-Shaanxi, and compared with drought areas detected by SPEI calculated with data from OMS(SPEI-OMS). Drought intensity, trend and temporal ranges for mutations of SPEI-ITPCAS were analyzed by using the cumulative drought intensity(CDI) index and the Mann-Kendall test. The results indicated that drought areas detected from SPEI-ITPCAS were closer to the historical drought records than those detected from SPEI-OMS. Severe and exceptional drought events with SPEI-ITPCAS lower than –1.0 occurred most frequently in summer, followed by spring. There was a general drying trend in spring and summer in Shaanxi Province and a significant wetting trend in autumn and winter in northern Shaanxi Province. On seasonal and annual scales, the regional and temporal ranges for mutations of SPEI-ITPCAS were different and most mutations occurred before the year 1990 in most regions of Shaanxi Province. The results reflect the response of different regions of Shaanxi Province to climate change, which will help to manage regional water resources.展开更多
In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Ad...In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Administration (CMA) for years and have been proven effective in reliably managing the complexities of large-scale meteorological related workflows. Based on the previous work on the platforms, we argue that a minimum set of guidelines including workflow scheme, module design, implementation standards and maintenance consideration during the whole establishment of the platform are highly recommended, serving to reduce the need for future maintenance and adjustment. A significant gain in performance can be achieved through the workflow-based projects. We believe that a good workflow system plays an important role in the weather forecast service, providing a useful tool for monitoring the whole process, fixing the errors, repairing a workflow, or redesigning an equivalent workflow pattern with new components.展开更多
Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the pr...Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the problems,this paper proposes a DDoS attack information fusion method based on CNN for multi-element data.Firstly,according to the distribution,concentration and high traffic abruptness of DDoS attacks,this paper defines six features which are respectively obtained from the elements of source IP address,destination IP address,source port,destination port,packet size and the number of IP packets.Then,we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment.The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features,and obtain multi-element fusion feature(MEFF)value.Finally,the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series.Experimental results show that the information fusion method proposed can effectively fuse multi-element data,reduce the missing rate and total error rate,memory resource consumption,running time,and improve the detection rate.展开更多
A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define...A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.展开更多
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th...With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.展开更多
Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be i...Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.展开更多
Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires ...Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.展开更多
In this paper,lightning protection technical service of oil depot is taken as research object. It is proposed using various meteorological data to carry out multiple lightning protection technical services( risk asses...In this paper,lightning protection technical service of oil depot is taken as research object. It is proposed using various meteorological data to carry out multiple lightning protection technical services( risk assessment,potential forecast,monitoring and early warning of lightning disasters) based on conventional detection of lightning protection system,to improve technical content of lightning protection service and benefits of disaster prevention and mitigation. It mainly analyzes the application of lightning monitoring data in pre-assessment of lightning disaster,to guide site selection of new oil depot; guidance of lightning disaster status assessment on regular inspection of lightning protection system,to determine lightning protection detection time and update and maintenance time of lightning protection system; potential prediction and early warning of lightning,to guide daily operation of oil depot.展开更多
A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralizatio...A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralization,was selected for interpretation.The median+2 MAD(median absolute deviation)method of exploratory data analysis(EDA)and C-A(concentration-area)fractal modeling were then applied to the Mahalanobis distance,as defined by Zn,Cu and Pb from the factor analysis to set the thresholds for defining multi-element anomalies.As a result,the median+2 MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal model.The soil anomaly identified by the median+2 MAD method on the Mahalanobis distances defined by three principal elements(Zn,Cu and Pb)rather than thirteen elements(Co,Zn,Cu,V,Mo,Ni,Cr,Mn,Pb,Ba,Sr,Zr and Ti)was the more favorable reflection of the ore body.The identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for validation.The results showed that the median+2 MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization,which is therefore useful at the reconnaissance drilling stage.展开更多
