WO2022194117A1 - Procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique - Google Patents

Procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique Download PDF

Info

Publication number
WO2022194117A1
WO2022194117A1 PCT/CN2022/080805 CN2022080805W WO2022194117A1 WO 2022194117 A1 WO2022194117 A1 WO 2022194117A1 CN 2022080805 W CN2022080805 W CN 2022080805W WO 2022194117 A1 WO2022194117 A1 WO 2022194117A1
Authority
WO
WIPO (PCT)
Prior art keywords
particle
observation
weight
state
particles
Prior art date
Application number
PCT/CN2022/080805
Other languages
English (en)
Chinese (zh)
Inventor
赵玉新
杨硕
邓雄
赵廷
郝日栩
刘延龙
赵恒德
Original Assignee
哈尔滨工程大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 哈尔滨工程大学 filed Critical 哈尔滨工程大学
Priority to GB2209053.4A priority Critical patent/GB2605726A/en
Publication of WO2022194117A1 publication Critical patent/WO2022194117A1/fr

Links

Images

Classifications

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

Definitions

  • the invention relates to a data assimilation method based on statistical observation and localized equalization weight particle filtering, belonging to the field of atmospheric and ocean data assimilation.
  • Data assimilation is a research method that can organically combine numerical models and observations, two basic means of oceanographic research. Data assimilation refers to a method of continuously incorporating new observational data in the dynamic operation of the dynamic model on the basis of considering the spatiotemporal distribution of the data and the errors of the observational field and background field.
  • the trajectory predicted by the model simulation can be gradually corrected to make it closer to the real trajectory, and the accuracy of the model simulation prediction can be improved.
  • the main purpose of data assimilation is to combine observational data with theoretical model results, absorbing the advantages of both, in order to obtain results that are closer to reality. Forecasting of model states provides more accurate initial fields and optimizes ocean model parameters to improve the climate forecasting capabilities of ocean models.
  • data assimilation algorithm mainly relies on accurate observation data and reasonable numerical model. According to the correlation between the assimilation algorithm and the model, the data assimilation algorithm is divided into two categories: continuous data assimilation algorithm and sequential data assimilation algorithm.
  • the patent application of Patent Application No. 201910038258.3 uses the coupled data assimilation and parameter optimization method based on the optimal observation time window. This patent uses the coupled data assimilation and parameter optimization method based on the optimal observation time window. Technical field of parameter optimization and numerical prediction.
  • the adaptive localization method calculates the correlation coefficient between the observation data and the model variable according to any observation data and model variables given in the ensemble Kalman filter assimilation system; and then uses the grouped correlation coefficient to estimate the relationship between the observation data and the model variable.
  • the original localization function according to the profile of the correlation coefficient, the position of the satellite observation is estimated, and the obtained adaptive localization parameter is used to predict the typhoon in the regional model.
  • the prediction result is compared with the prediction result without using the present invention, The error relative to the observation is obviously reduced, and at the same time, the use of the present invention also significantly improves the forecast of the typhoon in the stage of rapid intensification.
  • 201910430413.6 is a water quality model particle filter assimilation method based on multi-source observation data.
  • the patent constructs a two-dimensional water quality model; initializes the state variables and parameters of the particles; generates the boundary conditions of the particles; resamples to obtain a new set of particles; The parameters are recursively pushed from time t to time t+1; at update time, the boundary conditions of particles continue to be generated, until the operation is completed at all times, and the particle filter assimilation of the two-dimensional water quality model is realized.
  • the multi-source observation data of water quality is reasonably integrated into the two-dimensional water quality model by using the particle filter algorithm, and the parameters of the two-dimensional water quality model are dynamically updated, which improves the simulation accuracy and prediction ability of the two-dimensional water quality model.
  • Particle filter algorithm is an ensemble data assimilation method. Since particle filter algorithm is not constrained by the assumption of model state quantity and error Gaussian distribution, it is suitable for any nonlinear non-Gaussian dynamic system. It also uses the Monte Carlo sampling method to approximate the posterior probability density distribution of the state quantity, which can better represent the change information of the nonlinear system.
  • the particle filter algorithm is simple and easy to implement. At the same time, compared with the current mainstream Kalman filter series algorithms, the particle filter algorithm does not have complex operations such as matrix transposition and inversion, so the calculation efficiency is higher.
  • the particle filter algorithm Compared with the Kalman filter series algorithms, which directly update the state value of particles, the particle filter algorithm only updates the weight of the particle when updating the particle, and the actual state value of the particle remains unchanged, which can avoid the update process. There is a situation where the particle state value is outside its physical value range.
