A cloud scheme and system for simulating, assimilating, and evaluating a land surface process model

Abstract

The present invention relates to a cloud scheme and system for simulating, assimilating, and evaluating a land surface process model. The steps include: 1) cloud integration and cloud processing of land surface data, integrating various land surface data sources and providing cloud processing; 2) cloud integration and cloud processing of human activity data, integrating data in all aspects of human activities; 3) cloud integration and cloud processing of natural change data, integrating data of various indicators of natural changes; 4) cloud integration and cloud simulation of the land surface process model, integrating various land surface process models and using the cloud-integrated data for simulation; 5) cloud integration and cloud assimilation of assimilation algorithms, integrating various data assimilation algorithms and using the cloud data for assimilation; 6) cloud evaluation of the land surface process model, integrating various evaluation methods and using cloud observations for evaluation; 7) cloud query of data, exporting query results. The present invention reduces the workload of downloading and processing data for running the land surface process model on a PC, and promotes the in-depth utilization of Earth big data in aspects such as the evolution of the land surface layer and land-atmosphere interaction.

Description

Technical Field

[0001] The present invention relates to distributed cloud database management technology, remote sensing big data processing technology, observation data integration technology, geographic information system technology, land surface process model technology, data assimilation technology, and data product display technology. Specifically, the present invention uses distributed cloud database management technology, remote sensing big data processing technology, observation data integration technology, and geographic information system technology to simulate, assimilate, and evaluate land surface process models and output simulation results according to user needs. Background Art

[0002] Land surface process models are an important method for studying the mechanisms of physical and biochemical processes in the atmosphere, hydrosphere, biosphere, and land surface, as well as the laws of energy and material transfer between them. They play an irreplaceable and important role in simulating energy distribution in the climate system, material circulation in ecosystems, surface and underground runoff, and soil moisture evolution. They are of great significance for modeling and parameterizing various components of land surface processes such as land-air flux, land surface hydrological processes, land surface ecological processes, and soil freeze-thaw process models, and have important applications in the fields of ecological environment, soil physics, hydrology, and global change.

[0003] Land surface process model simulation requires atmospheric forcing data, soil and vegetation attribute data, land use data, human activity data, surface parameter data and other data. On the one hand, these data are currently distributed on various websites. On the other hand, the required data volume is large and the data types are relatively complex. When simulating different areas, a large amount of relevant data needs to be downloaded, which causes great inconvenience to the land surface process model simulation. More importantly, there is a lack of an effective evaluation platform between the simulation results of different land surface process models, which seriously limits the development and application of land surface process models. Therefore, there is an urgent need to develop a comprehensive platform for land surface process model simulation and evaluation.

[0004] At present, various cloud platforms have provided great convenience for the calculation, networking and storage of the exponentially growing massive earth big data. Users can complete complex calculations and modeling without downloading the original data, providing a powerful tool for the study of earth system science. With the help of earth observation data on cloud platforms, the advantages of ground observation products and land surface model simulation are organically combined through land surface data assimilation methods, laying a methodological foundation for effectively improving the simulation accuracy of land surface process models. However, there are currently no land surface process models running on cloud platforms, which seriously lags behind the rapidly developing cloud platforms. This is very unfavorable for the effective use of earth big data, the full realization of the application potential of land surface process models, the comprehensive evaluation of land surface process models, and the overall study of earth system science under climate change.

[0005] In order to improve the comprehensive performance of cloud computing, modeling, and verification of land surface process models, a cloud solution and system for simulating, assimilating, and evaluating land surface process models are invented using cloud platform interconnection technology. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an integrated technical method and system for the input, simulation, assimilation, evaluation, and output of land surface process models in view of the deficiencies of the prior art. The integrated technical method and system comprehensively use cloud server interconnection technology, remote sensing data processing technology, observation data integration technology, land surface process model technology, and data assimilation technology to simulate, predict, and analyze the land surface layer.

[0007] The basic idea of the present invention is as follows: First, the input data, parameter data, and ground observation data of the land surface process model scattered on various cloud platforms are interconnected into a multi-source database management cloud platform. Then, the land surface process model is embedded into the cloud platform using the applicable cloud platform programming language and simulation and assimilation are achieved with the support of the multi-source database management cloud platform. Furthermore, the display form of the simulation results of the land surface process model is designed to complete the output of the simulation results of the land surface process model and the performance comparison and evaluation between them. Among them, the entire process requires multi-source earth information system cloud interconnection technology to provide smooth data flow and information processing.

