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1-Mecikalski-MultiSensorCI1-4h_v2.pdf
Preview of Storm Initiation Forecasting: A Multi-sensor Approach
🔗 Source: cwg.eumetsat.int
📊 Size: 1.82 MB
👤 Author: Li, Xiang
⬇️ Downloads: 60

Summary

Mecikalski et al. present a statistical method for 1-4 hour forecasts of convective storm initiation (CI), combining observational and numerical weather prediction (NWP) data. The goal is to create a probabilistic CI nowcast product with 30-minute updates and 5 km resolution.

Motivation:
- 1-4 hour CI forecasting is challenging despite extensive research on nowcasting.
- CI processes involve interactions of mesoscale and synoptic scale settings, land-surface processes, and orography.

Approach:
1. CI Event Detection: Define CI as ≥ 35 dBZ radar echo at the surface or -10°C level, with a 20 km radius Region Of Interest (ROI).
2. Training Database: Characterize pre-thunderstorm conditions using NASA, NOAA, and RAP model fields within the ROI for 5 hours before CI.
3. Machine Learning: Develop statistical models (Random Forest, SVM, Logistic Regression) relating background conditions to CI occurrence.
4. Generate Predictions: Create CI predictions for 1, 2, 3, and 4 hours into the future.

Study Domain & Data Sources:
- Training: 2014-07-22 04Z to 2014-07-24 00Z; Testing: 2014-08-07 00Z to 2014-08-09 00Z, focusing on weaker synoptic settings in SE U.S.
- Data sources include MRMS radar, GOES, topography, precipitation, MODIS vegetation and land cover, RAP model fields, and LIS soil moisture/temperature.

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Description

Researchers propose a statistical method combining observational and numerical weather prediction data for 1-4 hour forecasts of convective storm initiation.

Technical Information

  • File Format: PDF
  • File Size: 1.82 MB
  • Pages: 21
  • Language: EN
  • Author: Li, Xiang
  • Total Downloads: 60
  • Last Updated: 6 days ago

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