CN107169258A - A kind of multi-source weather information data assimilation method and its application in rainfall forecast - Google Patents
A kind of multi-source weather information data assimilation method and its application in rainfall forecast Download PDFInfo
- Publication number
- CN107169258A CN107169258A CN201610576857.7A CN201610576857A CN107169258A CN 107169258 A CN107169258 A CN 107169258A CN 201610576857 A CN201610576857 A CN 201610576857A CN 107169258 A CN107169258 A CN 107169258A
- Authority
- CN
- China
- Prior art keywords
- data
- assimilation
- data assimilation
- weather information
- mrow
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
Landscapes
- Environmental & Geological Engineering (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Radar Systems Or Details Thereof (AREA)
- Image Processing (AREA)
Abstract
The present invention relates to a kind of multi-source weather information data assimilation method and its application in rainfall forecast, comprise the following steps：The selection of data assimilation scheme；The structure of multi-source weather information database；Multi-source weather information data assimilates；The determination of optimal assimilation data set.Merits and demerits angle of the present invention from different weather informations, there is provided a kind of multi-source weather information data assimilation method of standardization, the precision of rainfall forecast can not only be improved, largely simplify data assimilation process, and increase multi-source weather information can make up the deficiency of different meteorological datas, give full play to the advantage of Various types of data, make the result of data assimilation relatively reliable, rational data assimilation scheme is provided for relevant departments such as meteorology, water conservancies, with general applicability.
Description
Technical field
The present invention relates to numerical value rainfall forecast and data assimilation field, and in particular to a kind of multi-source weather information data
Assimilation method and its application in rainfall forecast.
Background technology
Catchment horizontal space yardstick very little, has randomness and sudden again, therefore numerical value rainfall forecast is numerical value
The difficult point of weather forecast, short heavy rain during especially for intensity great calendar, it is difficult to obtain the forecast result with certain precision.
Data assimilation be by not in the same time, the meteorological data of different type, separate sources, different resolution and ambient field
The data set of generation time, space and physics uniformity, in terms of numerical value air forecast precision is improved, particularly rainfall forecast,
There is very important effect.
The technology of assimilation single kind data is more ripe at present, and can judge the quality of its assimilation effect, but right
The situation that a variety of data are assimilated simultaneously is less, also without a kind of method weighed and choose assimilation data.This has data assimilation
There is significant limitations, it is impossible to embody the advantage of various data completely, make assimilation result more excellent.It is widely used at present and effect is preferable
Data assimilation method have three-dimensional variation data assimilation and Ensemble Kalman Filter method.
The essence of Three-dimensional Variational Data Assimilation is to solve for a situational variables so that one is weighed situational variables and ambient field and sight
The cost functional of distance reaches minimum between survey field.The cost functional can be expressed from the next：
Wherein X is the optimal solution of required Numerical Prediction Models original state；XbFor ambient field；B assists for background field error
Variance matrix；Y0For observation vector；H is Observation Operators, and pattern variable is projected into observation space by model space；R is observation
Error co-variance matrix, R=E+F, E is Instrument observation error co-variance matrix, and F is observation representive error covariance matrix,
Three-dimensional Variational Data Assimilation can embody the nonlinear restriction relation of complexity.Due to the Observation Operators of complexity can be used, thus more have
There is indirect or nonlinear correlation observational data beneficial to assimilation and pattern variable.
Ensemble Kalman Filter method solves the estimation of background error covariance matrix in practical application with the thought of set
The problem of with forecast difficulty, available for the data assimilation of nonlinear system, while reducing the amount of calculation of data assimilation.Set card
Kalman Filtering is comprising prediction and updates two steps：
(1) predict：
In formula,It is the state analysis value of i-th of set of k moment,It is k+1 moment status predication values, Mk,k+1It is
The k moment is to k+1 moment state change relations, wi,kIt is model error.
(2) update：
Wherein vi,k~N (0, Qk),
In formula,It is i-th
The individual state analysis value for being integrated into the k+1 moment, Kk+1It is gain matrix,It is the observation data at k+1 moment, Hk+1It is the k+1 moment
Observation Operators, vi,kIt is observation error,It is the assay value of all set,It is predicated error variance matrix,It is
Analyze field error variance matrix.