[0008] A cloud solution for simulating, assimilating, and evaluating a land surface process model implemented by the present invention specifically includes the following steps:

[0009] S1: Cloud integration and cloud processing of land surface data: centrally manage various open-source land surface data sets through the cloud platform and provide cloud processing;

[0010] S2: Cloud integration and cloud processing of human activity data: import open-source data sets in various aspects of human activities through the cloud platform;

[0011] S3: Cloud integration and cloud processing of natural change data: import open-source data of various natural change indicators through the cloud platform;

[0012] S4: Cloud integration and cloud simulation of land surface process models: integrate various land surface process models and perform simulations using cloud data;

[0013] S5: Cloud integration and cloud assimilation of assimilation algorithms: integrate various data assimilation algorithms and perform assimilation using cloud remote sensing products or observation data;

[0014] S6: Cloud evaluation of land surface process models: integrate model simulation evaluation methods and evaluate model simulations using cloud observation data;

[0015] S7: Data cloud query: query and export cloud platform data, products, and model simulation / assimilation results.

[0016] The characteristics of the above steps are as follows:

[0017] Step S1: Cloud integration and cloud processing of land surface data, which is used to centrally manage various open-source land surface datasets through a cloud platform and provide an interface for converting them into GeoTiff format files. The open-source land surface datasets include: forcing datasets such as Princeton Global Meteorological Forcing, Climatic Research Unit - National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project, etc.; remote sensing datasets such as HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS, etc.; parameter datasets such as land use GLC2000, ESA GlobCover, FROM_FLC10, soil datasets such as HWSD, Future Water, global soil dataset for earth system modeling, elevation datasets such as SRTM DEM, etc.; observation datasets such as the global water, heat and carbon flux observation network FluxNet, the glacier observation network GTN-G, the permafrost observation network, etc. The cloud processing includes converting data files in formats such as TXT, HDF, L1B, binary, Netcdf, Shapefile, etc. into GeoTiff format and completing projection conversion, regional cropping, spatio-temporal resampling, etc.;

[0018] Step S2: Cloud integration and cloud processing of human activity data, which is used to centrally manage the open-source data sets of all aspects of human activities through a cloud platform and provide a data import interface. Human activities mainly involve changing the surface function state, changing the surface material cycle, changing the surface energy cycle, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, economic development, etc. The open-source data sets include: GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database, etc. The data import interface is to convert the data format of human activity data concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete processing such as projection transformation, regional cropping, and spatio-temporal resampling;

[0019] Step S3: Cloud integration and cloud processing of natural change data, which is used to centrally share the open-source data of various natural change indicators through a cloud platform and provide an import interface for indicator data. Natural changes mainly involve meteorological oscillations, atmospheric circulation, climate variability, ocean circulation, etc. The open-source data sets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, Trans- index, quasi-Biennial oscillation, Pacific decadal oscillation, etc. The data import interface is to convert the data format of natural change indicator data concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete processing such as projection transformation, regional cropping, and spatio-temporal resampling;

[0020] Step S4: Cloud integration and simulation of land surface process models, which is used to integrate various land surface process models and perform simulations using cloud-integrated data. That is, various open-source land surface process models are integrated and uniformly managed on the cloud platform, and a source code editing interface and common function interfaces are provided. The land surface process models include CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM, etc. The simulation of land surface process models uses cloud-platform integrated data for online simulation;

[0021] Step S5: Cloud integration and assimilation of assimilation algorithms, which is used to integrate various open-source data assimilation algorithms into the cloud platform and provide a source code editing interface and extensible function interfaces, and perform assimilation using remotely sensed products or observational data integrated in the cloud to improve the simulation accuracy of land surface process models. The assimilation algorithms include: sequential assimilation, continuous assimilation, ensemble-variational method, etc. Assimilation mainly uses remotely sensed products or observational data integrated in the cloud platform for online assimilation to improve the simulation of land surface process models. The assimilation code editing interface and extensible function interfaces mainly provide editing methods such as modification, addition, deletion, and copying of assimilation methods;

[0022] Step S6: Cloud evaluation of land surface process models, which is used to integrate various open-source evaluation methods for land surface process model simulations on the cloud and provide application interfaces and extensible evaluation function interfaces, and evaluate the simulation results of land surface process models using observational data integrated in the cloud. The evaluation methods include: root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar chart, Nash coefficient, spatial gradient, etc. The application interfaces and extensible evaluation function interfaces of evaluation methods mainly provide editing methods such as addition and modification of evaluation methods;

[0023] Step S7: Cloud data query, which is used for users to query cloud platform data, products, and land surface process model simulation / assimilation results by time and range, and export the query results in GeoTiff format.

