May 21 - July 31
A Comparison of Mesoscale Analysis Systems Used for Severe Weather Forecasting
Rebecca Steeves, Dustan Wheatley, and Michael Coniglio
What is already known:
- An accurate depiction of the mesoscale environment is important to identifying the severe convective weather threat. Numerical weather prediction is an important part of operational severe convective storm forecasting.
- Evaluating the error and bias characteristics of mesoscale analysis systems (single model and ensemble based) used for severe weather forecasting can assist the forecaster in interpreting the model systems' output.
What this study adds:
- A preliminary investigation into the use of a WRF-based mesoscale ensemble system for operational severe convective storm forecasting.
- From the 19 cases used and specific parameters/characteristics analyzed, WRF-based ensemble produced comparable or slightly lower errors to other analysis systems. These results support continued exploration of a WRF-based mesoscale ensemble system for use in operation on a larger scale.
Abstract:
The relative performances of several mesoscale analysis systems are evaluated with regard to severe convective weather forecasting, by exploring their ability to reproduce soundings collected in pre-convective and near storm environments observed during the Verification of the Origins of Rotation in Tornados Experiment 2 (VORTEX2) field phase. This was done to investigate a greater use of mesoscale ensemble forecasts in the operational setting. Soundings that matched the geographical locations and release times of the VORTEX2 soundings were extracted from datasets of the Rapid Update Cycle (RUC) model, the Surface Objective Analysis (SFCOA) developed by the Storm Prediction Center, and a Weather Research and Forecasting (WRF) mesoscale ensemble system, developed at the National Severe Storms Laboratory (NSSL). Parameters and characteristics important to severe weather forecasting are extracted from the systems’ datasets at the observed sounding locations and compared to the observations. Results show that the mesoscale ensemble forecasts, in many cases, produce smaller errors than the other mesoscale analyses considered when calculating the planetary boundary layer height, surface based convective available potential energy, the surface based lifted condensation level, and near surface temperatures and dew points. Findings thus far display the potential of the mesoscale ensemble models to produce an accurate depiction of the mesoscale environment.