The content of the invention
The present invention devises a kind of multi-source weather information data assimilation method and its application in rainfall forecast, and synthesis is examined
The merits and demerits of routine observation data and non-conventional observation data is measured, its technical problem solved is how to choose not of the same race
The data of class, play multi-source weather information advantage, and the effect for assimilating multi-source data is more excellent, so as to improve the forecast essence of rainfall
Degree.
In order to solve above-mentioned technical problem, present invention employs following scheme：
A kind of multi-source weather information data assimilation method, including following steps：
The selection of step 1, data assimilation method；
Step 2, structure multi-source weather information database；
Step 3, to multi-source weather information carry out data assimilation；
Step 4, the optimal assimilation data set of determination.
Further, the preferably three-dimensional variation data assimilation method of the current application effect of data assimilation scheme selection in step 1
With ensemble Kalman filter assimilation method, data assimilation method set is formed.
Further, step 2 is from the American National Center for Atmospheric Research NCAR earth's surface observation data provided and souding upper-air observation
Data, band radar data, satellite remote sensing date, rocket sounding data, form weather information database.Wherein, NCAR is provided
Earth's surface observation data and souding upper-air observation data it is more accurate, but data volume is few, particularly East Asia Region；Band radar data are more smart
Really, it is raster data, a range of spatial data can be covered；Satellite remote sensing date is coverage maximum in all data
, but its resolution ratio is relatively poor, and precision is relatively low；Rocket sounding data are very accurate, but cost is high, and data volume is few.
Observation data eventually for data assimilation are rainfall data, and its unit is mm.But variety classes observational data
Original observation content it is different.It is that main meteorological will that NCAR earth's surfaces, which observe data and souding upper-air observation data, rocket sounding data,
The observation data of element, including：Air pressure (MPa), humidity (%rh), temperature (DEG C), precipitation (mm), wind speed (m/s)；Radar it is original
Observational data is reflectivity (cm2/m3) and radial velocity (m/s), after obtain rainfall data by data conversion；Satellite remote sensing
Original observational data is image, and image need to be interpreted, and obtains rainfall data.
Further, the data assimilation of step 3.1 is the 5 kinds of data provided for step 2, same using 2 kinds of data of step 1
Change method carries out data assimilation, forms 30 kinds of data assimilation schemes：
Although current data assimilation can assimilate to any appropriate data, assimilatory efficiency and effect are also required to examine
Consider.The species for assimilating data is more, and efficiency is lower, and the error in different pieces of information can make assimilation performance unstable, influence assimilation effect
Really.But assimilation data class is fewer, the representativeness of data is not strong, considers the assembled scheme from two class data.So, on
All it is that the combinations of two groups of data is assimilated in state form 11-30.
It is not that two kinds of data sums are assimilated when assimilating to a variety of data simultaneously, but in operation, according to
It is secondary that two kinds of data are assimilated, such as：Band radar data and satellite remote sensing date, are first carried out with radar data to initial data
Assimilation, then assimilate the result after radar data assimilation with satellite remote sensing date, obtain final assimilation data.
Further, step 3.2 judges the quality of each data assimilation scheme using the relative error of rainfall forecast：
In formula, αiFor the relative error of the rainfall forecast after i-th of data assimilation scheme assimilation, pi' it is that i-th of data is same
Forecast rainfall after the assimilation of change scheme, piFor corresponding actual measurement rainfall, i takes 1,2,3 ... ..., and 29,30.
Further, the overall relative error m and the entirety of Ensemble Kalman Filter method of step 3.3 contrast Three-dimensional Variational Data Assimilation
Relative error n：
If m<N, then the whole structure of Three-dimensional Variational Data Assimilation is better than Ensemble Kalman Filter method, if m>N, then gather Kalman
Filter method whole structure is better than Three-dimensional Variational Data Assimilation, if m=n, Three-dimensional Variational Data Assimilation is integrally imitated with Ensemble Kalman Filter method
It is really identical.
Further, in order that multi-source data assimilation scheme is easier, whole structure preferably assimilation method, such as three are chosen
Variational Assimilation is tieed up, then is taken in 15 Three-dimensional Variational Data Assimilation schemes, the assimilation data that 5 minimum schemes of relative error are used,
The optimal assimilation data set in the research area is constituted, carrying out data assimilation for rainfall forecast next time provides foundation.
