Abstract ID: 150
Mesoscale Convective Systems in the Arabian Peninsula: Subseasonal to Seasonal Forecast and Tracking Capability through High Resolution Regional Climate Modeling
Lead Author: Christopher Castro
The University of Arizona, United States of America
Keywords: subseasonal to seasonal forecast, regional climate modeling, mesoscale convecitive system, MCS tracking
Abstract: Precipitation events, especially convective extremes, in the Arabian Peninsula (AP) often are not well forecasted. Many of the top convective extremes are observed near Jeddah during the cool season and produced flash flooding in the city and neighboring Mecca. Improving forecast capability of these organized convections at longer forecast lead time is critical for public safety and disaster risk mitigation.
Two sets of dynamically downscaled regional climate modeling subseasonal to seasonal (S2S) reforecasts were completed for the entire AP subcontinent. We dynamically downscaled the European Centre of Medium-range Weather Forecasts (ECMWF) using Weather Research and Forecasting Model (WRF) at convective-permitting resolution (4 km). First for the top 20 convective extreme events and second for 20 years of cool season (November to April) from 1998-2017. Both event-based and climatology reforecast downscaling were initialized at 1-week to 4-week lead time.
MCS tracking method is calibrated specifically for the AP MCSs. Cloud top temperature from both satellite-based observation (MERGIR) and WRF downscaled ERA5 Reanalysis developed at King Abdullah University Science and Technology (KAUST) are used to calibrate the tracking code and create MCS tracking baseline climatology. WRF S2S ensembles have reasonable forecast capability to represent both the extreme events and the 20-year cool season climatology across different lead times. Our result highlights the value-added using regional climate modeling at the spatial resolution sufficient to resolve convection. Further, tracking results using the WRF S2S reforecast ensembles are compatible to the observed tracks, for both extreme events and seasonal climatology. WRF exhibits lower forecast capability near southern Red Sea that led to lesser MCS tracks over the coastal region due to sea surface temperature bias from driving ECMWF S2S reforecasts.
Christoforus Bayu Risanto (The University of Arizona)
Hsin-I Chang (The University of Arizona)
Thang Luong (King Abdullah University of Science and Technology)
Ibrahim Hoteit (King Abdullah University of Science and Technology)