Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.
Bibliographical noteKAUST Repository Item: Exported on 2023-06-06
Acknowledgements: This work is supported by Binational Consortium of Regional Scientific Development and Inovation at The University of Arizona and the Consejo Nacional de Ciencia y Technología de México. Yoland L. Serra’s contributions to this work were funded by National Science Foundation Grant AGS-1261226. We thank Modhi Ali Alshammari and Chayan Roychaudury for reading and giving feedback on the manuscript. We also thank the editor and the reviewers that improved our manuscript. The authors declare there is no conflict of interest.
ASJC Scopus subject areas
- Atmospheric Science