Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, bu...Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.展开更多
Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations i...Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance(GRAPES-3DVar),cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system.This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV)and adjust the atmospheric and cloud parameters based on infrared radiance observations.In this paper,we investigate a heavy rainfall over Guangdong province on May 26,2007,which is right after the onset of a South China Sea monsoon.In this case,channels of the Moderate Resolution Imaging Spectroradiometer(MODIS)for observing water vapor(Channel 27)and cloud top altitude(Channel 36)are selected for the assimilation.The process of heavy rainfall is simulated by the Weather Research and Forecasting(WRF)model.Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field.The tendency of adjustment agrees well with the satellite observations.The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.展开更多
The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional outlier detection a...The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately.In order to improve the detection accuracy and reduce the computational complexity,an outlier detection algorithm for flue temperature data based on the CLOF(Clipping Local Outlier Factor,CLOF)algorithm is proposed.The algorithm preprocesses the normalized data using the cluster pruning algorithm,and realizes the high accuracy and high efficiency outlier detection in the outliers candidate set.Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant,the main parameters of the CLOF algorithm are selected according to the experimental results,and the outlier detection effect of the Isolation Forest algorithm,the 3-sigma principle,the conventional LOF algorithm and the CLOF algorithm are compared and analyzed.The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy.Compared with the outlier detection results of the Isolation Forest algorithm and 3-sigma principle,the accuracy of the CLOF detection results is increased,and the amount of data calculation is significantly reduced.展开更多
The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs ...The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs for online predicting temperature field.Firstly,to establish the mapping relationship between temperature field and operating parameters as well as multi-point temperature data,a data set was constructed.In the data set,the temperature fields were obtained through the inversion of thermal radiation imaging model,while the operating parameters were collected from the distributed control system of the unit.Then,a transpose convolutional neural network(TCNN)model was developed to obtain the mapping relationship based on the data set.In the simulation study,multi-point temperature data were obtained through the forward calculation of the thermal radiation imaging model.The impact of the quantity and location of multi-point temperature data on generalization ability of the TCNN model was analyzed.In the experimental study,multi-point temperature data were measured by image probes.A comparative analysis was conducted to evaluate generalization ability of the TCNN model with and without the addition of multi-point temperature data,benchmarking against existing methods.With the addition of multi-point temperature data,the mean absolute percentage errors of predicted temperature fields are all less than 1.6%at four stable loads,while the maximum relative error of average value of predicted temperature field decreases from 7.24%to 2.77%during variable load process.The proposed prediction method has promising potential for online combustion monitoring in the furnace.展开更多
The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radio...The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.展开更多
The Field bus device based on HART field communications protocol has been widely used in industrial control. In the pro-duction of petroleum, the method which using the HART bus to transfer the temperature and pressur...The Field bus device based on HART field communications protocol has been widely used in industrial control. In the pro-duction of petroleum, the method which using the HART bus to transfer the temperature and pressure data collected from the oilwell, can improve the traditional collecting method's shortage, prevent the failure of the data transfer caused of the rupture of the oiltransfer pole, moreover, can enhance the collection accuracy.展开更多
The purpose of this review article is to discuss the development and associated estimation of uncertainties in the global and hemispheric surface temperature records. The review begins by detailing the groups that pro...The purpose of this review article is to discuss the development and associated estimation of uncertainties in the global and hemispheric surface temperature records. The review begins by detailing the groups that produce surface temperature datasets. After discussing the reasons for similarities and differences between the various products, the main issues that must be addressed when deriving accurate estimates, particularly for hemispheric and global averages, are then considered. These issues are discussed in the order of their importance for temperature records at these spatial scales： biases in SST data, particularly before the 1940s; the exposure of land-based thermometers before the development of louvred screens in the late 19th century; and urbanization effects in some regions in recent decades. The homogeneity of land-based records is also discussed; however, at these large scales it is relatively unimportant. The article concludes by illustrating hemispheric and global temperature records from the four groups that produce series in near-real time.展开更多
In order to study the temperature distribution and the corresponding temperature effects on pre-stressed concrete（PC） curved box girder bridge in Shandong Province, this paper builds and adopts an automatic remote r...In order to study the temperature distribution and the corresponding temperature effects on pre-stressed concrete（PC） curved box girder bridge in Shandong Province, this paper builds and adopts an automatic remote real-time temperature collection system to collect temperature data on site, and further uses the software ANSYS for analysis. Based on the comparisons between the measured data and the simulation results, the following conclusions can be drawn： 1 Our temperature monitoring system is reliable; 2 The corresponding measured data of the web plate and flange plate exposed to the sun, vary more severely than that at other positions, so these plates need higher standard design and construction requirements; 3 In the cold wave where still is sunshine, the box girder temperature effect behaves as sine-like curve.展开更多
