Storm Identification and Tracking Algorithm for Modeling of Rainfall Fields Using 1-h NEXRAD Rainfall Data in Texas | Academic Article individual record
abstract

A method to identify and track rainfall structures using 1-h accumulated NEXt generation RADar (NEXRAD) rainfall data is presented and used to analyze the dynamics of storm features over an area in Texas. Storm features are identified from a Gaussian mixture model using the expectation maximization algorithm. The method assigns NEXRAD pixels to storm features, simultaneously producing a smooth fitted function to the rainfall intensity distribution. Once the storm features are identified, they are tracked using inverse cost functions and using the fact that continuous features overlap each other from frame to frame in the accumulated data. The inverse cost functions also account for storm feature merging, splitting, birth, and death. Application of this storm identification and tracking algorithm for Brazos County (1,500 km2) in southeastern Texas distinguishes several characteristics of the storm feature dynamics. From September through April, storm features are predominantly of a frontal nature, with storm features following geostrophic flow along low pressure fronts moving in from the north. In summer (May-August), storm features are convective in nature following random track directions. Both types of storm features have durations of 1-3 h in Brazos County due to the county's relatively small size compared to the measured average storm speed of 40 km/h and due to the fact that most storms only intersect the county over part of their area. © 2009 ASCE.

author list (cited authors)
Choi, J., Olivera, F., & Socolofsky, S. A.
publication date
2009
citation count

10