[0024] The technical solution of the present invention to solve the above technical problems is as follows: A cloud system for land surface process model simulation, assimilation, and evaluation includes a land surface data cloud integration and cloud processing module, a human activity data cloud integration and cloud processing module, a natural change data cloud integration and cloud processing module, a land surface process model cloud integration and cloud simulation module, an assimilation algorithm cloud integration and cloud assimilation module, a land surface process model cloud evaluation module, and a data cloud query module;

[0025] The land surface data cloud integration and cloud processing module is used to centrally manage various open-source land surface datasets through a cloud platform and provide an interface for converting them into GeoTiff format files. The open-source datasets include: forcing datasets such as Princeton Global Meteorological Forcing, Climatic Research Unit-National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project, etc.; remote sensing datasets such as HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS, etc.; parameter datasets such as land use GLC2000, ESA GlobCover, FROM_FLC10, soil datasets such as HWSD, Future Water, global soil dataset for earth system modeling, elevation datasets such as SRTM DEM, etc.; observation datasets such as the global water, heat and carbon flux observation network FluxNet, the glacier observation network GTN-G, the permafrost observation network, etc. The preprocessing includes converting data files in formats such as TXT, HDF, L1B, binary, Netcdf, Shapefile, etc. into GeoTiff format and completing projection conversion, regional cropping, spatio-temporal resampling, etc.;

[0026] The human activity data cloud integration and cloud processing module is used to centrally manage the open-source datasets in all aspects of human activities through a cloud platform and provide a data import interface. Human activities mainly involve changing the surface function state, changing the surface material cycle, changing the surface energy cycle, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, economic development, etc. The open-source datasets include: GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database, etc. The data import interface is to convert the data format of human activity data concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete processing such as projection conversion, regional cropping, spatio-temporal resolution resampling, etc.;

[0027] The natural change data cloud integration and cloud processing module is used to centrally share the open-source data of various natural change indicators through a cloud platform and provide an import interface for indicator data. Natural changes mainly involve meteorological oscillations, atmospheric circulation, climate variability, ocean circulation, etc. The open-source datasets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, Trans- index, quasi-Biennial oscillation, Pacific decadal oscillation, etc. The data import interface is to convert the data format of natural change indicator data concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete processing such as projection conversion, regional cropping, etc.;

[0028] The cloud integration and cloud simulation module of the land surface process model is used to integrate various land surface process models and perform simulations using the integrated data in the cloud. That is, various open-source land surface process models are integrated and uniformly managed on the cloud platform, and a source code editing interface and common function interfaces are provided. The land surface process models include CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM, etc. The simulation of the land surface process model uses the integrated data in the cloud platform for online simulation;

[0029] The cloud integration and cloud assimilation module of the assimilation algorithm is used to integrate various open-source data assimilation algorithms into the cloud platform and provide a source code editing interface and extensible function interfaces, and perform assimilation using the remotely sensed products or observational data integrated in the cloud to improve the simulation accuracy of the land surface process model. The assimilation algorithms include: sequential assimilation, continuous assimilation, ensemble-variational method, etc. Assimilation mainly uses the remotely sensed products or observational data integrated in the cloud platform for online assimilation to improve the simulation of the land surface process model. The assimilation code editing interface and extensible function interfaces mainly provide editing methods such as modification, addition, deletion, and copying of the assimilation method;

[0030] The cloud evaluation module of the land surface process model is used to integrate various open-source evaluation methods for the simulation of the land surface process model in the cloud and provide application interfaces and extensible evaluation function interfaces, and evaluate the simulation results of the land surface process model using the observational data integrated in the cloud. The evaluation methods include: root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar chart, Nash coefficient, spatial gradient, etc. The application interfaces and extensible evaluation function interfaces of the evaluation methods mainly provide editing methods such as addition and modification of the evaluation methods;

[0031] The data cloud query module is used for users to query the cloud platform data, products, and the simulation / assimilation results of the land surface process model by time and range, and export the query results in the GeoTiff format.

