CN110968926A - Method for predicting atmospheric parameters based on improved background error covariance matrix - Google Patents

Method for predicting atmospheric parameters based on improved background error covariance matrix Download PDF

Info

Publication number
CN110968926A
CN110968926A CN201811147232.4A CN201811147232A CN110968926A CN 110968926 A CN110968926 A CN 110968926A CN 201811147232 A CN201811147232 A CN 201811147232A CN 110968926 A CN110968926 A CN 110968926A
Authority
CN
China
Prior art keywords
atmospheric
parameter
forecast
parameters
background error
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
Application number
CN201811147232.4A
Other languages
Chinese (zh)
Other versions
CN110968926B (en
Inventor
何杰颖
张升伟
董晓龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
National Space Science Center of CAS
Original Assignee
National Space Science Center of CAS
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by National Space Science Center of CAS filed Critical National Space Science Center of CAS
Priority to CN201811147232.4A priority Critical patent/CN110968926B/en
Publication of CN110968926A publication Critical patent/CN110968926A/en
Application granted granted Critical
Publication of CN110968926B publication Critical patent/CN110968926B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Algebra (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a method for predicting atmospheric parameters based on an improved background error covariance matrix, which comprises the following steps: step 1), inputting satellite observation data into a WRF model; outputting atmospheric parameter variables for weather forecast at a specified time and a specified spatial resolution grid point; step 2) calculating the variable variance of the same parameter and the covariance of different parameters in the atmospheric parameters; step 3) drawing a relation curve of the atmospheric parameter variable and the forecast duration, and obtaining a time period when the atmospheric parameter variable and the forecast duration have linear correlation according to the relation curve; step 4) constructing a background error covariance matrix, wherein main diagonal elements are the variation of the variance of the same parameter variable in the atmospheric parameters along with the forecast duration, and off-diagonal elements are the variation of the covariance of different parameter variables in the atmosphere along with the forecast duration; and 5) inputting the satellite observation data and the background error covariance matrix into a WRF (weighted round robin) assimilation model, so as to obtain a predicted value of the atmospheric parameter in the forecast duration.

Description

Method for predicting atmospheric parameters based on improved background error covariance matrix
Technical Field
The invention relates to the field of atmospheric prediction and data assimilation systems, in particular to a method for predicting atmospheric parameters based on an improved background error covariance matrix.
Background
The prior information in data assimilation systems is generally derived from predictions, and background error covariance is related to prediction values and true values, and affects the overall assimilation effect in the assimilation system, namely, the expected value of the difference between the two, namely, the product of the difference and the transpose of the difference between the two. A more accurate background error covariance matrix is beneficial to improving the data assimilation effect, because assimilation depends on prior knowledge, uncertainty of the prior knowledge is generally expressed by a probability density function and is generally simplified into a Gaussian form, and errors can be transmitted to a real-time observation information domain through a control variable or the variable, so that the balance of an iterative algorithm in an assimilation system is influenced. Static atmospheric variables are embedded in a background error covariance matrix, an assimilation process is influenced to a large extent, and the static matrix with large errors can amplify noise in an unmeasurable variable mode, so that the assimilation stability and applicability are influenced.
The methods commonly used at present are based on reanalysis data for 24 hours or 12 hours, and are not suitable for use in extreme weather assimilation systems that change rapidly for short periods of time. The state updating analysis method needs intensive measured data, and the aggregation method needs a single aggregation Kalman matrix or B matrix to disturb the model. In the WRFDA model developed by NCAR, control variables in different expression forms can be generated using an open-source executable file, thereby satisfying the operation of WRFDA, but up to now, this model has not taken deep consideration of hydrogel particles and is not suitable for extreme weather conditions with high-speed spatiotemporal changes.
Disclosure of Invention
The invention aims to solve the problems that all the background error covariance matrixes commonly used at present do not deeply predict the particle parameters of the water condensate in the atmosphere, are not suitable for an extreme weather assimilation system which rapidly changes for a short time, and cannot predict the extreme weather condition which changes at a high speed and in a space-time mode.
In order to achieve the above object, the present invention provides a method for improving a background error covariance matrix to predict atmospheric parameters, comprising:
step 1), inputting satellite observation data into a WRF model; outputting atmospheric parameter variables for weather forecast at a specified time and a specified spatial resolution grid point;
step 2) calculating the variable variance of the same parameter and the covariance of different parameters in the atmospheric parameters;
step 3) drawing a relation curve of the atmospheric parameter variable and the forecast duration, and obtaining a time period when the atmospheric parameter variable and the forecast duration have linear correlation according to the relation curve;
step 4) constructing a background error covariance matrix, wherein main diagonal elements are the variation of the variance of the same parameter variable in the atmospheric parameters along with the forecast duration, and off-diagonal elements are the variation of the covariance of different parameter variables in the atmosphere along with the forecast duration;
and 5) inputting the satellite observation data and the background error covariance matrix into a WRF (weighted round robin) assimilation model, so as to obtain a predicted value of the atmospheric parameter in the forecast duration.
As a modification of the above method, the step 1) includes:
step 1-1), 0, 6, 12 and 18 hours of reanalysis data are taken as background field data and input into a WRF model, and a regional lattice point is selected to be 15 kilometers;
step 1-2) outputting atmospheric parameters at a specified time and on a specified spatial resolution grid point, wherein the atmospheric parameters comprise thermodynamic parameters and water condensate parameters; the thermodynamic parameters include temperature and humidity, the water condensate parameters include cloud, rain, snow, ice, and aragonite particles;
step 1-3) setting atmospheric parameters of forecast samples
Figure BDA0001817069310000027
7, superscript-f as prediction samples, each one
Figure BDA0001817069310000028
Water condensate density including temperature, humidity and cloud, rain, snow, ice and aragonite particles 7 parameter values, where cloud, rain, snowDensity of hydraulics of ice and aragonite particles ρhThe value is obtained by the following steps:
step 1-3-1) setting the parameter mixing ratio of the water condensate as Qh，QhRespectively adopt Qcl、Qr、Qs、Qi、QgThe value of (d); wherein QclDenotes the cloud water mixing ratio, QrDenotes the cloud-rain mixture ratio, QsIndicating snow mix ratio, QiDenotes the ice mixing ratio, QgRepresents the mixing ratio of the aragonite particles;
step 1-3-2) calculating different hydrogel densities rho of cloud, rain, snow, ice and aragonite particlesh，ρhRespectively using rhocl，ρr，ρs，ρi，ρgTo represent the values of cloud density, rain density, snow density, ice density and shot density, respectively, in g/m3；
Figure BDA0001817069310000021
Wherein QvFor water-vapor mixing ratio, pvIs the water vapor density.
As a modification of the above method, the step 2) includes:
step 2-1) calculating background errors of k atmospheric parameters
Figure BDA0001817069310000022
Figure BDA0001817069310000023
Figure BDA0001817069310000024
A is the background error of the ith parameter variable in the atmospheric parameter at the time t and is expressed as an analysis sample; at is expressed as an increment of time,
Figure BDA0001817069310000025
indicating the ith atmospheric parameter at the initial momentThe sample is analyzed and the results of the analysis,
Figure BDA0001817069310000026
i is more than or equal to 1 and less than or equal to k as a forecast sample of the ith atmospheric parameter after the time delta t;
step 2-2) calculating the variance of the background error of the atmospheric parameter
Figure BDA0001817069310000031
Figure BDA0001817069310000032
Step 2-3) calculating covariance of background errors of different atmospheric parameters
Figure BDA0001817069310000033
Figure BDA0001817069310000034
Wherein j is more than or equal to 1 and less than or equal to k, and i is not equal to j.
As an improvement of the above method, the relationship curve of the atmospheric parameter variable and the forecast time in step 3) includes a relationship curve of a thermodynamic parameter variable and the forecast time and a relationship curve of a hydraulic condensate parameter variable and the forecast time.
As an improvement of the above method, said step 2) the variance and covariance are expressed as a function σ x of Δ ti 2(Δ t) and σ xixj(Δt)：
Figure BDA0001817069310000035
Figure BDA00018170693100000511
Where at is expressed as a forecast duration,
Figure BDA0001817069310000037
is an offset.
As a modification of the above method, the forecast time of the thermodynamic parameter variable in step 3) is 9 hours, and the forecast time of the hydraulic condensate parameter variable is 4.5 hours.
As an improvement of the above method, the change in the atmospheric parameter over the forecast duration follows brownian motion in time.
As an improvement of the above method, the background error covariance matrix in step 4) dynamically changes with the forecast duration, and the update speed is consistent with the update speed of the satellite transmission observation data.
As an improvement of the above method, the step 5) specifically includes:
step 5-1), inputting satellite observation data and a background error matrix to a WRF assimilation model to obtain a cost function j (x):
Figure BDA0001817069310000038
wherein x is an atmospheric parameter variable, xbThe method comprises the following steps of (1) obtaining atmospheric parameters of a known background field, y is satellite observation data, H is an observation operator, R is an observation error matrix, T is a transposition, and B is a background error covariance matrix;
and 5-2) deriving the cost function j (x), and converging the background error covariance matrix when j' (x) is 0 to obtain a predicted value of the atmospheric parameter.
The invention has the advantages that:
the method for predicting the atmospheric parameters by using the improved background error covariance matrix provided by the invention has the advantages that each atmospheric state parameter is converged within the prediction time by using the improved background error covariance matrix, the problem that the current general background error covariance matrix predicts the hydraulic substance parameters in a divergent state in a data assimilation mode is solved, and the method has a good effect in an atmospheric data assimilation system through verification.
Drawings
FIG. 1 is a diagram of WRF mode input and output of the prior art;
FIG. 2 is a diagram of data from the reanalysis of NCEP6 hours provided by the data research division of the national atmospheric research center;
FIG. 3 is a schematic diagram of a background error covariance matrix selection area of the present invention;
FIG. 4(a) is a diagram illustrating a background error covariance matrix data source according to the present invention;
FIG. 4(b) is a schematic diagram of the calculation of the background error covariance matrix data according to the present invention;
FIG. 5(a) is a schematic diagram showing the correlation between the parameters of the hydrogel of the present invention and the forecast time;
FIG. 5(b) is a schematic diagram illustrating the correlation between thermodynamic parameters and forecast time according to the present invention;
FIG. 6 is a flow chart of a method for predicting atmospheric parameters based on an improved background error covariance matrix according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The invention provides a method for improving a background error covariance matrix to predict atmospheric parameters, which aims at an observation data assimilation mode with high regional space-time resolution, inputs microsatellite observation data in the assimilation mode, has the time resolution of 10-15 minutes and the spatial resolution of 10km, and constantly calculates the increment between an observed brightness temperature value and a brightness temperature value simulated by an advanced radiation transmission mode by utilizing Kalman filtering.
The background error covariance matrix is a matrix which dynamically changes along with the forecast duration, is different from a static concept in a time window in the existing assimilation mode, and the updating speed of the background error covariance matrix is consistent with the input satellite observation data, so that the assimilation system can better realize compromise optimization of background errors and observation errors, and the data assimilation effect is improved.
The invention provides a method for predicting atmospheric parameters by improving a background error covariance matrix, wherein the background error covariance matrix is composed of variances and covariances of atmospheric temperature, humidity, cloud, rain, snow, ice and aragonite particles.
Reanalysis grid data of FNL, one of data sets provided by national environmental prediction center (NCEP) by national atmospheric research center data research division, was used as input, once every 6 hours (1 degree by 1 degree or 2.5 degrees by 2.5 degrees), and 32 layers were set from the ground surface to the high altitude, from 1000hpa (mb) to 10hpa (mb). The parameters comprise surface air pressure, sea surface air pressure, potential height, temperature, sea surface temperature, soil condition, ice coverage, relative humidity, u and v wind speeds, vertical movement, vorticity, ozone and other assimilated parameters, and high spatial and temporal resolution atmospheric parameters are output through a WRF model. Forecasting time interval is 15 minutes, and selecting area grid points as 15 kilometers; different area ranges and grid point sizes can be set according to requirements; by calculating the parameter characteristics of forecast data and reanalysis data with the same time difference, the change response of atmospheric thermodynamics and water condensate parameters along with time under extreme weather conditions is searched, and the change of each parameter in different time scale ranges is proved to follow the Brownian motion in time, see formulas (1) to (5), and then a background error covariance matrix is calculated through the relation.
First, the background error of k atmospheric parameters is calculated
Figure BDA0001817069310000051
1≤i≤k：
Figure BDA0001817069310000052
Figure BDA0001817069310000053
For the background error of the ith parameter variable in the atmospheric parameters at the time t, the superscript f is expressed as a forecast sample, and a is expressed as an analysis sample; at is expressed as an increment of time,
Figure BDA0001817069310000054
an analysis sample representing the ith atmospheric parameter at the initial time,
Figure BDA0001817069310000055
a forecast sample of the ith atmospheric parameter after the time delta t;
secondly, calculating the variance of the background error of the atmospheric parameter
Figure BDA0001817069310000056
Figure BDA0001817069310000056
1≤i≤k：
Figure BDA0001817069310000057
Thirdly, calculating the covariance of background errors of different atmospheric parameters
Figure BDA0001817069310000058
Figure BDA0001817069310000058
1≤i≤k,1≤j≤k,i≠j：
Figure BDA0001817069310000059
The variance and covariance are expressed as a function σ x of Δ t according to the above formulai 2(Δ t) and σ xixj(Δt)：
Figure BDA00018170693100000510
Figure BDA00018170693100000511
Where at, expressed as the forecast duration, is an integer multiple of at,
Figure BDA00018170693100000512
is an offset;
Δt＝15mins (6)
and obtaining linear correlation between the atmospheric parameter variable and the forecast time length, wherein the background error variance and the covariance are reduced along with the shortening of the time increment delta t.
As shown in fig. 1, 6 hours per world 1 of useoUsing the grid reanalysis data FNL as an initial field driving weather forecast mode WRF to obtain weather forecasts on grid points at specified time and specified spatial resolution, and extracting the cloud and rain such as temperature, humidity, air pressure, water vapor content and the like output by forecastingThe atmospheric environment data is used as a data source of the background error covariance matrix, and the data can be directly extracted according to the latitude and longitude and the height of the storage area.
As shown in table 1:
TABLE 1
Figure BDA0001817069310000061
As shown in FIG. 3, the selected area is an Atlantic area frequently issued by hurricanes, the Domain is 25-35 degrees N, -70-60 degrees W, the time span is 2015-2017 years, 10 typical typhoons are covered, and the data is an FNL atmospheric parameter data set issued by initial re-analysis data NCEP all over the world.
As shown in fig. 4(a), using the WRF mode, in combination with the ambient field data of 0, 6, 12, and 18 hours, the atmospheric parameters of 9 hours in the future are predicted, and the time interval for inputting the ambient field data is 15 minutes. And extracting the atmospheric parameters required in the background error covariance matrix aiming at the output data, and performing correlation calculation, wherein:
water condensate parameters include density parameters of cloud, rain, snow, ice, and shot particles;
hydrogel parameter mixing ratio (Q)h) Respectively by Qcl、Qr、Qs、Qi、QgRepresents; wherein QcDenotes the cloud water mixing ratio, QrDenotes the cloud-rain mixture ratio, QsIndicating snow mix ratio, QiDenotes the ice mixing ratio, QgThe mixing ratio of the aragonite particles is shown.
The concrete transformation and extraction formula is that referring to figure 4(b) and formulas (7), (8) and (9), the hydrogel density rhohThe unit is g/m3，ρhRespectively using rhocl，ρr，ρs，ρi，ρgThe value of (d);
Figure BDA0001817069310000062
Figure BDA0001817069310000071
Figure BDA0001817069310000072
here, MhAny hydrogel mass per unit volume V of (a);
Mddry air mass per unit volume V;
Mvmass of water vapor per unit volume V.
As shown in fig. 4(a), the initial field is selected for 3 days at 4 moments each day, the forecast time interval is 15 minutes, the total forecast duration is 9 hours, and there are 36 forecast times, and each forecast time has 12 forecast samples, which serve as a basic data source for verifying brownian operation rules.
As shown in fig. 5, water condensate parameters such as Cloud (Cloud), Rain (Rain), Snow (Snow), Ice (Ice) and aragonite (Graupel), and thermodynamic parameters such as atmospheric Temperature (Temperature) and water Vapor (Vapor), all have different degrees of linear relationship with the duration of the forecast. The abscissa is the forecast time, t-1 represents the first forecast time, t-2 represents the second forecast time, and so on, and the ordinate is the variance. Therefore, the correlation between the water condensate parameter and the forecast time is stronger, the quasi-linear relation gradually weakens and disappears along with the increase of the time, the background field and the observation field of the whole system are integrated, the forecast time of the water condensate parameter is set to be 4.5 hours, and the thermodynamic parameter can meet the quasi-linear relation within 9 hours.
The improved background error covariance matrix provided by the invention utilizes a WRF mode, based on forecast field data and forecast duration of different initial times, and takes 15 minutes as a time interval to analyze the Brownian motion rule of atmospheric parameters, and utilizes the rule to calculate parameter variance and covariance in the background error matrix, namely each element of the matrix, so as to obtain a background error covariance matrix which dynamically changes along with the input of satellite observation data, wherein the background error covariance matrix is a square matrix, a symmetric matrix and a positive matrix, the diagonal is not 0, and the dimension is equal to the variable number.
As shown in table 2, the main diagonal element is the variation of the atmospheric parameter variance with the duration of the forecast, i.e. the variance shown in the table, and the off-diagonal element is the variation of the covariance of the relevant element with the duration of the forecast, i.e. the covariance shown in the diagonal, where both the variance and the covariance are functions of time and satisfy the brownian theory.
The Brownian motion analyzes the variation of the atmospheric parameter covariance and covariance of the same forecast time length along with the forecast time length according to different starting time and forecast time, so as to analyze the relation between the background error covariance matrix and the forecast time length.
The background error covariance matrix is applied to a satellite microwave assimilation system, a group of background error covariance matrices with smaller errors with the actual atmospheric state are provided every 15 minutes, and the forecasting errors are closer to the true values under the condition that the observation errors are not changed, so that the atmospheric parameters converged within the forecasting time length are obtained, namely the atmospheric parameters are more consistent with atmospheric thermodynamics and water condensate parameters.
TABLE 2
Figure BDA0001817069310000081
As shown in fig. 6, a flow of a method for predicting atmospheric parameters based on an improved background error covariance matrix according to the present invention is provided, which includes:
step 1), inputting satellite observation data into a WRF model; outputting atmospheric parameter weather forecast on a specified time and a specified spatial resolution grid point;
step 2) extracting atmospheric parameters output by weather forecast, and calculating to obtain parameter variable variance and covariance of the hydraulics;
step 3) taking the appointed time as a horizontal coordinate and the variance of the parameter variable of the hydrogel as a vertical coordinate, and drawing a relation curve of atmospheric thermodynamics and the parameter variable of the hydrogel and the forecast duration;
step 4) obtaining a relation curve, wherein the variance and covariance of atmospheric water condensate parameters are functions of time in a certain time period, and the changes of the atmospheric water condensate parameters follow Brownian motion in time in different time scale ranges; thereby obtaining a certain time period when atmospheric thermodynamics and hydraulic condensate parameter variables have linear correlation with the forecast duration;
step 5) taking the atmospheric water condensate parameter variances at different moments in a certain time period with linear correlation as main diagonal elements, and constructing a background error covariance matrix by taking the covariance of the relevant elements as off-diagonal elements;
and 6) applying the background error covariance matrix in a satellite microwave assimilation system, providing a group of background error covariance matrices with smaller errors with the actual atmospheric state every 15 minutes, and under the condition that the observation errors are not changed, enabling the difference between the predicted value and the true value to meet the local minimum, thereby obtaining the atmospheric parameters converged within the prediction duration, and enabling the cost function to be minimum, namely, the atmospheric thermodynamics and the hydraulic condensate parameters to be more consistent.
The step 6) cost function can be expressed as:
Figure BDA0001817069310000082
the right side of the equation consists of two parts, and a background error and an observation error jointly determine a cost function; in the actual atmospheric parameter forecasting process, a background error matrix and observation data are brought in, and derivation operation is carried out on the background error matrix and the observation data according to a cost function formula;
in the formula, x is an atmospheric state variable, xbIs an atmospheric state parameter of a known background field, y is satellite observation data, H is an observation operator, R-1Is the observation error matrix and B is the error covariance matrix. And (4) deriving a cost function formula j (x), and converging the background error covariance matrix when j' (x) is 0 to obtain a predicted value of the atmospheric state parameter.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (9)

1. A method for predicting atmospheric parameters based on an improved background error covariance matrix, comprising:
step 1), inputting satellite observation data into a WRF model; outputting atmospheric parameter variables for weather forecast at a specified time and a specified spatial resolution grid point;
step 2) calculating the variable variance of the same parameter and the covariance of different parameters in the atmospheric parameters;
step 3) drawing a relation curve of the atmospheric parameter variable and the forecast duration, and obtaining a time period when the atmospheric parameter variable and the forecast duration have linear correlation according to the relation curve;
step 4) constructing a background error covariance matrix, wherein main diagonal elements are the variation of the variance of the same parameter variable in the atmospheric parameters along with the forecast duration, and off-diagonal elements are the variation of the covariance of different parameter variables in the atmosphere along with the forecast duration;
and 5) inputting the satellite observation data and the background error covariance matrix into a WRF (weighted round robin) assimilation model, so as to obtain a predicted value of the atmospheric parameter in the forecast duration.
2. The method of claim 1, wherein the step 1) comprises:
step 1-1), 0, 6, 12 and 18 hours of reanalysis data are taken as background field data and input into a WRF model, and a regional lattice point is selected to be 15 kilometers;
step 1-2) outputting atmospheric parameters at a specified time and on a specified spatial resolution grid point, wherein the atmospheric parameters comprise thermodynamic parameters and water condensate parameters; the thermodynamic parameters include temperature and humidity, the water condensate parameters include cloud, rain, snow, ice, and aragonite particles;
step 1-3) setting atmospheric parameters of forecast samples
Figure FDA0001817069300000011
Superscript-f is expressed as forecast samples, each
Figure FDA0001817069300000012
Including temperature, humidity and water condensate density of cloud, rain, snow, ice and aragonite particles of 7 parameter values, where the water condensate density ρ of the cloud, rain, snow, ice and aragonite particleshThe value is obtained by the following steps:
step 1-3-1) setting the parameter mixing ratio of the water condensate as Qh，QhRespectively adopt Qcl、Qr、Qs、Qi、QgThe value of (d); wherein QclDenotes the cloud water mixing ratio, QrDenotes the cloud-rain mixture ratio, QsIndicating snow mix ratio, QiDenotes the ice mixing ratio, QgRepresents the mixing ratio of the aragonite particles;
step 1-3-2) calculating different hydrogel densities rho of cloud, rain, snow, ice and aragonite particlesh，ρhRespectively using rhocl，ρr，ρs，ρi，ρgTo represent the values of cloud density, rain density, snow density, ice density and shot density, respectively, in g/m3；
Figure FDA0001817069300000013
Wherein QvFor water-vapor mixing ratio, pvIs the water vapor density.
3. The method for predicting atmospheric parameters based on the improved background error covariance matrix of claim 1, wherein the step 2) comprises:
step 2-1) calculating background errors of k atmospheric parameters
Figure FDA0001817069300000021
Figure FDA0001817069300000022
Figure FDA0001817069300000023
A is the background error of the ith parameter variable in the atmospheric parameter at the time t and is expressed as an analysis sample; at is expressed as an increment of time,
Figure FDA0001817069300000024
an analysis sample representing the ith atmospheric parameter at the initial time,
Figure FDA0001817069300000025
i is more than or equal to 1 and less than or equal to k as a forecast sample of the ith atmospheric parameter after the time delta t;
step 2-2) calculating the variance of the background error of the atmospheric parameter
Figure FDA0001817069300000026
Figure FDA0001817069300000027
Step 2-3) calculating covariance of background errors of different atmospheric parameters
Figure FDA0001817069300000028
Figure FDA0001817069300000029
Wherein j is more than or equal to 1 and less than or equal to k, and i is not equal to j.
4. The method according to claim 3, wherein the relationship between the atmospheric parameter variable and the forecast time of step 3) comprises a relationship between a thermodynamic parameter variable and the forecast time and a relationship between a hydrogel parameter variable and the forecast time.
5. The method of claim 4, wherein the method further comprises predicting the atmospheric parameter based on the improved background error covariance matrixIn that said step 2) variance and covariance are expressed as a function σ x of Δ ti 2(Δ t) and σ xixj(Δt)：
Figure FDA00018170693000000210
Figure DEST_PATH_BDA00018170693100000511
Where at is expressed as a forecast duration,
Figure FDA00018170693000000212
is an offset.
6. The method for predicting atmospheric parameters based on the improved background error covariance matrix of claim 4, wherein the forecast duration of the thermodynamic parameter variables of step 3) is 9 hours, and the forecast duration of the hydraulic parameter variables is 4.5 hours.
7. The improved background error covariance matrix-based prediction of atmospheric parameters of claim 6, wherein changes in the atmospheric parameters over a forecasted duration follow brownian motion in time.
8. The method for predicting atmospheric parameters based on the improved background error covariance matrix of claim 1, wherein the background error covariance matrix of step 4) is dynamically changed with the forecast duration at a rate consistent with the update rate of the satellite transmitted observation data.
9. The method for predicting atmospheric parameters based on the improved background error covariance matrix according to any one of claims 1-8, wherein the step 5) comprises:
step 5-1), inputting satellite observation data and a background error matrix to a WRF assimilation model to obtain a cost function j (x):
Figure FDA0001817069300000031
wherein x is an atmospheric parameter variable, xbThe method comprises the following steps of (1) obtaining atmospheric parameters of a known background field, y is satellite observation data, H is an observation operator, R is an observation error matrix, T is a transposition, and B is a background error covariance matrix;
and 5-2) deriving the cost function j (x), and converging the background error covariance matrix when j' (x) is 0 to obtain a predicted value of the atmospheric parameter.
CN201811147232.4A 2018-09-29 2018-09-29 Method for predicting atmospheric parameters based on improved background error covariance matrix Active CN110968926B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811147232.4A CN110968926B (en) 2018-09-29 2018-09-29 Method for predicting atmospheric parameters based on improved background error covariance matrix

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811147232.4A CN110968926B (en) 2018-09-29 2018-09-29 Method for predicting atmospheric parameters based on improved background error covariance matrix

Publications (2)

Publication Number Publication Date
CN110968926A true CN110968926A (en) 2020-04-07
CN110968926B CN110968926B (en) 2023-05-02

Family

ID=70027269

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811147232.4A Active CN110968926B (en) 2018-09-29 2018-09-29 Method for predicting atmospheric parameters based on improved background error covariance matrix

Country Status (1)

Country Link
CN (1) CN110968926B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111881590A (en) * 2020-07-30 2020-11-03 中国科学院空天信息创新研究院 Spatial analysis method for concentration of atmospheric particulate matter
CN114048433A (en) * 2021-10-26 2022-02-15 南京大学 Mixed assimilation system and method based on ensemble Kalman filtering framework
CN116975523A (en) * 2023-09-22 2023-10-31 南京气象科技创新研究院 Data assimilation background error covariance characteristic statistical method for strong convection weather typing

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008157745A (en) * 2006-12-22 2008-07-10 Central Res Inst Of Electric Power Ind Method and program for predicting snow accretion
CN106339568A (en) * 2015-07-08 2017-01-18 中国电力科学研究院 Numerical weather prediction method based on mixed ambient field
CN107273995A (en) * 2016-04-08 2017-10-20 株式会社日立制作所 Urban Air Pollution Methods
CN107991722A (en) * 2017-12-25 2018-05-04 北京墨迹风云科技股份有限公司 Method for building up, Forecasting Methodology and the prediction meanss of weather prediction model

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008157745A (en) * 2006-12-22 2008-07-10 Central Res Inst Of Electric Power Ind Method and program for predicting snow accretion
CN106339568A (en) * 2015-07-08 2017-01-18 中国电力科学研究院 Numerical weather prediction method based on mixed ambient field
CN107273995A (en) * 2016-04-08 2017-10-20 株式会社日立制作所 Urban Air Pollution Methods
CN107991722A (en) * 2017-12-25 2018-05-04 北京墨迹风云科技股份有限公司 Method for building up, Forecasting Methodology and the prediction meanss of weather prediction model

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111881590A (en) * 2020-07-30 2020-11-03 中国科学院空天信息创新研究院 Spatial analysis method for concentration of atmospheric particulate matter
CN114048433A (en) * 2021-10-26 2022-02-15 南京大学 Mixed assimilation system and method based on ensemble Kalman filtering framework
CN114048433B (en) * 2021-10-26 2022-06-21 南京大学 Mixed assimilation system and method based on ensemble Kalman filtering framework
CN116975523A (en) * 2023-09-22 2023-10-31 南京气象科技创新研究院 Data assimilation background error covariance characteristic statistical method for strong convection weather typing
CN116975523B (en) * 2023-09-22 2023-12-12 南京气象科技创新研究院 Data assimilation background error covariance characteristic statistical method for strong convection weather typing

Also Published As

Publication number Publication date
CN110968926B (en) 2023-05-02

Similar Documents

Publication Publication Date Title
CN110363327B (en) ConvLSTM and 3D-CNN-based short rainfall prediction method
Caillaud et al. Modelling Mediterranean heavy precipitation events at climate scale: an object-oriented evaluation of the CNRM-AROME convection-permitting regional climate model
Gowan et al. Validation of mountain precipitation forecasts from the convection-permitting NCAR ensemble and operational forecast systems over the western United States
Kumar et al. Assimilation of INSAT‐3D hydro‐estimator method retrieved rainfall for short‐range weather prediction
Zapotocny et al. A two-season impact study of satellite and in situ data in the NCEP Global Data Assimilation System
Schiro et al. Tropical continental downdraft characteristics: mesoscale systems versus unorganized convection
Jones et al. Assimilation of satellite infrared radiances and Doppler radar observations during a cool season observing system simulation experiment
Pu et al. The impact of aircraft dropsonde and satellite wind data on numerical simulations of two landfalling tropical storms during the tropical cloud systems and processes experiment
CN110968926A (en) Method for predicting atmospheric parameters based on improved background error covariance matrix
Huang et al. A simultaneous multiscale data assimilation using scale-dependent localization in GSI-based hybrid 4DEnVar for NCEP FV3-based GFS
Prasad et al. Improvements in medium range weather forecasting system of India
Xian et al. All‐sky assimilation of the MWHS‐2 observations and evaluation the impacts on the analyses and forecasts of binary typhoons
Squitieri et al. On the forecast sensitivity of MCS cold pools and related features to horizontal grid spacing in convection-allowing WRF simulations
Karbou et al. Global 4DVAR assimilation and forecast experiments using AMSU observations over land. Part II: Impacts of assimilating surface-sensitive channels on the African monsoon during AMMA
Huang et al. On the use of cost-effective valid-time-shifting (VTS) method to increase ensemble size in the GFS hybrid 4DEnVar system
Tian et al. An assimilation test of Doppler radar reflectivity and radial velocity from different height layers in improving the WRF rainfall forecasts
Maldonado et al. Parameter sensitivity of the WRF–LETKF system for assimilation of radar observations: Imperfect-model observing system simulation experiments
Jia et al. Skillful seasonal prediction of North American summertime heat extremes
Snaiki et al. A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate
Ramanujam et al. On the effect of non-raining parameters in retrieval of surface rain rate using TRMM PR and TMI measurements
Candy et al. The assimilation of microwave humidity sounder observations in all‐sky conditions
Stensrud et al. Importance of cold pools to NCEP mesoscale Eta Model forecasts
Rowntree Large-scale land–atmosphere models and data needs
Tsuyuki et al. The JMA mesoscale 4D-Var system and assimilation of precipitation and moisture data
Lewis et al. Upstream Large-Scale Control of Subtropical Low-Cloud Climatology

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