CN114840616A - Dynamic atmospheric natural environment modeling method based on space-time interpolation - Google Patents
Dynamic atmospheric natural environment modeling method based on space-time interpolation Download PDFInfo
- Publication number
- CN114840616A CN114840616A CN202111622550.3A CN202111622550A CN114840616A CN 114840616 A CN114840616 A CN 114840616A CN 202111622550 A CN202111622550 A CN 202111622550A CN 114840616 A CN114840616 A CN 114840616A
- Authority
- CN
- China
- Prior art keywords
- atmospheric
- data
- space
- environmental
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/211—Schema design and management
- G06F16/212—Schema design and management with details for data modelling support
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- Remote Sensing (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a dynamic atmospheric natural environment modeling method based on space-time interpolation, which comprises the following implementation steps: the method comprises the following steps: collecting typical atmospheric environment data; step two: modeling atmospheric environment data by time-space interpolation; step three: comprehensive statistical analysis of environmental data; through the steps, the invention provides a dynamic atmospheric natural environment modeling method based on space-time interpolation, which analyzes environmental factors aiming at typical atmospheric environmental factors and space-time positions, comprehensively considers the influence of time and geographic positions, establishes a space-time dynamic change model with geographic position precision reaching 0.5 degrees through a space-time interpolation method, provides atmospheric natural environment dynamic change data, and performs statistical analysis on the atmospheric natural environment data in a time dimension to form the dynamic atmospheric natural environment modeling method based on the space-time interpolation. The method is scientific, easy to realize and more in line with engineering practice.
Description
Technical Field
The invention provides a dynamic atmospheric natural environment modeling method based on space-time interpolation. The dynamic atmospheric natural environment modeling method is based on typical atmospheric environment data collection, atmospheric environment data space-time interpolation modeling and environmental data comprehensive statistical analysis. The method is characterized in that the influences of time and geographic positions are comprehensively considered aiming at typical atmospheric environmental factors and spatial-temporal positions, a spatial-temporal dynamic change model with the geographic position precision reaching 0.5 degrees is established through a spatial-temporal interpolation method, atmospheric natural environment dynamic change data are provided, environmental factors are comprehensively analyzed, and a dynamic atmospheric natural environment modeling method is formed. The method is suitable for the technical field related to natural environment modeling.
Background
Under the condition of the atmospheric natural environment, the natural environment condition of the product not only has continuous change on the time scale, but also has different change rules on the space scale, the brought environmental effect is different, and the model which accords with the space-time change rule of the atmospheric natural environment factor is established under the influence of the difference of the environmental factors, so that the method has important significance. In order to obtain natural environment data of an area outside an observation station, a statistical method is generally combined with a geographic information system, and estimation is performed based on an observed value of an existing observation station, that is, spatial interpolation of the natural environment data, and the spatial interpolation method is divided into two types: one is a deterministic method and the other is a geostatistical method. The deterministic interpolation method is to create a fitted surface based on the similarity between information points or the smoothness of the whole surface, such as an inverse distance weighted average interpolation method, a trend surface method, a spline function method and the like; the geostatistical interpolation method is to quantify the spatial autocorrelation among sample points by using the statistical rules of the sample points, so as to construct a spatial structure model of the sample points around the points to be predicted, such as a kriging interpolation method. The deterministic interpolation method is characterized in that the interpolation result at the sample point is basically consistent with the actual value of the original sample point, and if the non-deterministic interpolation method is used, the interpolation result at the sample point is not necessarily consistent with the actual value of the sample, and the difference is quite far. Whatever the interpolation method, there is a distance attenuation effect, i.e. points closer in spatial position are more likely to have similar observations; and the farther away the points are, the less likely the eigenvalues are similar. In the atmospheric natural environment, the mutual influence effect between points far away from each other is small, neglect processing can be carried out, blocks are divided in the environment area to be measured according to connectivity, a complex whole is decomposed into a series of local units, errors are reduced, and interpolation and prediction accuracy is improved. Therefore, to accurately describe the actual environmental conditions experienced by the product, it is necessary to describe the temporal and spatial variation rules of the product at the same time to perform dynamic atmospheric natural environment modeling. Based on the method, the invention provides a dynamic atmosphere natural environment modeling method based on space-time interpolation.
