Meteorological Data Filtering With Boundary-Aware Moving Average
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Solution Overview
Problem
Current meteorological data filtering methods, such as Lanczos filtering, suffer from boundary issues, edge effects, limited frequency domain characteristics, and high computational complexity, leading to inaccurate data processing and increased uncertainty in high-precision applications like weather forecasting.
Innovation Solution
A method involving improved moving average filtering, including data cleaning, weighted moving average filtering, trend analysis, and boundary processing, with techniques like zero-padding and mirror extension, to ensure data continuity and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Lanczos filtering is used for meteorological data processing, then frequency domain characteristics are improved, but boundary processing accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing mirror extension and zero-padding on the meteorological data before applying Lanczos filtering. This preprocessing ensures that boundary data points have sufficient neighboring points for the filter kernel, eliminating the boundary effect where the filter cannot fully cover boundary data points. The mirror extension creates virtual data points by mirroring the boundary values, while zero-padding adds zero values at the boundaries, both enabling the filter to operate effectively at data boundaries.
2Measurement precision
If Lanczos filtering is used for meteorological data processing, then low-pass filtering properties are improved, but edge effects causing ringing artifacts worsen
Solution Approach 1:
The patent applies preliminary anti-action by using mirror extension to create symmetric boundary conditions before filtering. This preliminary action counteracts the potential ringing artifacts by ensuring that the filter kernel has symmetric data points at the boundaries, preventing the unnatural fluctuations that would otherwise occur. The mirror extension creates a symmetric extension of the data at the boundaries, which anti-cacts against the edge effects that cause ringing.
3Reliability
If Lanczos filtering is used for meteorological data processing, then filtering effectiveness is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively applying Lanczos filtering only to the extended portion of the data (the mirror extension and zero-padding regions) rather than the entire dataset. The core filtering is performed on the original data with modified boundary conditions, while the excessive computation is avoided by not applying the full Lanczos kernel to all data points. This partial application reduces computational complexity while maintaining filtering effectiveness for the critical boundary regions.
Data Source
AI summary
The present disclosure provides a method, medium, and device for processing meteorological data based on improved moving average filtering. The method includes: collecting and cleaning original meteorological data to obtain to-be-processed meteorological data; performing weighted moving average filtering on the to-be-processed meteorological data to obtain filtered meteorological data; conducting trend analysis and boundary processing on the filtered meteorological data; and reconstructing the filtered meteorological data after being subjected to the trend analysis and boundary processing to ensure the data continuity and integrity. The present disclosure provides higher precision and reliability in the meteorological data processing process.