Forecast Calibration System for Long-Range Temperature Prediction
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Solution Overview
Problem
Current weather forecasting models face challenges in accurately predicting long-range temperature forecasts due to inherent biases and underdispersion, leading to incomplete representation of atmospheric uncertainty, especially for longer-range predictions.
Innovation Solution
The implementation of a forecast calibration system that uses Nonhomogeneous Gaussian Regression to calibrate ensemble forecasts by fitting climatology models to observational and reforecast data, generating a calibration function to correct for biases and improve forecast accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If physical models are used to simulate atmospheric processes for long-range forecasts, then the forecast coverage extends to 14-45 day lead times, but inherent biases and underdispersion reduce forecast accuracy and reliability
Solution Approach 1:
A calibration function serves as an intermediary between the physical forecast model output and the final temperature prediction. This calibration function, trained on historical reanalysis data, corrects systematic biases and adjusts dispersion characteristics of the model forecasts, thereby improving accuracy while maintaining extended forecast lead times
Solution Approach 2:
The calibration process transforms the forecast parameters by learning the relationship between model-predicted temperatures and actual reanalysis temperatures. This parameter transformation corrects biases and adjusts the distribution characteristics (underdispersion) of the forecast ensemble, enhancing reliability without sacrificing the long-range forecast capability
2Adaptability or versatility
If ensemble forecasts are used to represent atmospheric uncertainty, then the forecast captures multiple possible outcomes, but underdispersion leads to incomplete representation of uncertainty
Solution Approach 1:
The calibration function acts as a mediator that adjusts the ensemble dispersion characteristics. By learning from historical data, it scales and transforms the ensemble spread to better match actual atmospheric variability, ensuring that the calibrated ensemble properly represents uncertainty without being overly confident or dispersed
Solution Approach 2:
The calibration process uses feedback from historical reanalysis data to continuously improve the ensemble uncertainty representation. The calibration function learns from past forecast-observation pairs and adjusts the ensemble dispersion accordingly, creating a feedback loop that enhances the reliability of uncertainty quantification in long-range forecasts
Data Source
AI summary
In an approach, a computer receives an observation dataset that identifies one or more ground truth values of an environmental variable at one or more times and a reforecast dataset that identifies one or more predicted values of the environmental variable produced by a forecast model that correspond to the one or more times. The computer then trains a climatology on the observation dataset to generate an observed climatology and trains the climatology on the reforecast dataset to generate a forecast climatology. The computer identifies observed anomalies by subtracting the observed climatology from the observation dataset and forecast anomalies by subtracting the forecast climatology from the reforecast dataset. The computer then models the observed anomalies as a function of the forecast anomalies, resulting in a calibration function, which the computer can then use to calibrate new forecasts received from the forecast model.