CN109063083A - A kind of multi-source weather information data assimilation method - Google Patents
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Abstract
The present invention relates to a kind of multi-source weather information data assimilation methods, comprising the following steps: the selection of data assimilation scheme；The building of multi-source weather information database；The assimilation of multi-source weather information data；The determination of best assimilation data set.The present invention is from the advantages of different weather informations and disadvantage angle, provide a kind of standardized multi-source weather information data assimilation method, the precision of rainfall forecast not only can 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, keep the result of data assimilation relatively reliable, reasonable data assimilation scheme is provided for relevant departments such as meteorology, water conservancies, there is general applicability.
Description
Technical field
The present patent application is the applying date on 07 20th, 2016, application No. is: 201610576857.7, it is entitled " a kind of
The divisional application of the application for a patent for invention of multi-source weather information data assimilation method and its application in rainfall forecast ".This hair
It is bright to be related 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 technique
Catchment horizontal space scale very little, and there is randomness and sudden, therefore numerical value rainfall forecast is numerical value
The difficult point of weather forecast, short heavy rain when especially for intensity great calendar are difficult to obtain the forecast result with certain precision.
Data assimilation is by different moments, different type, separate sources, the meteorological data of different resolution and ambient field
The data set for generating time, space and physics consistency, in terms of improving numerical value atmosphere forecast precision, especially rainfall forecast,
There is very important effect.
The technology of assimilation single kind data have been relatively mature at present, and can judge the superiority and inferiority of its assimilation effect, but right
The case where a variety of data are assimilated simultaneously is less, also without a kind of method of measurement and selection assimilation data.This has data assimilation
There is significant limitations, the advantage of various data cannot be embodied completely, keeps assimilation result more excellent.It is widely used at present and effect is preferable
Data assimilation method have three-dimensional variation data assimilation and set Kalman filtering method.
The essence of Three-dimensional Variational Data Assimilation is to solve for a situational variables, so that a measurement situational variables and ambient field and sight
The cost functional for surveying distance between field reaches minimum.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 is background field error association
Variance matrix；Y0For observation vector；H is Observation Operators, and pattern variable is projected to observation space by model space；R is observation
Error co-variance matrix, R=E+F, E are Instrument observation error co-variance matrix, and F is observation representive error covariance matrix,
Three-dimensional Variational Data Assimilation can embody complicated nonlinear restriction relationship.Since complicated Observation Operators can be used, thus more have
There is indirect or nonlinear correlation observational data with pattern variable conducive to assimilation.
The thought of Ensemble Kalman Filter method set solves the estimation of background error covariance matrix in practical application
Difficult problem, can be used for the data assimilation of nonlinear system, while reducing the calculation amount of data assimilation with forecast.Set card
Kalman Filtering includes to predict and update two steps:
(1) it predicts:
In formula,It is the state analysis value of i-th of k moment set,It is k+1 moment status predication value, Mk,k+1It is
The k moment is to k+1 moment state change relationship, wi,kIt is model error.
(2) it updates:
Wherein vi,k~N (0, Qk), In formula,It is to be integrated into k i-th
The state analysis value at+1 moment, Kk+1It is gain matrix,It is the observation data at k+1 moment, Hk+1It is the observation calculation at k+1 moment
Son, vi,kIt is observation error,It is the assay value of all set,It is prediction varivance matrix,It is that analysis field is missed
Poor variance matrix.
Summary 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 advantages of having measured routine observation data and non-conventional observation data and disadvantage, solve the technical issues of be how to choose it is 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, 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 the following steps:
The selection of step 1, data assimilation method；
Step 2, building multi-source weather information database；
Step 3 carries out data assimilation to multi-source weather information；
Step 4 determines best assimilation data set.
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, surface observation data and souding upper-air observation that step 2 selects American National Center for Atmospheric Research NCAR to provide
Data, band radar data, satellite remote sensing date, rocket sounding data form weather information database.Wherein, NCAR is provided
Surface observation data and souding upper-air observation data it is more accurate, but data volume is few, especially East Asia Region；Band radar data are more smart
Really, it is raster data, a certain range of spatial data can be covered；Satellite remote sensing date is that coverage area is maximum in all data
, but its resolution ratio is relatively poor, and precision is lower；Rocket sounding data are very accurate, but at high cost, and data volume is few.
