Global medium-term numerical forecast GRAPES_GFS

Abstract

The invention discloses global medium-term numerical forecast GRAPES_GFS. The global medium-term numerical forecast GRAPES_GFS is characterized in that in the aspect of large-scale parallel computing,the improvement of an existing framework is proposed for the shortcomings of an existing semi-implicit semi-Lagrangian integration scheme in terms of computational efficiency and scalability, and a solving algorithm with high efficiency and high performance is proposed based on the semi-implicit integral scheme and linearization system. The non-equidistant difference is adopted to improve the calculation accuracy of the background temperature profile and solve the problem of large errors of the background temperature profile at the level of drastic change of model vertical stratification thickness. The digital filtering module is reconstructed and optimized to improve the stability and computational efficiency of digital filtering. A 3D-Var integrated single-point test system is established.

Description

technical field [0001] The invention belongs to the technical field of global medium-term numerical weather forecasting, and in particular relates to a GRAPES (Global / Regional Assimilation and Prediction System) global medium-term numerical forecasting method (GlobalForecast System), referred to as GRAPES_GFS. Background technique [0002] At present, the global medium-range numerical weather prediction system is the core of the numerical forecasting system, which not only provides boundary conditions and background information for regional mesoscale numerical forecasting, but also serves as the basis for global ensemble forecasting. The continuous development of global medium-range forecasting models and assimilation technology has largely promoted the overall research and forecasting level of numerical forecasting in the world[. Therefore, the development and continuous improvement of the global medium-range forecasting system is the key task of the weather forecasting cen...

AI Technical Summary

Problems solved by technology

The traditional deviation correction of observation data mainly depends on the difference between observation and background. There are two main problems in business application: (1) when there is a significant deviation in the model, the observation information will be wrongly corrected; (2) when the quality control is not When perfect, it will cause unreasonable observation bias estimation and correction

As far as the global model is concerned, the calculation accuracy of the semi-implicit semi-Lagrangian algorithm of the dynamical framework is low, and how to improve mass conservation is not considered, and the computational noise processing related to the semi-Lagrangian scheme has not been fully considered in the algorithm design Insufficient consideration of large-scale terrain processing and sub-grid terrain dynamics, insufficient optimization of model physical processes, and lack of careful assessment and adjustment of the coordination and balance between physical processes related to clouds and precipitation have seriously affected the accuracy of the model. Forecast stability and precision

As far as the global assimilation system is concerned, the isobaric surface analysis adopted by the quasi-operational version leads to unnecessary interpolation errors in the assimilation to the model space, inconsistencies between the analyzed variables and the predicted variables, the lack of reasonable estimation and optimization of the background error covariance, and the quality of the observation data. The problem of rough control and bias correction technology and lack of high-quality satellite data assimilation application technology affects the accuracy of assimilation

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