Schematic Diagram
The following schematic diagram shows the four steps to generate a four-member ensemble forecast.
- Step 1: The process starts with a current deterministic multivariate prediction and a set of historical predictions from a deterministic weather model. The multivariate prediction includes surface temperature, humidity, wind speed, and so on. Corresponding observations to each historical forecasts are also collected.
- Step 2: A number of historical predictions are identified based on their similarity to the current multivariate prediction. This similarity is also time-dependent, meaning that, instead of point-to-point comparison, it also compares the trend of each weather variable within a short time range.
- Step 3: The corresponding observations associated with the identified historical predictions are selected.
- Step 4: These observations become ensemble members in the final forecast.
Simplified Example for Temperature Forecasts
Please navigate through the following slides to see the example.
Animation credited to Laura Clemente-Harding and Guido Cervone
- Step 1: A deterministic model has been running for a week and a new prediction is generated from the model. Red dots are temperature observations, and black dots are model predictions.
- Step 2: By comparing current and historical model predictions (black dots), most similar past forecasts are identified.
- Step 3: The corresponding observations are selected that are associated with the identified past predictions.
- Step 4: These observations become ensemble members in the final forecast.
Of course, in reality, the similarity metric is a time-dependent and multivariate metric.
References
- Analog Ensemble Package
- A Beginners Introduction to the Analog Ensemble Technique
- Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble
- Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble
- Probabilistic Weather Prediction with an Analog Ensemble
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