Observation data eventually for data assimilation are rainfall data, unit mm.But variety classes observational data
Original observation content it is different.NCAR surface observation data and souding upper-air observation data, rocket sounding data are that main meteorological is wanted
The observation data of element, comprising: 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 convert to obtain rainfall data by data；Satellite remote sensing
Original observational data is image, need to be interpreted to image, and rainfall data are obtained.
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 any appropriate data, assimilatory efficiency and effect are also required to examine
Consider.The type for assimilating data is more, and efficiency is lower, and the error in different data can make assimilation performance unstable, influences assimilation effect
Fruit.But assimilation data class is fewer, and data are not representative, and comprehensively considers the assembled scheme for selecting two class data.On so
State all is that the combinations of two groups of data is assimilated in 11-30 of table.
It is not that the sum of two kinds of data are assimilated simultaneously when assimilating to a variety of data, but in operation, according to
Secondary to assimilate to two kinds of data, such as: band radar data and satellite remote sensing date first carry out initial data with radar data
Assimilation, then with satellite remote sensing date assimilation radar data assimilation after as a result, obtaining final assimilation data.
Further, step 3.2 judges the superiority and inferiority of each data assimilation scheme using the relative error of rainfall forecast:
In formula, αiThe relative error of rainfall forecast after assimilating for i-th of data assimilation scheme, 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 entirety of whole the relative error m and Ensemble Kalman Filter method of step 3.3 comparison Three-dimensional Variational Data Assimilation
Relative error n:
If m<n, the overall effect of Three-dimensional Variational Data Assimilation gathers Kalman if m>n better than Ensemble Kalman Filter method
Filter method overall effect is better than Three-dimensional Variational Data Assimilation, if m=n, Three-dimensional Variational Data Assimilation is integrally imitated with Ensemble Kalman Filter method
Fruit is identical.
Further, in order to keep multi-source data assimilation scheme easier, overall effect preferably assimilation method, such as three are chosen
Variational Assimilation is tieed up, then is taken in 15 Three-dimensional Variational Data Assimilation schemes, assimilates data used by the smallest 5 schemes of relative error,
The best assimilation data set in the research area is constituted, data assimilation is carried out for rainfall forecast next time and foundation is provided.
Selecting the method for showing preferable 5 data assimilation schemes formation data set to be forecast can be described as set in advance
Report.Rainfall forecast is carried out respectively based on this 5 data assimilation schemes, and finally available 5 rainfall forecasts are as a result, since forecast has
There is very big uncertainty, which cannot affirm the result is that closest to the following rainfall occurred, therefore by this 5 rainfall forecasts
As a result it is all used as forecast result, therefore DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM can evade the risk of a certain forecast result inaccuracy to a certain extent.
A kind of forecasting procedure of rainfall, it is characterised in that: pre- for rainfall next time using above-mentioned best assimilation data set
Report carries out data assimilation and provides application.
This has the advantages that for multi-source weather information data assimilation method and its forecast applied to rainfall
(1) present invention provides a kind of standardized multi-source gas from the advantages of different weather informations and disadvantage angle
Image information data assimilation method, the precision of rainfall forecast not only can be improved in data assimilation, but also increasing 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, keeps the result of data assimilation relatively reliable, for gas
As the relevant departments such as, water conservancy provide reasonable data assimilation scheme, there is general applicability.
(2) present invention is that a certain research area has determined best assimilation data set, and multi-source data assimilation scheme can promote assimilation
Precision and stability, and optimum data collection is selected, and can largely simplify data assimilation process.
Specific embodiment
Below with reference to embodiment, the present invention will be further described:
The technical scheme adopted by the invention is that the advantages of being based on routine observation data and non-conventional observation data and disadvantage,
It proposes a kind of multi-source weather information data assimilation method, follows the steps below to implement:
(1) the preferably three-dimensional variation data assimilation of current application effect and set card the selection of data assimilation method: are chosen
Kalman Filtering assimilation.
(2) multi-source weather information database is constructed: the surface observation for selecting American National Center for Atmospheric Research NCAR to provide
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), establishes 30 kinds of data assimilation methods,
It is shown in Table 1.
