How Do Microphysical Processes Influence Large‐Scale Precipitation Variability and Extremes?. Issue 3 (10th February 2018) - Hagos, Samson; Ruby Leung, L.; Zhao, Chun; Feng, Zhe; Sakaguchi, Koichi
Abstract: Convection permitting simulations using the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) are used to examine how microphysical processes affect large‐scale precipitation variability and extremes. An episode of the Madden‐Julian Oscillation is simulated using MPAS‐A with a refined region at 4‐km grid spacing over the Indian Ocean. It is shown that cloud microphysical processes regulate the precipitable water ( PW ) statistics. Because of the non‐linear relationship between precipitation and PW, PW exceeding a certain critical value ( PW cr ) contributes disproportionately to precipitation variability. However, the frequency of PW exceeding PW cr decreases rapidly with PW, so changes in microphysical processes that shift the column PW statistics relative to PW cr even slightly have large impacts on precipitation variability. Furthermore, precipitation variance and extreme precipitation frequency are approximately linearly related to the difference between the mean and critical PW values. Thus observed precipitation statistics could be used to directly constrain model microphysical parameters as this study demonstrates using radar observations from DYNAMO field campaign. Plain Language Summary: Because of nonlinearity and the broad range of scales involved, understanding the process through which in‐cloud processes influences large‐scale precipitation variability and extremes has been challenging. Through high‐resolution modeling and theoretical/statistical analysis, this study reveals a direct link between frequency of precipitation extremes and these in‐cloud processes. An application of the findings of this study for estimating important but difficult to observe in‐cloud parameters is demonstrated using radar observations of rainfall statistics. Key Points: Precipitation variance and extreme precipitation frequency are linearly related to mean minus critical precipitable water (PW) Because of non‐linearity, the difference between the mean PW and critical PW is very sensitive to cloud microphysical parameters These findings are used to show that observed precipitation statistics could be used to directly constrain model microphysical parameters