CN104573378A - Method for optimizing land surface process mode - Google Patents
Method for optimizing land surface process mode Download PDFInfo
- Publication number
- CN104573378A CN104573378A CN201510033103.2A CN201510033103A CN104573378A CN 104573378 A CN104573378 A CN 104573378A CN 201510033103 A CN201510033103 A CN 201510033103A CN 104573378 A CN104573378 A CN 104573378A
- Authority
- CN
- China
- Prior art keywords
- surface process
- process parameterization
- data
- parameterization
- value
- 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.)
- Granted
Links
Landscapes
- Image Processing (AREA)
Abstract
The invention relates to a method for optimizing a land surface process mode and aims at solving the technical problem that the time consumed by a traditional data assimilation algorithm is long. The method comprises the following steps: S1 operating the land surface process mode and calculating a simulation value of each physical quantity in a land surface process; S2 correcting a simulated and assimilated physical quantity by a data assimilation method to obtain an assimilation value of a land surface variable; S3 constructing a target function and introducing the assimilation value into the land surface process mode; changing a parameter value of the land surface process mode to a minimum target function through a numerical value optimization method; repeating the steps S1 to S3 to obtain an optimal value time sequence of mode parameters; and S4 replacing original mode parameters by the optimal value time sequence of the mode parameters and improving the land surface process mode. According to the method, a land surface process mode parameterization scheme is improved from a physical mechanism so that the simulation precision of the mode is improved; high-precision mode output data are obtained by directly operating the improved land process mode and a year-by-year and day-by-day low-efficiency assimilation process is avoided.
Description
Technical field:
The present invention relates to satellite remote sensing field, particularly relate to a kind of method utilizing surface process parameterization to export data assimilation value optimization surface process parameterization.
Background technology:
Land surface process study mainly obtains face, land variable spatial and temporal distributions by Simulation and observation two kinds of means.Wherein, land surface emissivity refer to can affect weather and climate change occur in the process controlling momentum between ground vapour, heat and exchange of moisture in top (comprising biosphere) and soil; Face, land variable comprises the physical quantity such as earth's surface and root region soil moisture, temperature, flux of energy; For the method simulated and surface process parameterization.Due to the height heterogeneity of face, land variable and the restriction of land table observation condition, at present with observation or the means simulated all cannot obtain complete set, reliably, on a large scale, the land of long-term sequence shows variable spatial and temporal distributions data, can be applied in general circulation model and Using A Regional Climate Model.The coefficient that surface process parameterization parameter will use when being and using surface process parameterization to calculate face, land variable, such as in the holard Parameterization Scheme of one of surface process parameterization Parameterization Scheme, described surface process parameterization parameter comprises saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant etc.
Land data assimilation utilizes various observation data (comprising the ground routine observation, satellite remote sensing, radar data etc. of different spaces and temporal resolution) exactly, in conjunction with surface process parameterization and data assimilation algorithm, optimize and calculate face, land variable spatial and temporal distributions.Land surface models comprises data (observation data, surface process parameterization input data and surface process parameterization export data), predictor, Observation Operators, estimation of error and data assimilation algorithm etc.Described predictor is used for describing the mechanism of moisture, heat and momentum-exchange between ground vapour system, is generally surface process parameterization; Described Observation Operators is used for setting up the relation between surface process parameterization output data (hereinafter referred " output data ") and observation data, when observation data is consistent with the physical significance exporting data, Observation Operators can think interpolation algorithm, for an output interpolation of data to observation station; When observation data be remotely-sensed data, with export the physical significance of data inconsistent time, Observation Operators uses surface microwave radiative transfer model usually, will export data and be converted to the observed quantity (such as surface microwave Brightness temperature value) of satellite; Data assimilation algorithm couples predictor, Observation Operators, on the basis considering prediction error and observational error, utilize observation data to be optimized output data, realize exporting Data correction.In this instructions, the output data after correcting are called output data assimilation value.
