Cheng-Zhi Zou received
a B.S. degree in Atmospheric Physics from the University of Science and
Technology of China in 1982. He then received his M.S. in Atmospheric
Physics in Institute of Atmospheric Physics, the Chinese Academy of
Sciences, in 1985. He was a research scientist in the Chinese Academy of
Meteorological Sciences from 1986 to 1987. He received his Ph.D. in
Meteorology from the University of Oklahoma in 1995. During this period
of time, his research mainly focused on nonlinear dynamics and
development of a zonally averaged climate model.
From 1997 to 2000, Cheng-Zhi worked for NOAA/NESDIS as a contracting
scientist. During this period of time, he switched his research from
climate modeling to using satellite data to study the climate and
weather problems. Cheng-Zhi joined NESDIS/Center for Satellite
Applications and Research (formerly Office of Research and Applications)
in February 2001. Since then, his research has been focusing on
satellite climatology and meteorology, including instrument calibration,
algorithm development and validation, satellite data production and
applications, etc. The following briefly describes the projects he has
recently been involved.
MSU calibration and climate trend:
Cheng-Zhi has developed an
intercalibration technique using simultaneous nadir overpasses (SNO)
and re-calibrated the MSU instruments on different NOAA satellites.
With the SNO calibration, he produced a deep-layer atmospheric
temperature dataset that are well-intercalibrated for climate
research. The new dataset has removed intersatellite biases at the
radiance level, thus yielding more accurate atmospheric temperature
trend analysis. The following link provides more detailed information
on the MSU calibration and trend work. https://www.star.nesdis.noaa.gov/smcd/emb/mscat/index.php
Polar Wind Algorithm Development and Data Production:
Cheng-Zhi has developed an algorithm to retrieve the atmospheric winds from
TOVS/AMSU temperature soundings plus SSM/I- or QuikSCAT- based surface
winds over the middle and high latitudes. The atmospheric wind
profiles derived form this algorithm have small biases and therefore
are suitable for climate studies over the polar region. Currently,
more than 20 years of polar wind profiles based on the TOVS
observations have been generated over the Arctic Ocean by Jennifer
Francis's group using this algorithm.
Impact of satellite surface products on numerical weather
forecasting:
Cheng-Zhi and his colleagues conducted impact experiments
of the NESDIS near real-time weekly AVHRR green vegetation fraction on
the WRF model forecasting. The greener weekly GVF products help to
reduce the warm biases found in the operational WRF model.
Coastal studies using SAR observations and MM5 simulation:
Cheng-Zhi and his colleagues have been using fine-resolution
observations from SAR satellites and high-resolution MM5 simulation to
study many coastal and boundary layer phenomena. At a recent
publication, they simulated the development and decay processes of
atmospheric vortex streets off Aleutian Volcanic Islands using MM5.
The simulation agrees fairly well with the SAR observations.
Recent Awards
Administrator's Award, NOAA, 2013:
for developing a science-quality long-term dataset of upper
atmospheric temperatures from NOAA's microwave and infrared satellite
measurements.
Silver Medal, Department of Commerce, 2007: For developing a
calibration technique breakthrough enabling detection of reliable long-
term atmospheric temperature trends form satellite data.
Cheng-Zhi Zou
Cheng-Zhi Zou received
a B.S. degree in Atmospheric Physics from the University of Science and
Technology of China in 1982. He then received his M.S. in Atmospheric
Physics in Institute of Atmospheric Physics, the Chinese Academy of
Sciences, in 1985. He was a research scientist in the Chinese Academy of
Meteorological Sciences from 1986 to 1987. He received his Ph.D. in
Meteorology from the University of Oklahoma in 1995. During this period
of time, his research mainly focused on nonlinear dynamics and
development of a zonally averaged climate model.