TRMM precipitation analysis in extreme storms in South America: Underestimation of near-surface rain

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in 1997 primarily to determine rainfall in the tropics and subtropics, the structure of convection, and to provide validation for climate models. Analysis of the 14 years of TRMM data has led to groundbreaking contributions, particularly in remote locations of the world. Extreme convection is often associated with orographic features, and the Andes in subtropical South America help spawn some of the most intense convection in the world. Understanding the hydrological impacts of various types of extreme storms is the main objective of this study. Methods developed in the Houze research group are used to correct Z-R relations applied to the TRMM data for precipitation processes occurring in the lowest 2.5 km of the atmosphere. Precipitation over South America calculated using a Z-R relation to which this correction has been applied is compared in this study to the standard TRMM near-surface rain product to determine the underestimation of rainfall in South America from particularly intense convective systems, separated into three distinct storm types. Analysis of the near-surface rainfall data provided by the TRMM PR algorithm shows consistent underestimation, which is problematic when attempting to determine the precipitation climatology in regions with frequent extreme weather systems. This underestimation is even greater for more intense convective storms. An underestimation of up to 39% of the TRMM PR monthly volumetric rain rate averaged over 13 years (1999-2011) has been observed. In addition, an analysis of the underestimate of precipitation from specific radar elements within larger systems will be shown to demonstrate the relationship between extreme convection and rainfall bias in regions with intense storms. This systematic miscalculation can lead to improper analysis of precipitation systems and biased hydrological impacts, which will not allow for accurate forecasting and climate modeling.