Quasi-ensemble-variation based mixed data assimilation method

Abstract

The invention discloses a quasi-ensemble-variation based mixed data assimilation method which comprises the following steps of: selecting 12-hour and 24-hour forecast data stored per 6 hours in historical forecast data of a past month, adjacent to forecast moment, and taking the data as a quasi-ensemble forecast sample; calculating the difference between 24-hour forecast and 12-hour forecast at the same moment, and obtaining quasi-ensemble forecast errors; calculating a mean value of the quasi-ensemble forecast errors, and substituting the mean value and the quasi-ensemble forecast errors into an unbiased estimation formula to obtain unbiased estimation; and substituting the unbiased estimation into a quasi-ensemble-variation assimilation algorithm, and carrying out mixed assimilation. The method is used for calculating historical forecast errors to obtain quasi-ensemble background errors, and is applied to quasi-ensemble-variation mixed data assimilation. The quasi-ensemble background errors are generated through adjacent historical forecast results without real ensemble forecast, so that the calculated amount for the ensemble forecast is effectively reduced and the efficiency for business data assimilation and forecast is improved.

Description

technical field [0001] The invention relates to a mixed data assimilation method based on quasi-ensemble-variation, and belongs to the technical field of data assimilation in numerical weather forecasting. Background technique [0002] The quality of numerical weather prediction is jointly determined by the numerical prediction model and the initial field of the model. At present, the model structure and physical process scheme of numerical forecasting have tended to be perfected, which can more accurately describe and simulate the evolution of real weather systems. Therefore, the task of improving the accuracy of numerical weather prediction falls more on how to improve the initial field of the model—the numerical weather prediction has higher and higher requirements for the accuracy of the initial conditions. With the development of software and hardware technology and observation systems, the continuous upgrading of the global meteorological observation network, the cont...

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Problems solved by technology

It can be seen from formula (2) that when the ensemble forecast error covariance is introduced into the mixture, the calculation of the ensemble members is required, and if the ensemble members are too few, the ensemble forecast error covariance will be dissatisfied with the rank and the variables are not coordinated. Although the mixture The assimilation scheme alleviates this problem, but the hybrid assimilation method still needs a certain amount of ensemble forecast results as the calculation samples of the error covariance of the ensemble background field at each assimilation time. If samples of 120 ensemble members are required, 120 times model forecast

For some research and business units whose computing conditions are not very sufficient, it still brings a lot of computing pressure, and even affects the efficiency of business forecasting

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