1 data assimilation scheme of table
The superiority and inferiority of each data assimilation scheme is judged using relative error:
In formula, αiThe relative error of rainfall forecast after assimilating for i-th of data assimilation scheme, 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) best assimilation data set: the whole relative error m and set Kalman of comparison Three-dimensional Variational Data Assimilation method is determined
The whole relative error n of filter method:
If m<n, the overall effect of Three-dimensional Variational Data Assimilation method gathers card if m>n better than Ensemble Kalman Filter method
Kalman Filtering method overall effect is better than Three-dimensional Variational Data Assimilation method, if m=n, Three-dimensional Variational Data Assimilation method and set Kalman
Filter method overall effect is identical.
It chooses overall effect preferably assimilation method and then takes 15 Three-dimensional Variational Data Assimilation sides such as Three-dimensional Variational Data Assimilation method
In method scheme, assimilates data used by the smallest 5 schemes of relative error, constitutes the best assimilation data set in the research area,
Data assimilation is carried out for rainfall forecast next time, and foundation is provided.
Above in conjunction with embodiment, an exemplary description of the invention, it is clear that realization of the invention is not by above-mentioned side
The limitation of formula as long as using the various improvement that the inventive concept and technical scheme of the present invention carry out, or not improved is sent out this
Bright conception and technical scheme directly apply to other occasions, are within the scope of the invention.
Claims (3)
1. a kind of multi-source weather information data assimilation method, including the following steps:
The selection of step 1, data assimilation method；
Step 2, building multi-source weather information database；
Step 3 carries out data assimilation to multi-source weather information；
Step 3.1, totally 30 kinds of scheme for determining data assimilation:
It is not that the sum of two kinds of data are assimilated simultaneously when assimilating to a variety of data, it is but in operation, successively right
Two kinds of data are assimilated；
The superiority and inferiority of each data assimilation scheme uses relative error in step 3.2, judgment step 3.1:
In formula, αiThe relative error of rainfall forecast after assimilating for i-th of data assimilation scheme, pi' it is i-th of data assimilation side
Forecast rainfall after case assimilation, piFor corresponding actual measurement rainfall, i takes 1,2,3 ... ..., and 29,30；
Step 3.3, according to the relative error in step 3.2, determine the excellent assimilation method of overall effect:
Calculate the whole relative error m of three-dimensional variation data assimilation method and the whole relative error n of Ensemble Kalman Filter method:
If m<n, the overall effect of three-dimensional variation data assimilation method gathers card if m>n better than Ensemble Kalman Filter method
Kalman Filtering method overall effect is better than three-dimensional variation data assimilation method, if m=n, three-dimensional variation data assimilation method and collection
It is identical to close Kalman filtering method overall effect；
Step 4 determines best assimilation data set；Step 4 is the assimilation method excellent according to the determining overall effect of step 3.3,
In 15 three-dimensional variation data assimilation method schemes or 15 Ensemble Kalman Filter method schemes, the smallest 5 sides of relative error
Assimilate data used by case, constitute the best assimilation data set in the research area, carries out data assimilation for rainfall forecast next time
Foundation is provided, the precision of rainfall forecast can be improved, and increases multi-source weather information to make up different meteorological datas not
Foot.
2. multi-source weather information data assimilation method according to claim 1, it is characterised in that: the data assimilation in step 1
Scheme selection applies the preferably three-dimensional variation data assimilation of relatively wide and effect and Ensemble Kalman Filter assimilation method at present.
3. multi-source weather information data assimilation method according to claim 1, it is characterised in that: the meteorology in step 2
The souding upper-air observation number of surface observation data, NCAR offer that information database is provided by American National Center for Atmospheric Research NCAR
According to, band radar data, satellite remote sensing date and rocket sounding data constitute.
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| CN111783361A (en) * | 2020-07-07 | 2020-10-16 | 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 |
| CN113406590A (en) * | 2021-06-11 | 2021-09-17 | 兰州大学 | Method for inhibiting false convection |
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| 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 |
| 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 |
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| CN109063083B (en) | 2021-05-07 |
| CN107169258B (en) | 2018-08-17 |
| CN109001845B (en) | 2019-12-10 |
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