Numerical: Ensemble
1. Global Ensemble Prediction System (GEPS)
Description:
The GEPS performs physics-based calculations to generate probabilistic forecasts of atmospheric elements from the current day up to 16 days into the future. Once a week, on Thursdays at 00 UTC, the forecast extends up to 32 days. The system produces multiple scenarios to estimate forecast uncertainties arising from the atmosphere's nonlinear behavior. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions and physics parameters, which are randomly perturbed using a Stochastic Parameter Perturbation (SPP) method. A control member without perturbations is also available.
Available Variables:
Air Temperature: Predicted temperatures at various atmospheric levels.
Precipitation: Forecasted amounts and types of precipitation.
Wind Speed and Direction: Predicted wind vectors at different altitudes.
Humidity: Forecasted relative humidity levels.
Sea-Level Pressure: Predicted atmospheric pressure at sea level.
Cloud Cover: Forecasted cloud fraction and distribution.
Potential AI/ML Applications:
Developing probabilistic weather forecasting models.
Assessing forecast uncertainty for risk management.
Enhancing decision support systems in sectors like agriculture and energy.
2. North American Ensemble Forecast System (NAEFS)
Description:
NAEFS is a collaborative effort that combines ensemble forecasts from multiple North American meteorological agencies, including Environment and Climate Change Canada (ECCC) and the National Centers for Environmental Prediction (NCEP) in the United States. By integrating outputs from different ensemble systems, NAEFS aims to improve forecast accuracy and provide a comprehensive probabilistic outlook for the North American region.
Available Variables:
Temperature: Ensemble-based temperature forecasts at various levels.
Precipitation: Probabilistic forecasts of precipitation amounts and types.
Wind: Ensemble forecasts of wind speed and direction.
Humidity: Probabilistic humidity forecasts.
Pressure: Ensemble-based sea-level pressure forecasts.
Potential AI/ML Applications:
Integrating multi-model ensemble data for improved regional forecasts.
Developing machine learning models to interpret ensemble spread and predict forecast confidence.
Enhancing early warning systems for extreme weather events.
3. Regional Ensemble Prediction System (REPS)
Description:
The REPS conducts physics-based calculations to provide probabilistic predictions of atmospheric elements from the current day up to 3 days into the future. The system utilizes 20 ensemble members, each perturbed through their initial and boundary conditions as well as physical tendencies. A control member without perturbations is also available. Geographical coverage includes Canada and the United States, with data available at a horizontal resolution of 10 km and on ten vertical levels. Predictions are performed four times a day.
Available Variables:
Air Temperature: Predicted temperatures at various atmospheric levels.
Precipitation: Forecasted precipitation amounts and types.
Wind Speed and Direction: Predicted wind vectors at different altitudes.
Humidity: Forecasted relative humidity levels.
Cloud Cover: Forecasted cloud fraction and distribution.
Potential AI/ML Applications:
Enhancing short-term regional weather forecasts.
Developing probabilistic models for severe weather prediction.
Supporting emergency response planning with high-resolution ensemble data.
4. Canadian Seasonal to Inter-annual Prediction System (CanSIPS)
Description:
CanSIPS is designed to provide seasonal to inter-annual climate predictions. It combines multiple climate models to generate ensemble forecasts, offering insights into climate variability and aiding in long-term planning across various sectors.
Available Variables:
Temperature Anomalies: Predicted deviations from average temperatures over extended periods.
Precipitation Anomalies: Forecasted deviations from average precipitation levels.
Sea Surface Temperature: Predicted temperatures of the ocean surface, crucial for understanding climate patterns.
Soil Moisture: Forecasted soil moisture content, important for agriculture and water resource management.
Potential AI/ML Applications:
Developing models to predict seasonal climate trends.
Assessing the impact of climate variability on agriculture, water resources, and energy demand.
Informing policy and decision-making related to climate adaptation and mitigation strategies.
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