But, existing Land data assimilation process by predictor and various Observation Operators separately the mode of error weighting realize optimizing, optimization just in a kind of mathematical meaning, do not improve the prediction ability of surface process parameterization self, in assimilation process, landside procedure schema Output rusults carries out timing needs to carry out a large amount of calculating, and efficiency is lower.
Summary of the invention:
The present invention be in order to solve traditional Land data assimilation technology landside procedure schema export data carry out timing need calculate in a large number, inefficient technical matters, proposes a kind of method optimizing surface process parameterization:
The method of optimization surface process parameterization of the present invention comprises the steps:
S1, utilize surface process parameterization to input data, run surface process parameterization, calculate the analogue value of face, land variable, generate surface process parameterization and export data;
Described surface process parameterization input packet is containing air force data and Land Surface Parameters;
S2, to correct described surface process parameterization with data assimilation algorithm and export data, obtain exporting data assimilation value;
S3, establishing target function, introduce in described surface process parameterization by described output data assimilation value, changes surface process parameterization parameter value make described target function value minimum by numerical optimization;
The surface process parameterization parameter that described target function value is corresponding time minimum, as surface process parameterization parameter optimization value;
S4, step S1 ~ S3 is repeated to (a certain year as the past) multiple period (each month as this year) in historical range, obtain surface process parameterization parameter optimization value time series; Replace original surface process parameterization parameter by described surface process parameterization parameter optimization value time series, form the surface process parameterization of Optimal Parameters scheme.
Further, the present invention uses the surface process parameterization of described Optimal Parameters scheme and intends the described surface process parameterization input data of research period, directly calculates the surface process parameterization intending the research period and exports data.
Described air force data comprise temperature near the ground, air pressure near the ground, surface air specific humidity, full blast near the ground speed, the shortwave radiation that faces down, the long-wave radiation that faces down, surface rainfall rate.
Described Land Surface Parameters comprises ground mulching type and shared ratio, soil texture ratio, leaf area index; The described soil texture refers to sand or clay.
Described step S1 comprises further: the process that runs up (Spin-up) of surface process parameterization: by surface process parameterization acquisition model equilibrium state described in the described surface process parameterization input data run intended before the research period; With described pattern equilibrium state for starting condition, by the described surface process parameterization input data intending the research period, run described surface process parameterization and calculate described surface process parameterization output data.
Preferably, described data assimilation algorithm comprises at least one in the variational method, ensemble Kalman filter algorithm, EKF filter algorithm, particle filter algorithm.
In described step S2, selectively, described data assimilation algorithm uses the deviation of face, land variables model data and face, land variable observation data to correct surface process parameterization and exports data.
In described step S2, selectively, described data assimilation algorithm uses the deviation of the satellite remote sensing date of simulation and the satellite remote sensing date of observation to correct surface process parameterization and exports data; The satellite remote sensing date of described simulation uses surface microwave radiative transfer model to export data to described surface process parameterization to calculate.
Preferably, in described step S3, described objective function is root-mean-square error function.
Preferably, in described step S3, described numerical optimization is complex hybrid evolutionary algorithm (SCE-UA).
The method of the invention optimizes the parameter value in surface process parameterization Parameterization Scheme, and from physical mechanism, Improvement and perfection surface process parameterization Parameterization Scheme, improves the forecast precision of surface process parameterization self.Compared with existing Land data assimilation technology, this method has evaded the problem of assimilating large, the consuming time length of calculated amount year by year, especially, when the LAND ATMOSPHERE COUPLING SYSTEM pattern utilizing surface process parameterization to be coupled with atmospherical model carries out climate simulation and prediction, the advantage that the method for the invention improves counting yield is more remarkable.The present invention can promote the application of remote sensing in earth system science and global change research due.
Accompanying drawing illustrates:
Fig. 1 is the method flow diagram optimizing surface process parameterization.
Fig. 2 is the embodiment that assimilation website observation data optimizes surface process parameterization.
