Precipitation Prediction Modeling With Reanalysis Variables and ResNet
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Solution Overview
Problem
Existing methods for seasonal precipitation prediction in complex climate regions with dense population and industry are inadequate, failing to provide accurate and reliable forecasts for drought and flood disasters, which significantly impact economic and social development.
Innovation Solution
A modeling method that utilizes reanalysis data from weather forecast centers like ECMWF to obtain circulation variable information and observed precipitation data, training a precipitation prediction model using a dual-module architecture combining an adapter module and ResNet50 for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used for seasonal precipitation prediction, then the prediction process is simple, but the prediction accuracy is insufficient for complex climate regions
Solution Approach 1:
The precipitation prediction model is divided into two independent modules: a circulation variable extraction module that processes reanalysis data to obtain circulation variables, and a precipitation prediction module that uses these variables to predict precipitation. This segmentation allows each module to be optimized independently, improving overall prediction accuracy while managing complexity through modular design.
Solution Approach 2:
Circulation variables serve as an intermediary between the reanalysis data and the precipitation prediction. The extraction module transforms raw reanalysis data into meaningful circulation variables (such as atmospheric circulation patterns), which then serve as inputs to the prediction module. This intermediary step improves prediction accuracy by focusing on the most relevant features while managing data complexity.
2Measurement precision
If more data sources are integrated into the model, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The model uses a unified reanalysis data product (ERA5) that provides multiple types of atmospheric variables simultaneously. This single data source serves multiple functions: providing circulation variables for prediction, offering quality control metrics, and supplying various atmospheric parameters. This multi-functionality approach improves prediction accuracy through comprehensive data while avoiding the complexity of integrating and harmonizing multiple separate data sources.
Solution Approach 2:
The model transforms raw reanalysis data into derived circulation variables through parameter changes and mathematical transformations. Instead of directly using all raw parameters, the extraction module computes specific circulation indices and patterns that are most relevant for precipitation prediction. This parameter transformation approach improves prediction accuracy by focusing on meaningful features while reducing data processing complexity through selective transformation.
Data Source
AI summary
The present disclosure provides a modeling method for precipitation prediction model. The specific scheme is: obtaining circulation variable information of a specified region in each time period of at least two time periods before a specified time moment based on reanalysis results of a weather forecast center; obtaining observed precipitation of the specified region in a next time period after the specified time moment based on a pre-collected observation dataset of the specified region; training a precipitation prediction model of the specified region based on the circulation variable information of the specified region in each time period of at least two time periods before the specified time moment and the observed precipitation of the specified region in the next time period after the specified time moment.