  • the equal-weight particle filtering method proposed by British professor Van Leeuwen uses the proposed density idea to effectively improve the particle degradation and particle depletion problems in traditional particle filtering. It can achieve more traditional methods by using fewer aggregated particles. The assimilation effect of particles.
  • the weighted particle filter method based on statistical observation can effectively improve its dependence on future observation information, making it better applied to the field of real-time data assimilation and effectively improving the assimilation quality.
  • the equal-weight particle filter method lacks a corresponding localization scheme, which makes it difficult to adapt to the complex gridded high-latitude patterns.
  • the purpose of the present invention is to provide a suitable localization scheme for the equal-weight particle filter, solve the limitation of the application of the method in the complex grid mode, and make the method have better potential and value of practical application.
  • Provided is a method for data assimilation based on localized weighted particle filtering based on statistical observations.
  • Step 1 Obtain the initial background field of the mode integral
  • the initial field of the model is introduced into the model integral equation, and the model equation is integrated first to make the model equation reach a chaotic state.
  • This method can avoid the occurrence of the fluctuation problem of the model equation.
  • the model variable that reaches the chaotic state is used as the initial background field, and the background field.
  • Step 2 Determine whether the start time of statistical observation is reached, and accumulate the observations to obtain the mean value of the statistical observation
  • the starting moment of the statistical observation calculation is determined, and selecting the appropriate ⁇ can effectively improve the reliability of the statistical observation, and can better guide the ensemble particles to approach the historical observation at the time of assimilation.
  • the statistical observation starts the historical observation at the corresponding position of the assimilation observation is accumulated to obtain the mean value.
  • the calculation of the mean value can effectively avoid the sudden jump in the historical information of the corresponding observation, and the statistical mean value is used to replace the future observation information in the traditional method.
  • Step 3 Calculate the proposed density according to the mean value of the statistical observations.
  • the particle weight is calculated using the average weight method and the particle state is adjusted.
  • Step 4 At a given assimilation time, use the average weight method to calculate the particle weight, and adjust the observed particle state at the corresponding position
  • the adjusted particles are brought into the weight formula in the equal-weight particle filter, and the weight of the particles in the set is re-determined according to the formula to ensure that the set is in the set. According to the observation, the particles of , can obtain a relatively close and optimal weight, and further adjust the state of the aggregated particles.
  • Step 5 Use the resampling method to adjust the aggregated particles to keep the number of particles stable, and update the state of the particles at the corresponding position of the observation
  • the resampling method is used to adjust the aggregated particles at the corresponding position of the observation, mainly to ensure that the aggregated particles with poor weight performance in the equal-weight method are adjusted, and the particles with smaller weights are proposed to maintain the stability of the aggregated particle number.
  • Step 6 Use the localization function to determine the particle weights around the assimilated observation location
  • the localization function After updating the state of the set of particles at the corresponding position observed at the time of assimilation, the localization function is used to continue to adjust the state of the set of particles within the influence radius of the observation.
  • the localization function refer to the localization scheme in the localized particle filter method, and use the localization function in the calculation process to describe the positional relationship between the weight of the aggregated particles in the area and the given observation.
  • the assimilation observations of refer to the localization parameters to determine the weights of the adjusted ensemble particles.
  • Step 7 Update the particle weight according to the localization weight, and update the surrounding particle state
  • the state of the corresponding aggregate particle needs to be adjusted by this weight.
  • the weight of the aggregate particle needs to be normalized.
  • the linear combination of the prior particles updates the state of the aggregated particles after localization, so that the observed nearby aggregated particles can be adjusted according to the non-corresponding point observations.
  • Step 8 Calculate the state posterior estimate of the localized weighted particle filter for statistical observations
  • the overall ensemble mean is reintroduced into the model equation, and the model integral is updated during the assimilation process to obtain the final assimilation analysis result.
  • the second step is specifically: judging whether the start time of the statistical observation is reached, and accumulating the observations to obtain the mean value of the statistical observation;
  • ⁇ is generally The selection is 0.8-0.9.
  • ⁇ is generally selected as 0.5-0.8; the statistical method is used to calculate the average value of the observations before the assimilation time. represents the time series of the observation information in the observation interval at the observation position to be assimilated. Assuming that tj is the start time of the statistical observation according to ⁇ , the mean value of the statistical observation can be expressed as:
  • the third step is specifically: calculating the proposed density adjustment set particle according to the mean value of statistical observation, at a given assimilation moment, using the average weight method to calculate the particle weight, and adjust the particle state;