Fig. 3 is the embodiment of the data-optimized surface process parameterization of assimilation satellite remote sensing observation.
Embodiment:
Below in conjunction with accompanying drawing, the embodiment of this method is described.
The present invention shown in Fig. 1 optimizes the process flow diagram of the method for surface process parameterization, comprises following steps:
S1, utilize surface process parameterization to input data, run surface process parameterization, calculate the analogue value of face, land variable, generate surface process parameterization and export data;
The computation process of step S1 referred to as:
Surface process parameterization exports data=surface process parameterization (parameter value, surface process parameterization input data) formula 1
In formula 1, described surface process parameterization input packet is containing air force data and Land Surface Parameters; Described surface process parameterization includes but not limited to common land model (CLM), variable under ooze ability mode (VIC), public land surface model (CoLM), simple biosphere pattern (SiB2), biosphere-propagation in atmosphere scheme (BATS) etc.; Described surface process parameterization exports the analogue value that data are face, land variablees, and face, the land variable that the holard Parameterization Scheme of such as surface process parameterization calculates is soil moisture;
The parameter value that described parameter value will use when being and using surface process parameterization to calculate face, land variable, the parameter that the holard Parameterization Scheme of such as surface process parameterization comprises has: saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant.
S2, to correct described surface process parameterization with data assimilation algorithm and export data, obtain exporting data assimilation value;
The computation process of step S2 referred to as:
Export data assimilation value=data assimilation algorithm (surface process parameterization exports data, observation data) formula 2
S3, establishing target function, described output data assimilation value is introduced described surface process parameterization, calculated by numerical optimization, changing surface process parameterization parameter value makes described objective function minimum, the surface process parameterization parameter that described target function value is corresponding time minimum, as surface process parameterization parameter optimization value;
The computation process of step S3 referred to as:
According to formula 1, due to
There is an optimal value in the parameter value that the surface process parameterization changed exports data=surface process parameterization (parameter value of change, surface process parameterization input data) then described change, makes:
Objective function (surface process parameterization of change exports data, exports data assimilation value)=MIN
S4, step S1 ~ S3 is repeated to (a certain year as the past) day part (each month as this year) in historical range, obtain surface process parameterization parameter optimization value time series; Replace original surface process parameterization parameter by described surface process parameterization parameter optimization value time series, form the surface process parameterization of Optimal Parameters scheme.
Fig. 2 represents that assimilation website observation data optimizes the embodiment of surface process parameterization, comprises following steps
S11, operation surface process parameterization, calculate the analogue value of face, land variable, generates surface process parameterization and export data.
Step S11 comprises further:
S111, according to studying a question the requirement of spatial and temporal resolution, prepare the air force data of certain spatial and temporal resolution, comprise temperature near the ground, air pressure near the ground, surface air specific humidity, full blast near the ground speed, the shortwave radiation that faces down, the long-wave radiation that faces down, surface rainfall rate;
S112, prepare the Land Surface Parameters of certain spatial and temporal resolution, comprise the ratio, leaf area index etc. of ground mulching type and shared ratio thereof, the soil texture (sand and clay), as pattern carries surface data collection, can the direct surface data collection that carries of using forestland, then do not need this step;
The process that runs up (Spin-up) of S113, surface process parameterization: utilize the air force data and Land Surface Parameters operation surface process parameterization of intending research period long-term sequence in the past, with acquisition model equilibrium state;
S114, with described pattern equilibrium state for starting condition, utilize and intend the research air force data of period and Land Surface Parameters drives surface process parameterization, calculate the analogue value of face, land variable.
S12, to correct described surface process parameterization with data assimilation algorithm and export data, obtain exporting data assimilation value.The time step observed there being website, website observation (as soil moisture) is assimilated with data assimilation algorithm (as the variational method, ensemble Kalman filter, EKF filter, particle filter scheduling algorithm), utilize the deviation between face, land variables model data and face, land variable observation data to export data to correct surface process parameterization, obtain the assimilation number intending assimilation face, land variable;
S13, establishing target function, introduce surface process parameterization by described output data assimilation value, changes surface process parameterization parameter value minimum to described objective function by numerical optimization.
