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- Author or Editor: Irina V. Chernykh x
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Abstract
A method developed in the former Soviet Union for predicting cloud amounts is supplemented with a new method of determining the base and tops of clouds. Criteria for predicting a cloud layer are 0 ≤ T″(z) and R″(z) ≤ 0, where T″ is the second derivative of the vertical profile of temperature and R″ is the second derivative of the relative humidity. This test was found from an analyses of United States radiosonde data.
Cloud amount (sky cover) is predicted from a relationship between cloud amount and dewpoint depression within the predicted cloud layer and the temperature at that level. This relationship is based on data from the former Soviet Union and data from the Indian 0cean and divides cloud amount into four categories: 0%–20%, 20%–60%, 60%–80%, and 80%–100% coverage.
The new composite method is evaluated using data from several United States radiosonde stations within different climates. Evaluation data was selected to include only situations in which the observer (providing the “truth”) could me only one cloud layer. Consequently, the evaluation is biased toward stratified cloud conditions. The method will provide cloud information that can be used in models of radiosonde sensors to adjusted temperature data.
Abstract
A method developed in the former Soviet Union for predicting cloud amounts is supplemented with a new method of determining the base and tops of clouds. Criteria for predicting a cloud layer are 0 ≤ T″(z) and R″(z) ≤ 0, where T″ is the second derivative of the vertical profile of temperature and R″ is the second derivative of the relative humidity. This test was found from an analyses of United States radiosonde data.
Cloud amount (sky cover) is predicted from a relationship between cloud amount and dewpoint depression within the predicted cloud layer and the temperature at that level. This relationship is based on data from the former Soviet Union and data from the Indian 0cean and divides cloud amount into four categories: 0%–20%, 20%–60%, 60%–80%, and 80%–100% coverage.
The new composite method is evaluated using data from several United States radiosonde stations within different climates. Evaluation data was selected to include only situations in which the observer (providing the “truth”) could me only one cloud layer. Consequently, the evaluation is biased toward stratified cloud conditions. The method will provide cloud information that can be used in models of radiosonde sensors to adjusted temperature data.
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.
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.
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.
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.
