Tropical Cyclone Prediction System Ensemble Forecasting
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Solution Overview
Problem
Current tropical cyclone forecasting systems provide limited accuracy beyond five days, relying on historical forecast errors and failing to provide reliable predictions of storm tracks and characteristics, which hampers civic planning and emergency preparations.
Innovation Solution
A tropical cyclone prediction system (TCPS) that combines ensemble data from various global meteorological models, assigns scaling factors based on historical accuracy, and uses predictive track data to provide probabilistic forecasts of storm tracks, intensity, and landfall characteristics, offering uncertainty measures beyond traditional forecasting limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional forecasting systems are used, then historical forecast errors are relied upon, but forecasting accuracy beyond five days deteriorates
Solution Approach 1:
The system segments the forecasting process into multiple independent model runs (ensemble members), each providing a possible track scenario. This segmentation allows the system to capture uncertainty and provide reliable probabilistic forecasts beyond five days by aggregating results from multiple segmented model executions rather than relying on a single deterministic forecast.
Solution Approach 2:
The system performs preliminary actions by pre-computing multiple ensemble member tracks and storing them for later analysis. This preliminary computation of various possible tracks allows the system to provide accurate probabilistic forecasts at extended lead times without requiring real-time re-computation, thus improving reliability beyond the traditional five-day limit.
2Measurement precision
If ensemble data from multiple models is combined, then forecasting accuracy improves, but system complexity increases
Solution Approach 1:
The system merges track data from multiple independent meteorological models into a unified ensemble forecast product. By combining forecasts from different models (e.g., GFS, ECMWF, UKMET) into a single probabilistic framework, the system achieves improved track prediction precision while managing complexity through standardized data integration procedures.
Solution Approach 2:
The system introduces an intermediary statistical framework that mediates between raw model outputs and final probabilistic forecasts. This intermediary layer standardizes and harmonizes data from multiple diverse models, enabling accurate combination while isolating the complexity of individual model differences from the final prediction process.
3Loss of information
If probabilistic forecasts with uncertainty measures are provided, then civic planning and emergency preparation improve, but information processing requirements increase
Solution Approach 1:
The system changes the parameter representation from deterministic single-track forecasts to probabilistic distributions characterized by key parameters (mean track, spread, confidence intervals). This parameter transformation efficiently captures uncertainty information in a compact form that improves civic planning while maintaining processing efficiency through reduced data dimensionality compared to full ensemble members.
Data Source
AI summary
A method of predicting information related to a characteristic of a tropical cyclone includes obtaining a plurality of model tracks corresponding to the tropical cyclone from at least one forecast center. A factor from a set of factors is assigned to each of the plurality of model tracks. A value for at least one characteristic for the tropical cyclone at the intersection of each of the plurality of model tracks with a boundary is predicted. A set of probabilities for the value of the at least one characteristic corresponding to an actual value of the at least one characteristic at the time of intersection of the tropical cyclone with the boundary is calculated.