The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from whi...The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.展开更多
Data temperature is a response to the ever-growing amount of data.These data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one time.This leads ...Data temperature is a response to the ever-growing amount of data.These data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one time.This leads to the concept of hot and cold data.Cold data can be migrated away from high-performance nodes to free up performance for higher priority data.Existing studies classify hot and cold data primarily on the basis of data age and usage frequency.We present this as a limitation in the current implementation of data temperature.This is due to the fact that age automatically assumes that all new data have priority and that usage is purely reactive.We propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their movement.We identify new metadata variables and user-defined variables to extend the current data temperature value.We further establish rules and conditions for limiting unnecessary movement of the data,which helps to prevent wasted input output(I/O)costs.We also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data temperature.The proposed system provides higher accuracy,increases performance,and gives greater user control for optimal positioning of data within multi-tiered storage solutions.展开更多
Wavetet transform was used to analyze the scaling law of temperature data (passive scalar) in Rayleigh-Bénard convection flow from two aspects. The first one was to utilize the method of extended self similarity,...Wavetet transform was used to analyze the scaling law of temperature data (passive scalar) in Rayleigh-Bénard convection flow from two aspects. The first one was to utilize the method of extended self similarity, presented first by Benzi et al., to study the scaling exponent of temperature data. The obtained results show that the inertial range is much wider than that one determined directly from the conventional structure function, and find the obtained scaling exponent agrees well with the one obtained from the temperature data in an experiment of wind tunnel. The second one was that, by extending the formula which was proposed by A. Arneodo et al. for extracting the scaling exponent ζ(q) of velocity data to temperature data, a newly defined formula which is also based on wavelet transform, and can determine the scaling exponent ξ(q) of temperature data was proposed. The obtained results demonstrate that by using the method which is named as WTMM (wavelet transform maximum modulus) ξ(q) correctly can be extracted.展开更多
In this paper,we present a novel ocean visualization framework,which focuses on analyzing multidimensional and spatiotemporal ocean data.GPU-based visualization methods are explored to effectively visualize ocean data...In this paper,we present a novel ocean visualization framework,which focuses on analyzing multidimensional and spatiotemporal ocean data.GPU-based visualization methods are explored to effectively visualize ocean data.An improved ray casting algorithm for heterogeneous multisection ocean volume data is presented.A two-layer spherical shell is taken as the ocean data proxy geometry,which enables oceanographers to obtain a real geographic background based on global terrain.An efficient ray sampling technique including an adaptive sampling technique and a preintegrated transfer function is proposed to achieve high-effectiveness and high-efficiency rendering.Moreover,an interactive transfer function is also designed to analyze the 3D structure of ocean temperature and salinity anomaly phenomena.Based on the framework,an integrated visualization system called i4Ocean is created.The visualization of ocean temperature and salinity anomalies extracted interactively by the transfer function is demonstrated.展开更多
Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro...Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.展开更多
A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the...A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the first eight channel observations of TIROS-N/HIRS2.Analyses of several examples indicate that this method can obtain much more accurate temperatures in the lower atmosphere than a statistical technique, and that the surface temperature and emissivity retrieved are also reasonable.展开更多
The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the for...The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.展开更多
Projecting the future distribution of permafrost under different climate change scenarios is essential,especially for the Qinghai–Tibet Plateau(QTP).The altitude-response model is used to estimate future permafrost c...Projecting the future distribution of permafrost under different climate change scenarios is essential,especially for the Qinghai–Tibet Plateau(QTP).The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs(RCP2.6,RCP4.5,RCP6.0,and RCP8.5).The simulation results show the following:(1)from now until 2070,the permafrost will experience different degrees of significant degradation under the four RCP scenarios.This will affect 25.68%,40.54%,45.95%,and 62.84%of the current permafrost area,respectively.(2)The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs.(1)In RCP2.6,the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070.(2)In RCP4.5,the rate of permafrost loss during the period 2030–2050(about 12.73%)is higher than between 2050 and 2070(about 8.33%).(3)In RCP6.0,the permafrost loss rate for the period 2030–2050(about 16.52%)is similar to that for 2050–2070(about 16.67%).(4)In RCP8.5,there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070:the rate is only about 3.70%for the first period but about 29.49%during the second.展开更多
基金Speical Scientific Research Project for Public Welfare (Meteorological) Industry (GYHY200906002)Project of National Natural Science Foundation (41075083)
文摘Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.
