CN113407524A - Climate system mode multi-circle layer coupling data assimilation system - Google Patents
Climate system mode multi-circle layer coupling data assimilation system Download PDFInfo
- Publication number
- CN113407524A CN113407524A CN202110735477.4A CN202110735477A CN113407524A CN 113407524 A CN113407524 A CN 113407524A CN 202110735477 A CN202110735477 A CN 202110735477A CN 113407524 A CN113407524 A CN 113407524A
- Authority
- CN
- China
- Prior art keywords
- assimilation
- data
- mode
- subsystem
- ocean
- 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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Abstract
The invention discloses a climate system mode multi-circle layer coupling data assimilation system, which comprises a climate system mode, an ocean assimilation subsystem, a sea ice assimilation subsystem, a land assimilation subsystem and an atmosphere assimilation subsystem, and is characterized in that: the climate system modes include an ocean component mode, an sea ice component mode, a land component mode, an atmospheric component mode, and a coupler; the ocean assimilation subsystem comprises a quality control module, a data fusion module and a data assimilation module for satellite remote sensing ocean surface temperature data, satellite remote sensing ocean surface height data and ocean temperature and salt profile observation data. The invention provides a multi-circle-layer multi-source data assimilation system based on a climate system mode coupling framework, which can more effectively extract and utilize observation data information of different circle layers and realize organic fusion of observation data and a climate system mode, thereby providing a more coordinated and reliable multi-circle-layer multi-variable assimilation analysis field.
Description
Technical Field
The invention relates to the technical field of climate, in particular to a climate system mode multi-circle layer coupling data assimilation system.
Background
Climate pattern prediction is a core scientific problem in the field of climate research and business. Data assimilation is one of key technical means for solving the climate mode prediction problem, and the method forms reliable estimation on the component states of atmosphere, sea ice, land and the like through comprehensive analysis of multi-source observation data of all circle layers of a climate system so as to generate an accurate and reliable climate mode initial field. According to the mode frame of the assimilation system and the different of the contained climate system components, the assimilation mode can be divided into single component assimilation of a non-coupled frame and multi-component assimilation of a coupled frame. The accuracy of the weather mode prediction initial value is directly influenced by the level of the assimilation technology capability, and the weather mode prediction initial value is an important ring for determining weather prediction success or failure.
In climate forecast services for more than 20 years in the past, the adoption of a sea-air coupling mode or a sea-land-air-ice multi-component coupling climate system mode to develop sub-season-annual scale climate forecast has become a consensus of main international business organizations; however, in the main climate mode business prediction system, most modes still adopt a means of individual assimilation of each component, that is, components such as ocean, atmosphere, land, sea ice and the like are respectively assimilated in an uncoupled and offline manner, and then initialization of a coupling mode is completed by utilizing various component assimilation products. In addition, although individual organizations begin to try to assimilate data in a coupled mode framework, assimilation algorithms for sea ice, land and other components are relatively simple, and have significant limitations in improving the climate system mode assimilation analysis capability and prediction performance as much as possible. The second generation climate mode business prediction system used by the China weather service bureau in 2015 is also constructed based on a sea-land-gas-ice coupling climate system mode, but only realizes the use of each component non-coupling individual assimilation product, and does not develop a matched coupling data assimilation technology, which becomes a prominent problem restricting the improvement of the climate mode business prediction level in China. Therefore, a climate system mode multi-turn layer coupling data assimilation system is provided.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a climate system mode multi-circle layer coupling data assimilation system, so as to solve the problems in the background art.
The invention provides the following technical scheme:
the climate system mode multi-circle layer coupling data assimilation system comprises a climate system mode, an ocean assimilation subsystem, a sea ice assimilation subsystem, a land assimilation subsystem and an atmosphere assimilation subsystem, wherein the climate system mode comprises an ocean component mode, an ocean ice component mode, a land component mode, an atmosphere component mode and a coupler; the ocean assimilation subsystem comprises a quality control module, a data fusion module and a data assimilation module for satellite remote sensing ocean surface temperature data, satellite remote sensing ocean surface height data and ocean temperature and salt profile observation data; the sea ice assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing sea ice density data; the land assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing land surface temperature data; the atmosphere assimilation subsystem comprises a data processing and converting module for analyzing atmosphere data and a data assimilation module.
Preferably, the multi-source data of each component is assimilated under the mode coupling framework of the climate system, so that a multi-circle-layer multivariable coordinated assimilation analysis field is obtained.
Preferably, the marine assimilation subsystem can realize the quality control of AVHRR satellite remote sensing sea surface temperature data and AVISO satellite remote sensing sea surface height data and the quality control of ARGO, GTSPP, TAO and other marine temperature salt profile observation data, and further complete the fusion and coordinated assimilation of multi-source marine observation data.
