Improving Week 2 Weather Forecasts Through "Re-Forecasting"
| Thomas M. Hamill and Jeffrey S. Whitaker NOAA/ESRL Physical Sciences Division Science Writer: Susan Bacon University of Colorado |
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Ensemble Forecasts of Weather from Past DecadesBetween model errors and chaos, a two-week forecast of the weather taken directly from the computer has nearly no skill at all. It is hard to determine what aspects of the weather remain predictable versus which are unpredictable. It can also be hard to determine whether, say, a cold, wet forecast indicates snow or just a systematic tendency for the model to be too cold or too wet. Whitaker and Hamill address both these problems by generating a collection, or "ensemble" of 2-week computer forecasts for each day during the last 23 years. Each member of the ensemble was started from only a slightly different estimate of the starting weather condition. The Whitaker and Hamill approach does not result in a perfect forecast, they found that they could make a much better forecast. They used the data set of old forecasts to understand and correct for the effects of chaos and model error in the current forecast. First, with an archive of old forecasts and the actual weather that happened, they were able to determine errors in the model, where it was consistently too warm or too cold, too wet or too dry. They could then adjust the current forecasts to back out these errors. |
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Figure 1. Here we show the forecast bias as a function of the time of year at St. Louis, Missouri. The bias is the average forecast error (positive bias indicates the forecast is too warm). The bias was determined by comparing the 23 years of past week 2 forecasts to the actual observed week 2 temperature. With a knowledge of these biases (too warm in winter and too cold in summer), the current forecasts can be corrected, making them more accurate. |
| Second, by running an ensemble of forecasts, the consistency between these forecasts provided a way of distinguishing between situations that were predictable and those that were unpredictable. If the ensemble of forecasts were all very different from each other, then the forecast was largely unpredictable. However, in situations where the forecasts were all indicating similar temperature or precipitation anomalies, that consistency provided an indication that the forecast that day was predictable. Whitaker and Hamill demonstrated the validity of this technique using a relatively crude version of the weather forecast model that the National Weather Service (NWS) uses to make their forecasts. Only by using this simpler model was it computationally feasible to run the current ensemble of forecasts and ensembles for the past 23 years. Nonetheless, Whitaker and Hamill's forecasts proved to be more skillful than the operational week 2 forecasts produces by the NWS. These forecasts were produced subjectively with forecasters manually synthesizing different computer forecasts but without the aid of a database of past forecasts. As a result of Whitaker and Hamill's efforts, the NWS will be adopting their approach as a starting point for making week 2 forecasts. |
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