Engineering topics

Doelling, David R.

Publications and source records attributed to Doelling, David R..

39 records · Page 3

NASA NTRS1995

Cloud Properties Derived From GOES-7 for Spring 1994 ARM Intensive Observing Period Using Version 1.0.0 of ARM Satellite Data Analysis Program

This document describes the initial formulation (Version 1.0.0) of the Atmospheric Radiation Measurement (ARM) program satellite data analysis procedures. Techniques are presented for calibrating geostationary satellite data with Sun synchronous satellite radiances and for converting narrowband radiances to top-of-the-atmosphere fluxes and albedos. A methodology is documented for combining geostationary visible and infrared radiances with surface-based temperature observations to derive cloud amount, optical depth, height, thickness, temperature, and albedo. The analysis is limited to two grids centered over the ARM Southern Great Plains central facility in north-central Oklahoma. Daytime data taken during 5 Apr. - 1 May 1994, were analyzed on the 0.3 deg and 0.5 deg latitude-longitude grids that cover areas of 0.9 deg x 0.9 deg and 10 deg x 14 deg, respectively. Conditions ranging from scattered low cumulus to thin cirrus and thick cumulonimbus occurred during the study period. Detailed comparisons with hourly surface observations indicate that the mean cloudiness is within a few percent of the surface-derived sky cover. Formats of the results are also provided. The data can be accessed through the World Wide Web computer network.

Minnis, Patrick↗

NASA NTRS1990

Examination of the use of narrowband radiances for earth radiation budget studies

The relationship between narrowband and broadband thermal radiances is explored to determine the accuracy of outgoing longwave radiation derived from narrowband data. Infrared window (11.5 microns) data from the Geostationary Operational Environmental Satellite are correlated with longwave (5.0 - 50.0 microns) data from the Earth Radiation Budget Experiment. A simple quadratic fit between the narrowband and longwave fluxes results in a standard error of the estimate of 4.5-5.3 percent for data which are matched closely in time and space. Use of matched regional flux data with temporal differences up to a half hour yield standard errors of 4.6-5.9 percent. About one fourth of the magnitude of the error may be attributed to limb-darkening and temporal differences in the matched fluxes. The relationship shows a significant dependence on the relative humidity of the atmosphere above the radiating surface. Although this dependency accounts for only about 2 percent of the standard error, it reduces the monthly mean regional errors by more than 10 percent. Data taken over land produced a slightly different relationship than data taken over water. The differences appear to be due to the higher altitudes of the land radiating surfaces. Cloud amount and height also influence the narrowband-broadband relationship. Inclusion of these parameters does not affect the standard errors but it reduces the montly mean regional errors by another 10 percent. Better humidity and temperature data and knowledge of cloud microphysics may be required to further improve the relationship. Using the best fits, it is concluded that the monthly mean outgoing flux may be determined with an rms uncertainty of 1.7 percent using a single infrared window channel with coincident cloud and humidity data. Regional concentrations of the errors, however, make the use of narrowband data too unreliable for monitoring the longwave flux for climate change studies.

Young, David F.↗

NASA NTRS1990

On the role of satellite-measured narrowband radiances for computing the earth's radiation balance

The capabilities of determining the planetary albedo with a single channel narrowband radiometer are examined by matching GOES and ERBE scanner data. This relationship depends on surface type and solar zenith angle, among other variables. Preliminary results show that the rms error in the monthly mean albedo using a four-term regression fit for three surface types is 0.013, or 6.1 percent.

Doelling, David R.↗