TOWARDS HIGH-RESOLUTION GLOBAL SATELLITE PRECIPITATION ESTIMATION

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Presentation transcript:

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.

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