WO2020103677A1 - Procédé et dispositif de traitement de données d'éléments météorologiques de prédiction météorologique numérique - Google Patents

Procédé et dispositif de traitement de données d'éléments météorologiques de prédiction météorologique numérique

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Publication number
WO2020103677A1
WO2020103677A1 PCT/CN2019/115125 CN2019115125W WO2020103677A1 WO 2020103677 A1 WO2020103677 A1 WO 2020103677A1 CN 2019115125 W CN2019115125 W CN 2019115125W WO 2020103677 A1 WO2020103677 A1 WO 2020103677A1
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WIPO (PCT)
Prior art keywords
element data
meteorological element
meteorological
time
error correction
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PCT/CN2019/115125
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English (en)
Chinese (zh)
Inventor
丁宇宇
董天仁
王蔚青
丁明月
郭树锋
张宇
马文珍
耿琴兰
隆文喜
苟晓侃
杨凡
李国栋
Original Assignee
国网青海省电力公司
北京金风慧能技术有限公司
国网青海省电力公司信息通信公司
国家电网有限公司
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Application filed by 国网青海省电力公司, 北京金风慧能技术有限公司, 国网青海省电力公司信息通信公司, 国家电网有限公司 filed Critical 国网青海省电力公司
Publication of WO2020103677A1 publication Critical patent/WO2020103677A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • 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

Definitions

  • the present application relates to the technical field of data processing, for example, to a method and device for meteorological element data processing of numerical weather forecast.
  • Numerical weather forecast data such as wind speed and wind direction are used as inputs, and the forecasted meteorological elements are converted into wind farm output power prediction and photovoltaic output power prediction through prediction algorithms. Therefore, accurate prediction of numerical weather forecast data can provide important decision support for power dispatching, and is one of the important determinants of the prediction accuracy of new energy generation power.
  • Model post-processing methods for wind speed forecasting include model output statistics, Kalman filtering, Back Propagation (BP) neural network, and adaptive partial least squares. Among them, the most widely used method is Model Output Statistics (MOS).
  • MOS Model Output Statistics
  • the embodiments of the present application provide a meteorological element data processing method and device for numerical weather forecasting, to at least solve that the numerical weather forecasting in related technologies does not use the information of nearby observation data as forecast parameters, resulting in low reliability of the obtained meteorological forecast results The problem.
  • the present application provides a meteorological element data processing method for numerical weather forecasting, including: acquiring first meteorological element data predicted by a numerical weather forecast of a weather station, wherein the first meteorological element data is in Predicted data of the weather station acquired before the first time point of the current day from the second time point of the previous day; obtaining second meteorological element data observed at the meteorological station, wherein the second meteorological element The data is the data observed between the second time point of the previous day and the third time point of the day; the third corresponding to the area where the time interval coincides in the first meteorological element data and the second meteorological element data is selected Meteorological element data, and the first error correction coefficient is obtained according to the third meteorological element data; the second error correction coefficient of the current forecast period is determined by an error conversion model according to the first error correction coefficient, wherein the forecast period is A time period between the time when the first meteorological element data is obtained according to the numerical weather forecast and the time when the first meteorological element data is released; the first meteorological element data according to the second
  • the present application further provides a meteorological element data processing apparatus for numerical weather forecasting, including: a first acquiring unit configured to acquire first meteorological element data predicted by a numerical weather forecast of a weather station, wherein The first meteorological element data is the data predicted by the meteorological station acquired before the first time point of the current day from the second time point of the previous day; the second acquiring unit is set to acquire the observations obtained at the meteorological station The second meteorological element data of, wherein the second meteorological element data is data observed between the second time point of the previous day and the third time point of the day; the third acquisition unit is set to select the The first meteorological element data corresponds to the third meteorological element data corresponding to the region where the time interval coincides in the second meteorological element data, and a first error correction coefficient is obtained according to the third meteorological element data; the first determining unit is set to The first error correction coefficient determines the second error correction coefficient of the current forecast period through an error conversion model, wherein the forecast period is from the time when the first meteorological element data is obtained according to
  • the present application further provides a storage medium, the storage medium includes a stored program, wherein the program executes the above method.