Step S13 comprises further:
S131, establishing target function (as root-mean-square error function etc.), with the fitting degree of the analogue value and assimilation number that describe land surface state variable;
S132, to (a certain year as the past) specific time period (each month as this year) in historical range, each parameter value respectively in given surface process parameterization Parameterization Scheme (as holard Parameterization Scheme) is (as saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant etc.) feasible solution scope, surface process parameterization is run by the method for step S11, utilize optimized algorithm (as complex hybrid evolutionary algorithm SCE-UA etc.) minimization objective function, by iterating, pattern simulation value and assimilation number is made to reach best-fit by given objective function metric form, the optimum solution of each parameter value in day part is found within the scope of the feasible solution of each parameter value,
S14, replace original surface process parameterization parameter by described surface process parameterization parameter optimization value time series, form the surface process parameterization Parameterization Scheme improved.
Step S14 comprises further:
S141, repetition S11, S12, S13, until interested historical range (a certain year as the past) terminates, obtain each parameter (comprising saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant etc.) the optimal value time series in surface process parameterization Parameterization Scheme (such as holard Parameterization Scheme).
S142, utilize abovementioned steps to obtain surface process parameterization Parameterization Scheme (as holard Parameterization Scheme) in each parameter optimization value time series modified parameters scheme;
S15, the described land surface emissivity using the surface process parameterization of described modified parameters scheme and plan to study the period input data, directly calculate the described surface process parameterization output data intending the research period.
Fig. 3 represents that assimilation satellite remote sensing date optimizes the embodiment of surface process parameterization, comprises following steps:
S21, operation surface process parameterization, calculate the analogue value of face, land variable, export data as surface process parameterization.
Step S21 comprises further:
The requirement of the spatial and temporal resolution that S211, basis study a question, prepare the air force data of certain spatial and temporal resolution, comprise temperature near the ground, air pressure near the ground, surface air specific humidity, full blast near the ground speed, the shortwave radiation that faces down, face down long-wave radiation, surface rainfall rate etc.;
S212, prepare the Land Surface Parameters of certain spatial and temporal resolution, comprise the ratio, leaf area index etc. of ground mulching type and shared ratio thereof, the soil texture (sand and clay), as pattern carries surface data collection, can the direct surface data collection that carries of using forestland, then do not need this step;
The process that runs up (Spin-up) of S213, surface process parameterization: utilize the air force data and Land Surface Parameters operation surface process parameterization of intending research period long-term sequence in the past, with acquisition model equilibrium state;
S214, equilibrium state are in mode starting condition, utilize the air force data of intending the research period and Land Surface Parameters to drive surface process parameterization, calculate the analogue value of face, land variable.
S22, to correct described land surface emissivity with data assimilation algorithm and export data, obtain exporting data assimilation value.
Step S22 comprises further:
S221, export the input of data (as parameters such as surface soil water, surface temperature, the snow deposit degree of depth and canopy surface temperature and vegetation pattern, vegetation ratio, soil typess) as surface microwave radiative transfer model with surface process parameterization, run earth's surface microwave mode, generate the satellite remote sensing date of simulation;
S222, there iing the time step of satellite remote sensing observation, satellite remote sensing observation (as surface microwave Brightness temperature value) is assimilated with data assimilation algorithm (as the variational method, ensemble Kalman filter, EKF filter, particle filter scheduling algorithm), correct surface process parameterization based on the deviation between the satellite remote sensing date of simulating and the satellite remote sensing date of observation and export data, obtain the assimilation number of face, the land variable intending assimilation;
S23, establishing target function, introduced surface process parameterization by described output data assimilation value, calculated by numerical optimization, changes surface process parameterization parameter value minimum to described objective function.