Preferably, the sea ice assimilation subsystem can realize the quality control and the data assimilation of the AVHRR satellite sea ice density data; the land assimilation subsystem can realize quality control and data assimilation of land surface temperature data of satellites such as MODIS, FY and the like; the atmospheric assimilation subsystem can realize data processing conversion and data assimilation of atmospheric analysis data such as NCEP-R1, NCEP-FNL, ERA-Interim and the like.
Preferably, the assimilation algorithm of the ocean component mode, the sea ice component mode, the land component mode and the atmospheric component mode may adopt a relatively complex assimilation method such as set kalman filtering, set optimal interpolation, three-dimensional variation, optimal interpolation, and the like, and may also adopt a simple initialization method for reanalyzed data.
The invention provides a multi-circle layer coupling data assimilation system based on a climate system mode coupling frame, which can more effectively extract and utilize observation data information of different circle layers and realize organic fusion of observation data and a climate system mode, thereby providing a more coordinated and reliable multi-circle layer multivariable assimilation analysis field.
Drawings
FIG. 1 is a schematic diagram of a climate system mode multi-turn layer coupled data assimilation system according to an embodiment of the present invention
Fig. 2 is a comparison graph of the assimilation effect of the climate system mode multi-turn coupled data assimilation system provided by the embodiment of the invention compared with other systems at home and abroad.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution:
the climate system mode multi-circle layer coupling data assimilation system comprises a climate system mode, an ocean assimilation subsystem, a sea ice assimilation subsystem, a land assimilation subsystem and an atmosphere assimilation subsystem, wherein the climate system mode comprises an ocean component mode, an ocean ice component mode, a land component mode, an atmosphere component mode and a coupler; the ocean assimilation subsystem comprises a quality control module, a data fusion module and a data assimilation module for satellite remote sensing ocean surface temperature data, satellite remote sensing ocean surface height data and ocean temperature and salt profile observation data; the sea ice assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing sea ice density data; the land assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing land surface temperature data; the atmosphere assimilation subsystem comprises a data processing and converting module for analyzing the atmosphere data and a data assimilation module. The climate system mode multi-circle layer coupling data assimilation system can include but is not limited to existing data, more observation data can be added for each climate system circle layer, such as satellite remote sensing soil humidity data, land surface temperature and soil humidity data observed by a meteorological station, satellite remote sensing sea surface salinity data, satellite remote sensing sea ice thickness data, atmospheric multi-source observation data and the like, and the performance of the assimilation system can be further improved through the continuously-increased observation data assimilation; may include, but is not limited to, existing algorithm modules, may be augmented with climate system mode component modules, such as introduction of atmospheric chemistry, geochemistry, and carbon cycle modules, upgrading the climate system mode to the earth system mode; more calculation modules can be added for each assimilation component, such as a deviation correction module for observation data, a quality control module and a complex assimilation algorithm module for original atmospheric observation data, a fusion module for multi-source data of each circle layer and the like, and the performance of the assimilation system can be further improved through the abundant algorithm modules. The assimilation system is a national climate center climate system mode multi-circle layer coupling data assimilation system, but the invention is not limited to the national climate center assimilation system.
Furthermore, the climate system mode (comprising sea, sea ice, land and atmospheric components) is coupled with the frame to assimilate the data of various sources of each component, so as to obtain a multi-circle multi-variable coordination assimilation analysis field.
Furthermore, the marine assimilation subsystem can realize the quality control (threshold value and extreme value inspection, spatial continuity inspection, data sparseness and the like) of the AVHRR satellite remote sensing sea surface temperature data and the AVISO satellite remote sensing sea surface height data, the quality control (record reliability inspection, threshold value and extreme value inspection, vertical gradient inspection, spatial continuity inspection and the like) of the ARGO, GTSPP, TAO and other marine temperature and salt profile observation data, and further complete the fusion and coordinated assimilation of the multi-source marine observation data.
Furthermore, the sea ice assimilation subsystem can realize the quality control (threshold value and extreme value check, space continuity check, data sparseness and the like) and data assimilation of the AVHRR satellite sea ice density data; the land assimilation subsystem can realize quality control (threshold and extreme value inspection, space continuity inspection, data sparseness and the like) and data assimilation of the satellite land surface temperature data such as MODIS, FY and the like; the atmospheric assimilation subsystem can realize data processing conversion (horizontal resolution conversion, vertical coordinate conversion and the like) and data assimilation of atmospheric analysis data such as NCEP-R1, NCEP-FNL, ERA-Interim and the like.