Select the method for showing preferable 5 data assimilation schemes formation data set to be forecast and can be described as set in advance
Report.Rainfall forecast is carried out respectively based on this 5 data assimilation schemes, 5 rainfall forecast results can be finally obtained, due to forecast tool
There is very big uncertainty, it is impossible to which which result is closest to the rainfall of following generation certainly, therefore by this 5 rainfall forecasts
As a result all as forecast result, therefore DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM can evade the inaccurate risk of a certain forecast result to a certain extent.
A kind of forecasting procedure of rainfall, it is characterised in that：The use of above-mentioned optimal assimilation data set is that rainfall next time is pre-
Report carries out data assimilation and provides application.
This is used for multi-source weather information data assimilation method and its forecast applied to rainfall has the advantages that：
(1) there is provided a kind of multi-source gas of standardization from the merits and demerits angle of different weather informations by the present invention
Image information data assimilation method, data assimilation can not only improve the precision of rainfall forecast, and increase multi-source weather information can
To make up the deficiency of different meteorological datas, the advantage of Various types of data is given full play to, makes the result of data assimilation relatively reliable, for gas
As the relevant departments such as, water conservancy provide rational data assimilation scheme, with general applicability.
(2) present invention determines optimal assimilation data set for a certain research area, and multi-source data assimilation scheme can lift assimilation
Precision and stability, and optimum data collection is selected, it can largely simplify data assimilation process again.
Embodiment
With reference to embodiment, the present invention will be further described：
The technical solution adopted in the present invention is the merits and demerits based on routine observation data and non-conventional observation data,
A kind of multi-source weather information data assimilation method is proposed, is implemented according to following steps：
(1) selection of data assimilation method：Choose the preferably three-dimensional variation data assimilation of current application effect and set blocks
Kalman Filtering is assimilated.
(2) multi-source weather information database is built：The earth's surface observation provided from American National Center for Atmospheric Research NCAR
Data and souding upper-air observation data, band radar data, satellite remote sensing date, rocket sounding data, form weather information database.
(3) data assimilation is carried out to multi-source weather information：On the basis of (1), (2), 30 kinds of data assimilation methods are set up,
It is shown in Table 1.
The data assimilation scheme of table 1
The quality of each data assimilation scheme is judged using relative error：
In formula, αiFor the relative error of the rainfall forecast after i-th of data assimilation scheme assimilation, pi' it is that i-th of data is same
Forecast rainfall after the assimilation of change scheme, piFor corresponding actual measurement rainfall, i takes 1,2,3 ... ..., and 29,30.
(4) optimal assimilation data set is determined：Contrast the overall relative error m and set Kalman of Three-dimensional Variational Data Assimilation method
The overall relative error n of filter method：
If m<N, then the whole structure of Three-dimensional Variational Data Assimilation method is better than Ensemble Kalman Filter method, if m>N, then set blocks
Kalman Filtering method whole structure is better than Three-dimensional Variational Data Assimilation method, if m=n, Three-dimensional Variational Data Assimilation method and set Kalman
Filter method whole structure is identical.
Whole structure preferably assimilation method, such as Three-dimensional Variational Data Assimilation method are chosen, then takes 15 Three-dimensional Variational Data Assimilation sides
In method scheme, the assimilation data that 5 minimum schemes of relative error are used constitute the optimal assimilation data set in the research area,
Data assimilation is carried out for rainfall forecast next time, and foundation is provided.
Exemplary description is carried out to the present invention above in conjunction with embodiment, it is clear that realization of the invention is not by above-mentioned side
The limitation of formula, as long as employing the various improvement of inventive concept and technical scheme of the present invention progress, or not improved sends out this
Bright design and technical scheme directly applies to other occasions, within the scope of the present invention.
Claims (8)
1. a kind of multi-source weather information data assimilation method, including following steps：
The selection of step 1, data assimilation method；
Step 2, structure multi-source weather information database；
Step 3, to multi-source weather information carry out data assimilation；
Step 4, the optimal assimilation data set of determination.
2. multi-source weather information data assimilation method according to claim 1, it is characterised in that：Data assimilation in step 1
The preferably three-dimensional variation data assimilation of scheme selection application at present relatively wide and effect and ensemble Kalman filter assimilation method.
3. multi-source weather information data assimilation method according to claim 1 or claim 2, it is characterised in that：The gas in step 2
Image information database observes the souding upper-air observation number that data, NCAR are provided by the American National Center for Atmospheric Research NCAR earth's surfaces provided
According to, band radar data, satellite remote sensing date and rocket sounding data constitute.
