Characterization of the Rainfall Associated with Atmospheric Rivers during the Ifloods Campaign over the Central United States

Friday, 19 December 2014
Munir Ahmad Nayak1, Gabriele Villarini1, David Anthony Lavers2 and Allen Bradley1, (1)The University of Iowa, IIHR-Hydroscience & Engineering, Iowa City, IA, United States, (2)European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Atmospheric rivers (ARs) are narrow and long regions in the atmosphere that transport large quantities of water vapor across the mid-latitudes. ARs have been linked to large flood events over different regions of the globe. Over the central United States, ARs play a major role in causing extreme precipitation and flooding, including the 1993 and 2008 flood events. The goal of this work is to characterize rainfall associated with AR events over the central United States.

Here we focus on the rainfall events during the Iowa Flood Studies (IFloodS) campaign, which took place through a partnership between the National Aeronautics and Space Administration (NASA) and the Iowa Flood Center at the University of Iowa during April–June 2013. This field campaign focused on improving the understanding of strengths and weaknesses of satellite-based rainfall estimates, resulting in the collection of a large amount of both ground based and remotely-sensed data.

The accumulated precipitation during IFloodS was some of the largest since the middle of the 20th century over most of the central United States. Many of the heavy rainfall events during the campaign were associated with ARs, with large regions experiencing more than 70% of their rainfall during the occurrence of these events. As a preliminary step, we evaluate how well different satellite- and radar-based precipitation products captured the rainfall associated with the ARs. More specifically, we focus on rainfall estimates from Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA; both real-time and research version), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), National Oceanic and Atmospheric Administration (NOAA)-Climate Prediction Center (CPC) Morphing Technique (CMORPH), and National Centers for Environmental Prediction (NCEP) Stage IV. We use gridded rainfall provided by CPC (Daily US Unified Gauge-Based Analysis of Precipitation) as reference. Stage IV is the product that shows the closest agreement with the reference data, and is the one that we use to characterize the rainfall distribution in AR events during the IFloodS campaign.