[0032] The beneficial effects of the present invention are: compared with the simulation, assimilation, and evaluation of the land surface process model on a PC, through the integrated management of the land surface database scattered on various websites, the present invention reduces the workload of downloading and processing driving data, parameter data, and assimilation data in the simulation and assimilation processes of the land surface process model, improves the efficiency of simulating the land surface layer by the land surface process model, enriches the means of comparative evaluation of the land surface process model, and has broad application prospects in promoting the comprehensive utilization of earth big data, predicting the evolution of the land surface layer, and land-air interaction, etc. Brief Description of the Drawings

[0033] Figure 1 The flowchart of the cloud solution for simulating, assimilating, and evaluating a land surface process model according to the present invention;

[0034] Figure 2Cloud system flowchart for simulating, assimilating, and evaluating a land surface process model according to the present invention;

[0035] In the accompanying drawings, the list of components represented by each reference numeral is as follows:

[0036] 1. Cloud integration and cloud processing module for land surface data, 2. Cloud integration and cloud processing module for human activity data, 3. Cloud integration and cloud processing module for natural change data, 4. Cloud integration and cloud simulation module for land surface process model, 5. Cloud integration and cloud assimilation module for assimilation algorithm, 6. Cloud evaluation module for land surface process model, 7. Data cloud query module. Detailed implementation manners

[0037] Now, a specific implementation manner of the present invention will be described in detail with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0038] As Figure 1 shown, a cloud solution for simulating, assimilating, and evaluating a land surface process model. The main steps of the present invention include:

[0039] S1 Land surface data cloud integration and cloud processing: centrally manage various scattered open-source land surface databases required for running land surface process models through a cloud platform and provide preprocessing. The land surface data required for running land surface process models includes forcing datasets, remote sensing datasets, parameter datasets, and observation datasets, etc. Optionally, the forcing datasets include Princeton Global Meteorological Forcing, Climatic Research Unit-National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project, etc.; the remote sensing datasets include HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS, etc.; the parameter datasets include land use GLC2000, ESA GlobCover, FROM_FLC10, soil dataset HWSD, Future Water, global soil dataset for earth system modeling, elevation dataset SRTM DEM, etc.; the observation datasets include the global water, heat, and carbon flux observation network FluxNet, the glacier observation network GTN-G, the permafrost observation network, etc. The preprocessing includes converting data files in formats such as TXT, HDF, L1B, binary, Netcdf, Shapefile, etc. into the GeoTiff format and simultaneously completing functions such as projection conversion, regional cropping, and spatio-temporal resolution resampling, and importing them into the land surface process model according to the research period, research area, and the simulated time and space resolutions.

[0040] S2 Cloud integration and cloud processing of human activity data: Import and preprocess the open-source datasets of various aspects of human activities required for running the land surface process model through the data import interface provided by the cloud platform. Human activities involve changes in surface functional states, surface material cycles, surface energy cycles, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, economic development, etc. Optionally, the open-source datasets include GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database, etc. The data import interface is a function of the cloud platform that enables the import of human activity datasets into the land surface process model. The preprocessing is to complete processing such as format conversion, projection conversion, regional cropping, resampling of time resolution and spatial resolution while importing the human activity datasets into the land surface process model.

[0041] S3 Cloud integration and cloud processing of natural change data: Import and preprocess the open-source data of various indicators of natural changes required for running the land surface process model through the data import interface of the cloud platform. Natural changes mainly involve meteorological oscillations, atmospheric circulation, climate variability, ocean circulation, etc. Optionally, the open-source datasets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, Trans- index, quasi-Biennial oscillation, Pacific decadal oscillation, etc. The data import interface is a functional function provided by the cloud platform for importing natural change indicator data into the land surface process model. The preprocessing is to complete processing such as format conversion, projection transformation, regional cropping, resampling of time resolution and spatial resolution when importing the natural change datasets into the land surface process model.