By using wireless communication technology, when wired network of grassroots station is failure, wireless backup system of meteorological data transmission can automatically switch, and transmit the data at the statio...By using wireless communication technology, when wired network of grassroots station is failure, wireless backup system of meteorological data transmission can automatically switch, and transmit the data at the station with line fault to the destination by short message way. The system has simple operation and low operation cost, and transmission fault of real-time meteorological data due to line problem at the station can be effectively decreased. At present, the system has been tested successfully and put into business operation.展开更多
Based on convenience and safety of historical data application, B/S mode is used instead of database management structure of C/S mode, and it can not only combine database and network, but also realize the safe use of...Based on convenience and safety of historical data application, B/S mode is used instead of database management structure of C/S mode, and it can not only combine database and network, but also realize the safe use of historical data online. High-level programming language is used to develop a online management and application system of historical meteorological data based on B/S mode. System data import function can import ground report file sequence of Xuzhou （ including five county-level stations） into the database, construct Oracle database of Xuzhou and the five stations since 1953, and establish data tables of time, day, ten-day, monthly, quarterly and annual historical data, weather information and so forth. Management software of database server is established to realize instruction-level management and scheduling of database and a balanced distribution of resources among users. At the same time, a Web-based management application interface is set up to meet users＇ needs to retrieve a variety of repositories, and it provides statistical query of time, day, ten-day, monthly, quarterly and annual historical data and climate data for each meteorological element, thereby meeting the needs of meteorological research and all sectors of society for statistical query of meteorological data.展开更多
With the development of meteorological services, there are more and more types of real-time observation data, and the timeliness requirements are getting higher and higher. The monitoring methods of existing meteorolo...With the development of meteorological services, there are more and more types of real-time observation data, and the timeliness requirements are getting higher and higher. The monitoring methods of existing meteorological observation data transmission can no longer meet the needs. This paper proposes a new monitoring model, namely the “integrated monitoring model” for provincial meteorological observation data transmission. The model can complete the whole network monitoring of meteorological observation data transmission process. Based on this model, the integrated monitoring system for meteorological observation data transmission in Guangdong Province is developed. The system uses Java as the programming language, and integrates J2EE, Hibernate, Quartz, Snmp4j and Slf4j frameworks, and uses Oracle database as the data storage carrier, following the MVC specification and agile development concept. The system development uses four key technologies, including simple network management protocol, network connectivity detection technology, remote host management technology and thread pool technology. The integrated monitoring system has been put into business application. As a highlight of Guangdong’s meteorological modernization, it has played an active role in many major meteorological services.展开更多
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program of Ministry of Science and Technology of the People's Republic of China[grant number 2022QZKK0101]the Science and Technology Department of the Tibet Program[grant number XZ202301ZY0035G]。
文摘Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.
基金the Fifth Batch of Innovation Teams of Wuzhou Meteorological Bureau"Wuzhou Innovation Team for Enhancing the Comprehensive Meteorological Observation Ability through Digitization and Intelligence"Wuzhou Science and Technology Planning Project(202402122,202402119).
文摘[Objective]In response to the issue of insufficient integrity in hourly routine meteorological element data files,this paper aims to improve the availability and reliability of data files,and provide high-quality data file support for meteorological forecasting and services.[Method]In this paper,an efficient and accurate method for data file quality control and fusion processing is developed.By locating the missing measurement time,data are extracted from the"AWZ.db"database and the minute routine meteorological element data file,and merged into the hourly routine meteorological element data file.[Result]Data processing efficiency and accuracy are significantly improved,and the problem of incomplete hourly routine meteorological element data files is solved.At the same time,it emphasizes the importance of ensuring the accuracy of the files used and carefully checking and verifying the fusion results,and proposes strategies to improve data quality.[Conclusion]This method provides convenience for observation personnel and effectively improves the integrity and accuracy of data files.In the future,it is expected to provide more reliable data support for meteorological forecasting and services.
基金Supported by Wuzhou Science and Technology Planning Project(202202047).
