Scientific Theme: Advanced Modeling and Observing Systems

Theme report: Advanced Modeling and Observing Systems
TexAQS/GoMACCS experiment, and can be easily modified to provide forecasts for future NOAA/CIRES field
studies.
MILESTONE CSD02.2:
Gather remote-sensing observations during the 2006 Texas field study to investigate the
capability of models to represent important boundary layer parameters.
ACCOMPLISHMENTS FOR CSD02.2:
Optical remote sensors were employed to gather information on ozone, aerosol, winds, turbulence and mixing
during a six-week period in August and September as part of the Texas study. The TOPAZ airborne ozone lidar
characterized ozone and aerosol distributions in the Houston and Dallas areas on 20 days during the period; data
were obtained during approximately 70 flight hours. From the lidar measurements, boundary-layer backgroundozone
concentrations, mixing-layer depths over different land surfaces and water, and ozone flux in plumes
downwind of Houston and Dallas were computed. Data from flights along the east Texas border were used to
estimate ozone imported into Texas from Louisiana and its effect on pollution levels. Initial analysis indicates no
obvious correlation between mixing-layer depths and ozone concentrations. Estimates of flux showed that the ozone
exported from the Dallas-Fort Worth area was about one-third that observed downwind of Houston.
During the same period, ozone and Doppler lidars were operated continuously from the R/V Ronald H. Brown in the
Gulf of Mexico, Galveston Bay, and the Houston ship channel. The Doppler lidar provided high resolution profiles
of horizontal wind speed and direction and vertical velocity speed and variance. From the vertical wind
measurements, estimates of mixing strength and mixing-layer depth were computed and compared with in situ
chemical and aerosol measurements on the ship as well as with the lidar-measured vertical profiles of ozone. Results
indicate that several events characterized by downward mixing of upper-level aerosol were observed. Additionally,
while in the ship channel, the lidar observed strong nocturnal low-level jet events that were associated with an
observed absence of elevated pollution levels on subsequent days. The composite lidar observational set from the
three instruments provide excellent data for comparison with model computations of ozone formation, transport, and
mixing processes in the southeastern Texas region.
PSD09: Environmental Monitoring and Prediction
GOALS:
Improve numerical model performance through development of new data streams that directly impact forecast
ability and through focused observational campaigns supporting geophysical process studies.
MILESTONE PSD09.1:
Develop inter-satellite bias correction and apply both diurnal sampling and inter-satellite bias
corrections to the High-Resolution Radiation Sounder (HIRS) satellite radiances. Determine the
impact these corrections have on the radiances by comparing with HIRS simulated radiances
from the GFDL climate model.
ACCOMPLISHMENTS FOR PSD09.1:
Corrections to the HIRS/2 satellite observations were developed to eliminate sampling errors due to orbital drift,
diurnal differences between satellites, and instrument bias inherent to each satellite. Climate model simulations of
HIRS observations were used to predict and correct the local time drift of HIRS observations for all of the satellites.
Diurnal differences between satellite observations were corrected to have all ascending orbits to a 1930 LST
observation, and all descending orbits were corrected to a 0730 LST observation time. Inter-satellite biases due to
instrument bias for HIRS brightness temperatures for channels 2 through 12 were corrected to NOAA-10 HIRS
observations. A final set of corrected HIRS clear-sky brightness temperature data has been compiled over a 26-year
period (1979-2004) and is now being used to compare trends in clear-sky greenhouse gas trapping in climate model
simulations. Other studies planned for these data include comparing trends between climate model OLR and HIRSderived
OLR and spatial trends in CO2.
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