H53G-0938:
On the Characterization of Rainfall Associated with U.S. Landfalling North Atlantic Tropical Cyclones Based on Satellite Data and Numerical Weather Prediction Outputs

Friday, 19 December 2014
Beda Nidhi Luitel, IIHR—Hydroscience and Engineering, Iowa City, IA, United States, Gabriele Villarini, The University of Iowa, IIHR-Hydroscience & Engineering, Iowa City, IA, United States and Gabriel Andres Vecchi, Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States
Abstract:
When we talk about tropical cyclones (TCs), the first things that come to mind are strong winds and storm surge affecting the coastal areas. However, according to the Federal Emergency Management Agency (FEMA) 59% of the deaths caused by TCs since 1970 is due to fresh water flooding. Heavy rainfall associated with TCs accounts for 13% of heavy rainfall events nationwide for the June-October months, with this percentage being much higher if the focus is on the eastern and southern United States.

This study focuses on the evaluation of precipitation associated with the North Atlantic TCs that affected the continental United States over the period 2007 – 2012. We evaluate the rainfall associated with these TCs using four satellite based rainfall products: 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); Climate Prediction Center (CPC) MORPHing technique (CMORPH). As a reference data we use gridded rainfall provided by CPC (Daily US Unified Gauge-Based Analysis of Precipitation). Rainfall fields from each of these satellite products are compared to the reference data, providing valuable information about the realism of these products in reproducing the rainfall associated with TCs affecting the continental United States.

In addition to the satellite products, we evaluate the forecasted rainfall produced by five state-of-the-art numerical weather prediction (NWP) models: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC). The skill of these models in reproducing TC rainfall is quantified for different lead times, and discussed in light of the performance of the satellite products.