Environmental Monitoring Branch
Research Scientist
Recent Publications
To see Dr. Kuligowski's complete list of publications, abstracts, and citation metrics, visit his
ResearcherID page.
Bob Kuligowski received a B.S.
degree in Meteorology from Penn State University in 1991. Following three
years as an operational weather forecaster at Accu-Weather, Inc., he
returned to Penn State for graduate work, receiving his M.S. in Meteorology
in 1996. To enhance his background in hydrology, he then switched to the
Department of Civil and Environmental Engineering at Penn State for his
Ph.D., which was completed in 2000. His primary research interest is in
estimating and predicting precipitation, as evidenced by his Master's
work on using artificial neural networks to predict short-term precipitation
from recent observations, and his Ph.D. work on assimilating satellite-based
sounding estimates into a mesoscale numerical weather prediction model to
improve fine-scale precipitation forecasts.
Bob has been a Meteorologist at NOAA/NESDIS/STAR since November 1999
and performs research and development on satellite-based rainfall
estimation and nowcasting tools.
Algorithm Development:
Developed the Self-Calibrating Multivariate Precipitation
Retrieval (SCaMPR), which retrieves rain rate estimates using infrared
data from geostationary satellites for flash flood applications;
calibration is automatically updated in real time using microwave-
based rainfall rate estimates as target data. This algorithm has been
running experimentally over the United States since November
2004.
Chairing the GOES-R Algorithm Working Group (AWG) Hydrology
Algorithm Team which is developing three algorithms for the prototype
ground system for the next generation of NOAA GOES:
The Rainfall Rate algorithm, based on a modified version of
the SCaMPR algorithm which uses the enhanced capabilities of the
Advanced Baseline Imager (ABI) onboard GOES-R;
The Rainfall Potential algorithm, which provides nowcasts of
rainfall accumulation for the 0-3 hour time frame based on the
NOAA National Severe Storms Laboratory (NSSL) K-Means rainfall
nowcasting model;
The Probability of Rainfall algorithm, which retrieves the
probability of measurable rainfall at the pixel level during the
next 0-3 hours based on a statistical model developed at
STAR.
Collaborating with the Hydrologic Research Center to provide
satellite-derived rainfall rates as input to a Flash Flood Guidance
system over Central America and the Mekong Delta.
Collaborating with the Nile Forecast Center of Egypt to improve
their capability for estimating rainfall from satellite data.
Dept. of Commerce • NOAA • NESDIS • STAR • Last revised: March 15, 2012
Bob Kuligowski
Bob Kuligowski received a B.S.
degree in Meteorology from Penn State University in 1991. Following three
years as an operational weather forecaster at Accu-Weather, Inc., he
returned to Penn State for graduate work, receiving his M.S. in Meteorology
in 1996. To enhance his background in hydrology, he then switched to the
Department of Civil and Environmental Engineering at Penn State for his
Ph.D., which was completed in 2000. His primary research interest is in
estimating and predicting precipitation, as evidenced by his Master's
work on using artificial neural networks to predict short-term precipitation
from recent observations, and his Ph.D. work on assimilating satellite-based
sounding estimates into a mesoscale numerical weather prediction model to
improve fine-scale precipitation forecasts.