Step S23 comprises further:
S231, establishing target function (as root-mean-square error function etc.), with the fitting degree of the analogue value and assimilation number that describe land surface state variable;
S232, to (a certain year as the past) multiple period (each month as this year) in historical range, each parameter respectively in given surface process parameterization Parameterization Scheme (as holard Parameterization Scheme) is (as saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant etc.) feasible solution scope, surface process parameterization is run by the method for step S21, utilize optimized algorithm (as complex hybrid evolutionary algorithm SCE-UA etc.) minimization objective function, by iterating, pattern simulation value and assimilation number is made to reach best-fit by given objective function metric form, the optimum solution of each parameter value in day part is found within the scope of the feasible solution of each parameter value,
S24, replace original surface process parameterization parameter by described surface process parameterization parameter optimization value time series, form the surface process parameterization Parameterization Scheme improved.
Step S24 comprises further:
S241, repetition S21, S22, S23, until interested historical range (a certain year as the past) terminates, can obtain each parameter (as saturated hydraulic conductivity, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant etc.) the optimal value time series in surface process parameterization Parameterization Scheme (as holard Parameterization Scheme).
S242, utilize abovementioned steps to obtain surface process parameterization Parameterization Scheme (as holard Parameterization Scheme) in each parameter optimization value time series modified parameters scheme;
S25, the described land surface emissivity using the surface process parameterization of described modified parameters scheme and plan to study the period input data, directly calculate and intend surface process parameterization output data described in the research period.
The satellite remote sensing observation used in above method is Low Frequency Ground microwave brightness temperature data.All satellite sensors that these type of data can be provided, such as be mounted in the Advanced Microwave scanning radiometer AMSR-E on NASA AQUA satellite, U.S. Nimbus series Nimbus-5, the 6 electrically scanning microwave radiometer ESMR carried, the multi-channel microwave radiometer SMMR of Nimbus-7, the extraordinary TRMMMi-crowave Imager SSM/I of U.S.'s defence weather satellite plan (DMSP), the TRMMMi-crowave Imager TMI of task TRMM lift-launch is measured in the tropical rainfall of the U.S. and Japan's associating, China's second generation polar orbiting meteorological satellite No. 3, wind and cloud (FY-3), soil moisture and Ocean Salinity satellite SMOS, soil moisture active-passive explorer satellite SMAP etc., the Low Frequency Ground microwave brightness temperature data provided may be used to the method to optimize surface process parameterization.The present invention is to optimize unsaturated soil water model, by development based on unsaturated soil water model and EKF filter algorithm and in conjunction with the soil moisture assimilation scheme of oozing capability model VIC under variable, take the moon as assimilation window, utilize optimized algorithm---complex hybrid evolutionary algorithm (SCE-UA) minimization objective function, the soil moisture of simulation and assimilation is made to reach best-fit by given objective function metric form, obtain assimilating period (i.e. interested historical range mentioned above) each parameter of unsaturated soil water model (saturated hydraulic conductivity in 1986, saturated soil humidity, the saturated soil flow of water, Clapp and Hornberger empirical constant) optimal value sequence, then each parameter optimization value time series of optimization is utilized to improve unsaturated soil water model, the model of improvement is finally utilized to carry out the numerical experiments of 1986-1993, result shows: optimize the parameter value in unsaturated soil water model, make unsaturated soil water model more perfect on physical mechanism, improve the analog capability of model self.This example provides example for utilizing Land data assimilation theory and the research of method Improvement and perfection surface process parameterization Parameterization Scheme, has good reference function.
