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Oleg A. Alduchov and Robert E. Eskridge

Abstract

Algorithms, based on Magnus's form equations, are described that minimize the difference between several relationships between temperature and water vapor pressure at saturation that are commonly used in archiving data. The work was initiated in connection with the development of a unified upper-air dataset that will use measurements gathered from the late 1930s to the present and archived in several data centers. The conversion of field measurements to archived humidity values within the databases that are being used have not been consistent and in some cases are unknown. A goal of this work was to develop a uniform and accurate method to convert these data to various humidity variables without regard to the equations used in archiving the original data. Archived temperature values are recorded to 0.1°C. This precision creates a temperature dependent range in uncertainty in saturation vapor pressure. A procedure was developed to take this into account when the error minimizing equations were derived.

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Oleg A. Alduchov and Robert E. Eskridge

Abstract

Correct radiosonde station elevation (balloon release height) is important in quality control of radiosonde soundings. Incorrect heights introduce errors in calculated temperature trends and numerical forecasts. Radiosonde metadata frequently contain erroneous information regarding station elevations. Soundings are produced at the station using the correct station elevation and the hydrostatic equation to determine the height of each pressure level in the sounding. It is easy to do a reverse calculation using the soundings to calculate the station elevation (release point). From a time series of calculated station elevations, the change dates and station elevations can be determined for individual stations. A method was developed to calculate station elevation and change dates automatically from time series of calculated station elevations.

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Irina V. Chernykh, Oleg A. Alduchov, and Robert E. Eskridge

Clouds are important to climate and climate trends. To determine trends in cloud-base heights and cloud-top heights, the Comprehensive Aerological Reference Data Set (CARDS) and the method of Chernykh and Eskridge are used to diagnose cloud base, top, and amount. Trends in time series of cloud bases and tops at 795 radiosonde stations from 1964 to 1998 are presented.

It was found that trends in cloud-base height and cloud-top height are seasonally dependent and a function of cloud cover amount. There was a small increase in multilayer cloudiness in all seasons. Geographical distributions of decadal changes of cloud bases and tops were spatially nonuniform and depended upon the season.

To estimate the errors made in calculating the heights of cloud boundaries, an analysis was made of the response of the thermistors and hygristors. Thermistors and hygristors are linear sensors of the first order. From this it is shown that the distance between calculated inflection points (cloud boundaries) of observed and true values is exactly equal to the time constant of the sensor times the balloon speed. More accurate cloud boundaries can be determined using this finding.

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Robert E. Eskridge, Oleg A. Alduchov, Irina V. Chernykh, Zhai Panmao, Arthur C. Polansky, and Stephen R. Doty

The possibility of anthropogenic climate change and the possible problems associated with it are of great interest. However, one cannot study climate change without climate data. The Comprehensive Aerological Reference Data Set (CARDS) project will produce high-quality, daily upper-air data for the research community and for policy makers. CARDS intends to produce a dataset consisting of radiosonde and pibal data that is easy to use, as complete as possible, and as free of errors as possible. An attempt will be made to identify and correct biases in upper-air data whenever possible. This paper presents the progress made to date in achieving this goal.

An advanced quality control procedure has been tested and implemented. It is capable of detecting and often correcting errors in geopotential height, temperature, humidity, and wind. This unique quality control method uses simultaneous vertical and horizontal checks of several meteorological variables. It can detect errors that other methods cannot.

Research is being supported in the statistical detection of sudden changes in time series data. The resulting statistical technique has detected a known humidity bias in the U.S. data. The methods should detect unknown changes in instrumentation, station location, and data-reduction techniques. Software has been developed that corrects radiosonde temperatures, using a physical model of the temperature sensor and its changing environment. An algorithm for determining cloud coverforthis physical model has been developed. A numerical check for station elevation based on the hydrostatic equations has been developed, which has identified documented and undocumented station moves. Considerable progress has been made toward the development of algorithms to eliminate a known bias in the U.S. humidity data.

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