HR: 17:15h
AN: H54A-04    [Abstracts]
TI: Remotely Sensed Precipitation Estimation using Multi-Spectral IR and MW
AU: * Khanbilvardi, R
EM: rk@ce.ccny.cuny.edu
AF: National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology (NOAA-CREST) Center, Civil Engineering Department City College of City University of New York Steinman Hall 140th Street and Convent Avenue, New York, NY 11374, United States
AU: Mahani, S E
EM: mahani@ce.ccny.cuny.edu
AF: National Oceanic and Atmospheric Administration-Cooperative Remote Sensing Science and Technology (NOAA-CREST) Center, Civil Engineering Department City College of City University of New York Steinman Hall 140th Street and Convent Avenue, New York, NY 11374, United States
AB: Retrieving more accurate distribution and quantity of precipitation, which are the key parameters for the most of hydrologic applications such as real time precipitation forecasting, severe weather monitoring, water resources management, and flood forecasting, is a major area of emphasis within the hydrologic community. However, accurate high spatial and temporal resolution precipitation (both rainfall and snowfall) estimation is still a challenging problem. Rainfall intensity can be captured through different ground and remote observation sources. But, ground-based traditional techniques such as rain gauge and radar networks have limitations, particularly on spatial coverage. Although, satellite is the only possible source of collecting information with no spatial limitation, precipitation estimates from satellite imagery have greater uncertainties particularly on estimating precipitation intensity. Hence, application of remote sensing data for precipitation estimation, particularly over the remote and mountainous regions, where there is usually heavier precipitation and cannot completely be covered by ground-based rain gauge and radar networks, is a challenging research area. Currently, a number of research efforts are directed to estimate actual precipitation using remotely sensed infrared observations, which identify cloud-top temperature, from geostationary satellites. Using multi-sensor satellite-based observations can provide information from various cloud properties and as a result can improve precipitation estimates. Improving precipitation estimates using combination of satellite-based infrared (IR) with microwave (MW) information will be discussed in this presentation. Distribution and intensity of both rainfall and snowfall are enhanced using cloud-top IR from Geostationary Operational Environmental Satellite (GOES) in conjunction with multi frequency microwave information from Advanced Microwave Sounding Units (AMSU). Preliminary investigation indicates that the higher microwave frequency (89 GHz and 150 GHz) is more sensitive to precipitation. In addition to remotely sensed cloud information some ground surface as well as meteorological measurements, such as topography (DEM), temperature, relative humidity, and wind speed and direction, is also used to enhance snowfall/rainfall detection and estimation.
DE: 1800 HYDROLOGY
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
DE: 1855 Remote sensing (1640)
SC: Hydrology [H]
MN: 2007 Joint Assembly