4. data assimilation method is carried out to multi-source weather information according to claim 1,2 or 3, it is characterised in that:Step
3.1st, totally 30 kinds of the scheme of data assimilation is determined：
5. multi-source weather information data assimilation method according to claim 4, it is characterised in that：Step 3.2, judgment step
The quality of each data assimilation scheme uses relative error in 3.1:
In formula, αiFor the relative error of the rainfall forecast after i-th of data assimilation scheme assimilation, p 'iFor i-th of data assimilation side
Forecast rainfall after case assimilation, piFor corresponding actual measurement rainfall, i takes 1,2,3 ... ..., and 29,30.
6. multi-source weather information data assimilation method according to claim 5, it is characterised in that：Step 3.3, according to step
Relative error in 3.2, determines the excellent assimilation method of whole structure：
Calculate the overall relative error m and the overall relative error n of Ensemble Kalman Filter method of three-dimensional variation data assimilation method：
<mrow>
<mi>m</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>15</mn>
</munderover>
<msub>
<mi>&alpha;</mi>
<mrow>
<mn>2</mn>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
<mrow>
<mi>n</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>15</mn>
</munderover>
<msub>
<mi>&alpha;</mi>
<mrow>
<mn>2</mn>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
If m<N, then the whole structure of three-dimensional variation data assimilation method is better than Ensemble Kalman Filter method, if m>N, then set blocks
Kalman Filtering method whole structure is better than three-dimensional variation data assimilation method, if m=n, three-dimensional variation data assimilation method and collection
Close Kalman filtering method whole structure identical.
7. the multi-source weather information data assimilation method according to any one of claim 1-6, it is characterised in that：Step 4
It is the excellent assimilation method of whole structure determined according to step 3.3, in 15 three-dimensional variation data assimilation method schemes or 15
In Ensemble Kalman Filter method scheme, the assimilation data that 5 minimum schemes of relative error are used constitute the research area most
Good assimilation data set, carries out data assimilation for rainfall forecast next time and provides foundation, can improve the precision of rainfall forecast, and
Increase multi-source weather information can make up the deficiency of different meteorological datas.
8. a kind of forecasting procedure of rainfall, it is characterised in that：Under the optimal assimilation data set in usage right requirement 7 is
Rainfall forecast carries out data assimilation and provides application.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810829167.7A CN109063083B (en) | 2016-07-20 | 2016-07-20 | Multi-source meteorological information data assimilation method |
| CN201911195801.7A CN110927827B (en) | 2016-07-20 | 2016-07-20 | Data assimilation method applied to rainfall forecast |
| CN201610576857.7A CN107169258B (en) | 2016-07-20 | 2016-07-20 | A kind of multi-source weather information data assimilation method and its application in rainfall forecast |
| CN201810828271.4A CN109001845B (en) | 2016-07-20 | 2016-07-20 | rainfall forecasting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610576857.7A CN107169258B (en) | 2016-07-20 | 2016-07-20 | A kind of multi-source weather information data assimilation method and its application in rainfall forecast |
Related Child Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810828271.4A Division CN109001845B (en) | 2016-07-20 | 2016-07-20 | rainfall forecasting method |
| CN201911195801.7A Division CN110927827B (en) | 2016-07-20 | 2016-07-20 | Data assimilation method applied to rainfall forecast |
| CN201810829167.7A Division CN109063083B (en) | 2016-07-20 | 2016-07-20 | Multi-source meteorological information data assimilation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107169258A true CN107169258A (en) | 2017-09-15 |
| CN107169258B CN107169258B (en) | 2018-08-17 |
Family
ID=59848820
Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911195801.7A Active CN110927827B (en) | 2016-07-20 | 2016-07-20 | Data assimilation method applied to rainfall forecast |
| CN201810828271.4A Active CN109001845B (en) | 2016-07-20 | 2016-07-20 | rainfall forecasting method |