Further, the assimilation algorithm of the ocean component mode, the sea ice component mode, the land component mode and the atmospheric component mode can adopt relatively complex assimilation methods such as set Kalman filtering, set optimal interpolation, three-dimensional variation and optimal interpolation, and can also adopt a simple initialization method aiming at reanalysis data.
The climate system mode multi-turn layer coupling data assimilation system can be deployed on a high-performance computer, and the operation process comprises the following five steps:
first, data preprocessing
Decompressing data files, reading effective records and combining the observation data in an assimilation window aiming at ocean temperature and salt profile observation data such as ARGO, GTSPP, TAO and the like;
decompressing data files and reading observation data in a merging and assimilating window aiming at AVHRR satellite remote sensing sea surface temperature and sea ice intensity data, AVISO satellite remote sensing sea surface height data and MODIS or FY satellite land surface temperature data;
aiming at atmosphere analysis data such as NCEP-R1, NCEP-FNL, ERA-Interim and the like, unifying horizontal grids of the data to a mode resolution, and converting a vertical isobaric surface into a mixed coordinate surface of the mode;
second, data quality control
Aiming at ocean temperature and salt profile observation data such as ARGO, GTSPP, TAO and the like, the main control flow comprises the following steps: judging and rejecting repeated records; checking the observation time to ensure whether the measurement time in the observation record is sequentially increased; checking the observation depth to ensure whether the measurement depth in the observation record is sequentially increased; comparing the observed sea-land distribution with the mode sea-land distribution, and removing the measuring points which are possibly positioned on land; checking a climate threshold value and an extreme value, and rejecting records which obviously exceed the range of the observed quantity and extreme abnormal values with larger deviation compared with climate state data; checking vertical gradients, and eliminating unreasonable observation of vertical gradients of adjacent levels of thermohalites; respectively carrying out spatial continuity inspection on the temperature and salinity variables, screening all observations in a horizontal space, and removing observation points with obvious abnormality compared with the average value in the adjacent range;
aiming at AVHRR satellite remote sensing sea surface temperature and sea ice density data, AVISO satellite remote sensing sea surface height data and MODIS or FY satellite land surface temperature data, the main control flow comprises the following steps: thinning the data to a resolution equivalent to the mode; comparing the observed sea-land distribution with the mode sea-land distribution, and removing the measuring points which are possibly positioned on land; checking a climate threshold value and an extreme value, and rejecting records which obviously exceed the range of the observed quantity and extreme abnormal values with larger deviation compared with climate state data; carrying out space continuity check on each variable, screening all observations in a horizontal space, and removing observation points with obvious abnormality compared with the average value of the adjacent range;
in addition, for multiple observation data (such as satellite remote sensing sea surface temperature data and multiple ocean temperature and salt profile data) possibly related to a single assimilation component, data fusion is carried out, quality control steps such as repeated recording inspection and variable space continuity inspection are carried out on the fused data, and observation entering an assimilation system is guaranteed to be reliable;
third, data assimilation analysis
Aiming at the characteristics of each component assimilation observation data and analysis variable, the data assimilation under a coupling mode framework is realized based on various assimilation algorithms with different complexity degrees, and the method mainly comprises the following steps:
and realizing the coordination and assimilation of satellite sea surface temperature data, satellite sea surface height data and temperature and salt profile data based on a set optimal interpolation method. The set optimal interpolation method is essentially derived from a set Kalman filtering assimilation method, keeps the main advantages of the set Kalman filtering method, does not adopt a dynamic sample to estimate the background error covariance, estimates a background error covariance matrix through a static set sample, and has the characteristics of low calculation cost and convenient service use;
the assimilation of sea ice density data is realized based on an optimal interpolation method. The method adopts a parameterization mode to estimate the covariance of the background error, and adopts a local design to simplify assimilation analysis and calculation;
the assimilation of satellite terrestrial surface temperature data is realized based on an integrated Kalman filtering method. The method carries out assimilation analysis aiming at single-point variables on a land pattern grid, and estimates the covariance of background errors by utilizing dynamic samples generated by random disturbance;
the assimilation of atmosphere analysis data adopts a simple relaxation approximation method to directly adjust the atmospheric temperature, the wind field and the humidity variable field in a mode spectrum space;
integration of assimilation test
A data assimilation system (comprising a climate system mode and various assimilation subsystems) is operated on a high-performance computer, wherein the sea, sea ice and land assimilation subsystems assimilate observed data once a day, and the atmospheric assimilation subsystem assimilates data once every 6 hours and analyzes the data again. Information transmission among the assimilation subsystems is realized through a mode coupler, and the coupling frequency is once every half hour;
the integration time of the assimilation test is determined according to the requirement, long-term assimilation analysis tests of historical data for more than ten years can be carried out, and real-time data assimilation analysis tests for recent days can also be carried out;
fifth, assimilation result processing and evaluation
And after the assimilation test is finished, performing post-processing on the mode output data, wherein the post-processing comprises merging and arranging output files, extracting a key variable output field, converting the mode atmosphere vertical coordinate surface variable into a conventional isobaric surface variable, and the like. On the basis, the assimilation performance of the system is checked by comparing with other observed or reanalyzed data.