Disclosure of Invention
(1) The purpose of the invention is as follows:
the invention aims to provide a dynamic atmospheric natural environment modeling method based on space-time interpolation, which aims at a series of problems of difficult atmospheric natural environment data collection, difficult query, difficult analysis and the like, provides a dynamic atmospheric natural environment modeling method, and is a dynamic atmospheric natural environment modeling method comprising typical atmospheric environment data collection, atmospheric environment data space-time interpolation modeling and environmental data comprehensive statistical analysis. The method comprises the steps of collecting typical atmospheric environmental factors of different empty positions by considering the difference of the environmental factors of different regions, taking the influence of time and geographic positions into consideration by comprehensive statistical analysis of the environmental factors, establishing a space-time dynamic change model with geographic position accuracy reaching 0.5 degrees by adopting a Krigin interpolation method, carrying out dynamic atmospheric natural environment modeling, and finally providing a dynamic atmospheric natural environment modeling method based on space-time interpolation.
(2) The technical scheme is as follows: based on the theory and thought, the invention provides a dynamic atmospheric natural environment modeling method based on space-time interpolation, which comprises the following specific implementation steps:
the method comprises the following steps: collecting typical atmospheric environment data;
atmospheric environmental factors mainly include temperature, humidity, solar radiation, rainfall, pollutants and the like; the invention aims at the products working in the atmospheric environment, and the environmental parameters to be considered comprise temperature and relative humidity; the temperature is an important factor influencing the corrosion failure of the material, and can influence the condensation of water vapor and the solubility of gas and salts in a water film, and the atmospheric corrosion rate is higher in places with high average temperature; if pollutants such as chloride ions exist in the atmospheric environment and are deposited or dissolved in a material surface liquid film, a corrosion micro-battery or an oxygen concentration battery can be formed on the surface of the material, so that the surface of the material is corroded and fails; the higher the relative humidity in the atmosphere is, the more the metal surface is easy to dewing, the longer the existence time of an electrolyte film on the surface is, the higher the corrosion failure speed is, and the influence on the working performance of a product is also generated; therefore, for the above typical environmental data, in order to meet the requirement that the accuracy of the final differential model geographic position reaches 0.5 degree longitude and latitude, an environmental observation station from which data is collected needs to be screened:
firstly, screening and collecting environmental data (observation stations, documents and data networks) according to different environmental factor data sources;
writing a program to process and arrange the scientific format file data;
step two: modeling atmospheric environment data by time-space interpolation;
the collected and sorted environmental factor data are incomplete and missing, so that when the overall environmental factor information of the target region position is required to be obtained in a covering manner, the atmospheric environmental data spatial-temporal interpolation modeling is required to be carried out on the environmental data of the existing geographic position, and the overall environmental factor data of the target region is obtained;
the kriging interpolation method is widely applied to an interpolation calculation method of geological statistics and has the characteristics of linearity, unbiased property and minimum estimation variance; the idea of the kriging interpolation method is to give corresponding weight values to observation data of spatial known sample points so as to estimate corresponding data of unknown points; because of the continuity of the atmospheric environment, the temperature and the relative humidity which are environmental variables can be assumed to meet the second-order stationary assumption and the intrinsic assumption, the calculation can be carried out by using a common kriging interpolation method; suppose that one environmental factor variable is Z (x), which is known everywhereThe observed values of the physical positions are respectively: z (x) 1 ),Z(x 2 ),Z(x 3 )，…,Z(x n )；
Then at x 0 The estimated value of the variable is obtained by the following formula:
in the formula: z * (x 0 ) Represents x 0 At an estimate of this variable, Z (x) i ) Representative observed value (i ═ 1, …, n), λ i The weight coefficient can be expressed as the contribution degree of the observed value of each position to the estimated value; the problem thus turns into λ i Since the expression already satisfies the linear requirement, the remaining two constraints can be used to solve: unbiased and minimum estimated variance; thus, the output solution equation set is:
in the formula: μ is the Lagrangian constant, c (x) i ,x j ) Is x i And x j The covariance function of (a); since the relationship between the variation function γ (-) and the covariance function c (-) approximates this trade-off, the system of equations can be solved by substituting the variation function into the system of equations to replace the covariance function, and the function that varies with distance in space is represented by a variation function model, which is commonly known as: gaussian model, exponential model, spherical model, etc.;
the following steps: "Gaussian model" means
The method comprises the following steps: "index model" means
The following steps: "spherical model" means
Wherein Δ x is x j -x i J > i; e is an index; a is a variation, represents a space correlation range uniformly possessed by the parameter, and reflects the speed of space change of the parameter, wherein the larger a is, the larger the space correlation range is, and the smaller the space change speed of the parameter is; c 0 The gold block constant is caused by parameter errors and microstructures and represents a part of parameter random variation; c 0 + C is a base station value and reflects the maximum variation amplitude of the parameter in numerical value; c is the arch height and represents the structural change part of the parameter;
step three: comprehensive statistical analysis of environmental data;
for different regions, the change of environmental factors has certain difference, for example, the temperature difference is large in different regions, the severe change of air temperature also affects the working condition of the product, if the temperature of the product is reduced in the working process, the temperature of the surface of the material is lower than the ambient temperature, and the water vapor in the atmosphere is condensed and condensed on the surface of the material, so that the corrosion is accelerated; meanwhile, environmental factors of the same place at different times have certain differences, for example, high-temperature drought in summer and low-temperature humidity in winter in some areas can cause the working environment of the product to change remarkably, and the working performance of the product is directly influenced; therefore, the environment is classified in time and space according to the characteristics of the environment factors in different areas, the environment factors are classified and screened in different longitudes and latitudes according to the time dimension accurate to the hour, and then a dynamic atmospheric natural environment modeling method based on space-time interpolation is formed through comprehensive statistical analysis of environment data;
through the steps, the effect of accurately estimating the specific environmental factors of the given time information of a certain area through the space information such as longitude and latitude is achieved, and the practical problem that the environmental factors are difficult to count, model and predict is solved.
(3) The advantages and the effects are as follows:
the invention relates to a dynamic atmospheric natural environment modeling method based on space-time interpolation, which has the advantages that:
the method analyzes environmental factors aiming at typical atmospheric environmental factors and space-time positions, comprehensively considers the influence of time and geographic positions, establishes a space-time dynamic change model with the geographic position precision reaching 0.5 ℃ by a Krigin interpolation method, provides atmospheric natural environment dynamic change data, and performs statistical analysis on the atmospheric natural environment data in a time dimension to form a dynamic atmospheric natural environment modeling method based on the space-time interpolation.
Secondly, the method provided by the invention is simple and convenient to calculate, easy to realize, more in line with engineering practice, convenient for engineering technicians to master and use, scientific in method, good in manufacturability and convenient to apply and popularize.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a map of the pacific ocean area-southeast asia area.
Fig. 3 is a result of interpolation calculation of the pacific sea area atmospheric temperature data.
Fig. 4 is a graph of annual temperature and relative humidity changes in the south sea area.
FIG. 5 is a graph of monthly temperature probability density distribution in the Hainan region.
Detailed Description
The present invention will be described in further detail below with reference to fig. 1 by taking a part of data in the atmospheric natural environment data of a certain sea area as an example. The invention provides a dynamic atmospheric natural environment modeling method based on space-time interpolation, which is shown in figure 1 and comprises the following specific implementation steps:
the method comprises the following steps: typical atmospheric environment data collection
Selecting a pacific sea area-southeast Asia area as a target sea area for analysis, wherein the specific longitude and latitude are as follows as shown in figure 2:
TABLE 1 sea area latitude and longitude
In order to meet the requirement that the accuracy of the final interpolation model geographic position reaches 0.5 degree longitude and latitude, an environment observation station from which data is collected needs to be screened. The sources of the different environmental factor data are shown in the following table:
table 2 environmental factor Collection data sources
Collected data is in a NetCDF scientific format and cannot be directly read, and is arranged into an EXCEL processable format after being converted by a writing program, and required environmental factors are screened;
the following steps: "NetCDF", which refers to a network Common Data format (network Common Data Form), is developed by scientists of the University of america (University Corporation for atmospherical Research) for the characteristics of scientific Data, and is a description and coding standard of Data oriented to array type and suitable for network sharing; the method is widely applied to the fields of atmospheric science, hydrology, oceanography, environmental simulation, geophysical and the like. The user can conveniently manage and manipulate the data set in a variety of ways;
step two: atmospheric environment data space-time interpolation modeling
Carrying out gridding processing on longitude and latitude by taking 0.5 degree as a unit in space according to the sorted environmental factor data, processing the environmental factors in time dimension accurate to hours in time, carrying out interpolation calculation and natural environment modeling based on the data, calculating autocorrelation coefficients by removing trend terms, solving a kriging equation set, carrying out error analysis and other steps, and carrying out space-time dynamic modeling on the atmospheric natural environment; specifically, when performing kriging interpolation calculation and modeling, a trend term needs to be removed from original data at first, because the periodic variation significantly affects the stationarity of variable distribution, and meanwhile, an obvious void effect appears when a time variation function is fitted, which is not beneficial to modeling of a space-time variation function, so that during an interpolation experiment, seasonal factors are removed at first, and a corresponding seasonal term is added after interpolation; then calculating the attenuation rule of the autocorrelation coefficient of the sequence along with the delay time period number, and judging the stationarity of the trend removing item data by using an autocorrelation graph test method; then, respectively constructing a space-time variation function for the data without the trend term, fitting, solving a Krigin equation set and interpolating to calculate space-time distribution data of the environmental factors; taking the atmospheric temperature environmental data of pacific ocean waters 2007 in 1 month and 1 day and 8 month and 1 day as an example, the environmental profile calculation results are shown in fig. 3, and the units are all degrees centigrade; it can be seen that the air temperature is lower for month 1, while the air temperature is higher for month 8 overall than for month 1;
step three: comprehensive statistical analysis of environmental data
The method comprises the steps of performing spatial classification on the geographic positions of environmental factors according to longitude and latitude information through environmental factor big data given by a space-time interpolation modeling calculation result, performing time classification on the change of the environmental factors according to different time information, and performing comprehensive statistical analysis on the environmental data;
the coverage range of the Pacific ocean sea area is large, the Pacific ocean sea area comprises different climate types, and the statistical property change of environmental factors in the coverage range is large, so that the target sea area can be reduced to a south sea area (105-118E, 4-21N); according to the atmospheric temperature environmental data, the change of the annual temperature and the relative humidity of the south sea area in the last 14 years is calculated and shown in fig. 4, the annual average temperature can be always maintained at a relatively stable stage, the annual change is small, and the change range is only between 1 ℃; meanwhile, the annual average relative humidity and the average temperature have the condition of presenting opposite rising and falling trends, because the temperature is possibly reduced due to the increase of the relative humidity and is consistent with the actual condition;
for the research of the distribution of the environmental factors, the target area is further reduced to Hainan province (108 degrees E-111 degrees E,18 degrees N-20 degrees N) in consideration, according to the atmospheric temperature environmental data, the monthly temperature probability density distribution of the Hainan province is calculated and is shown in figure 5, and the situation that 1 month-12 months show more typical normal distribution can be seen, meanwhile, the temperature is gradually increased from 1 month, and the temperature gradually decreases after reaching 8 months;
through the steps, a dynamic atmospheric natural environment modeling method based on space-time interpolation is formed;
in conclusion, the invention designs a dynamic atmospheric natural environment modeling method which considers typical atmospheric environment data collection, atmospheric environment data space-time interpolation modeling and environmental data comprehensive statistical analysis; the method is characterized by screening environmental factors aiming at typical atmospheric environmental factors and space-time positions, comprehensively considering the influence of time and geographic positions, establishing a space-time dynamic change model with the geographic position precision reaching 0.5 ℃ by adopting a Krigin interpolation method, providing atmospheric natural environment dynamic change data, and carrying out statistical analysis on the atmospheric natural environment data through a time dimension to form a dynamic atmospheric natural environment modeling method based on space-time interpolation.