文摘With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,it is very important to improve the efficiency of decision-making by quickly obtaining accurate meteorological observation data.However,the traditional method of meteorological data collection and transmission has a large delay in data acquisition due to the conversion of public network and internal network,which affects the timeliness of emergency decision-making.This paper proposes a solution based on the Internet of things platform combined with MQTT protocol,which aims to realize the efficient and reliable real-time collection and transmission of meteorological data,shorten the data acquisition time,improve the emergency response speed,and meet the needs of temporary observation.
基金funded by the National Key R&D Program of China(Grant Nos.2018YFB0504900 and 2015AA123700)
文摘In this paper,the latest progress,major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed.First,the latest three FengYun(FY)meteorological satellites(FY-2H,FY-3D,and FY-4A)and their primary objectives are introduced Second,the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated,including the latest geostationary(FY-2/4)and polar-orbit(FY-3)satellites.Third,the radiometric calibration techniques and accuracies of reflective solar bands,thermal infrared bands,and passive microwave bands for FY meteorological satellites are discussed.It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods,including the vicarious China radiance calibration site calibration,pseudo invariant calibration site calibration,deep convective clouds calibration,and lunar calibration.Fourth,recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length.The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models.Lastly,the latest progress in radiative transfer,absorption and scattering calculations for satellite remote sensing is summarized,and some important research using a new radiative transfer model are illustrated.
基金National 973 Program of China, No.2010CB950900Swedish Research Links, No.2006-24724-44416-13
文摘Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.
基金supported by the National Natural Science Foundation of China (No.40771021)the Ministry of Education ofChina (No.20070027020)the Ministry of Science & Technology of China (Nos.2006BAD20B03 and 2006BAD20B02).
文摘Taibus County, Inner Mongolia, China, lies in a farming-pastoral ecotone, where severe wind erosion and various aeolian sand hazards are prevalent and fixed and semi-fixed sand dunes occur frequently. This study was conducted to investigate the relationships between sand transportation rate and wind speed for the fixed and semi-fixed sand dunes based on field measurements. The annual quantity of soil erosion by wind was estimated using meteorological wind data. The results indicated that the sand transportation rate in Taibus County in 2000 was 57.38 kg cm-1 year-1 for the semi-fixed dunes and 4.56 kg cm-1 year-1 for the fixed dunes. The total duration of erosive winds covered 12.5% of the time of the year, and spring posed the highest potential of sand transportation. Wind with low speed (≤ 17 m s-1) and high frequency plays a dominant role in sand transportation, while strong wind (≥ 17 m s-1) with low frequency significantly enhanced the sand transportation. Erosive wind speed, directions, and frequency were three crucial dynamic factors influencing sand hazards in the farming-pastoral ecotone. The dominant factors intensifying sand and dust storms in Taibus County might be related to the favorable wind condition in combination with the durable drought, which led to land desertification and vegetation degradation.
基金National Natural Sciences Foundation of China,No.41471463President’s International Fellowship Initiative CAS
文摘Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available for climate analysis in Pakistan and India, the present study emphases on an extensive quality control and homogenization of daily maximum temperature, minimum temperature and precipitation data in the Jhelum River basin, Pakistan and India. A combination of different quality control methods and relative homogeneity tests were applied to achieve the objective of the study. To check the improvement after homogenization, correlation coefficients between the test and reference series calculated before and after the homogenization process were compared with each other. It was found that about 0.59%, 0.78% and 0.023% of the total data values are detected as outliers in maximum temperature, minimum temperature and precipitation data, respectively. About 32% of maximum temperature, 50% of minimum temperature and 7% of precipitation time series were inhomogeneous, in the Jhelum River basin. After the quality control and homogenization, 1% to 11% improvement was observed in the infected climate variables. This study concludes that precipitation daily time series are fairly homogeneous, except two stations （Naran and Gulmarg）, and of a good quality. However, maximum and minimum temperature datasets require an extensive quality control and homogeneity check before using them into climate analysis in the Jhelum River basin.
基金supported by the National Key R&D Program of China (Project No.2020YFC2200800,Task No.2020YFC2200803)the Key Projects of the Natural Science Foundation of Heilongjiang Province (Grant No.ZD2021E001)。
文摘Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.
