Meteorological large model prediction method based on data correction model

By combining topological-optimal transmission quantum annealing observation screening with diffusion inverse integral and symplectic decomposition pulse network, the problems of insufficient spatial skeleton identification and real-time performance in large meteorological model prediction are solved, and high-precision, low-power meteorological forecasts are achieved.

CN121348469AInactive Publication Date: 2026-01-16ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY
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Patent Information

Application Number
CN202511903637.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-17
Publication Date
2026-01-16
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing meteorological big model prediction methods have large errors in path and intensity under extreme convection and rapid typhoon intensification scenarios, and existing assimilation methods have difficulty in automatically identifying the spatial skeleton, resulting in insufficient real-time performance of probabilistic forecast products.

Method used

The topology-optimal transmission quantum annealing observation screening output weighted covariance is adopted, and then diffusion inverse integration is driven by the physics-observation composite gradient. Combined with symplectic decomposition pulse network in parallel inference on neuromorphic chip, energy is monitored in real time and the threshold is adaptive, so as to achieve high-precision and low-power uncertainty prediction.

Benefits of technology

It achieves high-precision meteorological large model forecasting, reduces power consumption, improves the real-time nature of forecasts and the ability to generate real-time uncertainty, and reduces path and intensity errors.

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Abstract

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously improved.
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