Claims (2)
1. A dynamic atmospheric natural environment modeling method based on space-time interpolation is characterized in that: the implementation steps are as follows:
the method comprises the following steps: collecting typical atmospheric environment data;
atmospheric environmental factors include temperature, humidity, solar radiation, rainfall and pollutants; for products operating in atmospheric environments, the environmental parameters to be considered include temperature and relative humidity; the temperature is an important factor influencing the corrosion failure of the material, and can influence the condensation of water vapor and the solubility of gas and salts in a water film, and the atmospheric corrosion rate is higher in places with high average temperature; if pollutants exist in the atmospheric environment and are deposited and dissolved in a material surface liquid film, a corrosion micro-battery and an oxygen concentration difference battery can be formed on the surface of the material, so that the surface of the material is corroded and loses efficacy; the higher the relative humidity in the atmosphere is, the more the metal surface is easy to dewing, the longer the existence time of an electrolyte film on the surface is, the higher the corrosion failure speed is, and the influence on the working performance of a product is also generated; therefore, for the above typical environmental data, in order to meet the requirement that the accuracy of the final differential model geographic position reaches 0.5 degree longitude and latitude, an environmental observation station from which data is collected needs to be screened:
firstly, screening and collecting environmental data according to different environmental factor data sources;
writing a program to process and arrange the scientific format file data;
step two: modeling atmospheric environment data by time-space interpolation;
the collected and sorted environmental factor data are incomplete and missing, so that when the overall environmental factor information of the target region position is required to be obtained in a covering manner, the atmospheric environmental data spatial-temporal interpolation modeling is required to be carried out on the environmental data of the existing geographic position, and the overall environmental factor data of the target region is obtained;
the kriging interpolation method is to give corresponding weight values to observation data of spatial known sample points to estimate corresponding data of unknown points; because the continuity of the atmospheric environment can assume the environmental variables of temperature and relative humidity, which all satisfy the second-order stationary assumption and the intrinsic assumption, the calculation can be carried out by using a common kriging interpolation method; and setting a certain environment factor variable as Z (x), wherein the observed values of the environment factor variable at all known geographic positions are respectively as follows: z (x) 1 ),Z(x 2 ),Z(x 3 )，…,Z(x n )；
Then at x 0 The estimated value of the variable is obtained by the following formula:
in the formula: z * (x 0 ) Represents x 0 At an estimate of this variable, Z (x) i ) Representative of observed values, i ═ 1, …, n, λ i Is a weight coefficient expressed as the degree of contribution of the observed value of each position to the estimated value; the problem thus turns into λ i Since the expression already satisfies the linear requirement, the remaining two constraints are used to solve: unbiased and minimum estimated variance; thus, the output solution equation set is:
in the formula: μ is the Lagrangian constant, c (x) i ,x j ) Is x i And x j The covariance function of (a); since the relationship between the variation function γ (-) and the covariance function c (-) approximates this trade-off, the system of equations is solved by substituting the variation function into the equation system instead of the covariance function, and the function varying with distance in space is represented by a variation function model including: a gaussian model, an exponential model and a spherical model;
step three: comprehensive statistical analysis of environmental data;
for different regions, the change of environmental factors is different, the severe change of air temperature also influences the working condition of the product, if the temperature of the product is reduced in the working process, the temperature of the surface of the material is lower than the ambient atmospheric temperature, and the water vapor in the atmosphere is condensed and condensed on the surface of the material, so that the corrosion is accelerated; meanwhile, the environmental factors of the same place and different time have difference, which can cause the working environment of the product to change obviously and directly influence the working performance of the product; therefore, the environment is classified in time and space according to the characteristics of the environment factors in different areas, the environment factors are classified and screened in different longitudes and latitudes according to the time dimension accurate to the hour, and then the dynamic atmospheric natural environment modeling method based on the space-time interpolation is formed through comprehensive statistical analysis of the environment data.
2. The dynamic atmospheric natural environment modeling method based on spatio-temporal interpolation according to claim 1, characterized in that: in step two, the Gaussian model refers to
The index model refers to
The spherical model refers to
