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Long-Range Forecast Users Guide

Description of the maps

The forecasts charts are composed of 2 panels: the forecast (upper panel) and the expected skill (lower panel) of the forecast system for this particular forecast.

The forecast is presented in three categories: below normal, near normal and above normal. The method used to define these categories is explained in the next section. The above normal category is depicted with a red color while the below normal category uses blue color. The white areas are predicted as being near normal. Upper right corner legend provides information on the color used for each category. The lower left corner one, provide information on: the forecast field (temperature or precipitation anomaly), forecast validity period, issue date and climatological period used.

The expected skill for this forecast is shown using the historical percent correct. The skill map has 2 legends. The lower left corner, provide a generic description of the map (example: Historical Percent Correct, 1 to 3 month Temperature Anomalies Forecast) and the validity period for this map. The other legend, using color, indicate the correspondance between the color on the map and the expected skill for the Historical Percent Correct (between 0 and 100%). Only regions with percent correct value greater than 40% are highlight. The grey color indicate areas with percent correct values below 40%, indicating that the skill of the forecast (for those areas) is not significantly better than chance.

The percent correct were calculated using the historical CanSIPS re-forecasts (also known as hindcasts). Re-forecasts are calculated for the 1981-2010 period. Derome et al., 2000; Plante and Gagnon, 2000 give an example of how the historical forecasts are created, although for one of the older CanSIPS setups. The historical percent correct is an evaluation of the past performance of the forecasts. Although there is no warranty that this skill map will be an accurate estimation of future skill, this is the best estimate currently available.

Definition of the categories

The forecasts are categorized as below normal, near normal and above normal. The threshold used to define the category is 0.43 times the interannual seasonal standard deviation of the variable (i.e. temperature or precipitation). This choice makes the category equiprobable (same probability) on average. Get the observed climatology and threshold maps.

Category information for temperature forecast:

Category information for precipitation forecast:

How to use the maps (example for the temperature anomaly forecast)?

  1. Locate the area of interest on the skill map and make sure that the historical percent correct is higher than 40% (colored areas). If it is the case, go to step 2. If it is not the case, the confidence in the forecast skill is not significantly better than chance. The confidence you put in the forecast must be considered as very low. Therefore, it is not recommended to use the forecast for this particular area of interest.
  2. Locate the area of interest on the seasonal forecast map and note the forecast category (above, below or near normal).
  3. Using the climatology map for temperature for the same season, note the average seasonal temperature for the area of interest.
  4. From the threshold maps for the same season, at the area of interest note the threshold value.
  5. How, for each of those values (climatology and threshold), can we get the forecast value for each category?.
    • For the above normal category, the temperature is forecast to be warmer (greater) than the climatological value plus the threshold value
    • For the below normal category, the temperature is forecast to be colder (less) than the climatological value minus the threshold value
    • For the near normal category, the temperature is forecast to be near the climatological value plus or minus the threshold value

Usage of the maps, for the precipitation anomaly forecast, is done through the same steps, except that you have to look at the anomaly forecast, climatology and threshold maps for the precipitaton.

It has to be noted that the surface air temperature forecast is a prediction of the anomaly of the mean daily temperature at 2 metres (i.e. at standard observation Stevenson screen height). It is not a forecast of the maximum nor of the minimum daily temperature. For more information on what is predicted by Environment Canada seasonal forecasts please read this frequently asked questions page.

Examples

  1. If the climatological temperature for the area of interest is -18 Celsius and the threshold value is 1.2 Celsius, the above, below and near normal categories are defined by the following values:
    • above normal: temperature forecast to be equal to or warmer than -16.8°C.
      -16.8°C = -18.0°C + 1.2°C.
    • below normal: temperature forecast to be equal to or colder than -19.2°C.
      -19.2°C = -18.0°C - 1.2°C.
    • near normal: temperature forecast to be between -16.8°C and -19.2°C.
  2. If the climatological temperature for the area of interest is +6 Celsius and the threshold value is 0.3°C, the above, below and near normal categories are defined by the following values:
    • above normal: temperature forecast to be equal to or warmer than 6.3°C.
      6.3°C = 6.0°C + 0.3°C.
    • below normal: temperature forecast to be equal to or colder than 5.7°C.
      5.7°C = 6.0C - 0.3°C.
    • near normal: temperature forecast to be between 5.7°C and 6.3°C.
  3. If the climatological precipitation for the area of interest is 300 millimetres (mm) and the threshold value is 30 mm, the above, below and near normal categories are defined by the following values:
    • above normal: precipitation forecast to be equal to or greater than 330 mm (in water equivalent).
      330mm = 300mm + 30mm.
    • below normal: precipitation forecast to be equal to or less than 270mm (in water equivalent.
      270mm = 300mm - 30mm.
    • near normal: precipitation forecast to be between 270 and 330 mm (in water equivalent).

References

Derome J., G. Brunet, A. Plante, N. Gagnon, G. J. Boer, F. W. Zwiers, S. J. Lambert, J. Sheng, et H. Ritchie, 2001: Seasonal Predictions Based on Two Dynamical Models.Atmos. Ocean., 39, 485-501. [article] (Requires Acrobat Reader to view)

Plante A. et N. Gagnon, 2000: Numerical Approach to Seasonal Forecasting. In "Proceedings of the sixth workshop on operational meteorology", Halifax, Novembre 1999, 162-165. (Requires Acrobat Reader to view)

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