| CN201810829167.7A Active CN109063083B (en) | 2016-07-20 | 2016-07-20 | Multi-source meteorological information data assimilation method |
| CN201610576857.7A Active CN107169258B (en) | 2016-07-20 | 2016-07-20 | A kind of multi-source weather information data assimilation method and its application in rainfall forecast |
Family Applications Before (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911195801.7A Active CN110927827B (en) | 2016-07-20 | 2016-07-20 | Data assimilation method applied to rainfall forecast |
| CN201810828271.4A Active CN109001845B (en) | 2016-07-20 | 2016-07-20 | rainfall forecasting method |
| CN201810829167.7A Active CN109063083B (en) | 2016-07-20 | 2016-07-20 | Multi-source meteorological information data assimilation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (4) | CN110927827B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403073A (en) * | 2017-10-03 | 2017-11-28 | 中国水利水电科学研究院 | A kind of set Flood Forecasting Method that forecast rainfall is improved based on data assimilation |
| CN109212631A (en) * | 2018-09-19 | 2019-01-15 | 中国人民解放军国防科技大学 | Satellite observation data three-dimensional variation assimilation method considering channel correlation |
| CN109814179A (en) * | 2019-01-04 | 2019-05-28 | 南京信息工程大学 | A kind of emergency communication processing system based on cloud perception |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110927827B (en) * | 2016-07-20 | 2021-06-22 | 中国水利水电科学研究院 | Data assimilation method applied to rainfall forecast |
| CN110020462B (en) * | 2019-03-07 | 2023-04-07 | 江苏无线电厂有限公司 | Method for fusing meteorological data and generating numerical weather forecast |
| CN111090130B (en) * | 2020-02-12 | 2021-07-23 | 江苏省气象科学研究所 | Improved algorithm for radar-rain gauge joint precipitation estimation based on minimum functional boundary condition acquisition |
| CN111783361B (en) * | 2020-07-07 | 2021-03-12 | 中国人民解放军国防科技大学 | Numerical weather forecast mixed data assimilation method based on triple multi-layer perceptron |
| CN113406590B (en) * | 2021-06-11 | 2023-03-10 | 兰州大学 | Method for inhibiting false convection |
| CN113360854B (en) * | 2021-08-10 | 2021-11-05 | 中国人民解放军国防科技大学 | Data assimilation method based on adaptive covariance expansion |
| CN116631530B (en) * | 2023-05-29 | 2024-02-13 | 智感技术(天津)有限公司 | Pollutant diffusion risk identification method, device and equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101814117A (en) * | 2010-04-14 | 2010-08-25 | 北京师范大学 | Multi-source environment ecological information data assimilation method |
| US20150278154A1 (en) * | 2014-03-26 | 2015-10-01 | Korea Institute Of Atmospheric Prediction Systems | Method of transforming variables in variational data assimilation module using cubed-sphere grid based on spectral element method and hardware device performing the same |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4371934B2 (en) * | 2004-07-26 | 2009-11-25 | 株式会社東芝 | Weather prediction system and weather prediction method |
| US7558674B1 (en) * | 2006-04-24 | 2009-07-07 | Wsi, Corporation | Weather severity and characterization system |
| CN102221389B (en) * | 2011-04-11 | 2012-12-19 | 国家海洋信息中心 | Method for predicting tide-bound water level by combining statistical model and power model |
| CN104992057A (en) * | 2015-06-25 | 2015-10-21 | 南京信息工程大学 | Quasi-ensemble-variation based mixed data assimilation method |
| CN105069295B (en) * | 2015-08-10 | 2018-05-08 | 河海大学 | Satellite and surface precipitation measured value assimilation method based on Kalman filtering |
| CN105425319B (en) * | 2015-09-16 | 2017-10-13 | 河海大学 | Rainfall satellite heavy rain assimilation method based on ground survey Data correction |
| CN110927827B (en) * | 2016-07-20 | 2021-06-22 | 中国水利水电科学研究院 | Data assimilation method applied to rainfall forecast |
-
2016
- 2016-07-20 CN CN201911195801.7A patent/CN110927827B/en active Active
- 2016-07-20 CN CN201810828271.4A patent/CN109001845B/en active Active
- 2016-07-20 CN CN201810829167.7A patent/CN109063083B/en active Active
- 2016-07-20 CN CN201610576857.7A patent/CN107169258B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101814117A (en) * | 2010-04-14 | 2010-08-25 | 北京师范大学 | Multi-source environment ecological information data assimilation method |
| US20150278154A1 (en) * | 2014-03-26 | 2015-10-01 | Korea Institute Of Atmospheric Prediction Systems | Method of transforming variables in variational data assimilation module using cubed-sphere grid based on spectral element method and hardware device performing the same |
Non-Patent Citations (3)
| Title |