[0042] S4 Cloud integration and cloud simulation of land surface process models, integrating various land surface process models and using cloud-integrated data for simulation. Embed various open-source land surface process models into the cloud platform and provide a source code editing interface and common function interfaces. Optionally, the land surface process models include CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM, etc. Cloud platform simulation is to use cloud-integrated data to drive the online simulation of land surface process models. When embedding the land surface process models into the cloud platform according to the cloud platform construction language, optionally, the embedding implementation languages are Python, JavaScript, C, C++, Fortran, C#, and the implementation forms are dynamic link libraries, editable interfaces, etc.

[0043] S5 Cloud integration and cloud assimilation of assimilation algorithms, integrating various data assimilation algorithms and using cloud-based remote sensing products or observational data for assimilation. Embed various open-source data assimilation algorithms into the cloud platform and provide a source code editing interface and extensible function interfaces, and use cloud-integrated remote sensing products or observational data for assimilation to improve the simulation accuracy of land surface process models. Optionally, the assimilation algorithms include sequential assimilation, continuous assimilation, ensemble-variational methods, etc. Assimilation is mainly to use the remote sensing products or observational data integrated into the cloud platform for cloud assimilation to improve the cloud simulation of land surface process models. The assimilation algorithm code can be used to modify, add, delete, copy, etc. the assimilation methods through the editing interface and extensible function interfaces provided by the cloud platform.

[0044] S6 Cloud evaluation of land surface process models, integrating model simulation evaluation methods into the cloud platform and using cloud-based observational data to evaluate model simulations. The cloud integrates various open-source evaluation methods for land surface process model simulations and provides application interfaces and extensible evaluation function interfaces, and uses cloud-integrated observational data to evaluate the simulation results of land surface process models. Optionally, the evaluation methods include root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar chart, Nash coefficient, spatial gradient, etc. The application interfaces and extensible evaluation function interfaces of the cloud platform evaluation methods are mainly to provide editing such as adding and modifying evaluation methods;

[0045] S7 Data cloud query, querying and exporting cloud platform data, products, and model simulation / assimilation results. Users query the cloud platform data, products, and land surface process model simulation / assimilation results by time and range, and export the query results in GeoTiff format.

[0046] Such as Figure 2As shown in the figure, a cloud system for simulating, assimilating, and evaluating a land surface process model includes a land surface data cloud integration and cloud processing module 1, a human activity data cloud integration and cloud processing module 2, a natural change data cloud integration and cloud processing module 3, a land surface process model cloud integration and cloud simulation module 4, an assimilation algorithm cloud integration and cloud assimilation module 5, a land surface process model cloud evaluation module 6, and a data cloud query module 7;

[0047] The land surface data cloud integration and cloud processing module 1 is used to centrally manage various scattered open-source land surface databases required for running the land surface process model through a cloud platform and provide preprocessing. The data required for running the land surface process model includes forcing datasets, remote sensing datasets, parameter datasets, and observation datasets, etc. Optionally, the forcing datasets include Princeton Global Meteorological Forcing, Climatic Research Unit-National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project, etc.; the remote sensing datasets include HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS, etc.; the parameter datasets include land use GLC2000, ESA GlobCover, FROM_FLC10, soil dataset HWSD, Future Water, global soil dataset for earth system modeling, elevation dataset SRTM DEM, etc.; the observation datasets include the global water, heat and carbon flux observation network FluxNet, the glacier observation network GTN-G, the permafrost observation network, etc. The preprocessing includes converting data files in formats such as TXT, HDF, L1B, binary, Netcdf, Shapefile, etc. into the GeoTiff format and simultaneously completing processing such as projection conversion, regional cropping, and spatio-temporal resolution resampling, and importing them into the land surface process model according to the research period, research area, and simulated temporal and spatial resolutions.

[0048] The human activity data cloud integration and cloud processing module 2 is used to import and preprocess the open-source datasets of various aspects of human activities required for running the land surface process model through the data import interface provided by the cloud platform. Human activities involve changes in the surface functional state, changes in the surface material cycle, changes in the surface energy cycle, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, economic development, etc. Optionally, the open-source datasets include GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database, etc. The data import interface is a function of the cloud platform to implement the function of importing the human activity dataset into the land surface process model. The preprocessing is to complete the processing such as format conversion, projection conversion, regional cropping, resampling of time resolution and spatial resolution while importing the human activity dataset into the land surface process model.

