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Doelling, David R.

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

41 records · Page 3

Changes in Cirrus Cloudiness and their Relationship to Contrails

Condensation trails, or contrails, formed in the wake of high-altitude aircraft have long been suspected of causing the formation of additional cirrus cloud cover. More cirrus is possible because 10 - 20% of the atmosphere at typical commercial flight altitudes is clear but ice-saturated. Since they can affect the radiation budget like natural cirrus clouds of equivalent optical depth and microphysical properties, contrail -generated cirrus clouds are another potential source of anthropogenic influence on climate. Initial estimates of contrail radiative forcing (CRF) were based on linear contrail coverage and optical depths derived from a limited number of satellite observations. Assuming that such estimates are accurate, they can be considered as the minimum possible CRF because contrails often develop into cirrus clouds unrecognizable as contrails. These anthropogenic cirrus are not likely to be identified as contrails from satellites and would, therefore, not contribute to estimates of contrail coverage. The mean lifetime and coverage of spreading contrails relative to linear contrails are needed to fully assess the climatic effect of contrails, but are difficult to measure directly. However, the maximum possible impact can be estimated using the relative trends in cirrus coverage over regions with and without air traffic. In this paper, the upper bound of CRF is derived by first computing the change in cirrus coverage over areas with heavy air traffic relative to that over the remainder of the globe assuming that the difference between the two trends is due solely to contrails. This difference is normalized to the corresponding linear contrail coverage for the same regions to obtain an average spreading factor. The maximum contrail-cirrus coverage, estimated as the product of the spreading factor and the linear contrail coverage, is then used in the radiative model to estimate the maximum potential CRF for current air traffic.

Minnis, Patrick↗

Intercalibration of Meteorological Satellite Imagers Using VIRS, ATSR-2 and MODIS

Global retrievals of surface, cloud and radiative properties from geostationary (GEO) and low-Earth-orbit (LEO) meteorological satellites require accurate calibration of their imagers. An accurate and consistent calibration increases the reliability and effectiveness of long-term monitoring of climate changes. More emphasis has been placed on calibrating the thermal infrared (IR) channel. The lack of on-board calibration in the visible (VIS) channel has prompted efforts to characterize the degradation of the VIS sensor using vicarious post-launch calibration techniques that measure bright stable desert targets from space and aircraft or using satellite-to-satellite normalizations. While such inter-calibrations are valuable and widely used, the lack of a well-characterized calibration reference source and the lengthy time delay between updates have minimized their effectiveness in climate monitoring. To address these shortcomings, this paper examines the use of research satellite imagers to provide stable calibration references for the visible (VIS, approximately 0.65 micrometers) channels and develops a method for rapid intercalibration of existing satellites. Calibration coefficients are determined for the Geostationary Operational Environmental Satellites (GOES-8 to GOES-10), Geostationary Meteorological Satellite (GMS-5), Meteosat-7, and the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR). As a reference calibration source, this technique uses the self-calibrating sensors on the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Radiometers (VIRS) or the ERS-2 Along Track Scanning Radiometer (ATSR-2). GOES-8 is calibrated with VIRS and then its calibration is transferred to other GEO or LEO satellites. The absolute accuracy of this technique relies on the assumption that the on-board calibration is stable and well maintained. Minnis et al. assessed the VIRS calibration using comparisons with other self-calibrated satellite sensors including the broadband Clouds and Earth's Radiant Energy System (CERES) scanners, the ERS-2 Along Track Scanning Radiometer (ATSR-2), and the Terra Moderate-resolution Imaging Spectrometer (MODIS). Thus, the VIRS data can be confidently used as the initial reference source.

Nguyen, Louis↗

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↗

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.↗

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.↗