基金Natural Foundamental Research and Development Project"973"Program(2009CB421500)Natural Science Foundation of China(7035011)
文摘Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance(GRAPES-3DVar),cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system.This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV)and adjust the atmospheric and cloud parameters based on infrared radiance observations.In this paper,we investigate a heavy rainfall over Guangdong province on May 26,2007,which is right after the onset of a South China Sea monsoon.In this case,channels of the Moderate Resolution Imaging Spectroradiometer(MODIS)for observing water vapor(Channel 27)and cloud top altitude(Channel 36)are selected for the assimilation.The process of heavy rainfall is simulated by the Weather Research and Forecasting(WRF)model.Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field.The tendency of adjustment agrees well with the satellite observations.The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61973094)the Maoming Natural Science Foundation(Grant No.2020S004)the Guangdong Basic and Applied Basic Research Fund Project(Grant No.2023A1515012341).
文摘The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately.In order to improve the detection accuracy and reduce the computational complexity,an outlier detection algorithm for flue temperature data based on the CLOF(Clipping Local Outlier Factor,CLOF)algorithm is proposed.The algorithm preprocesses the normalized data using the cluster pruning algorithm,and realizes the high accuracy and high efficiency outlier detection in the outliers candidate set.Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant,the main parameters of the CLOF algorithm are selected according to the experimental results,and the outlier detection effect of the Isolation Forest algorithm,the 3-sigma principle,the conventional LOF algorithm and the CLOF algorithm are compared and analyzed.The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy.Compared with the outlier detection results of the Isolation Forest algorithm and 3-sigma principle,the accuracy of the CLOF detection results is increased,and the amount of data calculation is significantly reduced.
基金supported by the National Key R&D Program of China(2024YFB4104804).
文摘The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs for online predicting temperature field.Firstly,to establish the mapping relationship between temperature field and operating parameters as well as multi-point temperature data,a data set was constructed.In the data set,the temperature fields were obtained through the inversion of thermal radiation imaging model,while the operating parameters were collected from the distributed control system of the unit.Then,a transpose convolutional neural network(TCNN)model was developed to obtain the mapping relationship based on the data set.In the simulation study,multi-point temperature data were obtained through the forward calculation of the thermal radiation imaging model.The impact of the quantity and location of multi-point temperature data on generalization ability of the TCNN model was analyzed.In the experimental study,multi-point temperature data were measured by image probes.A comparative analysis was conducted to evaluate generalization ability of the TCNN model with and without the addition of multi-point temperature data,benchmarking against existing methods.With the addition of multi-point temperature data,the mean absolute percentage errors of predicted temperature fields are all less than 1.6%at four stable loads,while the maximum relative error of average value of predicted temperature field decreases from 7.24%to 2.77%during variable load process.The proposed prediction method has promising potential for online combustion monitoring in the furnace.
基金Supported by the National Innovation Project for Meteorological Science and Technology (CMAGGTD003-5)China Meteorological Administration Special Public Welfare Research Fund (GYHY201506002)National Key Research and Development Program of China (2017YFC1501801)。
文摘The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.
文摘The Field bus device based on HART field communications protocol has been widely used in industrial control. In the pro-duction of petroleum, the method which using the HART bus to transfer the temperature and pressure data collected from the oilwell, can improve the traditional collecting method's shortage, prevent the failure of the data transfer caused of the rupture of the oiltransfer pole, moreover, can enhance the collection accuracy.
文摘The purpose of this review article is to discuss the development and associated estimation of uncertainties in the global and hemispheric surface temperature records. The review begins by detailing the groups that produce surface temperature datasets. After discussing the reasons for similarities and differences between the various products, the main issues that must be addressed when deriving accurate estimates, particularly for hemispheric and global averages, are then considered. These issues are discussed in the order of their importance for temperature records at these spatial scales： biases in SST data, particularly before the 1940s; the exposure of land-based thermometers before the development of louvred screens in the late 19th century; and urbanization effects in some regions in recent decades. The homogeneity of land-based records is also discussed; however, at these large scales it is relatively unimportant. The article concludes by illustrating hemispheric and global temperature records from the four groups that produce series in near-real time.
基金Supported by the China Postdoctoral Science Foundation(2013M531560)the Technology Innovation Plan in Traffic of Shandong Province(2012A15)the Science&Technology Development Projects of Shandong Province(2014GSF120015)
文摘In order to study the temperature distribution and the corresponding temperature effects on pre-stressed concrete（PC） curved box girder bridge in Shandong Province, this paper builds and adopts an automatic remote real-time temperature collection system to collect temperature data on site, and further uses the software ANSYS for analysis. Based on the comparisons between the measured data and the simulation results, the following conclusions can be drawn： 1 Our temperature monitoring system is reliable; 2 The corresponding measured data of the web plate and flange plate exposed to the sun, vary more severely than that at other positions, so these plates need higher standard design and construction requirements; 3 In the cold wave where still is sunshine, the box girder temperature effect behaves as sine-like curve.
基金supported by the National Natural Science Foundation of China (Grant No. 11173038)
文摘The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.
