1. TOWARDS HIGH-RESOLUTION GLOBAL SATELLITE PRECIPITATION ESTIMATION
(PERSIANN: Precipitation Estimation from Remote Sensing Information (NASA and NOAA satellite data)
using Artificial Neural Network)
NEWS Project
PI: Soroosh Sorooshian
University of California, Irvine
PERSIANN
(0.25°  0.25°)
07/25-27/2006
PERSIANN CCS
(0.04°  0.04°)
07/24-27/2006
High resolution precipitation
data are needed for hydrologic
applications in the mountainous SW.
Severe storms propagate
from mountains to
low-elevated areas.

2. Flash Flood Monitoring
NEWS Project
PI: Soroosh Sorooshian
University of California, Irvine
In summer, 2006, Southwestern United States experienced a series of recorded flash floods due to the strong North American Monsoon.
This demo shows the potential of using satellite rainfall estimates to improve flooding warning.
Strong convection starts over mountains where radar
coverage is poor.
PERSIANN monitors lifetimes
of storm systems and provides information for early warning.
Radar beams (3-km above
ground level) are blocked
by mountains in SW.
Differences between PERSIANN and radar images exist.
Red: PERSIANN showed Rain but Radar showed No Rain
Blue: PERSIANN No Rain vs. Radar Rain