[0049] The natural change data cloud integration and cloud processing module 3 is used to import and preprocess the open-source data of various indicators of natural changes required for running the land surface process model through the data import interface of the cloud platform. Natural changes mainly involve meteorological oscillations, atmospheric circulation, climate variability, ocean circulation, etc. Optionally, the open-source datasets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, Trans- index, quasi-Biennial oscillation, Pacific decadal oscillation, etc. The data import interface is a functional function provided by the cloud platform to import the natural change index data into the land surface process model. The preprocessing is to complete the processing such as format conversion, projection transformation, regional cropping, resampling of time resolution and spatial resolution when importing the natural change dataset into the land surface process model.

[0050] The land surface process model cloud integration and cloud simulation module 4 is used to integrate various land surface process models and perform simulations using the integrated data in the cloud. Various open-source land surface process models are embedded into the cloud platform, and a source code editing interface and common function interfaces are provided. Optionally, the land surface process models include CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM, etc. Cloud simulation is to drive the land surface process model for online simulation using the integrated data in the cloud. When embedding the land surface process model into the cloud platform according to the cloud platform construction language, optionally, the embedding implementation languages are Python, JavaScript, C, C++, Fortran, C#, and the implementation forms include dynamic link libraries, editable interfaces, etc.

[0051] The assimilation algorithm cloud integration and cloud assimilation module 5 is used to integrate various data assimilation algorithms and perform assimilation using remote sensing products or observational data in the cloud. Various open-source data assimilation algorithms are embedded into the cloud platform, and a source code editing interface and extensible function interfaces are provided. The simulation accuracy of the land surface process model is improved by performing assimilation using the integrated remote sensing products or observational data in the cloud. Optionally, the assimilation algorithms include sequential assimilation, continuous assimilation, ensemble-variational methods, etc. Assimilation is mainly to perform cloud assimilation using the remote sensing products or data integrated into the cloud platform to improve the simulation of the land surface process model. The assimilation algorithm code can implement editing such as modification, addition, deletion, and copying of the assimilation method through the editing interface and extensible function interfaces provided by the cloud platform.

[0052] The land surface process model cloud evaluation module 6 is used to integrate model simulation evaluation methods into the cloud platform and evaluate model simulations using observational data in the cloud. Various open-source evaluation methods for land surface process model simulations are integrated in the cloud, and application interfaces and extensible evaluation function interfaces are provided. The simulation results of the land surface process model are evaluated using the integrated observational data in the cloud. Optionally, the evaluation methods include root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar chart, Nash coefficient, spatial gradient, etc. The application interfaces and extensible evaluation function interfaces of the cloud platform evaluation methods mainly provide editing such as addition and modification of evaluation methods;

[0053] The data cloud query module 7 is used to query and export cloud platform data, products, and model simulation / assimilation results. Users query the cloud platform data, products, and land surface process model simulation / assimilation results by time and range, and export the query results in GeoTiff format.