  • Step 3.1 Calculate the particle probability density at the corresponding position of the observation according to the mean value of the statistical observation
  • the particle filter method uses the conditional posterior probability density distribution based on Bayesian theory.
  • the posterior probability density distribution of can be expressed as
  • M represents the total number of aggregated particles, Represents the mean value of aggregate particles; the probability density of particles is calculated by this formula;
  • Step 3.3 Obtain the particle proposal density from the probability density of the aggregated particles
  • H indicates that the observation operator generally takes a value of 1
  • y indicates the observation information
  • the calculation of the proposed density is not limited to the observation information at the time of assimilation, The proposed density is based on the historical mean of statistical observations before the assimilation time, in order to adjust the ensemble particle position close to the observation information before the assimilation time;
  • Step 3.4 Select the optimal proposal density and calculate particle weights
  • ⁇ i represents the weight of the ith particle in the set, represents the state of the ith particle at time t j , represents the statistical observation information at time t j , represents the probability density, represents the proposal density, Represents the probability density of the particle state at time t j for the statistical observation, and finally obtains the weight of each particle passing through the proposed density; for the proposed density to adjust the aggregated particles to approach the statistical observation, the aggregated particle state can be expressed as:
  • ⁇ is the statistical observation start threshold
  • b is the scaling factor that controls the degree of relaxation to observations
  • Step 4 is specifically as follows: at a given assimilation moment, using the average weight method to calculate the particle weight, and adjust the observed particle state at the corresponding position;
  • Step 4.1 Calculate the weight of the particle on the basis of the proposed density-adjusted set of particles
  • y denotes the observation vector
  • x represents the state vector
  • the superscript T represents the matrix transposition
  • Q represents the mode error covariance matrix
  • R represents the observation error covariance matrix
  • Step 4.2 Adjust particle state according to weight
  • the state at time n can be expressed as:
  • y represents the observation vector
  • x represents the state vector
  • K QH T (HQH T +R) -1
  • Q is the mode error covariance
  • R is the observation error covariance
  • Step 6 is specifically: using the localization function to determine the particle weight around the corresponding position of the assimilation observation
  • the localization scheme in the particle filter method uses the localization function to describe the relationship between the weight of the aggregated particles in the area and the given observation position during the calculation process.
  • the weight in the particle filter represents the likelihood probability of the observation.
  • ⁇ i,j p(y
  • the localization operator l[y, xi ,r] is mainly used in local analysis to judge the relative position information of the set particle and the observation information; when the observation y and the set particle x i coincide, the maximum value of this function is 1 , when the distance between the two exceeds the given influence radius r, the function value is 0, indicating that the observation has no adjustment effect on the state of this set of particles; the vector weight of the final set of particles is expressed as:
  • h j is the measurement operator; it can be seen that the weight of the aggregated particles is not only related to the observation information, but also related to the relative position of the observation and the particle;
  • Step 7 is specifically: update the particle weight according to the localized weight, and update the surrounding particle state
  • the state of the corresponding aggregated particle needs to be adjusted by this weight.
  • the weight of the aggregated particle needs to be normalized to ensure that the sum of the weights in the aggregate is 1 , the normalized weight formula can be expressed as:
  • the particle state in the set is re-adjusted, and the posterior particle state can be expressed as:
  • k n is the nth sampled particle
  • vectors r 1 and r 2 can linearly combine new particles to form sampled particles and prior particles, and finally realize the posterior update of the localized particle state, where the vector
  • the calculation formulas of r 1 and r 2 can be expressed as:
  • the eighth step is specifically: calculating the state a posteriori estimated value of the localized weighted particle filter for statistical observation
  • calculate the posterior estimated set mean of the state Take the updated posterior estimated set mean as the initial value of the analysis model, and bring it back into the model integral equation for the next step of prediction and assimilation. Repeat the above steps within the assimilation time when there are available observations to obtain the final analysis field.
  • a field can act as a data field that reflects the current state of the environment.
  • the localization scheme is introduced into the statistical observation weighted particle filter, which can effectively improve the assimilation quality when the observation is sparse.
  • the root mean square error of the assimilation result of the statistical observation localization weighted particle filter is better than the localized particle filter method. Localization improvement of traditional equal-weight particle filter;
  • the statistical observation weighted particle filter can be better applied to the complex gridded medium and high-dimensional patterns, so that the particle filter method has better practical application potential.