文摘Data temperature is a response to the ever-growing amount of data.These data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one time.This leads to the concept of hot and cold data.Cold data can be migrated away from high-performance nodes to free up performance for higher priority data.Existing studies classify hot and cold data primarily on the basis of data age and usage frequency.We present this as a limitation in the current implementation of data temperature.This is due to the fact that age automatically assumes that all new data have priority and that usage is purely reactive.We propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their movement.We identify new metadata variables and user-defined variables to extend the current data temperature value.We further establish rules and conditions for limiting unnecessary movement of the data,which helps to prevent wasted input output(I/O)costs.We also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data temperature.The proposed system provides higher accuracy,increases performance,and gives greater user control for optimal positioning of data within multi-tiered storage solutions.
文摘Wavetet transform was used to analyze the scaling law of temperature data (passive scalar) in Rayleigh-Bénard convection flow from two aspects. The first one was to utilize the method of extended self similarity, presented first by Benzi et al., to study the scaling exponent of temperature data. The obtained results show that the inertial range is much wider than that one determined directly from the conventional structure function, and find the obtained scaling exponent agrees well with the one obtained from the temperature data in an experiment of wind tunnel. The second one was that, by extending the formula which was proposed by A. Arneodo et al. for extracting the scaling exponent ζ(q) of velocity data to temperature data, a newly defined formula which is also based on wavelet transform, and can determine the scaling exponent ξ(q) of temperature data was proposed. The obtained results demonstrate that by using the method which is named as WTMM (wavelet transform maximum modulus) ξ(q) correctly can be extracted.
基金supported by the National Natural Science Foundation of China[grant number 42030406]the Marine Science&Technology Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)[grant number 2018SDKJ0102]+2 种基金the National Key R&D Program of China[grant number 2016YFC1401008]the ESA-NRSCC Scientific Cooperation Project on Earth Observation Science and Applications:Dragon 5[grant number 58393]the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources[grant number KF-2020-05-085].
文摘In this paper,we present a novel ocean visualization framework,which focuses on analyzing multidimensional and spatiotemporal ocean data.GPU-based visualization methods are explored to effectively visualize ocean data.An improved ray casting algorithm for heterogeneous multisection ocean volume data is presented.A two-layer spherical shell is taken as the ocean data proxy geometry,which enables oceanographers to obtain a real geographic background based on global terrain.An efficient ray sampling technique including an adaptive sampling technique and a preintegrated transfer function is proposed to achieve high-effectiveness and high-efficiency rendering.Moreover,an interactive transfer function is also designed to analyze the 3D structure of ocean temperature and salinity anomaly phenomena.Based on the framework,an integrated visualization system called i4Ocean is created.The visualization of ocean temperature and salinity anomalies extracted interactively by the transfer function is demonstrated.
基金funded by National High Technology Research and Development Program of China (863 Program,2012AA092303)Project of Shanghai Science and Technology Innovation (12231203900)+2 种基金Industrialization Program of National Development and Reform Commission (2159999)National Science and Technology Support Program (2013BAD13B01)Shanghai Leading Academic Discipline Project
文摘Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.
文摘A physical method,based on the simplification of surface radiation terms in remote sensing equations, has been suggested to retrieve the surface temperature,vertical temperature profile and surface emissivity from the first eight channel observations of TIROS-N/HIRS2.Analyses of several examples indicate that this method can obtain much more accurate temperatures in the lower atmosphere than a statistical technique, and that the surface temperature and emissivity retrieved are also reasonable.
基金supported by the science and technology project of State Grid Corporation of China(Project Code:1400-202157207A-0-0-00)the National Natural Science Foundation of China[grant numbers 72273137].
文摘The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.
基金funded by the Basic Research Project of the Ministry of Science and Technology of China[no.2013FY110900]the Science and Technology Plan Project of Yunnan Province[no.2012CA021].
文摘Projecting the future distribution of permafrost under different climate change scenarios is essential,especially for the Qinghai–Tibet Plateau(QTP).The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs(RCP2.6,RCP4.5,RCP6.0,and RCP8.5).The simulation results show the following:(1)from now until 2070,the permafrost will experience different degrees of significant degradation under the four RCP scenarios.This will affect 25.68%,40.54%,45.95%,and 62.84%of the current permafrost area,respectively.(2)The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs.(1)In RCP2.6,the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070.(2)In RCP4.5,the rate of permafrost loss during the period 2030–2050(about 12.73%)is higher than between 2050 and 2070(about 8.33%).(3)In RCP6.0,the permafrost loss rate for the period 2030–2050(about 16.52%)is similar to that for 2050–2070(about 16.67%).(4)In RCP8.5,there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070:the rate is only about 3.70%for the first period but about 29.49%during the second.