Above embodiment is only for explaining method of the present invention, and be not limitation of the present invention, the those of ordinary skill of relevant technical field without departing from the spirit and scope of the present invention, can also make a variety of changes and be out of shape, such as use different surface process parameterization, different surface microwave radiative transfer models, different assimilation algorithms and different optimization methods to develop assimilation scheme, assimilate different website observation and satellite remote sensing observation.Therefore all equivalent technical schemes also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (10)
1. optimize a method for surface process parameterization, it is characterized in that, comprise the following steps:
S1, utilize surface process parameterization to input data, run surface process parameterization, calculate the analogue value of face, land variable, generate surface process parameterization and export data;
Described surface process parameterization input packet is containing air force data and Land Surface Parameters;
S2, to correct described surface process parameterization with data assimilation algorithm and export data, obtain exporting data assimilation value;
S3, establishing target function, introduce described surface process parameterization by described output data assimilation value, changes surface process parameterization parameter value make described target function value minimum by numerical optimization;
The surface process parameterization parameter that described target function value is corresponding time minimum, as surface process parameterization parameter optimization value;
S4, step S1 ~ S3 is repeated to multiple period in historical range, obtain surface process parameterization parameter optimization value time series; Replace original surface process parameterization parameter by described surface process parameterization parameter optimization value time series, form the surface process parameterization of Optimal Parameters scheme.
2. optimize the method for surface process parameterization as claimed in claim 1, it is characterized in that, also comprise following steps
Use the surface process parameterization of described Optimal Parameters scheme and intend the described surface process parameterization of research period and input data, directly calculate the described surface process parameterization intending the research period and export data.
3. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that,
Described air force data comprise temperature near the ground, air pressure near the ground, surface air specific humidity, full blast near the ground speed, the shortwave radiation that faces down, the long-wave radiation that faces down, surface rainfall rate.
4. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that,
Described Land Surface Parameters comprises ground mulching type and shared ratio, soil texture ratio, leaf area index; The described soil texture refers to sand or clay.
5. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that,
Described step S1 comprises further: by surface process parameterization acquisition model equilibrium state described in the described surface process parameterization input data run intended before the research period;
With described pattern equilibrium state for starting condition, by the described surface process parameterization input data intending the research period, run described surface process parameterization and calculate described surface process parameterization output data.
6. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that,
Described data assimilation algorithm comprises at least one in the variational method, ensemble Kalman filter algorithm, EKF filter algorithm, particle filter algorithm.
7. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that,
In described step S2, described data assimilation algorithm uses the deviation of face, land variables model data and face, land variable observation data to correct surface process parameterization and exports data.
8. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that, in described step S2,
Described data assimilation algorithm uses the deviation of the satellite remote sensing date of simulation and the satellite remote sensing date of observation to correct surface process parameterization and exports data;
The satellite remote sensing date of described simulation uses surface microwave radiative transfer model to export data to described surface process parameterization to calculate.
9. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that, in described step S3, described objective function is root-mean-square error function.
10. optimize the method for surface process parameterization as claimed in claim 1 or 2, it is characterized in that, in described step S3, described numerical optimization is complex hybrid evolutionary algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510033103.2A CN104573378B (en) | 2015-01-22 | 2015-01-22 | A kind of method for optimizing surface process parameterization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510033103.2A CN104573378B (en) | 2015-01-22 | 2015-01-22 | A kind of method for optimizing surface process parameterization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104573378A true CN104573378A (en) | 2015-04-29 |