  • Figure 1 shows the process of localized weighted particle filtering for statistical observation
  • Figure 2 is a comparison chart of the root mean square error of the localized average weight particle filter for statistical observation
  • Fig. 3 is the data assimilation flow chart of traditional equal weight particle filter
  • Fig. 4 is a flow chart of data assimilation based on statistical observation average weight particle filter
  • Figure 5 is a flow chart of data assimilation based on localized average weight particle filter based on statistical observations.
  • Step 1 Obtain the initial background field of the mode integral
  • the Lorenz-96 mode is selected because it has 40 state variables, which can better apply the localization scheme.
  • input the initial starting point of the mode into the model equation and integrate 1 million steps for spin-up to make the mode The variable reaches the chaotic state, to avoid the mode fluctuation deviation introduced in the assimilation integration process, and the mode variable that reaches the chaotic state is used as the initial background field of the mode. Based on this mode background field, the initial value of the particle filter set particles is obtained through the mode equation. , which can effectively improve the reliability of the ensemble particle state obtained by solving the mode equation.
  • Step 2 Determine whether the start time of statistical observation is reached, and accumulate the observations to obtain the mean value of the statistical observation
  • the statistical observation is used to replace the future observation information of the traditional method, and the aggregated particles are guided to be close to the historical statistical results observed at the time of assimilation, and the number of effective particles in the sampling process is increased, and an appropriate threshold ⁇ is selected to judge the appropriate Start statistical observations.
  • the choice of ⁇ is related to the time interval of the observation information that needs to be assimilated. When ⁇ is too small, more useless observations will be introduced into the statistics and the statistical error will be increased. When ⁇ is too large, the statistical observation information will be too small. It is not possible to correctly calculate the proposed density to guide the particle closer to the observation.
  • the calculation formula of ⁇ is:
  • t 0 is the previous observation time that needs to be assimilated
  • t n is the observation interval between two observations
  • t j represents the current time.
  • ⁇ is generally selected as 0.8-0.9
  • ⁇ is generally selected as 0.5-0.8.
  • Use statistical methods to calculate the mean of the observations before the assimilation time, using represents the time series of the observation information in the observation interval at the observation position to be assimilated. Assuming that tj is the start time of the statistical observation according to ⁇ , the mean value of the statistical observation can be expressed as:
  • Step 3 Calculate the proposed density according to the mean value of the statistical observations.
  • the particle weight is calculated using the average weight method and the particle state is adjusted.
  • Step 3.1 Calculate the probability density of particles at the corresponding position of the observation according to the mean value of the statistical observation
  • the particle filter method uses the conditional posterior probability density distribution based on Bayesian theory.
  • the posterior probability density distribution of can be expressed as
  • M represents the total number of aggregated particles, represents the aggregate particle mean.
  • the probability density of particles is calculated by this formula.
  • Step 3.3 Obtain the particle proposal density from the probability density of the aggregated particles
  • the proposed density in the uniformly weighted particle can be further obtained.
  • the proposed density obtained from the prior density is expressed as:
  • K n QH T (HQH T +R) -1
  • K n the model error covariance Q and the observation error covariance R expression for:
  • H indicates that the observation operator generally takes a value of 1
  • y indicates the observation information.
  • the calculation of the proposed density is not limited to the observation information at the time of assimilation.
  • the proposed density can use the historical mean value of the statistical observation before the time of assimilation. The purpose is to adjust the position of the set particles to be close to the observation information before the time of assimilation. .
  • Step 3.4 Select the optimal proposal density and calculate particle weights
  • the selection of the proposed density is considered to be an important criterion for controlling the position of particles and observation information and the calculation of particle weights. Selecting the appropriate and optimal proposed density can ensure the number of valid particles in sampling and the reliability of particle weights, which is also an equal-weight particle filter. The most important part in finding the optimal proposal density hypothesis The weight of each particle in the set based on statistical observations is expressed as:
  • ⁇ i represents the weight of the ith particle in the set
  • ⁇ j represents the state of the ith particle at time t j
  • the statistical observation information at time t j represents the probability density
  • the proposal density represents the probability density of the particle state at time tj for the statistical observation
  • the weight of each particle passing through the proposed density For the proposed density-adjusted ensemble particle approaching statistical observations, the ensemble particle state can be expressed as:
  • ⁇ is the statistical observation start threshold
  • b is the scaling factor that controls the degree of relaxation to observations.
  • Step 4 At a given assimilation time, use the average weight method to calculate the particle weight, and adjust the observed particle state at the corresponding position
  • Step 4.1 Calculate the weights of particles based on the proposed density-adjusted aggregate particles
  • the position of the particles in the proposed density adjustment set is calculated, and the particles are closer to the observation information at the assimilation time, so as to ensure that most of the particles get equal weights in the posterior probability density function at the assimilation time.
  • the proposed density brings each particle in the ensemble close to the observational information, so that it gets approximately the same weight.
  • the weight expression for each particle is:
  • Step 4.2 Adjust particle state according to weight
  • the state at time n can be expressed as:
  • y represents the observation vector
  • x represents the state vector
  • K QH T (HQH T +R) -1
  • Q is the mode error covariance
  • R is the observation error covariance
  • Step 5 Use the resampling method to adjust the aggregated particles to keep the number of particles stable, and update the state of the particles at the corresponding position of the observation
  • the resampling method is used to remove the particles with smaller weights in the set of particles at the corresponding position observed in the equal-weight particle filter, while ensuring the stability of the total number of particles in the set, and copy the particles with larger weights.
  • the equal-weight particle filtering method itself ensures that most of the particles are preserved, but in order to prevent a very small number of particles that do not meet the requirements of equal weight in the process of equal-weighting, the resampling method is used to perform final state adjustment to keep the number of particles stable.
  • Step 6 Use the localization function to determine the particle weight around the corresponding position of the assimilation observation
  • the localization function is used to continue to adjust the collective particle state within the influence radius r of the observation.
  • the localization function refers to the localization scheme in the localized particle filter method.
  • the localization function is used to describe the relationship between the weight of the aggregated particles in the area and the given observation position, and the weight in the particle filter is used. Indicates the likelihood probability of the observation.
  • the localization operator l[y,x i ,r] is used in the calculation. This operator is used to describe the relative position coefficient of the observation y and the set particle state x i , and the corresponding particle weight. Expressed as:
  • ⁇ i,j p(y
  • the localization operator l[y, x i , r] is mainly used in local analysis to judge the relative position information of aggregated particles and observation information.
  • the maximum value of this function is 1.
  • the function value is 0, which means that the observation has no adjustment effect on the state of the set particle. .
  • the vector weight of the final set of particles is expressed as:
  • the weight of the aggregated particles is not only related to the observation information, but also related to the relative position of the observation and the particle.
  • Step 7 Update the particle weight according to the localization weight, and update the surrounding particle state
  • the state of the corresponding aggregated particle needs to be adjusted by this weight.
  • the weight of the aggregated particle needs to be normalized to ensure that the sum of the weights in the aggregate is 1 , the normalized weight formula can be expressed as:
  • the particle state in the set is re-adjusted, and the posterior particle state can be expressed as:
  • k n is the nth sampled particle
  • vectors r 1 and r 2 can linearly combine new particles to form sampled particles and prior particles, and finally achieve a posteriori update of the localized particle state.
  • the calculation formula of the vectors r 1 and r 2 can be expressed as:
  • Step 8 Calculate the state posterior estimate of the localized weighted particle filter for statistical observations
  • calculate the posterior estimated set mean of the state Take the updated posterior estimated set mean as the initial value of the analysis model, and bring it back into the model integral equation for the next step of prediction and assimilation. Repeat the above steps within the assimilation time when there are available observations to obtain the final analysis field.
  • a field can act as a data field that reflects the current state of the environment.
  • the present invention proposes a data assimilation technology based on localized average weight particle filtering based on statistical observation.
  • the significant feature of the present invention is that a localization scheme adapted to its application is proposed for the statistical observation weighted particle filter method, which effectively solves the problem of the method for complex high-latitude grids.
  • the limitation of the application of the assimilation mode is that at the time of assimilation, the statistical observation method is used to update the state of the aggregated particles at the corresponding position of the observation, and then according to the position information and localization parameters of the assimilation observation, the observation is used to adjust the surrounding of the observation position, and the aggregated particles are available for observation sensitive.
  • the state of the nearby set of particles is adjusted to improve the utilization rate of the observation and improve the quality of assimilation.
  • the method proposed in this patent can effectively improve the assimilation ability of the traditional statistical observation weighted particle filter method under complex grid patterns and sparse observation conditions, can effectively improve the assimilation quality, and at the same time improve the application prospect of the statistical average weighted particle filter method.

Landscapes

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

Abstract

La présente invention concerne un procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique, consistant : à acquérir un champ de contexte initial d'intégration de mode ; à déterminer si un moment de début d'observation statistique est atteint, et à accumuler des observations pour calculer une valeur moyenne d'observation statistique ; à calculer une densité de propositions selon la valeur moyenne d'observation statistique pour ajuster des particules d'ensemble ; à un moment d'assimilation donné, à calculer des poids des particules au moyen d'un procédé de poids équivalents, et à ajuster les états des particules ; à utiliser un procédé de rééchantillonnage pour ajuster les particules d'ensemble pour maintenir stable le nombre des particules, et à mettre à jour les états des particules à des positions correspondant aux observations ; à utiliser une fonction de localisation pour déterminer les poids des particules entourant une position correspondant à une observation d'assimilation ; selon un poids de localisation, à mettre à jour les poids des particules et à mettre à jour les états des particules environnantes ; et à calculer une valeur d'estimation a posteriori d'état du filtre particulaire de poids équivalents localisé par observation statistique. La présente invention peut améliorer efficacement la qualité d'assimilation de données d'un mode de maillage non gaussien, et peut être mieux appliquée à l'assimilation de données en temps réel dans un mode de maillage complexe, ce qui permet d'améliorer la qualité d'assimilation.
PCT/CN2022/080805 2021-03-17 2022-03-15 Procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique WO2022194117A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
GB2209053.4A GB2605726A (en) 2021-03-17 2022-03-15 No title - not published

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110284192.3A CN113051529B (zh) 2021-03-17 2021-03-17 一种基于统计观测局地化均权重粒子滤波数据同化方法
CN202110284192.3 2021-03-17

Publications (1)

Publication Number Publication Date
WO2022194117A1 true WO2022194117A1 (fr) 2022-09-22

Family

ID=76512986

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/080805 WO2022194117A1 (fr) 2021-03-17 2022-03-15 Procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique

Country Status (2)

Country Link
CN (1) CN113051529B (fr)
WO (1) WO2022194117A1 (fr)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117111101A (zh) * 2023-06-26 2023-11-24 北京航空航天大学 消除双层空基导航增强自组网杠杆效应的故障检测方法
CN117235454A (zh) * 2023-11-10 2023-12-15 中国海洋大学 多时间尺度集合卡尔曼滤波在线资料同化方法
CN118260509A (zh) * 2024-05-31 2024-06-28 南京信息工程大学 基于六阶切线性隐式低通滤波的双局地混合变分同化方法
CN118280091A (zh) * 2024-06-03 2024-07-02 山东科技大学 一种基于时空事件链的煤矿灾害智能联动方法
CN118313226A (zh) * 2024-06-05 2024-07-09 中国人民解放军国防科技大学 基于粒子滤波的离散事件仿真数据同化方法、装置及设备
CN118335245A (zh) * 2024-04-30 2024-07-12 中国科学院大气物理研究所 一种在线耦合的大气化学传输模式与资料同化的系统
CN118503616A (zh) * 2024-07-16 2024-08-16 南京气象科技创新研究院 基于局地天气类型的局地背景误差协方差统计调用方法

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113051529B (zh) * 2021-03-17 2023-05-30 哈尔滨工程大学 一种基于统计观测局地化均权重粒子滤波数据同化方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102737155A (zh) * 2011-04-12 2012-10-17 中国科学院寒区旱区环境与工程研究所 基于贝叶斯滤波的通用数据同化方法
CN108073782A (zh) * 2017-11-06 2018-05-25 哈尔滨工程大学 一种基于观测窗口均权重粒子滤波的数据同化方法
CN113051529A (zh) * 2021-03-17 2021-06-29 哈尔滨工程大学 一种基于统计观测局地化均权重粒子滤波数据同化方法

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7167808B2 (en) * 2004-04-08 2007-01-23 Los Alamos National Security, Llc Statistical density modification using local pattern matching
CN102004856B (zh) * 2010-11-27 2011-12-21 中国海洋大学 高频观测资料实时数据的快速集合卡曼滤波同化方法
CN105157704A (zh) * 2015-06-03 2015-12-16 北京理工大学 一种基于贝叶斯估计的粒子滤波重力辅助惯导匹配方法
CN105046046B (zh) * 2015-06-09 2017-11-21 哈尔滨工程大学 一种集合卡尔曼滤波局地化方法
WO2018144534A1 (fr) * 2017-01-31 2018-08-09 The Regents Of The University Of California Accélération d'apprentissage machine fondée sur le matériel
CN107246873A (zh) * 2017-07-03 2017-10-13 哈尔滨工程大学 一种基于改进的粒子滤波的移动机器人同时定位与地图构建的方法
CN108536971B (zh) * 2018-04-13 2022-04-26 广州市建筑科学研究院有限公司 一种基于贝叶斯模型的结构损伤识别方法
CN109460539B (zh) * 2018-10-15 2020-05-26 中国科学院声学研究所 一种基于简化容积粒子滤波的目标定位方法
CN109783932B (zh) * 2019-01-14 2022-10-28 哈尔滨工程大学 一种结合最优观测时间窗口的强耦合数据同化方法
CN110119590B (zh) * 2019-05-22 2020-08-11 中国水利水电科学研究院 一种基于多源观测数据的水质模型粒子滤波同化方法
CN110378074A (zh) * 2019-08-21 2019-10-25 南京信息工程大学 基于粒子滤波的红外卫星辐射率资料云检测质量控制方法
CN110598614B (zh) * 2019-09-04 2022-08-26 南京邮电大学 一种结合粒子滤波的相关滤波目标跟踪方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102737155A (zh) * 2011-04-12 2012-10-17 中国科学院寒区旱区环境与工程研究所 基于贝叶斯滤波的通用数据同化方法
CN108073782A (zh) * 2017-11-06 2018-05-25 哈尔滨工程大学 一种基于观测窗口均权重粒子滤波的数据同化方法
CN113051529A (zh) * 2021-03-17 2021-06-29 哈尔滨工程大学 一种基于统计观测局地化均权重粒子滤波数据同化方法

