AI Wind Vector Prediction from Water Vapor Channels Under Cloud Interference
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Solution Overview
Problem
Existing methods for predicting wind vectors using satellite data are limited by the interference of cloud regions, particularly in water vapor absorption channels, which hinder accurate wind vector calculation.
Innovation Solution
A method utilizing an artificial intelligence network that processes satellite videos, including preprocessing and segmentation to distinguish cloud and clear sky regions, and employs learning networks for predicting wind vectors based on water vapor absorption channels, even in cloudy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If water vapor absorption channel is used for wind vector calculation, then wind vector can be obtained in clear sky regions, but cloud regions interfere with accurate calculation
Solution Approach 1:
The patent segments the satellite video into cloud regions and clear sky regions using a segmentation network. This allows the system to process only the clear sky regions for wind vector calculation using water vapor absorption channels, thereby eliminating cloud interference while maximizing the use of available data.
Solution Approach 2:
The patent introduces a segmentation network as an intermediary that identifies and separates cloud regions from clear sky regions. This intermediary component enables the subsequent wind vector calculation to proceed accurately by ensuring that only suitable regions are processed.
2Ease of operation
If traditional methods are used for wind vector prediction, then calculation is simple in clear sky regions, but prediction is difficult or impossible in cloudy conditions
Solution Approach 1:
The patent creates a universal system that handles both clear sky and cloudy conditions through a multi-functional architecture. The segmentation network identifies suitable regions, the learning network predicts wind vectors in clear sky regions, and the system synthesizes results to provide wind vector predictions across all regions including previously unusable cloudy areas.
Solution Approach 2:
The patent uses a learning network that learns from training data to copy and generalize wind vector prediction patterns. This allows the system to predict wind vectors in regions where direct calculation would be difficult, effectively copying successful prediction patterns from training examples to new situations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of wind vectors even in cloudy conditions, contributing to improved weather forecasting.
Implementation Method 1
A water vapor absorption channel in a satellite video may be used to calculate a wind vector based on the characteristics of water vapor
Data Source
AI summary
A wind vector prediction method includes receiving, by an analysis device, a weather video; inputting, by the analysis device, a first base video of a first time point for at least one water vapor absorption channel among a plurality of water vapor absorption channels included in the weather video and a reference video of a time point different from the first time point into a first learning network, and predicting, by the analysis device, a wind vector of a second time point after the first time point for the at least one water vapor absorption channel based on information output from the first learning network.