Wherein Δ x is x j -x i J > i; e is an index; a is a variation, represents a space correlation range uniformly possessed by the parameter, and reflects the speed of space change of the parameter, wherein the larger a is, the larger the space correlation range is, and the smaller the space change speed of the parameter is; c 0 The gold block constant is caused by parameter errors and microstructures and represents a part of parameter random variation; c 0 + C is a base station value and reflects the maximum variation amplitude of the parameter in numerical value; c is the arch height, representing the part of the parameter that varies structurally.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111622550.3A CN114840616A (en) | 2021-12-28 | 2021-12-28 | Dynamic atmospheric natural environment modeling method based on space-time interpolation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111622550.3A CN114840616A (en) | 2021-12-28 | 2021-12-28 | Dynamic atmospheric natural environment modeling method based on space-time interpolation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114840616A true CN114840616A (en) | 2022-08-02 |
Family
ID=82562644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111622550.3A Pending CN114840616A (en) | 2021-12-28 | 2021-12-28 | Dynamic atmospheric natural environment modeling method based on space-time interpolation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114840616A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116680518A (en) * | 2023-08-04 | 2023-09-01 | 北京市智慧水务发展研究院 | Space-time situation analysis method for surface water environment |
| CN116701371A (en) * | 2023-06-09 | 2023-09-05 | 中国科学院地理科学与资源研究所 | Method and device for interpolating missing values of atmospheric temperature data under covariance analysis |
| CN117313307A (en) * | 2023-06-26 | 2023-12-29 | 北京航空航天大学 | Climate model simulation temperature data correction method integrating space-time environment information |
| CN117992760A (en) * | 2024-04-07 | 2024-05-07 | 中国电子科技集团公司第十研究所 | Electromagnetic environment monitoring task planning method based on cognitive map |
-
2021
- 2021-12-28 CN CN202111622550.3A patent/CN114840616A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116701371A (en) * | 2023-06-09 | 2023-09-05 | 中国科学院地理科学与资源研究所 | Method and device for interpolating missing values of atmospheric temperature data under covariance analysis |
| CN116701371B (en) * | 2023-06-09 | 2024-03-22 | 中国科学院地理科学与资源研究所 | Method and device for interpolating missing values of atmospheric temperature data under covariance analysis |
| CN117313307A (en) * | 2023-06-26 | 2023-12-29 | 北京航空航天大学 | Climate model simulation temperature data correction method integrating space-time environment information |
| CN116680518A (en) * | 2023-08-04 | 2023-09-01 | 北京市智慧水务发展研究院 | Space-time situation analysis method for surface water environment |
| CN116680518B (en) * | 2023-08-04 | 2023-10-20 | 北京市智慧水务发展研究院 | Space-time situation analysis method for surface water environment |
| CN117992760A (en) * | 2024-04-07 | 2024-05-07 | 中国电子科技集团公司第十研究所 | Electromagnetic environment monitoring task planning method based on cognitive map |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114840616A (en) | Dynamic atmospheric natural environment modeling method based on space-time interpolation | |
| CN111401602B (en) | Assimilation method for satellite and ground rainfall measurement values based on neural network | |
| Hunter et al. | Climatologically aided mapping of daily precipitation and temperature | |
| Huang et al. | Assessing model performance of daily solar irradiance forecasts over Australia | |
| Fox et al. | The modular ocean data assimilation system (MODAS) | |
| Bezak et al. | Reconstruction of past rainfall erosivity and trend detection based on the REDES database and reanalysis rainfall | |
| Solaiman et al. | Development of probability based intensity-duration-frequency curves under climate change | |
| Körner et al. | Introducing Gradient Boosting as a universal gap filling tool for meteorological time series | |
| CN111260111A (en) | Runoff forecast improvement method based on meteorological big data | |
| Abaurrea et al. | Forecasting local daily precipitation patterns in a climate change scenario | |
| KR101575847B1 (en) | System and Method for variability diagnosis modeling of Western North Pacific surface sea temperature using northern hemisphere climatic index | |
| McNicholas et al. | Smartphone pressure collection and bias correction using machine learning | |
| Zheng et al. | Trend detection in regional-mean temperature series: maximum, minimum, mean, diurnal range, and SST | |
| Li et al. | An improved model for detecting heavy precipitation using GNSS-derived zenith total delay measurements | |
| Nicolet et al. | Assessing climate change impact on the spatial dependence of extreme snow depth maxima in the French Alps | |
| Kisi et al. | Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models | |
| Wang et al. | Spatial Variation of Extreme Rainfall Observed From Two Century‐Long Datasets | |
| Anuchaivong et al. | Statistical downscaling for rainfall forecasts using modified constructed analog method in Thailand | |
| Qin et al. | Application of geographically weighted regression model in the estimation of surface air temperature lapse rate | |
| Khouider et al. | A novel method for interpolating daily station rainfall data using a stochastic lattice model | |
| He et al. | An investigation of atmospheric temperature and pressure using an improved spatio-temporal Kriging model for sensing GNSS-derived precipitable water vapor | |
| Fan et al. | Simulation of average monthly ozone exposure concentrations in China: A temporal and spatial estimation method | |
| CN111967677A (en) | Prediction method and device for unconventional resource dessert distribution | |
| Selvi S et al. | Stochastic Modelling of Annual Rainfall at Tamil Nadu. | |
| Zhang et al. | Exploring mean annual precipitation values (2003–2012) in a specific area (36° N–43° N, 113° E–120° E) using meteorological, elevational, and the nearest distance to coastline variables |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |