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Off-line ensemble Kalman filtering paleoclimate data assimilation system and method based on analogy and electronic equipment

A Kalman filter and data assimilation technology, applied in climate sustainability, electrical digital data processing, complex mathematical operations, etc., can solve problems such as the influence of the curse of dimensionality, and achieve the effect of reducing errors and reducing the amount of calculation

Pending Publication Date: 2022-08-05
NANJING UNIV
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  • Summary
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  • Application Information

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Problems solved by technology

Another approach to ensemble-based paleoclimate data assimilation uses degenerate particle filters to select the optimal simulation from ensemble-mode simulations, but even with large ensemble numbers, the selected particles suffer from the curse of dimensionality influences

Method used

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  • Off-line ensemble Kalman filtering paleoclimate data assimilation system and method based on analogy and electronic equipment
  • Off-line ensemble Kalman filtering paleoclimate data assimilation system and method based on analogy and electronic equipment
  • Off-line ensemble Kalman filtering paleoclimate data assimilation system and method based on analogy and electronic equipment

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Experimental program
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Effect test

Embodiment 1

[0054] like figure 1 As shown, the analog-based off-line ensemble Kalman filtering paleoclimate data assimilation method of the present invention firstly uses observations in a set of state variable samples simulated by a control experiment according to different standards (minimum RMSE or correlation coefficient between the model and the observation). Maximum) filter the prior set members, and then use the obtained prior set members to apply the set square root filter to update the set average and set perturbation. The analytical field can be further improved by applying this assimilation method. Specific steps are as follows:

[0055] Step 1. Given a set of state variables and observations to control the experimental simulation

[0056] 1.1. Given a set of state variable samples that control the experimental simulation

[0057] x f Sample X from the state variables of a controlled experimental simulation n×T ={x 1 ,…,x T }, where T is the number of state variable samp...

Embodiment 2

[0088]The present invention assimilates observations based on the off-line ensemble Kalman filtering paleoclimate data assimilation method of analogy. Taking the Lorenz (2005) model as an example, the performance of the present invention is tested with a single-scale model II without model error, and compared with the traditional static capture, The error results of the 'offline' ensemble Kalman filtering (OEnKF, Hakim 2016) method are compared. Sensitivity test results show that the present invention is superior to the traditional assimilation method in different ensemble sizes, localization scales, observation errors and observation densities.

[0089] Step 1. Given a set of state variables and observations to control the experimental simulation

[0090] The L05 model single-scale mode II contains only one large-scale slow process variable. Let X be the slow process variable, the single-scale model II can be written as:

[0091]

[0092] The subscript n represents the g...

Embodiment 3

[0125] Based on the above paleoclimate data assimilation method, the present invention provides an off-line ensemble Kalman filter paleoclimate data assimilation system based on analogy, comprising: an acquisition module for acquiring the observation y whose error covariance matrix to be assimilated is R; the assimilation The module is built based on the assimilation framework of the ensemble Kalman filter; the assimilation module first interpolates the state variable sample x into the observation y before assimilating the observation, and then according to each state variable sample x of the state variable sample x j With respect to the principle that the root mean square error of the observation y is the smallest or the correlation coefficient is the largest, the first N samples sorted in order selected from the state variable sample x constitute members of the prior set; the assimilation module, at the time of assimilation, Use square root filtering to assimilate the observa...

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Abstract

The invention discloses an analogy-based off-line ensemble Kalman filtering paleoclimate data assimilation system and method and electronic equipment. The data assimilation method is carried out under an assimilation framework of ensemble Kalman filtering, and before assimilation observation, based on an analogy method, an ensemble prior member is selected through a certain standard (relative to an observed root-mean-square error or a correlation coefficient); and the set mean value and the set disturbance are updated instead of the traditional static set priori members which are randomly grabbed. Compared with an online cyclic assimilation method, the method has the advantage that the huge calculation amount of forward integration of the set mode is reduced. Compared with a general assimilation method of'off-line 'repeatedly using a randomly captured priori set, the method of the invention can construct more accurate priori set average, simultaneously capture background field error covariance information of'flow dependence', and further reduce errors after assimilation.

Description

technical field [0001] The invention relates to a method for reconstructing a paleoclimate field, which is an assimilation system, method and electronic equipment based on analogy and ensemble Kalman filtering. Background technique [0002] Understanding paleoclimate phenomena can improve forecasting skills for future climate change, and paleoclimate field reconstructions use paleoclimate proxies to assess climate status. Most paleoclimate field reconstruction methods use multiple linear regression to obtain climate field variables, but are limited by the nonlinear relationship between proxy indicators and climate variables. [0003] Correspondingly, paleoclimate data assimilation is a method to find the optimal estimate of the state of the climate system by combining climate model dynamical system constraints and proxy observation information. Among them, the traditional ensemble-based data assimilation technique uses the analysis field ensemble updated by observation at t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/16G06F119/02
CPCG06F30/20G06F17/16G06F2119/02Y02A90/10
Inventor 雷荔傈孙浩昊谈哲敏
Owner NANJING UNIV
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