"Extratropical cyclones cause much of the high impact and extreme weather conditions over the mid-latitudes, including heavy precipitation, high winds, coastal storm surges, and extreme cold events. On the other hand, lack of extratropical cyclone activity (ECA) in summer is linked to extreme heat. Hence skillful predictions of future cyclone activity will provide policy makers, emergency management, and stakeholders advanced warnings to prepare for mitigation measures. Unfortunately at present the National Weather Service does not provide any such forecast products in the subseasonal to seasonal time range.
The goal of this project is to improve the subseasonal prediction of ECA and its associated weather extremes. It has three specific objectives: i) Improve the understanding of the physical drivers that give rise to ECA predictability; ii) Improve the prediction of ECA and its drivers by focusing on the forecasting system set-up and model convection parameterizations; iii) Improve the forecasting of weather extremes associated with ECA variability. To achieve these objectives, the following tasks will be conducted: 1) Subseasonal prediction of ECA derived from multi-model ensemble hindcasts will be evaluated, and diagnostic and mechanistic model experiments will be conducted, to test the following hypothesis on ECA predictability: ECA predictability depends on the specific combinations of different drivers, such as the combination of the different phases of the Madden-Julian Oscillation and ENSO; 2) The choices of ensemble members, improved convection parameterizations that control diabatic heating and moisture sink profiles, as well as model resolutions will be investigated to improve the set-up of the forecasting system; 3) The impact of model biases and improvements in ECA prediction on the prediction of weather extremes will be quantified.
This project seeks to advance the subseasonal prediction of ECA and its associated weather extremes, thus contributing to NOAA’s goals to develop the capability to bridge weather and seasonal predictions, and to extend the lead times at which extreme events are skillfully predicted, thereby allowing emergency managers, water resource managers, and other stakeholders more time to prepare, hence this project is highly relevant to NOAA’s long term goals. This project seeks to understand the physical basis behind the subseasonal predictability of ECA, explore how the set-up of the prediction system and model convection parameterization impact system skill in predicting ECA and its drivers, and assess ECA predictability in the context of its impacts on weather extremes, thus this project is highly relevant to this competition."
Principal Investigator (s): Edmund Kar-Man Chang (Stony Brook University)
Co-PI (s):Minghua Zhang (Stony Brook University), Hyemi Kim (Stony Brook University), Wanqiu Wang (NOAA/CPC)
Task Force: S2S Prediction Task Force
Year Initially Funded:2016
Final Report: Final_Report_NA16OAR4310070 (2).pdf