  • the present application further provides a processor, the processor is configured to run a program, and the above method is executed when the program is running.
  • FIG. 1 is a flowchart of a method for processing meteorological element data of numerical weather forecast according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of the relationship between meteorological element data and time according to an embodiment of the present application
  • FIG. 3 is a flowchart of another meteorological element data processing method of numerical weather forecast according to an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a meteorological element data processing device for numerical weather forecast according to an embodiment of the present application.
  • the MOS method generally takes multiple forecast variable values as the forecast factors, and uses the actual weather or meteorological elements at the time of forecast as the forecast amount, and selects different models of sampling from years of historical data, thereby seeking for Statistical relations and laws, and establish corresponding regression equations.
  • the application of MOS technology in conventional weather forecast has been deeply researched and applied in practice.
  • the MOS forecast system has been established to provide a reference for daily short-term element forecasting; the use of MOS for fine wind forecasting has obtained results that are significantly higher than the original model ’s atmospheric mesoscale model (Mesoscale Model Version 5, MM5) model forecast level.
  • the Perfect Prediction (PP) method is to first establish the statistical relationship between the large-scale circulation and local meteorological elements based on the observation data, and then replace the observed large-scale circulation information with the output of the numerical forecast for forecasting. This method assumes that the output value of the model is completely consistent with the measured value, that is, it believes that the numerical prediction is completely correct.
  • Kalman Filter (KF) algorithm uses the state estimation value at the previous time and the observation value at the current time to obtain the optimal estimation of the state variable at the current time of the dynamic system, including the two steps of forecasting and analysis.
  • KF Kalman Filter
  • the predicted value of the current mode state is generated according to the previous mode state.
  • the analysis stage the observation data is introduced, and the mode state is re-analyzed using the minimum variance estimation method.
  • Aggregate forecasting refers to a set of different forecasting results for the same effective forecasting time. Differences between multiple forecasts can provide information about the probability distribution of the forecasted quantity. Multiple forecasts in a set forecast can have different initial conditions, boundary conditions, parameter settings, and can even be generated with completely independent numerical weather forecast models . Aggregate forecasting is a method of starting with some initial values with little correlation and obtaining some forecasted values. This is a classic concept of collective forecasting. In addition to considering the initial value problem, the uncertainty and randomness of many physical processes in the numerical model (such as parameterization schemes, etc.) are also considered, and some predicted values are obtained. This is a new set of predictions.
  • the relevant technology only considers the error of historical observation data and historical forecast results, and does not consider the comparison error of nearby observation data to the forecast results of the day, so it is impossible to use the observation data that is near after each numerical forecast result is released;
  • a method embodiment of a meteorological element data processing method of numerical weather forecast is provided, and the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and Although the logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from here.
  • FIG. 1 is a flowchart of a meteorological element data processing method of numerical weather forecast according to an embodiment of the present application. As shown in FIG. 1, the meteorological element data processing method of numerical weather forecast includes: S102 to S110.
  • S102 Obtain first meteorological element data predicted by a numerical weather forecast of a meteorological station, where the first meteorological element data is data predicted by the meteorological station acquired before the first time point of the day from the second time point of the previous day .
  • the data time resolution is 15 minutes.
  • numerical weather forecast refers to the calculation of the equations of hydrodynamics and thermodynamics in the process of weather evolution based on the actual atmospheric conditions and under certain initial and boundary conditions, through large-scale computers for numerical calculations, to predict the atmosphere for a certain period in the future Methods of movement status and weather phenomena.
  • S104 Obtain second meteorological element data observed at a meteorological station, where the second meteorological element data is data observed between the second time point of the previous day and the third time point of the same day.
  • the corresponding meteorological element data obtained in real time from 20:00 to 6:00 of the previous day of the meteorological station is obtained before 7 a.m., and is recorded as Or .
  • the data time resolution is also 15 minutes.
  • S106 Select the third meteorological element data corresponding to the region where the time interval coincides in the first meteorological element data and the second meteorological element data, and obtain the first error correction coefficient according to the third meteorological element data.
  • the area where the time interval of the meteorological element data predicted by the numerical weather forecast coincides with the observed meteorological element data is not less than 10 hours. If it is less than 10 hours, continue to obtain more observed meteorological element data until it coincides The area reaches 10 hours. In this embodiment, 10 hours is just an example, and the predetermined duration may be set according to actual needs.
  • S108 Determine the second error correction coefficient of the current forecast period through the error conversion model according to the first error correction coefficient, where the forecast period is from the time when the first meteorological element data is obtained according to the numerical weather forecast to the time when the first meteorological element data is released The time period between.
  • the first meteorological element data predicted by the numerical weather forecast of the meteorological station can be obtained, where the first meteorological element data is the meteorological station acquired before the first time point of the day at the second time point of the previous day Forecast data; at the same time obtain the second meteorological element data observed at the meteorological station, where the second meteorological element data is the data observed between the second time point of the previous day and the third time point of the day; And select the third meteorological element data corresponding to the area where the time interval coincides in the first meteorological element data and the second meteorological element data, and obtain the first error correction coefficient according to the third meteorological element data; through the error conversion model according to the first error correction coefficient Determine the second error correction coefficient for the forecast period of the day, where the forecast period is the period between the time when the first meteorological element data is obtained according to the numerical weather forecast and the time when the first meteorological element data is released; and the second error correction The coefficient revises the first meteorological element data to obtain the revised first meteorological element data.
  • the meteorological element data processing method of numerical weather forecast can realize the purpose of using the observation data reported from the time of the forecast of the numerical weather forecast to the release period to correct the subsequent forecast results of the forecast, so as to fully utilize the nearby observation data ( That is, the information of the second meteorological element data) improves the technical effect of the accuracy of the revised forecast, and thus solves the problem that the numerical weather forecast in the related technology does not use the information of the nearby observation data as the forecast parameter, resulting in the low reliability of the weather forecast problem.
  • selecting the third meteorological element data corresponding to the overlapping area of the first meteorological element data and the second meteorological element data may include: determining whether the overlapping area of the first meteorological element data and the second meteorological element data meets the time interval A predetermined condition is obtained, and the judgment result is obtained, wherein the predetermined condition is that the total duration corresponding to the overlapping area of the first meteorological element data and the second meteorological element data is greater than the predetermined duration; when the judgment result is the first meteorological element data and the second meteorological element When the time interval overlapping area in the data satisfies the predetermined condition, the weather element data corresponding to the time interval overlapping area is used as the third weather element data.
  • the first meteorological element data and the second meteorological element data are selected to coincide in the time interval
  • the third meteorological element data corresponding to the area includes: determining the time difference of the acquisition time of the second meteorological element data relative to the third time point; continuing to acquire the second meteorological element data based on the time difference until the time difference is zero.
  • obtaining the first error correction coefficient according to the third meteorological element data may include: one-to-one correspondence between the first meteorological element data and the second meteorological element data in the third meteorological element data corresponding to the time interval coincidence area according to time
  • the sequence determines the first error correction coefficient through the first formula, including: determining the error
  • the weather can intercept the first data element of numerical weather day time and the data F r O r overlapped portion of the second time meteorological elements day observation obtained, i.e., the day before the day of 6:00 to 20:00, and forecasts
  • the method may further include: determining an error conversion model; wherein, determining the error conversion model includes: Obtain the historical forecast meteorological element data and historical observation meteorological element data in the historical time period; determine the first historical error revision coefficient in the predetermined time interval and the second historical error revision coefficient in the historical forecast time period ; Train the first historical error revision coefficient and the second historical error revision coefficient to obtain the error conversion model.
  • FIG. 2 is a schematic diagram of the relationship between meteorological element data and time according to an embodiment of the present application.
  • the real-time meteorological element data includes: real-time predicted meteorological element data (ie, first meteorological element data) obtained by numerical weather forecast, Observed real-time observation meteorological element data (ie, second meteorological element data).
  • the first error correction coefficients ( ar , br ) are determined according to the real-time forecasting meteorological element data and real-time observation meteorological element data; Use the historical forecast meteorological element data and historical observation meteorological element data in the historical time period to train to obtain an error conversion model, and obtain (a ri , b ri ), (a fi , b fi ) according to the error conversion model, and obtain the minimum error time Coefficient (c, d).
  • F a is the corrected forecast meteorological element data
  • F f is the forecasted meteorological element data before the correction
  • F h and O h are the time series corresponding to the predicted meteorological element data and the observed meteorological element data in the historical time period.
  • determining the second error correction coefficient of the current forecast period through the error conversion model according to the first error correction coefficient may include: determining the error revision coefficient under the minimum error condition through the error conversion model; according to the minimum error condition The error correction coefficient and the first error correction coefficient determine the second error correction coefficient.
  • FIG. 3 is a flowchart of another meteorological element data processing method for numerical weather forecast according to an embodiment of the present application.
  • meteorological element data ie, second meteorological element data
  • values obtained from real-time observation are obtained.
  • the meteorological element data that is, the first meteorological element data
  • the method provided by the embodiment of the present application can use the numerical weather forecast results of the day and the observation results of the same period, and can be corrected based on the latest meteorological element data, which improves the correction effect of the forecast results of the numerical weather forecast, taking into account each time
  • a numerical weather forecasting meteorological element data processing apparatus is also provided.
  • the numerical weather forecasting meteorological element data processing apparatus of the present embodiment may be configured to execute the method provided by the present embodiment.
  • the meteorological element data processing device for numerical weather forecast provided by the embodiment of the present application will be described below.
  • FIG. 4 is a schematic diagram of a meteorological element data processing apparatus for numerical weather forecast according to an embodiment of the present application. As shown in FIG. 4, the apparatus includes: a first acquiring unit 41, a second acquiring unit 43, and a third acquiring unit 45, The first determination unit 47 and the fourth acquisition unit 49. The device will be described below.
  • the first acquiring unit 41 is configured to acquire the first meteorological element data predicted by the numerical weather forecast of the meteorological station, wherein the first meteorological element data is the second meteorological station acquired before the first time point of the current day. Predicted data from time.
  • the second obtaining unit 43 is configured to obtain the second meteorological element data observed at the weather station, wherein the second meteorological element data is observed between the second time point of the previous day and the third time point of the same day data.
  • the third obtaining unit 45 is configured to select the third meteorological element data corresponding to the region where the time interval coincides between the first meteorological element data and the second meteorological element data, and obtain the first error correction coefficient according to the third meteorological element data.
  • the first determining unit 47 is configured to determine the second error correction coefficient of the current forecast period based on the first error correction coefficient through the error conversion model, where the forecast period is from the time when the first meteorological element data is obtained according to the numerical weather forecast to the first The time period between the time when the meteorological element data is released.
  • the fourth obtaining unit 49 is configured to revise the first meteorological element data according to the second error correction coefficient to obtain the revised first meteorological element data.
  • the first acquiring unit 41 in this embodiment may be configured to execute S102 in the embodiment of the present application
  • the second acquiring unit 43 in this embodiment may be configured to execute S104 in the embodiment of the present application
  • the third acquiring unit 45 may be configured to execute S106 in the embodiment of the present application
  • the first determining unit 47 in the embodiment may be configured to execute S108 in the embodiment of the present application
  • the fourth acquiring unit 49 in the embodiment may be configured To execute S110 in the embodiment of the present application.
  • the above modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above embodiments.
  • the first meteorological element data predicted by the numerical weather forecast of the meteorological station may be acquired by the first acquiring unit, where the first meteorological element data is the meteorological station acquired before the first time point of the day.
  • the third acquiring unit includes: a determination subunit configured to determine whether the overlapping region of the first meteorological element data and the second meteorological element data satisfies a predetermined condition to obtain a determination result, wherein the predetermined condition is the first meteorological condition
  • the total duration corresponding to the overlapping area of the time interval between the element data and the second meteorological element data is greater than the predetermined duration
  • the first determining subunit is set to determine that the overlapping area of the time interval in the first meteorological element data and the second meteorological element data satisfies the judgment result
  • the meteorological element data corresponding to the overlapping time zone is used as the third meteorological element data.
  • the third acquiring unit further includes: a second determining subunit, configured to determine the second when the judgment result is that the overlapping region of the time interval in the first meteorological element data and the second meteorological element data does not satisfy the predetermined condition
  • the acquisition time of the meteorological element data is relative to the time difference at the third time point; the first acquisition subunit is set to continue to acquire the second meteorological element data based on the time difference until the time difference is zero.
  • the meteorological element data processing device of the numerical weather forecast further includes: a second determining unit, which is set to determine before determining the second error correction coefficient of the current forecast period through the error conversion model according to the first error correction coefficient Error conversion model; wherein, the second determination unit includes: a third acquisition subunit, which is set to acquire historical predicted meteorological element data and historical observation meteorological element data within the historical period; a fourth determination subunit, which is set to determine the historical period The first historical error revision coefficient within a predetermined time interval within each day, and the second historical error revision coefficient within the historical forecast period; the fourth acquisition subunit is set to revise the first historical error revision coefficient and the second historical error revision The coefficients are trained to obtain the error conversion model.
  • the second determination unit includes: a third acquisition subunit, which is set to acquire historical predicted meteorological element data and historical observation meteorological element data within the historical period; a fourth determination subunit, which is set to determine the historical period The first historical error revision coefficient within a predetermined time interval within each day, and the second historical error revision coefficient
  • the first determining unit includes: a fifth determining subunit, which is set to determine the error correction coefficient in the case of the smallest error through the error conversion model; and a sixth determining subunit, which is set according to the case in which the error is the smallest The error correction coefficient and the first error correction coefficient determine the second error correction coefficient.
  • the above-mentioned device includes a processor and a memory, and the above-mentioned first acquisition unit 41, second acquisition unit 43, third acquisition unit 45, first determination unit 47, fourth acquisition unit 49, etc. are all stored in the memory as program units, which are processed by The device executes the above-mentioned program units stored in the memory to realize the corresponding functions.
  • the above processor contains a core, and the core retrieves the corresponding program unit from the memory.
  • One or more kernels may be set, and the first meteorological element data may be revised according to the second error correction coefficient by adjusting the kernel parameters to obtain revised first meteorological element data.
  • the above memory may include non-permanent memory, random access memory (RAM) and / or non-volatile memory in computer-readable media, such as read-only memory (ROM) or Flash memory (flash RAM), the memory includes at least one memory chip.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM Flash memory
  • a storage medium includes a stored program, where the program executes the above method.
  • a processor is further provided, and the processor is configured to run a program, wherein the above method is executed when the program is running.
  • An embodiment of the present application also provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor.
  • the processor executes the program, the following steps are implemented: acquiring the weather value of the weather station
  • the first meteorological element data for forecasting and forecasting where the first meteorological element data is the data predicted by the meteorological station obtained before the first time point of the day from the second time point of the previous day;
  • the second meteorological element data where the second meteorological element data is data observed between the second time point of the previous day and the third time point of the day; the first meteorological element data and the second meteorological element data are selected
  • the third meteorological element data corresponding to the area where the time interval coincides, and the first error correction coefficient is obtained according to the third meteorological element data;
  • the second error correction coefficient of the current forecast period is determined by the error conversion model according to the first error correction coefficient, where the forecast
  • the time period is the time period between the time when the first meteorological element data is obtained according to the numerical weather
  • a computer program product is also provided in an embodiment of the present application.