[0054] The embodiments described in the present invention can enable those skilled in the art to understand the present invention more comprehensively without limiting the present invention in any way. For those of ordinary skill in the art, without departing from the principle of the present invention, the present invention can also be applied to climate models, ecological models, hydrological models, groundwater models, etc. and certain improvements and refinements can be made, and these should also be regarded as the protection scope of the present invention. For those of ordinary skill in the art, supplements, modifications or equivalent replacements can be made according to the above description, and all these improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A cloud method for simulating, assimilating, and evaluating a land surface process model, characterized in that, Including the following steps: S1: Cloud integration and cloud processing of land surface data: Integrate various land surface data sources and provide cloud processing, that is, centrally manage various open-source land surface data sets through a cloud platform and provide an interface for converting them into GeoTiff format files; The cloud integration and cloud processing of land surface data in S1 is to integrate various open-source land surface data sets into a cloud management platform and provide a preprocessing interface for converting them into GeoTiff format. The open-source land surface data sets include: forcing data sets Princeton Global Meteorological Forcing, Climatic Research Unit - National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project, remote sensing data sets HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS, parameter data sets land use GLC2000, ESA GlobCover, FROM_FLC10, soil data sets HWSD, Future Water, global soil dataset for earth system modeling, elevation data sets SRTM DEM, observation data sets global water-heat-carbon flux observation network FluxNet, glacier observation network GTN-G, permafrost observation network. The cloud processing includes converting data files in TXT, HDF, L1B, binary, Netcdf, Shapefile formats into GeoTiff format and completing projection conversion, regional cropping, and spatio-temporal resampling; S2: Cloud integration and cloud processing of human activity data: Integrate data from all aspects of human activities, that is, centrally manage the open-source datasets from all aspects of human activities through a cloud platform and provide a data import interface; For S2, cloud integration and cloud processing of human activity data, human activities involve changing the surface function state, changing the surface material cycle, changing the surface energy cycle, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, and economic development. The open-source datasets include: GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database. The data import interface is to convert the human activity data format concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete projection transformation, regional cropping, and spatio-temporal resampling processing; S3: Natural Variation Data Cloud Integration and Cloud Processing: Integrate data on various indicators of natural variation, that is, centrally share the open-source data on various indicators of natural variation through a cloud platform and provide an import interface for indicator data; for S3 natural variation data cloud integration and cloud processing, natural variation involves meteorological oscillations, atmospheric circulation, climate variability, and ocean circulation. The open-source data sets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, index, quasi-Biennial oscillation, Pacific decadal oscillation. The data import interface converts the data format of natural variation indicators centralized on the cloud platform into the data format required for land surface process model simulation and simultaneously completes projection conversion, regional cropping, and spatio-temporal resampling processing; S4: Cloud integration and cloud simulation of land surface process models: Integrate various land surface process models and use the cloud-integrated data for simulation, that is, integrate various open-source land surface process models on the cloud platform for unified management and provide a source code editing interface and common function interfaces; For S2, cloud integration and cloud simulation of land surface process models, the land surface process models include: CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM. The land surface process model simulation uses the cloud-platform integrated data for online simulation; S5: Cloud integration and cloud assimilation of assimilation algorithms: Integrate various data assimilation algorithms and use the remotely sensed products or observational data integrated in the cloud for assimilation to improve the simulation accuracy of land surface process models, that is, integrate various open-source data assimilation algorithms on the cloud platform and provide a source code editing interface and extensible function interfaces; For S3, cloud integration and cloud assimilation of assimilation algorithms, the assimilation algorithms include: sequential assimilation, continuous assimilation, ensemble-variational method. Assimilation is to use the remotely sensed products or observational data integrated in the cloud for online assimilation to improve the simulation of land surface process models. The assimilation code editing interface and extensible function interfaces provide editing methods for modifying, adding, deleting, and copying assimilation methods; S6: Cloud evaluation of land surface process models: Integrate the evaluation methods simulated by various land surface process models and use the observation data integrated in the cloud to evaluate the simulation results of land surface process models, that is, integrate the evaluation methods of various open-source land surface process model simulations in the cloud and provide application interfaces and extensible evaluation function interfaces; S2 Cloud evaluation of land surface process models, the evaluation methods include: root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar graph, Nash coefficient, spatial gradient, and the application interfaces and extensible evaluation function interfaces of the evaluation methods provide ways to add and modify the evaluation methods. S7: Cloud data query: Users can query the cloud platform data, products, and the simulation / assimilation results of land surface process models by time and range, and export the query results in GeoTiff format.

