Developing tracking capability for mesoscale convective systems in the Arabian Peninsula through observations and model-based subseasonal reforecasts

Organized mesoscale convective systems (MCSs) over the Arabian Peninsula (AP) are a major driver of extreme precipitation and flash flooding in the cool season (October - April). Existing criteria for MCS tracking methods do not capture this phenomenon over the AP, including several record-breaking MCS-driven extreme precipitation events that caused significant socioeconomic losses in the Kingdom of Saudi Arabia (KSA). In this study, we evaluate the MCS tracking capability and calibrate regional tracking criteria for the AP.

Based on several MCS-driven precipitation events over the past 20 years, AP MCS criteria are updated as follows: size over 20,000 km², cloud-top temperature less than 230 K, and merging/splitting duration over 3 hours. The AP MCS tracking criteria are also updated specifically for application to convective-permitting Weather Research and Forecasting model (WRF) output. WRF MCS size and durations are similar to observed MCSs, but the core cloud temperature threshold is lowered to 218 K.

The MCS tracking algorithm is then applied to a 20-year MERGIR brightness temperature (Tb) dataset and a corresponding 20-year subseasonal WRF (4-km grid spacing) ensemble reforecast product. The WRF subseasonal ensemble reforecasts are available at 1-week to 4-week lead times. Forecast skill is assessed using categorical statistics such as the Critical Success Index, combined with a neighborhood verification method to reduce double-penalty effects.

The AP MCS tracking results based on WRF subseasonal ensembles exhibit robust tracking capability in both early and late cool season, with respect to seasonal climatology and extreme convective case studies. The convective-permitting reforecast demonstrates subseasonal forecast skill and the potential to enhance early warning capabilities for public safety and disaster risk mitigation.