Probabilistic Weather Forecasting Using Doppler LIDAR Wind Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current weather forecasting models struggle to accurately predict extreme weather events like local downpours due to low reproducibility in numerical prediction models and the impact of air turbulence and ground surface changes, especially in urban areas.
Innovation Solution
A probabilistic weather forecasting system using Doppler LIDAR to acquire wind-condition pattern information, which is stored in a database system that associates this information with measured weather data, allowing for continuous updates and improved prediction accuracy by weighting wind-condition and measured weather information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic prediction using normal weather forecasting models is used, then the forecasting system can operate with standard models, but prediction accuracy of extreme weather in limited regions remains only several percent
Solution Approach 1:
The patent replaces deterministic numerical prediction models with a probabilistic forecasting system that uses Doppler LIDAR observations to detect wind-condition patterns. This substitution allows the system to capture air turbulence and random behaviors that deterministic models cannot reproduce, thereby improving prediction accuracy for extreme weather events while maintaining system operability.
Solution Approach 2:
The patent changes the fundamental parameter of prediction from deterministic values to probabilistic patterns. By using Doppler LIDAR to observe wind speed and direction patterns and comparing them against stored pattern information, the system transforms the prediction approach to account for randomness and turbulence, significantly improving extreme weather prediction accuracy.
2Measurement precision
If optimal forecasting models are constructed, then prediction accuracy can be improved, but it is difficult to continue highly accurate forecast continuously in urban areas where ground conditions change from moment to moment
Solution Approach 1:
The system uses Doppler LIDAR to continuously and autonomously observe current wind-condition patterns in the prediction region, eliminating the need for manual updates to forecasting models. The system self-adjusts by comparing real-time observations against stored pattern information, automatically adapting to changing urban ground conditions without requiring model reconstruction.
Solution Approach 2:
The patent pre-stores multiple wind-condition patterns and their associated weather outcomes in a database. When prediction is needed, the system quickly matches current Doppler LIDAR observations against these pre-stored patterns, enabling rapid adaptation to changing conditions without requiring time-consuming model recalibration.
3Measurement precision
If Doppler LIDAR observation and pattern matching is used, then accurate prediction of extreme weather can be achieved, but the system complexity increases compared to standard forecasting models
Solution Approach 1:
The patent creates simplified copies of complex atmospheric conditions by storing representative wind-condition patterns and their associated weather outcomes. Instead of modeling all physical processes, the system copies observed patterns and matches them against current Doppler LIDAR data, achieving high prediction accuracy with reduced computational complexity.
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
The system enables accurate prediction of extreme weather events by considering wind-speed variations and air turbulence, providing efficient and reliable forecasts even in rapidly changing urban environments.
Implementation Method 1
wind-condition information obtained from an air observation system using Doppler LIDAR
Data Source
AI summary
Provided is a weather forecasting system for predicting a weather phenomenon in a prediction target region. The weather forecasting system includes a storage unit and a first calculation unit. The storage unit stores wind-condition information and measured weather information. The wind-condition information is information obtained from an air observation system using Doppler LIDAR. The first calculation unit generates predicted weather information including presence of a local downpour, based on information related to wind convergence included in the wind-condition information and information related to instability of air included in the measured weather information.