|---|
| 文小航等: "中国东北半干旱区能量水分循环的同化模拟", 《中国科学：地球科学》 * |
| 田济扬等: "中尺度数值大气模式WRF在水文气象领域的研究", 《南水北调与水利科技》 * |
| 马旭林等: "数值天气预报中集合-变分混合资料同化及其研究进展", 《热带气象学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403073A (en) * | 2017-10-03 | 2017-11-28 | 中国水利水电科学研究院 | A kind of set Flood Forecasting Method that forecast rainfall is improved based on data assimilation |
| CN107403073B (en) * | 2017-10-03 | 2020-07-21 | 中国水利水电科学研究院 | Integrated flood forecasting method for improving and forecasting rainfall based on data assimilation |
| CN109212631A (en) * | 2018-09-19 | 2019-01-15 | 中国人民解放军国防科技大学 | Satellite observation data three-dimensional variation assimilation method considering channel correlation |
| CN109212631B (en) * | 2018-09-19 | 2020-12-01 | 中国人民解放军国防科技大学 | Satellite observation data three-dimensional variation assimilation method considering channel correlation |
| CN109814179A (en) * | 2019-01-04 | 2019-05-28 | 南京信息工程大学 | A kind of emergency communication processing system based on cloud perception |
| CN109814179B (en) * | 2019-01-04 | 2021-01-12 | 南京信息工程大学 | Emergency communication processing system based on cloud perception |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107169258B (en) | 2018-08-17 |
| CN109063083B (en) | 2021-05-07 |
| CN110927827B (en) | 2021-06-22 |
| CN110927827A (en) | 2020-03-27 |
| CN109001845A (en) | 2018-12-14 |
| CN109063083A (en) | 2018-12-21 |
| CN109001845B (en) | 2019-12-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107169258B (en) | A kind of multi-source weather information data assimilation method and its application in rainfall forecast | |
| CN108227041B (en) | Horizontal visibility forecasting method based on site measured data and mode result | |
| CN102004856B (en) | Rapid collective Kalman filtering assimilating method for real-time data of high-frequency observation data | |
| CN106405682B (en) | A kind of precipitation predicting method and device | |
| CN103529189B (en) | A kind of soil organism space distribution Forecasting Methodology based on quantitative and qualitative analysis auxiliary variable | |
| CN105891833A (en) | Method of identifying warm cloud precipitation rate based on Doppler radar information | |
| US20160203245A1 (en) | Method for simulating wind field of extreme arid region based on wrf | |
| CN104063718B (en) | The method with selection remotely-sensed data and sorting algorithm in area reckoning is recognized in crop | |
| CN104992071B (en) | A kind of initial disturbance method based on pooling information assimilation technique | |
| CN109543356A (en) | Consider the ocean interior temperature-salinity structure remote sensing inversion method of Space atmosphere | |
| CN108981616B (en) | Method for inverting effective leaf area index of artificial forest by unmanned aerial vehicle laser radar | |
| CN103792524A (en) | Radar quantitative precipitation estimation method based on cloud classification | |
| CN108256546A (en) | Method and system for detecting height of atmospheric boundary layer under non-precipitation condition | |
| CN114254802B (en) | Prediction method for vegetation coverage space-time change under climate change drive | |
| CN113592132B (en) | Rainfall objective forecasting method based on numerical weather forecast and artificial intelligence | |
| CN105046046B (en) | A kind of Ensemble Kalman Filter localization method | |
| Squitieri et al. | On the forecast sensitivity of MCS cold pools and related features to horizontal grid spacing in convection-allowing WRF simulations | |
| CN109685334A (en) | A kind of new hydrological model simulation evaluation method based on Multiscale Theory | |
| CN113435630A (en) | Basin hydrological forecasting method and system with self-adaptive runoff yield mode | |
| CN113095586A (en) | Short-term multi-meteorological-element forecasting method based on deep neural network | |
| CN108764527A (en) | A kind of Soil organic carbon pool space-time dynamic prediction suitable environment Variable Selection method | |
| CN106772697B (en) | Sea of clouds natural landscape forecasting procedure and system | |
| CN114910980A (en) | Tropical cyclone gale wind circle forecasting method based on subjective path strength forecasting and parameterized wind field model | |
| CN105447593A (en) | Rapid updating mixing assimilation method based on time lag set | |
| CN109657988B (en) | Tobacco leaf quality partitioning method based on HASM and Euclidean distance algorithm |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |