Introducing a 16-years (2000-2015) Atmospheric River Database using SSM/I and GOES Satellite Information

Abstract:

Pacific southwesterly Atmospheric Rivers (ARs) are in temporary conduits continually form to transport huge amounts of atmospheric water vapor from the tropical ocean surface to the U.S. West Coast. Such ARs’ frequent landfall over California in a winter season can sometimes bring up to 50% of California’s annual precipitation (snow and/or rain). However, the landfall of strong ARs can often cause extreme precipitations and lead to severe flooding damage in the state. To study the characters of AR, a 16-years (2000-2015) AR database is built which records ARs’ water vapor and precipitation dynamic images from SSM/I and GOES satellites and calculates their characteristics including the time, duration, location, coverage, intensity, transport strength, precipitation amount. The above-mentioned information is useful for California weather forecasting and water resource management, therefore this study is supported by and cooperated with California Department of Water Resources.

The AR dataset is a subset of the object-orientated satellite precipitation database PERSIANN-CONNECT (http://connect.eng.uci.edu). The PERSIANN-CONNECT put together connecting precipitation images at sequential time steps into “objects (events)” in the 4-D of space, time, and intensity. An AR-induced precipitation are typical kinds of “objects” differ from other such as the hurricanes, monsoons. Our results show that the object-oriented database can capture the evolution of AR and AR precipitation from its origination, development, landfall, and dissipation to recess. Therefore, the AR database is useful for the predictability and prediction diagnostic study of numerical weather/climate models. Our preliminary study using the AR database shows that the most frequent AR occurrences in a year are from November to January; the most severe AR landing events occurred during the winter of 2005 to 2006.The PERSIANN-CONNECT AR database indicates it is possible that we can identify AR 3-4 days before the AR landfall over the coastal region. As ARs exist globally, the results and methodology of our study are applicable and transferrable for risk characterization of AR-induced extreme precipitations in the context of a variable and changing climate.