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- Author or Editor: Robert E. Eskridge x
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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 removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.
The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.
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.
CREATING CLIMATE REFERENCE DATASETS
CARDS Workshop on Adjusting Radiosonde Temperature Data for Climate Monitoring
Homogeneous upper-air temperature time series are necessary for climate change detection and attribution. About 20 participants met at the National Climatic Data Center in Asheville, North Carolina on 11–12 October 2000 to discuss methods of adjusting radiosonde data for inhomogeneities arising from instrument and other changes. Representatives of several research groups described their methods for identifying change points and adjusting temperature time series and compared the results of applying these methods to data from 12 radiosonde stations. The limited agreement among these results and the potential impact of these adjustments on upper-air trends estimates indicate a need for further work in this area and for greater attention to homogeneity issues in planning future changes in radiosonde observations.
Homogeneous upper-air temperature time series are necessary for climate change detection and attribution. About 20 participants met at the National Climatic Data Center in Asheville, North Carolina on 11–12 October 2000 to discuss methods of adjusting radiosonde data for inhomogeneities arising from instrument and other changes. Representatives of several research groups described their methods for identifying change points and adjusting temperature time series and compared the results of applying these methods to data from 12 radiosonde stations. The limited agreement among these results and the potential impact of these adjustments on upper-air trends estimates indicate a need for further work in this area and for greater attention to homogeneity issues in planning future changes in radiosonde observations.