  • it When executed on a data processing device, it is suitable for executing a program initialized with the following method steps: acquiring first meteorological element data predicted by a numerical weather forecast of a weather station, Among them, the first meteorological element data is the data predicted by the meteorological station obtained before the first time point of the day from the second time point of the previous day; the second meteorological element data obtained by observation at the meteorological station is obtained.
  • the second meteorological element data is the data observed between the second time point of the previous day and the third time point of the day; the third meteorological element corresponding to the area where the time interval coincides between the first meteorological element data and the second meteorological element data is selected Element data, and the first error correction coefficient is obtained according to the third meteorological element data; the second error correction coefficient of the current forecast period is determined by the error conversion model according to the first error correction coefficient, where the forecast period is based on the numerical weather forecast A time period between the time of the first meteorological element data and the time when the first meteorological element data is released; the first meteorological element data is revised according to the second error correction coefficient to obtain the revised first meteorological element data.
  • the disclosed technical content may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the unit may be a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Integration into another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, units or modules, and may be in electrical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • multiple functional units in multiple embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. All or part of the technical solution of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions to make a computer device (which can be a personal computer, server, or network device) Etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.
  • the foregoing storage media include: Universal Serial Bus flash disk (Universal Serial Bus flash disk, U disk), ROM, RAM, mobile hard disk, magnetic disk or optical disk and other media that can store program code.

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Abstract

L'invention concerne un procédé et un dispositif de traitement de données d'éléments météorologiques d'une prédiction météorologique numérique. Ledit procédé comprend : l'acquisition de premières données d'éléments météorologiques prédites par une prédiction météorologique numérique au niveau d'une station météorologique (S102) ; l'acquisition de deuxièmes données d'éléments météorologiques obtenues par observation au niveau de la station météorologique (S104) ; la sélection de troisièmes données d'éléments météorologiques correspondant à une région de coïncidence d'intervalles de temps des premières données d'éléments météorologiques et des deuxièmes données d'éléments météorologiques, et l'obtention d'un premier coefficient de correction d'erreur en fonction des troisièmes données d'éléments météorologiques (S106) ; en fonction du premier coefficient de correction d'erreur, la détermination, au moyen d'un modèle de conversion d'erreur, d'un second coefficient de correction d'erreur à une période de prédiction du jour en cours (S108) ; et la révision, en fonction du second coefficient de correction d'erreur, des premières données d'éléments météorologiques, de façon à obtenir des premières données d'éléments météorologiques révisées (S110).
PCT/CN2019/115125 2018-11-21 2019-11-01 Procédé et dispositif de traitement de données d'éléments météorologiques de prédiction météorologique numérique WO2020103677A1 (fr)

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CN201811394322.3 2018-11-21
CN201811394322.3A CN109543295B (zh) 2018-11-21 2018-11-21 数值天气预报的气象要素数据处理方法及装置

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CN109946765A (zh) * 2019-04-02 2019-06-28 上海电气风电集团有限公司 风电场的流场的预测方法和系统
CN111737876A (zh) * 2020-06-29 2020-10-02 杭州电子科技大学 一种具有时空分布特征的茶叶开采期及采摘时长预测方法
CN114330641A (zh) * 2021-11-09 2022-04-12 国网山东省电力公司应急管理中心 基于复杂地形的深度学习的短期风速订正模型的建立方法
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CN114675349B (zh) * 2022-03-04 2024-04-26 国家气象中心(中央气象台) 数值模式产品分段式订正预报方法及系统
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CN116205138B (zh) * 2023-01-16 2023-11-03 中国气象局广州热带海洋气象研究所（广东省气象科学研究所） 风速预报订正方法及装置
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CN117452527A (zh) * 2023-12-26 2024-01-26 贵州省气象台(贵州省气象决策服务中心) 一种数字气象智能服务方法及系统
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