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
POTERJOY JONATHAN, ANDERSON JEFFREY L.: "Efficient Assimilation of Simulated Observations in a High-Dimensional Geophysical System Using a Localized Particle Filter", MONTHLY WEATHER REVIEW., WASHINGTON, DC., US, vol. 144, no. 5, 1 May 2016 (2016-05-01), US , pages 2007 - 2020, XP055967318, ISSN: 0027-0644, DOI: 10.1175/MWR-D-15-0322.1 *
POTERJOY JONATHAN: "A Localized Particle Filter for High-Dimensional Nonlinear Systems", MONTHLY WEATHER REVIEW., WASHINGTON, DC., US, vol. 144, no. 1, 1 January 2016 (2016-01-01), US , pages 59 - 76, XP055967320, ISSN: 0027-0644, DOI: 10.1175/MWR-D-15-0163.1 *
YANG SHUO: "Research on Ocean-atmosphere Coupled Model Data Assimilation Based on Particle Filter", BASIC SCIENCE, CHINA DOCTORAL DISSERTATIONS/MASTER'S THESES FULL-TEXT DATABASE (MASTER), no. 6, 15 June 2018 (2018-06-15), XP055967323 *
ZHAO, YUXIN ET AL.: "The statistical observation localized equivalent-weights particle filter in a simple nonlinear model", ACTA OCEANOLOGICA SINICA, vol. 41, no. 2, 1 February 2022 (2022-02-01), XP037779687, DOI: 10.1007/s13131-021-1876-1 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117111101A (zh) * 2023-06-26 2023-11-24 北京航空航天大学 消除双层空基导航增强自组网杠杆效应的故障检测方法
CN117111101B (zh) * 2023-06-26 2024-03-22 北京航空航天大学 消除双层空基导航增强自组网杠杆效应的故障检测方法
CN117235454A (zh) * 2023-11-10 2023-12-15 中国海洋大学 多时间尺度集合卡尔曼滤波在线资料同化方法
CN118335245A (zh) * 2024-04-30 2024-07-12 中国科学院大气物理研究所 一种在线耦合的大气化学传输模式与资料同化的系统
CN118260509A (zh) * 2024-05-31 2024-06-28 南京信息工程大学 基于六阶切线性隐式低通滤波的双局地混合变分同化方法
CN118280091A (zh) * 2024-06-03 2024-07-02 山东科技大学 一种基于时空事件链的煤矿灾害智能联动方法
CN118313226A (zh) * 2024-06-05 2024-07-09 中国人民解放军国防科技大学 基于粒子滤波的离散事件仿真数据同化方法、装置及设备
CN118503616A (zh) * 2024-07-16 2024-08-16 南京气象科技创新研究院 基于局地天气类型的局地背景误差协方差统计调用方法

Also Published As

Publication number Publication date
CN113051529B (zh) 2023-05-30
CN113051529A (zh) 2021-06-29

Similar Documents

Publication Publication Date Title
WO2022194117A1 (fr) Procédé d'assimilation de données basé sur un filtre particulaire de poids équivalents localisé par observation statistique
CN111222698B (zh) 面向物联网的基于长短时记忆网络的积水水位预测方法
CN111860982B (zh) 一种基于vmd-fcm-gru的风电场短期风电功率预测方法
CN109272146B (zh) 一种基于深度学习模型和bp神经网络校正的洪水预测方法
CN110276104B (zh) 一种集合气候模式下的分期设计洪水推求方法
CN109255728B (zh) 混沌相空间优化重构的光伏发电功率神经网络预测法
CN113255986B (zh) 一种基于气象信息和深度学习算法的多步日径流预报方法
CN110443417A (zh) 基于小波变换的多模型集成负荷预测方法
CN110689183B (zh) 一种集群光伏功率概率预测方法、系统、介质及电子设备
CN111695290A (zh) 一种适用于变化环境下的短期径流智能预报混合模型方法
CN107274028A (zh) 一种基于混合Copula函数的多风场出力预测方法
CN115239029B (zh) 考虑功率时序及气象相依特性的风电功率预测方法及系统
CN113281790A (zh) 一种北斗卫星钟差预报方法及装置
CN113486303A (zh) 一种基于修饰模型集成的长时间序列预测方法
CN116451873A (zh) 基于多尺度双时空网络区域风力发电功率预测方法及系统
CN113435630A (zh) 一种产流模式自适应的流域水文预报方法及系统
CN117293809A (zh) 一种基于大模型的多时空尺度新能源发电功率预测方法
CN117076738A (zh) 基于变网格模型的中长期气象预测方法、系统及介质
CN117235454A (zh) 多时间尺度集合卡尔曼滤波在线资料同化方法
CN115618720A (zh) 一种基于海拔高度的土壤盐渍化预测方法及系统
CN113984198B (zh) 一种基于卷积神经网络的短波辐射预测方法及系统
Tian et al. Wind speed forecasting based on Time series-Adaptive Kalman filtering algorithm
CN117391139A (zh) 基于改进UNet神经网络的天气现象预报订正方法
CN113051520B (zh) 一种基于统计观测均权重粒子滤波数据同化方法
CN111476402A (zh) 耦合气象信息与emd技术的风电发电能力预测方法

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 202209053

Country of ref document: GB

Kind code of ref document: A

Free format text: PCT FILING DATE = 20220315

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22770472

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22770472

Country of ref document: EP

Kind code of ref document: A1