Numerical: Precipitation
1. Regional Deterministic Precipitation Analysis (RDPA)
Description:
The RDPA offers a best estimate of precipitation amounts over recent 6-hour and 24-hour periods. It integrates data from in situ precipitation gauges, weather radar, satellite imagery, and numerical weather prediction models. The analysis covers North America, including Canada, the United States, and Mexico, at a horizontal resolution of 10 km. Analyses are produced four times daily for 6-hour intervals and twice daily for 24-hour intervals.
Available Variables:
Precipitation Amount (PR): Measured in millimeters (mm) over 6-hour and 24-hour intervals.
Confidence Index of the Analysis (CFIA): A unitless index ranging from 0 to 1, indicating the weight of observations in the analysis. A value close to 1 suggests a higher contribution from observations, while a value near 0 indicates reliance on the model's trial field.
Potential AI/ML Applications:
Developing models for precipitation forecasting.
Assessing the reliability of precipitation estimates based on the confidence index.
2. High Resolution Deterministic Precipitation Analysis (HRDPA)
Description:
The HRDPA provides a high-resolution estimate of 6-hour and 24-hour precipitation amounts. It combines data from in situ gauges, radar Quantitative Precipitation Estimates (QPEs), and a trial field from a numerical weather prediction system. The analysis is conducted at a 2.5 km resolution, offering more detailed spatial information. Analyses are produced four times daily for 6-hour amounts and twice daily for 24-hour amounts.
Available Variables:
Precipitation Amount (APCP): Accumulated precipitation in millimeters over specified intervals.
Confidence Index of the Analysis: Indicates the weight of observational data in the analysis, similar to the CFIA in RDPA.
Potential AI/ML Applications:
Enhancing localized precipitation forecasting models.
Analyzing fine-scale precipitation patterns for urban planning and water resource management.
3. High Resolution Ensemble Precipitation Analysis (HREPA)
Description:
The HREPA is an ensemble-based precipitation analysis that provides probabilistic estimates of precipitation. It utilizes multiple model runs to assess uncertainty and offers a range of possible precipitation outcomes.
Available Variables:
Ensemble Mean Precipitation: Average precipitation amount across ensemble members.
Precipitation Spread: Measure of variability among ensemble members, indicating forecast uncertainty.
Potential AI/ML Applications:
Developing probabilistic precipitation forecasts.
Assessing risk and uncertainty in precipitation predictions for decision-making processes.
4. High Resolution Deterministic Precipitation Analysis Averaged by Watershed (HRDPA Watershed)
Description:
This product provides precipitation analyses averaged over predefined watershed areas. It is derived from the HRDPA and offers insights into precipitation distribution within specific hydrological basins.
Available Variables:
Average Watershed Precipitation: Mean precipitation amount in millimeters over each watershed area.
Average Confidence Index: Mean confidence index for the analysis within each watershed, indicating the reliability of the precipitation estimates.
Potential AI/ML Applications:
Modeling hydrological responses and flood forecasting.
Managing water resources and planning for agricultural activities.
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