基金supported by the National Natural Science Foundation of China (41871307)the Shaanxi Coordinate Innovation Plan Project of Science and Technology (2016KTCL03-17)。
文摘The spatial pattern of meteorological factors cannot be accurately simulated by using observations from meteorological stations(OMS) that are distributed sparsely in complex terrain. It is expected that the spatial-temporal characteristics of drought in regions with complex terrain can be better represented by meteorological data with the high spatial-temporal resolution and accuracy. In this study, Standard Precipitation Evapotranspiration Index(SPEI) calculated with meteorological factors extracted from ITPCAS(China Meteorological Forcing Dataset produced by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences) was applied to identify the spatial-temporal characteristics of drought in Shaanxi Province of China, during the period of 1979–2016. Drought areas detected by SPEI calculated with data from ITPCAS(SPEI-ITPCAS) on the seasonal scale were validated by historical drought records from the Chinese Meteorological Disaster Canon-Shaanxi, and compared with drought areas detected by SPEI calculated with data from OMS(SPEI-OMS). Drought intensity, trend and temporal ranges for mutations of SPEI-ITPCAS were analyzed by using the cumulative drought intensity(CDI) index and the Mann-Kendall test. The results indicated that drought areas detected from SPEI-ITPCAS were closer to the historical drought records than those detected from SPEI-OMS. Severe and exceptional drought events with SPEI-ITPCAS lower than –1.0 occurred most frequently in summer, followed by spring. There was a general drying trend in spring and summer in Shaanxi Province and a significant wetting trend in autumn and winter in northern Shaanxi Province. On seasonal and annual scales, the regional and temporal ranges for mutations of SPEI-ITPCAS were different and most mutations occurred before the year 1990 in most regions of Shaanxi Province. The results reflect the response of different regions of Shaanxi Province to climate change, which will help to manage regional water resources.
文摘In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Administration (CMA) for years and have been proven effective in reliably managing the complexities of large-scale meteorological related workflows. Based on the previous work on the platforms, we argue that a minimum set of guidelines including workflow scheme, module design, implementation standards and maintenance consideration during the whole establishment of the platform are highly recommended, serving to reduce the need for future maintenance and adjustment. A significant gain in performance can be achieved through the workflow-based projects. We believe that a good workflow system plays an important role in the weather forecast service, providing a useful tool for monitoring the whole process, fixing the errors, repairing a workflow, or redesigning an equivalent workflow pattern with new components.
基金This work was supported by the Hainan Provincial Natural Science Foundation of China[2018CXTD333,617048]National Natural Science Foundation of China[61762033,61702539]+1 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444].
文摘Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the problems,this paper proposes a DDoS attack information fusion method based on CNN for multi-element data.Firstly,according to the distribution,concentration and high traffic abruptness of DDoS attacks,this paper defines six features which are respectively obtained from the elements of source IP address,destination IP address,source port,destination port,packet size and the number of IP packets.Then,we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment.The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features,and obtain multi-element fusion feature(MEFF)value.Finally,the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series.Experimental results show that the information fusion method proposed can effectively fuse multi-element data,reduce the missing rate and total error rate,memory resource consumption,running time,and improve the detection rate.
文摘A multivariate statistical analysis was performed on multi-element soil geochemical data from the Koda Hill-Bulenga gold prospects in the Wa-Lawra gold belt, northwest Ghana. The objectives of the study were to define gold relationships with other trace elements to determine possible pathfinder elements for gold from the soil geochemical data. The study focused on seven elements, namely, Au, Fe, Pb, Mn, Ag, As and Cu. Factor analysis and hierarchical cluster analysis were performed on the analyzed samples. Factor analysis explained 79.093% of the total variance of the data through three factors. This had the gold factor being factor 3, having associations of copper, iron, lead and manganese and accounting for 20.903% of the total variance. From hierarchical clustering, gold was also observed to be clustering with lead, copper, arsenic and silver. There was further indication that, gold concentrations were lower than that of its associations. It can be inferred from the results that, the occurrence of gold and its associated elements can be linked to both primary dispersion from underlying rocks and secondary processes such as lateritization. This data shows that Fe and Mn strongly associated with gold, and alongside Pb, Ag, As and Cu, these elements can be used as pathfinders for gold in the area, with ferruginous zones as targets.
