Building Western U.S. Atmospheric River Population Statistics: An Object Oriented Approach Based On a New Precipitation Data Set: PERSIANN-CONNECT

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 3 February 2014: 1:45 PM
Room C205 (The Georgia World Congress Center )
Scott Sellars, Univ. of California, Irvine, CA; and X. Gao, K. L. Hsu, and S. S. Sorooshian

Understanding the fundamental causes of regional variability in large scale and extreme precipitation events is one of the most important insights needed in water resource planning to provide society with a reliable source of usable water and to minimize the impacts of extreme precipitation events. The statistical analysis of populations making up these large scale and extreme precipitation events can provide very specific and useful information that will be vital to statistical methodologies of analyzing extreme events, by providing enhanced information regarding the similarities of the events, as well as the their characteristics.

Our case study focuses on Western U.S. large-scale precipitation events, including Atmospheric Rivers (ARs) (large plumes of moisture transported from the tropics). To accomplish this, we applied an object-oriented connectivity algorithm developed at the Center for Hydrometeorology and Remote Sensing (CHRS) at UCI, which segments gridded near global satellite precipitation data into 4D objects (longitude, latitude, time and intensity). The precipitation data used for populating the object-oriented database is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) .25-degree dataset, which covers from 60N to 60S and from March 1st, 2000 to January 1st, 2011. We define an object as a precipitation event connected in space and time with at least 1mm/hour and exist for a minimum of 24 hours (allowing for smaller events to not be included). This data set is called PERSIANN-CONNected objECT (CONNECT) and is stored in a PostgreSQL database accessible by the public here: (http://chrs.web.uci.edu/research/voxel/index.html). We currently have 55,173 precipitation objects globally, with 60 attributes or “characteristics”, including over 30 climate phenomena indices. Some characteristics include mean precipitation intensity, volume of precipitation, El Nino Southern Oscillation (ENSO) Phase, starting location and ending location. By searching the PERSIANN-CONNECT database of global objects, looking for large-scale storms that impact the Western U.S., we built a population of 499 precipitation objects (i.e. precipitation associated with Western Pacific storms), which included 181 AR objects (Using a list of land falling ARs provided by Dr. Marty Ralph) over a 10+ year period. We then separate the storms and AR objects by month so that attributes including physical and climate related characteristics could be statistically investigated.

This talk will comment on the seasonality of the large-scale storm count per month as well as the impact of the MJO and ENSO on this monthly storm count. Results suggest that although Western Pacific storms and ARs can occur during any month of the year, October, November and December have the largest AR count with 23, 35, 31, respectively for the 10+ years. With August, September and November having the highest percentages of AR events (48%, 63%, 56% respectively) to impact the Western U.S. Further discussion on the impacts of the phase of ENSO and MJO on the track, starting and ending location and intensity of these large scale storm occurrences will also highlight the potential benefit to analyzing meteorological events (in this case precipitation) as a population of objects.