Fig. 2 shows the characteristics of the sea temperature Root Mean Square Error (RMSE) of the climate system mode multi-turn layer coupled data assimilation system (CDA) provided by the embodiment of the invention, compared with the last generation assimilation system (BCC-GODAS2) of the national climate center and the two sets of assimilation systems (SODA, GODAS) in the united states, along with the change of the depth. It can be seen that the error of the climate system mode multi-turn layer coupled data assimilation system of the embodiment of the invention is obviously smaller than the error of the other three assimilation systems.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention and the equivalent alternatives or modifications according to the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (5)
1. Many rings of layer coupling data assimilation systems of climate system mode, including climate system mode, ocean assimilation subsystem, sea ice assimilation subsystem, land assimilation subsystem and atmosphere assimilation subsystem, its characterized in that: the climate system modes include an ocean component mode, an sea ice component mode, a land component mode, an atmospheric component mode, and a coupler; the ocean assimilation subsystem comprises a quality control module, a data fusion module and a data assimilation module for satellite remote sensing ocean surface temperature data, satellite remote sensing ocean surface height data and ocean temperature and salt profile observation data; the sea ice assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing sea ice density data; the land assimilation subsystem comprises a quality control module and a data assimilation module for satellite remote sensing land surface temperature data; the atmosphere assimilation subsystem comprises a data processing and converting module for analyzing atmosphere data and a data assimilation module.
2. The climate system mode multi-turn coupled data assimilation system of claim 1, wherein: and assimilating the multiple source data of each component under the mode coupling framework of the climate system, thereby obtaining a multi-circle-layer multivariable coordination assimilation analysis field.
3. The climate system mode multi-turn coupled data assimilation system of claim 1, wherein: the marine assimilation subsystem can realize the quality control of AVHRR satellite remote sensing sea surface temperature data and AVISO satellite remote sensing sea surface height data and the quality control of ARGO, GTSPP, TAO and other marine temperature and salt profile observation data, and further complete the fusion and coordinated assimilation of multi-source marine observation data.
4. The climate system mode multi-turn coupled data assimilation system of claim 1, wherein: the sea ice assimilation subsystem can realize the quality control and the data assimilation of the sea ice concentration data of the AVHRR satellite; the land assimilation subsystem can realize quality control and data assimilation of land surface temperature data of satellites such as MODIS, FY and the like; the atmospheric assimilation subsystem can realize data processing conversion and data assimilation of atmospheric analysis data such as NCEP-R1, NCEP-FNL, ERA-Interim and the like.
5. The climate system mode multi-turn coupled data assimilation system of claim 1, wherein: the assimilation algorithm of the ocean component mode, the sea ice component mode, the land component mode and the atmospheric component mode can adopt relatively complex assimilation methods such as set Kalman filtering, set optimal interpolation, three-dimensional variation, optimal interpolation and the like, and can also adopt a simple initialization method aiming at reanalysis data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110735477.4A CN113407524A (en) | 2021-06-30 | 2021-06-30 | Climate system mode multi-circle layer coupling data assimilation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110735477.4A CN113407524A (en) | 2021-06-30 | 2021-06-30 | Climate system mode multi-circle layer coupling data assimilation system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113407524A true CN113407524A (en) | 2021-09-17 |
Family
ID=77680448
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110735477.4A Pending CN113407524A (en) | 2021-06-30 | 2021-06-30 | Climate system mode multi-circle layer coupling data assimilation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113407524A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114841442A (en) * | 2022-05-10 | 2022-08-02 | 中国科学院大气物理研究所 | Strong coupling method and system applied to atmosphere-ocean observation data |
| CN116304491A (en) * | 2023-05-11 | 2023-06-23 | 长江三峡集团实业发展(北京)有限公司 | Assimilation method and system for marine anomaly observation data |
| CN116597319A (en) * | 2023-04-19 | 2023-08-15 | 北京师范大学 | Sea ice daily data comprehensive processing method and device and computing equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120017337A (en) * | 2010-08-18 | 2012-02-28 | 한국해양연구원 | Formaton method of artificial satelite exploration data for data assimilation |