文摘With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.
基金Funding Statement:The researchers would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.
文摘Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.
文摘In this paper,lightning protection technical service of oil depot is taken as research object. It is proposed using various meteorological data to carry out multiple lightning protection technical services( risk assessment,potential forecast,monitoring and early warning of lightning disasters) based on conventional detection of lightning protection system,to improve technical content of lightning protection service and benefits of disaster prevention and mitigation. It mainly analyzes the application of lightning monitoring data in pre-assessment of lightning disaster,to guide site selection of new oil depot; guidance of lightning disaster status assessment on regular inspection of lightning protection system,to determine lightning protection detection time and update and maintenance time of lightning protection system; potential prediction and early warning of lightning,to guide daily operation of oil depot.
文摘A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralization,was selected for interpretation.The median+2 MAD(median absolute deviation)method of exploratory data analysis(EDA)and C-A(concentration-area)fractal modeling were then applied to the Mahalanobis distance,as defined by Zn,Cu and Pb from the factor analysis to set the thresholds for defining multi-element anomalies.As a result,the median+2 MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal model.The soil anomaly identified by the median+2 MAD method on the Mahalanobis distances defined by three principal elements(Zn,Cu and Pb)rather than thirteen elements(Co,Zn,Cu,V,Mo,Ni,Cr,Mn,Pb,Ba,Sr,Zr and Ti)was the more favorable reflection of the ore body.The identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for validation.The results showed that the median+2 MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization,which is therefore useful at the reconnaissance drilling stage.
文摘By using wireless communication technology, when wired network of grassroots station is failure, wireless backup system of meteorological data transmission can automatically switch, and transmit the data at the station with line fault to the destination by short message way. The system has simple operation and low operation cost, and transmission fault of real-time meteorological data due to line problem at the station can be effectively decreased. At present, the system has been tested successfully and put into business operation.
文摘Based on convenience and safety of historical data application, B/S mode is used instead of database management structure of C/S mode, and it can not only combine database and network, but also realize the safe use of historical data online. High-level programming language is used to develop a online management and application system of historical meteorological data based on B/S mode. System data import function can import ground report file sequence of Xuzhou （ including five county-level stations） into the database, construct Oracle database of Xuzhou and the five stations since 1953, and establish data tables of time, day, ten-day, monthly, quarterly and annual historical data, weather information and so forth. Management software of database server is established to realize instruction-level management and scheduling of database and a balanced distribution of resources among users. At the same time, a Web-based management application interface is set up to meet users＇ needs to retrieve a variety of repositories, and it provides statistical query of time, day, ten-day, monthly, quarterly and annual historical data and climate data for each meteorological element, thereby meeting the needs of meteorological research and all sectors of society for statistical query of meteorological data.
文摘With the development of meteorological services, there are more and more types of real-time observation data, and the timeliness requirements are getting higher and higher. The monitoring methods of existing meteorological observation data transmission can no longer meet the needs. This paper proposes a new monitoring model, namely the “integrated monitoring model” for provincial meteorological observation data transmission. The model can complete the whole network monitoring of meteorological observation data transmission process. Based on this model, the integrated monitoring system for meteorological observation data transmission in Guangdong Province is developed. The system uses Java as the programming language, and integrates J2EE, Hibernate, Quartz, Snmp4j and Slf4j frameworks, and uses Oracle database as the data storage carrier, following the MVC specification and agile development concept. The system development uses four key technologies, including simple network management protocol, network connectivity detection technology, remote host management technology and thread pool technology. The integrated monitoring system has been put into business application. As a highlight of Guangdong’s meteorological modernization, it has played an active role in many major meteorological services.
