Canadian Ensemble Forecasts (MSC)

Products and information:

• Spaghetti plots
• Calibrated probability of equivalent precipitation
• Accumulated quantity of precipitation
• Sea level pressure centres
• GZ 500 maps
• Ensemble spread of trial fields
• Information on the system
• Digital data access

Spaghetti plots

In the spaghetti plots one can see both the position of a contour line and its uncertainty. We also show the standard deviation for the 500 hPa height (with the background colour). It may happen that the contour lines are far apart but that the gradients are not very important. In such a case, the forecasts may still be considered to be reliable.

Calibrated probability of equivalent precipitation

In these figures we show the probabilities that the accumulated amount of (melted or liquid) precipitation in a 12 hour period exceeds thresholds of 2, 5, 10 or 25mm. Raw probability intervals are at less than 22 % (at most 4 members are above the threshold), between 22 and 47 % (5 to 9 members above the threshold), between 47 and 72 % (10 to 14 members above the threshold) or more than 72 % (15 to 20 members above the threshold).

We performed a calibration of our precipitation forecasts. This allowed us to replace the raw probabilities of 22, 47 and 72 % by calibrated values.

Accumulated quantity of precipitation

First we show a contour plot of the ensemble mean forecast. On this plot we put small red numbers for the precipitation centres of the corresponding members. The verifying analysis is added on with the dashed red line. The next plots show the forecasts of the global GEM, control and the 20 ensemble members.

Sea level pressure centres

First we show a contour plot of the ensemble mean forecast. On this plot we put small red numbers for low centres and small blue numbers for high centres of the corresponding members. The verifying analysis is added on with the dashed red line. The next plots show the forecasts of the global GEM, control and the 20 ensemble members.

GZ 500 maps

First we show a contour plot of the ensemble mean forecast. On this plot we put small red numbers for low centres and small blue numbers for high centres of the corresponding members. The verifying analysis is added on with the dashed red line. The next plots show the forecasts of the global GEM, control and the 20 ensemble members.

Ensemble spread of trial fields

Every 6 hours, we compute the standard deviation, in Celsius, from the trial fields used in the assimilation cycles of the temperature at a level at approximately 5 km of altitude. Where the standard deviation of the trial fields is large, the incertitude is large. It is important to have good observations for these uncertain areas. One might target additional observations in such areas to try to diminish the uncertainty.

Information on the system

• The daily ensemble forecasts have been available on an operational basis since January 24, 1996. They were originally performed with eight members. Then, on August 24 1999, eight more members were added creating a 16-member ensemble forecast system. On January 12 2005, the Optimal Interpolation Technique for the analysis cycle was replaced with the Ensemble Kalman Filter Technique. Starting in July 2007, four more members were added to produce a 20-member ensemble. Since that time, the system is regularly upgraded (higher resolution, better initial conditions, better model, etc.).
• A description of the perturbed models currently used in the operational forecasting system can be viewed here.
• The MSC ensemble forecasts presented on the current page is part of North American Ensemble Forecast System (NAEFS). This initiative is an operational collaboration between the United States National Weather Services (NWS), the National Meteorological Service of Mexico (NMSM) and the MSC. Products are made using the merge of the MSC and the NCEP ensemble data (superensemble of 40 members). These products can be viewed on the following web site.
• Training on ensemble forecasting is available here. This training covers the basics concepts in probability and statistics as well as in numerical modelling. We also explain how the ensemble prediction systems work. The products generated with these forecasts are described and guidelines are provided to help the use of this kind of products. Additionally, several applications through case studies are presented.

Digital data access

Access to ensemble data in digital format:

• GEPS data in GRIB2 format
• NAEFS global ensemble city data in XML format
• Online access of MSC and other global ensemble archived data on the external WMO THORPEX TIGGE site