2. A cloud system for simulating, assimilating, and evaluating a land surface process model, characterized in that, It includes a land surface data cloud integration and cloud processing module, a human activity data cloud integration and cloud processing module, a natural change data cloud integration and cloud processing module, a land surface process model cloud integration and cloud simulation module, an assimilation algorithm cloud integration and cloud assimilation module, a land surface process model cloud evaluation module, and a data cloud query module; The land surface data cloud integration and cloud processing module is used to integrate various land surface data sources and perform preprocessing, that is, centrally manage various open-source land surface data sets on the cloud platform and provide an interface for converting them into GeoTiff format files; Land surface data cloud integration and cloud processing integrate various open-source land surface datasets into a cloud management platform and provide a preprocessing interface for conversion to the GeoTiff format. The open-source datasets include: forcing datasets such as Princeton Global Meteorological Forcing, Climatic Research Unit - National Centers for Environmental Prediction, Global Soil Wetness Project, Global change and hydrological observation project forcing dataset, European Centre for Medium-Range Weather Forecasts, Coupled Model Intercomparison Project; remote sensing datasets such as HJ-1A / B, MODIS Terra / Aqua and products, TM / ETM, AVHRR-16 / 17 / 18, Earth Observation Link, FY-2 / 4, Atmospheric Infrared Sounder, Tropical Rainfall Measuring Mission, International Satellite Cloud Climatology Project, GLASS; parameter datasets such as land use GLC2000, ESA GlobCover, FROM_FLC10, soil datasets such as HWSD, Future Water, global soil dataset for earth system modeling, elevation datasets such as SRTM DEM; observation datasets such as the global water, heat and carbon flux observation network FluxNet, the glacier observation network GTN-G, and the permafrost observation network. The preprocessing includes converting data files in formats such as TXT, HDF, L1B, binary, Netcdf, and Shapefile into the GeoTiff format and performing projection conversion, regional cropping, and spatio-temporal resampling. The human activity data cloud integration and cloud processing module is used to integrate data from all aspects of human activities, that is, to integrate open-source data from all aspects of human activities into the cloud platform for centralized management and provide a data import interface; for human activity data cloud integration and cloud processing, human activities involve changing the functional state of the earth's surface, changing the material cycle of the earth's surface, changing the energy cycle of the earth's surface, resource consumption, irrigation and agricultural fertilization, anthropogenic radiative forcing, and economic development. The open-source data sets include: GDW’s Global Dam and Reservoir Datasets, FAO's Global Information System on Water and Agriculture, Irrigation and agricultural fertilization database, World urban development database, Global energy consumption database, Deforestation database. The data import interface is to convert the human activity data format concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete projection conversion, regional cropping, and spatio-temporal resolution resampling processing; The natural change data cloud integration and cloud processing module is used to integrate data of various indicators of natural change, that is, to centrally share the open-source data of various indicators of natural change through a cloud platform and provide an import interface for indicator data; for the cloud integration and cloud processing of natural change data, natural change involves meteorological oscillations, atmospheric circulation, climate variability, and ocean circulation, and the open-source data sets include: Atlantic multidecadal oscillation, arctic oscillation, east Pacific / north Pacific oscillation, Pacific North American index, tropical Northern Atlantic index, index, quasi-Biennial oscillation, Pacific decadal oscillation. The data import interface is to convert the data format of natural change indicator data concentrated on the cloud platform into the data format required for land surface process model simulation and simultaneously complete projection conversion and regional cropping processing; The land surface process model cloud integration and cloud simulation module is used to integrate various land surface process models and perform simulations using the cloud-integrated data, that is, to integrate various open-source land surface process models on the cloud platform for unified management and provide a source code editing interface and common function interfaces; the land surface process models include: CLM, hyssib, mosaic, Noah-MP, VIC, BATS1e, CESM. The land surface process model simulation uses the cloud platform integrated data for online simulation; The assimilation algorithm cloud integration and cloud assimilation module is used to integrate various data assimilation algorithms and perform assimilation using the remotely sensed products or observational data integrated in the cloud to improve the simulation accuracy of the land surface process model, that is, to integrate various open-source data assimilation algorithms on the cloud platform and provide a source code editing interface and extensible function interfaces; the assimilation algorithms include: sequential assimilation, continuous assimilation, ensemble-variational method. Assimilation is to perform online assimilation using the remotely sensed products or observational data integrated in the cloud to improve the simulation of the land surface process model. The assimilation code editing interface and extensible function interfaces provide editing ways for modifying, adding, deleting, and copying assimilation methods; The cloud evaluation module of the land surface process model is used to integrate the evaluation methods simulated by various land surface process models and can use the observation data integrated in the cloud to evaluate the simulation results of the land surface process model, that is, to integrate various open-source land surface process model simulation evaluation methods in the cloud and provide application interfaces and extensible evaluation function interfaces; for the simulation evaluation of the land surface process model, the evaluation methods include: root mean square error, correlation coefficient, scatter plot, line graph, Taylor diagram, bar graph, Nash coefficient, spatial gradient, and the application interfaces of the evaluation methods and the extensible evaluation function interfaces provide ways to add and modify the evaluation methods. The data cloud query module is used to enable users to query the cloud platform data, products, and the simulation / assimilation results of the land surface process model by time and range, and export the query results in the GeoTiff format.

Images