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Michael J. Uddstrom

Abstract

The paper describes the results of an experiment where, for a series of flights, Philips RS4 and Väisälä RS80 radiosondes were mounted on the same balloon. It is shown that there are both random and systematic differences in the raw and derived data generated from these systems. At all levels above 1000 hPa, solar corrected RS4 temperature soundings are colder than those of the RS8O; resulting in a geopotential height difference of the order of 90 m at 50 hPa. The Väisälä RS8O Omega winds are similar to radar-derived wind profiles except in regions of changing vertical shear.

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Larry McMillin
,
Michael Uddstrom
, and
Alessandro Coletti

Abstract

Temperature sensors on radiosondes measure a temperature that is a balance between the temperature of the air and the temperature of the radiation environment of the sensor. Because of this balance, the temperature reported by a radiosonde differs from the true air temperature by an amount that is determined by the heat-transfer coefficient, the longwave emissivity of the sensor, the shortwave emissivity, the longwave flux on the sensor surface, the shortwave flux on the sensor surface, and the sensor temperature. Of these quantities, the heat-transfer coefficient is determined by properties of both the sensor and the atmosphere, the reflectivities are determined by the sensor, and the fluxes and air temperature are determined by the atmosphere. For a typical radiosonde, the radiative properties of the sensor can be determined, the coefficient of heat transfer can be estimated, and models exist for calculating the shortwave flux. In this paper, the authors show that a modification of the Elsasser formulation for infrared fluxes can be used to calculate the infrared flux. This provides sufficient information to solve for the temperature difference between the temperature sensor and the air. The method is used to calculate errors for some typical meteorological conditions for the white-coated VIZ sensor, made by VIZ Manufacturing Co.

The method was used to examine the range of radiation errors for typical conditions. Although the shortwave radiation error is generally recognized because it is observed in the day-to-night differences, it is demonstrated that the longwave radiation errors are significant and variable. The longwave error for the VIZ instrument can reach 3 K at 10 hPa for a winter profile and can exceed that when there is a stratospheric warming. While the longwave radiation error is generally considered to be a cooling effect, at 100 hPa the longwave radiation is a source of heating in a tropical atmosphere because the tropopause at 100 hPa is cold relative to the rest of the atmosphere that is radiating to the sensor. Clouds have significant effects on both the longwave and shortwave components. A cloud at the tropopause of a tropical atmosphere can change the error of a radiosonde above the cloud from a heating error to a cooling error. At sunrise and sunset, the change in the shortwave error is abrupt. Extremely accurate knowledge of the time and location of the radiosonde is required to reduce the uncertainty in temperature to less than 1 K at the upper levels at these times.

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Michael J. Uddstrom
,
Warren R. Gray
,
Richard Murphy
,
Niles A. Oien
, and
Talbot Murray

Abstract

Bayesian methods are used to develop a cloud mask classification algorithm for use in an operational sea surface temperature (SST) retrieval processing system for Advanced Very High Resolution Radiometer (AVHRR) local area coverage (LAC) resolution data. Both radiative and spatial features are incorporated in the resulting discriminant functions, which are determined from a large training sample of cloudy and clear observations. This approach obviates the need to specify the arbitrary thresholds used by hierarchical cloud-clearing methods, provides an estimate of the probability that an instantaneous field of view is cloudy (clear), and allows the skill of different cloud discriminant models to be objectively analyzed.

Results show that spatial information is of particular importance in reducing the false alarm rate of the cloudy class. However, while the use of complex textural measures such as gray-level difference statistics—as opposed to simple statistics such as the standard deviation—improves the skill of nighttime cloud-masking algorithms, they are of little advantage during daytime hours.

Cloud mask discriminant models having similar high Kuipers’ performance index scores (i.e., 0.935) are developed for both day and night satellite data from the Southern Hemisphere midlatitudes. Applied to LAC orbital (i.e., operational) data, the characteristics of the cloud masks appear to be similar to those derived from analysis of the training sample data. However, in this case, to enhance processing performance, a hybrid algorithm is employed—obviously cloudy instantaneous fields of view (IFOVs) are first removed via a gross threshold check and the Bayesian method applied only to the remaining IFOVs. This same (hybrid) algorithm is also applied to an ensemble of 30 days of AVHRR LAC data from the New Zealand region. Analysis of the resulting time-composited SST data (means and standard deviations) shows there is little evidence of a day–night bias in the performance of the Bayesian cloud-masking algorithm and that the resulting SST data may be used to determine the variability of oceanographic features.

Although this paper uses AVHRR data to demonstrate the principles of the Bayesian cloud-masking algorithm, there is no reason why the approach could not be used with other instruments.

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