| CN109426886A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of climatic prediction system |
| CN110175214A (en) * | 2019-02-01 | 2019-08-27 | 中国空间技术研究院 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
| CN112819254A (en) * | 2021-03-05 | 2021-05-18 | 兰州大学 | Assimilation planet scale and machine learning external forcing climate mode prediction method |
-
2021
- 2021-06-30 CN CN202110735477.4A patent/CN113407524A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120017337A (en) * | 2010-08-18 | 2012-02-28 | 한국해양연구원 | Formaton method of artificial satelite exploration data for data assimilation |
| CN109426886A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of climatic prediction system |
| CN110175214A (en) * | 2019-02-01 | 2019-08-27 | 中国空间技术研究院 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
| CN112819254A (en) * | 2021-03-05 | 2021-05-18 | 兰州大学 | Assimilation planet scale and machine learning external forcing climate mode prediction method |
Non-Patent Citations (1)
| Title |
|---|
| 吴统文等: ""国家气候中心短期气候预测模式系统业务化进展"", 《应用气象学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114841442A (en) * | 2022-05-10 | 2022-08-02 | 中国科学院大气物理研究所 | Strong coupling method and system applied to atmosphere-ocean observation data |
| CN114841442B (en) * | 2022-05-10 | 2024-04-26 | 中国科学院大气物理研究所 | Strong coupling method and system applied to atmosphere-ocean observation data |
| CN116597319A (en) * | 2023-04-19 | 2023-08-15 | 北京师范大学 | Sea ice daily data comprehensive processing method and device and computing equipment |
| CN116597319B (en) * | 2023-04-19 | 2024-02-02 | 北京师范大学 | Sea ice daily data comprehensive processing method and device and computing equipment |
| CN116304491A (en) * | 2023-05-11 | 2023-06-23 | 长江三峡集团实业发展(北京)有限公司 | Assimilation method and system for marine anomaly observation data |
| CN116304491B (en) * | 2023-05-11 | 2023-08-08 | 长江三峡集团实业发展(北京)有限公司 | Assimilation method and system for marine anomaly observation data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113407524A (en) | Climate system mode multi-circle layer coupling data assimilation system | |
| Tilinina et al. | Comparing cyclone life cycle characteristics and their interannual variability in different reanalyses | |
| CN111723482B (en) | Satellite CO-based 2 Method for inverting surface carbon flux by column concentration observation | |
| Lucas et al. | An observational analysis of Southern Hemisphere tropical expansion | |
| CN113297527B (en) | PM based on multisource city big data 2.5 Overall domain space-time calculation inference method | |
| Kang et al. | Development of an observation processing package for data assimilation in KIAPS | |
| CN108319772B (en) | Wave long-term data reanalysis method | |
| CN115204618B (en) | CCMVS region carbon source sink equalization inversion method | |
| CN114707688A (en) | Photovoltaic power ultra-short-term prediction method based on satellite cloud chart and space-time neural network | |
| CN114510513A (en) | Short-term meteorological forecast data processing method for ultra-short-term photovoltaic power prediction | |
| CN111210483B (en) | Simulated satellite cloud picture generation method based on generation of countermeasure network and numerical mode product | |
| CN111401602A (en) | Assimilation method for satellite and ground rainfall measurement values based on neural network | |
| CN113108918B (en) | Method for inverting air temperature by using thermal infrared remote sensing data of polar-orbit meteorological satellite | |
| CN113536576B (en) | Method and system for correcting distance integral power statistics of numerical forecast product | |
| CN112308973A (en) | Three-dimensional ocean element field intelligent construction method and system based on machine learning | |
| He et al. | A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2 | |
| CN114970184B (en) | Synchronous inversion high-resolution artificial CO 2 Emission and natural CO 2 Flux assimilation method and system | |
| CN114595876A (en) | Regional wind field prediction model generation method and device and electronic equipment | |
| CN114118511A (en) | Large-area multi-satellite combined coverage effectiveness evaluation method based on cloud amount prediction information | |
| CN113984198A (en) | Short wave radiation prediction method and system based on convolutional neural network | |
| US20230127492A1 (en) | Imputation method for surface ultraviolet irradiance based on feasible cloud information and machine learning | |
| Rayner et al. | Evaluating climate variability and change from modern and historical SST observations | |
| CN116486931A (en) | Full-coverage atmospheric methane concentration data production method and system coupled with physical mechanism | |
| Leufen et al. | Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction | |
| CN114169215B (en) | Surface temperature inversion method coupling remote sensing and regional meteorological model |
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 |