| CN104573378B CN104573378B (en) | 2017-11-03 |
Family
ID=53089424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510033103.2A Expired - Fee Related CN104573378B (en) | 2015-01-22 | 2015-01-22 | A kind of method for optimizing surface process parameterization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104573378B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106546958A (en) * | 2016-11-02 | 2017-03-29 | 中国水利水电科学研究院 | A kind of radar data assimilation method of optimization |
| CN107256293A (en) * | 2017-05-24 | 2017-10-17 | 南京雨后地软环境技术有限公司 | A kind of climatic ecology system coupling modeling method |
-
2015
- 2015-01-22 CN CN201510033103.2A patent/CN104573378B/en not_active Expired - Fee Related
Non-Patent Citations (8)
| Title |
|---|
| SHENGLEI ZHANG ET AL.: "A Microwave Wetland Surface Emissivity Calibration Scheme Using SCE-UA Algorithm and AMSR-E Brightness Temperature Data", 《ELSEVIER》 * |
| XIANGJUN TIAN ET AL.: "A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature", 《JOURNAL OF GEOPHYSICAL RESEARCH》 * |
| XIANGJUN TIAN ET AL.: "A microwave land data assimilation system: Scheme and preliminary evaluation over China", 《JOURNAL OF GEOPHYSICAL RESEARCH》 * |
| 张生雷等: "基于土壤水模型及站点资料的土壤湿度同化方法", 《地球科学进展》 * |
| 张生雷等: "扩展Kalman 滤波在土壤湿度同化中的应用*", 《中国气象学会2006年年会"气候系统模式发展与应用"分会场论文集》 * |
| 李得勤等: "SCE-UA 算法优化土壤湿度方程中参数的性能研究", 《大气科学》 * |
| 李新: "陆地表层系统模拟和观测的不确定性及其控制", 《中国科学：地球科学》 * |
| 李红祺等: "条件非线性最优扰动方法在陆面过程模式参数优化中的扩展应用初探*", 《物理学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106546958A (en) * | 2016-11-02 | 2017-03-29 | 中国水利水电科学研究院 | A kind of radar data assimilation method of optimization |
| CN106546958B (en) * | 2016-11-02 | 2018-03-13 | 中国水利水电科学研究院 | A kind of radar data assimilation method of optimization |
| CN107256293A (en) * | 2017-05-24 | 2017-10-17 | 南京雨后地软环境技术有限公司 | A kind of climatic ecology system coupling modeling method |
| CN107256293B (en) * | 2017-05-24 | 2020-09-15 | 南京雨后地软环境技术有限公司 | Climate-ecosystem coupling modeling method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104573378B (en) | 2017-11-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106570627B (en) | Method for calculating crop irrigation water demand under future climate conditions | |
| Zhang et al. | Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China | |
| CN106951980B (en) | Reservoir group adaptive scheduling method based on RCP (remote control protocol) scene | |
| CN110751094B (en) | Crop yield estimation method based on GEE comprehensive remote sensing image and deep learning method | |
| CN102945508B (en) | Model correction based wind power forecasting method | |
| Stathopoulos et al. | Wind power prediction based on numerical and statistical models | |
| CN103020487B (en) | A kind of photovoltaic plant irradiance predicted value modification method | |
| CN102651096B (en) | Method for estimating yield of winter wheat by assimilating characteristics of leaf area index time-sequence curve | |
| CN102736596B (en) | Multi-scale greenhouse environment control system based on crop information fusion | |
| CN104521699A (en) | Field intelligent irrigation on-line control management method | |
| CN104134095A (en) | Crop yield estimation method based on scale transformation and data assimilation | |
| CN114510824B (en) | Construction method of regional scale evapotranspiration model synchronously considering dynamic changes of vegetation canopy and root system | |
| CN111461909B (en) | Short-term prediction method for farmland evapotranspiration | |
| CN111898258A (en) | Two-dimensional drought disaster assessment method driven by hydrologic cycle variation | |
| CN105321120A (en) | Assimilation evapotranspiration and LAI (leaf area index) region soil moisture monitoring method | |
| CN105588930A (en) | Method for measuring parameters of soil in greenhouse | |
| CN104143043B (en) | A kind of Multifunctional climate data capture method | |
| CN112257225A (en) | NPP calculation method suitable for alpine grassland ecosystem | |
| CN116362402B (en) | Irrigation system optimizing system based on weather forecast and phenotype information monitoring | |
| CN111460744B (en) | Land utilization planning method and planning system for inland river upstream region | |
| Zhang et al. | Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain | |
| CN104573378A (en) | Method for optimizing land surface process mode | |
| CN118095034B (en) | Hydrologic forecasting method and system based on hydrologic model and LSTM dual coupling under influence of water taking activity | |
| CN106355264B (en) | Reference crop evapotranspiration combined prediction method | |
| WANG et al. | Development of a decision support system for irrigation management to control groundwater withdrawal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171103 Termination date: 20190122 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |