CN117634208B - Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data - Google Patents
Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data Download PDFInfo
- Publication number
- CN117634208B CN117634208B CN202311692397.0A CN202311692397A CN117634208B CN 117634208 B CN117634208 B CN 117634208B CN 202311692397 A CN202311692397 A CN 202311692397A CN 117634208 B CN117634208 B CN 117634208B
- Authority
- CN
- China
- Prior art keywords
- data
- subset
- observation
- observed
- cloud
- 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.)
- Active
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 49
- 238000003672 processing method Methods 0.000 title claims abstract description 22
- 230000004927 fusion Effects 0.000 claims abstract description 83
- 238000004458 analytical method Methods 0.000 claims abstract description 76
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 17
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000007499 fusion processing Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 63
- 238000004140 cleaning Methods 0.000 claims description 37
- 238000004364 calculation method Methods 0.000 claims description 31
- 238000000034 method Methods 0.000 claims description 29
- 230000001419 dependent effect Effects 0.000 claims description 21
- 238000003860 storage Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 description 9
- 238000002310 reflectometry Methods 0.000 description 7
- 238000003908 quality control method Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000012937 correction Methods 0.000 description 4
- 238000009499 grossing Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 239000013078 crystal Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004141 dimensional analysis Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- 239000008276 ice cloud Substances 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
Classifications
-
- 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
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Processing (AREA)
Abstract
The invention provides a three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data, comprising the following steps: acquiring a multisource meteorological data set in a forecast area; the multisource meteorological data set comprises a preliminary field data subset, a ground observation data subset and a space-based observation data subset; preprocessing the multi-source meteorological data set to obtain a standard multi-source data set; carrying out fusion processing on the standard multi-source data set to obtain a multi-source fusion data set; performing meteorological element analysis on the multisource fusion data set to obtain a three-dimensional cloud parameter simulation result; the three-dimensional cloud parameter simulation result comprises cloud top height, cloud bottom height, cloud water content and cloud ice content. The invention can realize the fusion of the mode background field and the observation data, and improves the cloud simulation analysis result of the site sparse area by fully utilizing various novel observation data, radars and satellite products, thereby improving the accuracy and the availability of the mesoscale numerical mode weather forecast.
Description
Technical Field
The invention relates to the technical field of meteorological statistics analysis and computers, in particular to a three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data.
Background
Cloud parameters are one of the key elements describing the medium and small scale atmospheric conditions. In a meteorological mode initial field, whether cloud parameter inversion is accurate and complete or not has a critical effect on the mesoscale mode proximity prediction. In addition, as weather detection technology continues to evolve and mature, more and more data and data subclasses of higher resolution real-time regular and irregular detection are provided. In the related art, forecasting through numerical mode is the most effective means for forecasting weather which is currently accepted, and is widely used for current production and life. For forecasting of three-dimensional cloud parameters, the forecasting method has lower accuracy, and particularly in some areas with lack of observation data, the common cloud simulation processing method has larger errors.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data, which are used for realizing the fusion of a mode background field and observation data, improving the accuracy of forecasting cloud parameters and further improving the accuracy and availability of weather forecast in a mesoscale numerical mode.
The embodiment of the invention discloses a three-dimensional cloud parameter simulation processing method based on multi-source meteorological data, which is characterized by comprising the following steps:
S1, acquiring a multi-source meteorological data set in a forecast area; the multisource meteorological data set comprises a preliminary field data subset, a ground observation data subset and a space-based observation data subset; the space-based observation data subset comprises a sounding data subset, a satellite data subset and a radar data subset;
S2, preprocessing the multi-source meteorological data set to obtain a standard multi-source data set;
s3, carrying out fusion processing on the standard multi-source data set to obtain a multi-source fusion data set;
s4, carrying out meteorological element analysis on the multisource fusion data set to obtain a three-dimensional cloud parameter simulation result; the three-dimensional cloud parameter simulation result comprises cloud top height, cloud bottom height, cloud water content and cloud ice content.
The preprocessing of the multisource meteorological data set to obtain a standard multisource data set comprises the following steps:
S21, performing data cleaning processing on the multi-source meteorological data set to obtain a cleaning data set;
S22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
S23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
S24, performing boundary check and category consistency check processing on the standard data set to obtain a standard multi-source data set.
The step of performing data protocol processing on the cleaning data set to obtain a protocol data set comprises the following steps:
s221, determining a data attribute range of a first guess field data subset in the cleaning data set;
S222, judging whether the data attribute of the observed data is within the data attribute range of the initial guess field data subset or not for each observed data of the initial guess field data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the initial guess field data subset;
S223, determining a data attribute range of a ground observation data subset in the cleaning data set;
S224, judging whether the data attribute of the observed data is in the data attribute range of the ground observed data subset or not for each observed data of the ground observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the ground observation data subset;
s225, determining a data attribute range of a space-based observation data subset in the cleaning data set;
S226, judging whether the data attribute of the observed data is in the data attribute range of the space-based observed data subset or not for each observed data of the space-based observed data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the space-based observation data subset;
S227, combining the initial guess field data subset, the ground observation data subset and the space-based observation data subset which are subjected to the judgment to obtain a protocol data set.
The step of carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set comprises the following steps:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
S232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
S233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise a first guess field normative data subset, a ground observation normative data subset and a space-based observation normative data subset;
S234, combining all the canonical data subsets to obtain a canonical data set.
Performing boundary check and category consistency check on the standard dataset to obtain a standard multi-source dataset, wherein the method comprises the following steps:
S241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the value range comprises an upper limit value of the value range and a lower limit value of the value range;
S242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the upper limit value or the lower limit value of the value range closest to the observed data;
S243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
S244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the initial guess field specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the initial guess field specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observed data of the boundary specification data subset corresponding to the initial guess field specification data subset to obtain the initial guess field specification data standard subset;
S246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the ground observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
S247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
S248, executing S246 and S247 on all the observed data of the boundary specification data subset corresponding to the ground observation specification data subset to obtain the ground observation specification data standard subset;
S249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the space-based observed specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the space-based observed specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
S2410, executing S249 on all the observation data of the boundary specification data subset corresponding to the space-based observation specification data subset to obtain the space-based observation specification data standard subset;
S2411, combining the initial guess field standard data standard subset, the ground observation standard data standard subset and the space-based observation standard data standard subset to obtain a standard multi-source data set.
The fusion processing is carried out on the standard multi-source data set to obtain a multi-source fusion data set, which comprises the following steps:
S31, setting a multisource meteorological fusion analysis data grid;
S32, carrying out interpolation processing on each type of standard subset in the multi-source fusion data set according to the multi-source meteorological fusion analysis data grid to obtain a corresponding fusion subset;
s33, merging all the fusion subsets to obtain a multi-source fusion data set.
The method for analyzing the meteorological elements of the multisource fusion dataset to obtain a three-dimensional cloud parameter simulation result comprises the following steps:
calculating the multisource fusion data set by using a cloud top height calculation model to obtain a cloud top height;
calculating the multisource fusion data set by using a cloud bottom height calculation model to obtain cloud top and bottom degrees;
Calculating the multisource fusion data set by using a cloud water content calculation model to obtain cloud water content;
calculating the multisource fusion data set by using a cloud ice content calculation model to obtain cloud ice content;
and combining the cloud top height, the cloud top bottom, the cloud water content and the cloud ice content to obtain a three-dimensional cloud parameter simulation result.
The embodiment of the invention in a second aspect discloses a three-dimensional cloud parameter simulation processing device based on multi-source meteorological data, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the three-dimensional cloud parameter simulation processing method based on the multi-source meteorological data.
In a third aspect of the embodiments of the present invention, a computer storage medium is disclosed, where the computer storage medium stores computer instructions that, when invoked, are configured to perform the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data.
The fourth aspect of the embodiment of the invention discloses an information data processing terminal which is used for realizing the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data.
The beneficial effects of the invention are as follows:
The application provides a multi-source meteorological fusion analysis and three-dimensional cloud simulation analysis method and device, wherein the method acquires multi-source meteorological data in a forecast area; fusing multisource meteorological data; and (3) carrying out wind analysis, ground observation data analysis and temperature analysis by using the background field on the grid and the observation data after quality control provided by the fusion module to obtain three-dimensional cloud simulation analysis, so that the cloud analysis precision is improved, and the accuracy of a numerical mode is improved.
The application realizes the fusion of the mode background field and the observation data, fully utilizes various novel observation data, radar and satellite products, improves the site sparse area simulation analysis method, and improves the accuracy and the availability of the mesoscale numerical mode weather forecast.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For a better understanding of the present disclosure, an embodiment is presented herein.
FIG. 1 is a flow chart of the method of the present invention.
In order to solve the technical problems of improving accuracy of numerical mode prediction and applicable scenes, a first aspect of the embodiment of the invention discloses a three-dimensional cloud parameter simulation processing method based on multi-source meteorological data, which comprises the following steps:
S1, acquiring a multi-source meteorological data set in a forecast area; the multisource meteorological data set comprises a preliminary field data subset, a ground observation data subset and a space-based observation data subset; the space-based observation data subset comprises a sounding data subset, a satellite data subset and a radar data subset;
The initial guess field data subset comprises temperature data, potential height data, warp direction wind speed data, weft direction wind speed data, relative humidity data, air pressure height data and other data with format errors in a forecast area; the temperature data, the warp direction wind speed data, the weft direction wind speed data and the relative humidity data are data values corresponding to grid points in a three-dimensional space coordinate system in the forecasting area; the potential height data and the air pressure height data are data corresponding to discrete height values in the forecasting area; the data of the initial guess field data subset is obtained by performing prediction processing on historical weather test data by adopting a weather parameter prediction method; the weather parameter prediction method can be a numerical prediction model or a statistical prediction method. The initial guess field data subset, the ground observation data subset and the space-based observation data subset all comprise a plurality of observation data;
The data attributes comprise temperature, potential height, warp wind speed, weft wind speed, relative humidity, air pressure height, wind speed, wind direction, dew point temperature and the like;
The ground observation data subset comprises temperature data, air pressure data, wind speed data, wind direction data and dew point temperature data of ground points in a forecast area; the ground observation data subset is obtained through a ground meteorological parameter measuring device; the ground observation data subset also comprises data with other format errors;
The sounding data subset comprises temperature data, air pressure data, wind speed data, wind direction data and dew point temperature data in a preset height range in a forecasting area; the sounding data subset is obtained through a meteorological parameter measuring device arranged on a sounding balloon; the sounding data subset also comprises data with other format errors;
The satellite data subset comprises infrared channel brightness temperature data in a forecast area; the satellite data subset is obtained through a meteorological parameter measuring device arranged on a satellite;
The radar data subset comprises reflectivity in a forecast area and Doppler frequency shift data of radar echoes; the radar data subset is obtained through meteorological radar measurement; the data in the space-based observation data subset are all data values corresponding to grid points in the forecast area;
S2, preprocessing the multi-source meteorological data set to obtain a standard multi-source data set;
the preprocessing comprises processing data of ASCII code, GRIB code or NetCDF format of the initial guess station and various detection data into a unified NetCDF format, and providing the data to a fusion module.
S3, carrying out fusion processing on the standard multi-source data set to obtain a multi-source fusion data set;
s4, carrying out meteorological element analysis on the multisource fusion data set to obtain a three-dimensional cloud parameter simulation result; the three-dimensional cloud parameter simulation result comprises cloud top height, cloud bottom height, cloud water content and cloud ice content;
The preprocessing of the multisource meteorological data set to obtain a standard multisource data set comprises the following steps:
S21, performing data cleaning processing on the multi-source meteorological data set to obtain a cleaning data set;
S22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
S23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
S24, performing boundary check and category consistency check processing on the standard data set to obtain a standard multi-source data set.
The data cleaning processing comprises filling in missing values, smoothing noise data and smoothing or deleting wild value points; the smooth noise data is obtained by firstly judging the noise data, and then carrying out smoothing treatment on the noise data according to the front and rear data of the noise data; the noise data is a value whose value is smaller than the detection sensitivity of the sensor of the observation data or larger than the measurement upper limit of the sensor of the observation data. The discrimination of the outlier point can adopt a Kalman filtering method. And for the determination of the filling value of the missing value, the measured value in a certain sampling interval before and after the missing value can be averaged.
The multi-source meteorological data set is subjected to data cleaning processing to obtain a cleaning data set, the cleaning data set is obtained by respectively carrying out data cleaning processing according to a data subset, and the cleaning data set comprises a first guess field data subset, a ground observation data subset and a space-based observation data subset after the data cleaning processing;
The step of performing data protocol processing on the cleaning data set to obtain a protocol data set comprises the following steps:
s221, determining a data attribute range of a first guess field data subset in the cleaning data set;
S222, judging whether the data attribute of the observed data is within the data attribute range of the initial guess field data subset or not for each observed data of the initial guess field data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the initial guess field data subset;
S223, determining a data attribute range of a ground observation data subset in the cleaning data set;
S224, judging whether the data attribute of the observed data is in the data attribute range of the ground observed data subset or not for each observed data of the ground observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the ground observation data subset;
s225, determining a data attribute range of a space-based observation data subset in the cleaning data set;
S226, judging whether the data attribute of the observed data is in the data attribute range of the space-based observed data subset or not for each observed data of the space-based observed data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the space-based observation data subset;
S227, combining the initial guess field data subset, the ground observation data subset and the space-based observation data subset which are subjected to the judgment to obtain a protocol data set.
The step of carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set comprises the following steps:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
S232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
The processing of aligning the acquisition time and the acquisition place of the observed data comprises the following steps:
When the acquisition time interval of the observed data is larger than the time interval of the unified data acquisition time, interpolation processing is carried out on the adjacent observed data to obtain an observed data value at the unified data acquisition time, and the observed data value is used as standard observed data;
when the acquisition time interval of the observed data is smaller than the time interval of the unified data acquisition time, sampling the observed data to obtain the observed data consistent with the unified data acquisition time, and taking the observed data as standard observed data;
When the acquisition space interval of the observed data is larger than the space interval of the unified data acquisition place, interpolation processing is carried out on the adjacent observed data to obtain an observed data value at the unified data acquisition place, and the observed data value is used as standard observed data;
And when the acquisition space interval of the observed data is smaller than the space interval of the unified data acquisition place, sampling the observed data to obtain the observed data consistent with the unified data acquisition place, and taking the observed data as standard observed data.
S233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise a first guess field normative data subset, a ground observation normative data subset and a space-based observation normative data subset;
s234, combining all the canonical data subsets to obtain a canonical data set;
performing boundary check and category consistency check on the standard dataset to obtain a standard multi-source dataset, wherein the method comprises the following steps:
S241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the value range comprises an upper limit value of the value range and a lower limit value of the value range;
S242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the upper limit value or the lower limit value of the value range closest to the observed data;
S243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
S244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the initial guess field specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the initial guess field specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observed data of the boundary specification data subset corresponding to the initial guess field specification data subset to obtain the initial guess field specification data standard subset;
S246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the ground observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
S247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
S248, executing S246 and S247 on all the observed data of the boundary specification data subset corresponding to the ground observation specification data subset to obtain the ground observation specification data standard subset;
S249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the space-based observed specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the space-based observed specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
S2410, executing S249 on all the observation data of the boundary specification data subset corresponding to the space-based observation specification data subset to obtain the space-based observation specification data standard subset;
S2411, carrying out combination processing on the initial guess field standard data standard subset, the ground observation standard data standard subset and the space-based observation standard data standard subset to obtain a standard multi-source data set;
And performing curve fitting on the curve to be approximated by using a function approximation method, and adopting an optimal consistent linear approximation method. The best consistent approximation polynomial f (Ix) has the expression:
f(Ix)＝αP1(Ix)P1+αP1-1(Ix)P1-1+…+α2(Ix)2+α1(Ix)+α0,
wherein P1 is the order of the best consistent approximation polynomial f (Ix), and alpha 0, alpha 1, alpha 2, …, alpha P1 is the coefficient of the best consistent approximation polynomial f (Ix);
The fusion processing is carried out on the standard multi-source data set to obtain a multi-source fusion data set, which comprises the following steps:
S31, setting a multisource meteorological fusion analysis data grid;
S32, carrying out interpolation processing on each type of standard subset in the multi-source fusion data set according to the multi-source meteorological fusion analysis data grid to obtain a corresponding fusion subset;
s33, merging all the fusion subsets to obtain a multi-source fusion data set.
The data distribution density of the multisource meteorological fusion analysis data grid is larger than the data acquisition density of a standard multisource data set;
the interpolation processing is performed according to the multi-source weather fusion analysis data grid, namely the interpolation calculation processing is performed on a plurality of data values according to a plurality of data values in a standard multi-source data set around each grid point in the multi-source weather fusion analysis data grid, so as to obtain the interpolated data values of the grid points.
And performing interpolation processing on each type of standard subset in the multi-source fusion data set according to the multi-source meteorological fusion analysis data grid to obtain a corresponding fusion subset, wherein the interpolation processing comprises the following steps:
interpolation processing is carried out on the initial guess field data according to the multisource meteorological fusion analysis data grid, so that initial guess field fusion data are obtained;
The interpolation processing is carried out on the initial guess field data to obtain initial guess field fusion data, which comprises the following steps:
the large scale preliminary guess field provided by the background field is interpolated into a grid for multisource weather fusion analysis, generating NetCDF format background fields lga (three-dimensional data fusion field) and lgb (ground data fusion field) for the analysis module.
Carrying out fusion processing on the ground observation data to obtain ground observation fusion data; specifically, the preprocessed ground observation data is compared with the quality of the actual observation value in time through a quality control program with a test property, an estimated value is used for filling in the inaccurate observation value, the time change of the data density is made up, ground observation fusion data are obtained, and the ground observation fusion data are provided for wind analysis and three-dimensional simulation cloud analysis. The estimated value can be obtained by linear interpolation according to the time and space resolution of the ground observation data.
Based on the radar data, carrying out radar data fusion; for each grid point of the multi-source meteorological fusion analysis data grid, calculating reflectivity by using a coordinate conversion method, and providing three-dimensional echo reflectivity, speed and the like of each radar for a wind analysis and three-dimensional simulation cloud analysis module. The radar data in any format can be unified into radar fusion data in a local rectangular coordinate format by a spatial interpolation method, and multiple radar data can be processed simultaneously. In addition, for the Doppler radar of SA/SB wave band, radar data fusion can be carried out by the reflectivity and the radial speed of the polar coordinate format. The fused radar data is named as Vxx, wherein xx represents the number of radars, and the Vxx comprises the reflectivity and the Nyquist speed of the 3-dimensional radar.
The method for analyzing the meteorological elements of the multisource fusion dataset to obtain a three-dimensional cloud parameter simulation result comprises the following steps:
calculating the multisource fusion data set by using a cloud top height calculation model to obtain a cloud top height;
calculating the multisource fusion data set by using a cloud bottom height calculation model to obtain cloud top and bottom degrees;
Calculating the multisource fusion data set by using a cloud water content calculation model to obtain cloud water content;
calculating the multisource fusion data set by using a cloud ice content calculation model to obtain cloud ice content;
Combining the cloud top height, the cloud top bottom, the cloud water content and the cloud ice content to obtain a three-dimensional cloud parameter simulation result;
the cloud top height calculation model can be realized by adopting a cloud top height calculation formula and a cloud top height calculation method in the weather forecast field, and particularly, the cloud top height calculation method based on infrared channel bright temperature data in a forecast area can be adopted;
the cloud bottom height calculation model can be realized by a cloud bottom height calculation formula, and the expression is as follows: cloud floor height= (air temperature-dew point temperature)/2.5;
The cloud ice content calculation model can be realized by adopting a cloud ice crystal number calculation method or an ice crystal particle number concentration estimation method in ice cloud;
The cloud water content calculation model can be obtained by calculating the cloud bottom height and humidity value by adopting an adiabatic specific water content calculation formula or method in atmospheric science;
The cloud ice content calculation model, the cloud water content calculation model, the cloud top height calculation model and the cloud bottom height calculation model can be realized by adopting a numerical simulation method, and particularly can be realized by adopting a WRF-Chem model, a CloudSat model and the like;
The cloud ice content calculation model, the cloud water content calculation model, the cloud top height calculation model and the cloud bottom height calculation model can also be realized by adopting a cloud disk paleometeorology model.
The method comprises the steps of carrying out meteorological element analysis on the multisource fusion dataset to obtain a three-dimensional cloud parameter simulation result, and further comprising:
s41, processing the multisource fusion data set by using a wind analysis model to obtain a cloud analysis result;
s42, processing the multi-source fusion data set by using a ground observation data analysis model to obtain a ground observation data analysis result;
s43, processing the multisource fusion data set by using a temperature analysis model to obtain a temperature analysis result;
s44, constructing and obtaining a meteorological element analysis result by using the cloud analysis result, the ground observation data analysis result and the temperature analysis result.
The processing the multisource fusion data set by using the wind analysis model to obtain a wind analysis result comprises the following steps:
S411, analyzing a background field without radar data by using a multi-layer iterative continuous correction technology;
firstly, continuous correction is carried out by using a weighting method, and the distance weight in the three-dimensional direction is as follows The instrument error reflected into the observed weight is/>The iteration result of each step is used as a background field of the next iteration, so that the iteration influence radius r of each step is reduced, and when the scale d of the mode is quite equal to the observation distance, the observation data and the instrument error, the iteration is stopped.
Smoothing the initial guess field, i.e. subtracting the background field to obtain the observed increment u 0, if zero increment occurs, weighting each lattice point, the weighting being inversely proportional to the square of the estimated errorStepwise iterations were performed with an analytical increment (mu i,j,k) of:
Where err b is denoted as the estimation error and μ i,j,k is the analysis increment with i, j, k number of grid points in the three-dimensional fusion analysis grid.
S412, analyzing lattice points with a plurality of radar radial speeds by using the result of the previous step as a new background field;
Firstly, performing deblurring treatment and other quality control, and adding a plurality of batches of radar radial speeds. And (3) carrying out two-by-two analysis on the multiple radar data on lattice points with multiple Doppler radar radial winds to obtain a two-dimensional wind field, and then taking the average field of the results.
S413, using the result of the previous step as a background field, analyzing the lattice covered by only one radar.
The vertical velocity is calculated using the radial wind of the radar, using the tangential wind of the background field, integrating the horizontal wind divergence, and determining the boundary conditions of the substrate from the ground wind and the terrain gradient.
In the (x, y, p) coordinate system u, v, ω represents wind in the x, y, p directions, respectively, where ω is the vertical velocity in the barometric altitude layer direction. The wind level divergence is:
It is possible to obtain a solution that,
Where ω p and ω p0 represent the vertical velocities at the barometric altitude level p and p 0, respectively, in hPa/s, ω p0 =0 when p 0 =1000 hPa, and thus the vertical velocity can be calculated for the level.
S42, processing the multi-source fusion data set by using a ground observation data analysis model to obtain a ground observation data analysis result, wherein the method comprises the following steps:
s421, when analyzing ground observation data, performing quality control based on the ground data fusion field lgb; wherein quality control is performed on observations that deviate from the background field lga by more than a threshold value prior to analyzing each field. Each observation data judges whether the value of the observation data is in a reasonable range according to the data attribute and the value range of the background field; when the observation data is in the value range, the observation data is not processed; and when the observed data value is not in the value range, setting the observed data value as a background field value.
S422, using observation increment for temperature, dew point temperature, horizontal wind component, average sea level air pressure, etc. to obtain interpolation of background field and observation data, and correcting temperature and dew point temperature by utilizing deviation of ground observation station height and multisource meteorological fusion analysis terrain height. When the interpolation of the ground observation height and the terrain height is carried out, the vertical change rate with the over-temperature is needed to be corrected. It is generally believed that the temperature drops by 0.98 c for every 100m of elevation.
T＝T0+Δh×δ
Wherein T 0 is the original temperature or dew point temperature before correction, Δh is the deviation between the height of the ground observation station and the terrain height of the multisource meteorological fusion analysis, and δ is the vertical change rate of temperature.
The reference background field in the modification discussed above is limited to a range not exceeding the observation threshold, which is advantageous in preventing the gradient from affecting too much of the sparse region of material.
S423, using direct observation data for visibility, firstly reading a ground data fusion field, then correcting the visibility of a region with higher relative humidity and a cloud vicinity region given by the previous cloud analysis, and using a background field value as the value of a low-visibility region if the visibility value of the ground data fusion field is higher.
S424 wherein for barometric pressure analysis reference altitude barometric pressure, ground barometric pressure and mean sea level barometric pressure are required. The air pressure of the reference height reduces the air pressure of the ground in the background field to the reference height through a poisson equation; the average sea level air pressure comes from a background field, and the background field air pressure mainly comes from the fused ground analysis field air pressure; the ground pressure at the station is calculated using the ground pressure at the background field and the average sea level pressure bias in the background field. S43, processing the multi-source fusion data set by using a temperature analysis model to obtain a temperature analysis result, wherein the method comprises the following steps:
in a specific embodiment, the temperature analysis requires the use of a background field, a ground analysis field generated at S42, S3 sounding fusion data, and the like. Quality control is first performed on the temperatures obtained from various observations, and if the temperature on any layer deviates from the background field or exceeds a threshold, the observations are discarded. The temperature analysis will output a three-dimensional temperature field and a height field, a two-dimensional boundary layer height and boundary layer head pressure.
S44, constructing and obtaining a meteorological element analysis result by using the wind analysis result, the ground observation data analysis result and the temperature analysis result.
And constructing a meteorological element analysis result set by the data generated by wind analysis, ground observation data analysis and temperature analysis.
The method further comprises S5, based on the wind analysis result, the ground observation data analysis result, the temperature analysis result and the cloud layer report data, performing three-dimensional cloud simulation analysis to obtain a three-dimensional cloud parameter simulation result, wherein the method comprises the following steps:
s51, analyzing vertical cloud information of cloud layer report data in the horizontal direction to obtain a three-dimensional initial guess cloud analysis field;
s52, determining the cloud top height and the three-dimensional cloud analysis field according to the three-dimensional temperature analysis field and the satellite cloud top temperature field;
and S53, correcting the cloud analysis field by utilizing the three-dimensional radar reflectivity to obtain cloud physical quantity.
The S51 includes:
in the step S3, the cloud layer report data is cloud layer information set in various data such as ground observation data, exploration data, satellite data, radar data and the like after fusion.
And reading layer-by-layer and class-by-class data, constructing a three-dimensional initial guess cloud analysis field, and providing cloud layer vertical and approximate horizontal analysis fields.
S52, determining the cloud top height and the three-dimensional cloud analysis field according to the three-dimensional temperature analysis field and the satellite cloud top temperature field;
according to the three-dimensional temperature analysis field generated in the S43 and the cloud top temperature field of the satellite, the cloud top height can be clearly known, the cloud top height field is interpolated to the first guess cloud analysis field, so that the cloud top height and the cloud three-dimensional analysis field are determined, and the difference between cloud report data and satellite data is eliminated by using a statistical method.
And S53, correcting the cloud analysis field by utilizing the three-dimensional radar reflectivity to obtain cloud physical quantity. Cloud analysis field correction was performed using radar data according to the following rule. And when the radar echo top is below a set value or no other cloud detection data exists, discarding radar echo data. When the satellite data can clearly display cloud information, both visible satellite data and radar data can be used. And when the visible light data shows no cloud and no radar echo exists or the radar echo intensity is smaller than the set intensity value, discarding the radar echo data. When the radar echo is strong, the visible light information is eliminated, and the cloud minimum information is reserved.
The embodiment of the invention in a second aspect discloses a three-dimensional cloud parameter simulation processing device based on multi-source meteorological data, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the three-dimensional cloud parameter simulation processing method based on the multi-source meteorological data.
In a third aspect of the embodiments of the present invention, a computer storage medium is disclosed, where the computer storage medium stores computer instructions that, when invoked, are configured to perform the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data.
The fourth aspect of the embodiment of the invention discloses an information data processing terminal which is used for realizing the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (8)
1. The three-dimensional cloud parameter simulation processing method based on the multi-source meteorological data is characterized by comprising the following steps of:
S1, acquiring a multi-source meteorological data set in a forecast area; the multisource meteorological data set comprises a preliminary field data subset, a ground observation data subset and a space-based observation data subset; the space-based observation data subset comprises a sounding data subset, a satellite data subset and a radar data subset;
S2, preprocessing the multi-source meteorological data set to obtain a standard multi-source data set;
s3, carrying out fusion processing on the standard multi-source data set to obtain a multi-source fusion data set;
s4, carrying out meteorological element analysis on the multisource fusion data set to obtain a three-dimensional cloud parameter simulation result; the three-dimensional cloud parameter simulation result comprises cloud top height, cloud bottom height, cloud water content and cloud ice content;
The preprocessing of the multisource meteorological data set to obtain a standard multisource data set comprises the following steps:
S21, performing data cleaning processing on the multi-source meteorological data set to obtain a cleaning data set;
S22, carrying out data protocol processing on the cleaning data set to obtain a protocol data set;
S23, carrying out unified processing on the acquired information of the protocol data set to obtain a standard data set;
S24, performing boundary check and category consistency check processing on the standard data set to obtain a standard multi-source data set;
performing boundary check and category consistency check on the standard dataset to obtain a standard multi-source dataset, wherein the method comprises the following steps:
S241, presetting a corresponding value range for each data attribute in each canonical data subset of the canonical data set; the value range comprises an upper limit value of the value range and a lower limit value of the value range;
S242, judging whether the value of each observation data of each canonical data subset of the canonical data set is in the value range according to the value range of the data attribute of each observation data; when the observation data is within the value range, the observation data is not processed; when the observed data is not in the value range, setting the observed data to be the upper limit value or the lower limit value of the value range closest to the observed data;
S243, after finishing S242 on all the observation data of each canonical data subset, obtaining a boundary canonical data subset corresponding to the canonical data subset;
S244, carrying out autoregressive-moving average modeling on the observed data of each type of data attribute of the boundary specification data subset corresponding to the initial guess field specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain regression models of the type of data attribute; calculating the independent variable by using the regression model to obtain regression data values; judging whether the absolute value of the difference between the regression data value and the corresponding factor variable value is larger than a set first regression judging threshold value or not; if the observed data is larger than the first regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the initial guess field specification data subset, and if the observed data is smaller than the first regression discrimination threshold, not processing the observed data;
S245, executing S244 on all the observed data of the boundary specification data subset corresponding to the initial guess field specification data subset to obtain an initial guess field specification data standard subset;
S246, carrying out cluster analysis processing on the observed data of each type of data attribute of the boundary specification data subset corresponding to the ground observation specification data subset, taking the data acquisition information of the observed data as an independent variable and the data value of the observed data as a dependent variable to respectively obtain the cluster result information of the type of data attribute; the clustering result information comprises clustering categories to which all the observation data of the class data attribute belong;
S247, determining the number of the observation data included in each cluster category; setting a data quantity threshold; deleting the observed data included in the clustering category with the number of the observed data smaller than the data quantity threshold value from the boundary specification data subset;
S248, executing S246 and S247 on all the observation data of the boundary specification data subset corresponding to the ground observation specification data subset to obtain a ground observation specification data standard subset;
S249, regarding the observed data of each type of data attribute of the boundary specification data subset corresponding to the space-based observed specification data subset, taking the data acquisition information of the observed data as a known independent variable, taking the data value of the observed data as a known dependent variable, constructing a curve to be approximated by using the known independent variable and the known dependent variable, and performing curve fitting on the curve to be approximated by using a function approximation method to obtain an optimal consistent approximation polynomial f (Ix) of the type of data attribute; calculating the known independent variable by using the optimal consistent approximation polynomial f (Ix) to obtain an approximate dependent variable; judging whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is larger than a set second regression judging threshold value; if the observed data is larger than the second regression discrimination threshold, deleting the observed data from the boundary specification data subset corresponding to the space-based observed specification data subset, and if the observed data is smaller than the second regression discrimination threshold, not processing the observed data;
s2410, executing S249 on all the observation data of the boundary specification data subset corresponding to the space-based observation specification data subset to obtain a space-based observation specification data standard subset;
S2411, combining the initial guess field standard data standard subset, the ground observation standard data standard subset and the space-based observation standard data standard subset to obtain a standard multi-source data set.
2. The three-dimensional cloud parameter simulation processing method based on multi-source meteorological data according to claim 1, wherein the performing data reduction processing on the cleaning data set to obtain a reduced data set comprises:
s221, determining a data attribute range of a first guess field data subset in the cleaning data set;
S222, judging whether the data attribute of the observed data is within the data attribute range of the initial guess field data subset or not for each observed data of the initial guess field data subset in the cleaning data set to obtain a first judging result; deleting observation data with the first judging result being no from the initial guess field data subset;
S223, determining a data attribute range of a ground observation data subset in the cleaning data set;
S224, judging whether the data attribute of the observed data is in the data attribute range of the ground observed data subset or not for each observed data of the ground observed data subset in the cleaning data set to obtain a second judging result; deleting observation data with the second judging result being no from the ground observation data subset;
s225, determining a data attribute range of a space-based observation data subset in the cleaning data set;
S226, judging whether the data attribute of the observed data is in the data attribute range of the space-based observed data subset or not for each observed data of the space-based observed data subset in the cleaning data set to obtain a third judging result; deleting observation data with the third judging result being no from the space-based observation data subset;
S227, combining the initial guess field data subset, the ground observation data subset and the space-based observation data subset which are subjected to the judgment to obtain a protocol data set.
3. The three-dimensional cloud parameter simulation processing method based on multi-source meteorological data according to claim 1, wherein the step of performing unified processing on acquired information on the protocol data set to obtain a specification data set comprises the following steps:
s231, setting unified data acquisition time and unified data acquisition place for the observation data with the same data attribute contained in the same data subset of the protocol data set;
S232, carrying out alignment processing on the acquisition time and the acquisition place of the observation data according to the unified data acquisition time and the unified data acquisition place on the observation data with the same data attribute contained in each data subset to obtain standard observation data;
S233, executing S232 on the observation data of each data attribute of each data subset to obtain a specification data subset corresponding to the data subset; the normative data subsets comprise a first guess field normative data subset, a ground observation normative data subset and a space-based observation normative data subset;
S234, combining all the canonical data subsets to obtain a canonical data set.
4. The three-dimensional cloud parameter simulation processing method based on multi-source meteorological data according to claim 1, wherein the fusing processing is performed on the standard multi-source dataset to obtain a multi-source fused dataset, and the method comprises the following steps:
S31, setting a multisource meteorological fusion analysis data grid;
S32, carrying out interpolation processing on each type of standard subset in the multi-source fusion data set according to the multi-source meteorological fusion analysis data grid to obtain a corresponding fusion subset;
s33, merging all the fusion subsets to obtain a multi-source fusion data set.
5. The three-dimensional cloud parameter simulation processing method based on multi-source meteorological data according to claim 1, wherein the performing meteorological element analysis on the multi-source fusion dataset to obtain a three-dimensional cloud parameter simulation result comprises:
calculating the multisource fusion data set by using a cloud top height calculation model to obtain a cloud top height;
calculating the multisource fusion data set by using a cloud bottom height calculation model to obtain cloud top and bottom degrees;
Calculating the multisource fusion data set by using a cloud water content calculation model to obtain cloud water content;
calculating the multisource fusion data set by using a cloud ice content calculation model to obtain cloud ice content;
and combining the cloud top height, the cloud top bottom, the cloud water content and the cloud ice content to obtain a three-dimensional cloud parameter simulation result.
6. A three-dimensional cloud parameter simulation processing device based on multi-source meteorological data, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data as claimed in any one of claims 1 to 5.
7. A computer storage medium storing computer instructions which, when invoked, are operable to perform the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data of any one of claims 1 to 5.
8. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the three-dimensional cloud parameter simulation processing method based on multi-source meteorological data according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311692397.0A CN117634208B (en) | 2023-12-11 | 2023-12-11 | Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311692397.0A CN117634208B (en) | 2023-12-11 | 2023-12-11 | Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117634208A CN117634208A (en) | 2024-03-01 |
| CN117634208B true CN117634208B (en) | 2024-05-10 |
Family
ID=90028724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311692397.0A Active CN117634208B (en) | 2023-12-11 | 2023-12-11 | Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117634208B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106547840A (en) * | 2016-10-13 | 2017-03-29 | 国家卫星气象中心 | A kind of parsing of global three-dimensional atmospheric data and management method |
| CN114896544A (en) * | 2022-03-15 | 2022-08-12 | 国家卫星气象中心(国家空间天气监测预警中心) | Three-dimensional cloud fusion analysis method for cooperation of wind cloud meteorological satellite and multiple data sources |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113051795B (en) * | 2021-03-15 | 2023-04-28 | 哈尔滨工程大学 | Three-dimensional Wen Yanchang analysis and prediction method for offshore platform guarantee |
-
2023
- 2023-12-11 CN CN202311692397.0A patent/CN117634208B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106547840A (en) * | 2016-10-13 | 2017-03-29 | 国家卫星气象中心 | A kind of parsing of global three-dimensional atmospheric data and management method |
| CN114896544A (en) * | 2022-03-15 | 2022-08-12 | 国家卫星气象中心(国家空间天气监测预警中心) | Three-dimensional cloud fusion analysis method for cooperation of wind cloud meteorological satellite and multiple data sources |
Non-Patent Citations (2)
| Title |
|---|
| 李红莉 ; 崔春光 ; 徐祥德 ; 刘瑞霞 ; .风云静止卫星资料在一次暴雨云分析中的应用.热带气象学报.2016,(01),全文. * |
| 风云静止卫星资料在一次暴雨云分析中的应用;李红莉;崔春光;徐祥德;刘瑞霞;;热带气象学报;20160328(01);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117634208A (en) | 2024-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sokol et al. | The role of weather radar in rainfall estimation and its application in meteorological and hydrological modelling—A review | |
| Bhuiyan et al. | A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula | |
| Grimaldi et al. | Remote sensing-derived water extent and level to constrain hydraulic flood forecasting models: Opportunities and challenges | |
| Forristall | Wave crest distributions: Observations and second-order theory | |
| CN109543353B (en) | Three-dimensional water vapor inversion method, device, equipment and computer readable storage medium | |
| Ebert et al. | Performance of satellite rainfall estimation algorithms during TOGA COARE | |
| Casson et al. | Simulation and extremal analysis of hurricane events | |
| JP6689396B2 (en) | Weather forecasting device, weather forecasting method, and program | |
| CN109100723A (en) | Upper-level winds inversion method based on Doppler radar data | |
| Atencia et al. | Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model | |
| JP2019211342A (en) | Weather analyzer, weather analysis method, and program | |
| Wong et al. | Development of a gridded daily hydrometeorological data set for Peninsular Malaysia | |
| CN113341389A (en) | Microwave scatterometer wind field inversion method and system based on maximum likelihood estimation | |
| Moron et al. | Weather types and rainfall over Senegal. Part II: Downscaling of GCM simulations | |
| CN111611731B (en) | Satellite data fusion method and device and electronic equipment | |
| CN117634208B (en) | Three-dimensional cloud parameter simulation processing method and device based on multi-source meteorological data | |
| CN114357770A (en) | Troposphere chromatography method | |
| Ramanujam et al. | On the effect of non-raining parameters in retrieval of surface rain rate using TRMM PR and TMI measurements | |
| CN116027333B (en) | Method for generating three-dimensional scanning elevation angle parameters of microwave rain-measuring radar | |
| Bakış et al. | Analysis and comparison of spatial rainfall distribution applying different interpolation methods in Porsuk river basin, Turkey | |
| CN115390160A (en) | Typhoon center automatic positioning method and device | |
| CN113064130B (en) | Method, device, storage medium and program product for determining particle spectral distribution | |
| Pejcic et al. | Polarimetric radar-based methods for evaluation of hydrometeor mixtures in numerical weather prediction models | |
| CN117610303B (en) | Fine simulation method and device for meteorological marine environment | |
| Augst et al. | Interpolation of operational radar data to a regular Cartesian grid exemplified by Munich’s airport radar configuration |
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 |