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Abstract
Ten of the most common radiosondes used throughout the world since 1960 have been evaluated concerning potential use of their temperature data for climate studies. The VIZ; Space Data Corp.; Chinese GZZ; Japanese RS2-80; Russian RKZ, MARS, and A-22; and Vaisala RS80, RS 12/15, and RS18/21 radiosondes were evaluated by modeling the temperature of the sensing element relative to the temperature of the air in which it is immersed. The difference, designated as the temperature error, was calculated under various environmental conditions. Validation and sensitivity analysis studies were performed on each radiosonde model as a means of estimating the environmental parameters that influence the temperature error and the resulting accuracy of the day and nighttime temperature profiles. Environmental parameters to which some sondes were sensitive include cloud cover, surface temperature, solar angle, ambient temperature profile, blackbody temperature, and the ventilation velocity. The ventilation velocity was found to depend strongly on the position of the sensor in the balloon wake. It is believed that the results of these analyses provide the best guidelines available to anyone wishing to perform climate studies using radiosonde data.
The research work presented in this paper indicates that climate trends can currently be estimated with a subset of the worldwide upper-air data. Trends can be calculated for monthly averaged, nighttime soundings with some confidence for the Vaisala RS80 (models not using the RSN80 and RSN86 corrections), Vaisala RS 12/15, Vaisala RS 18/21, Chinese GZZ (below 25 km), Russian RKZ, Russian MARS, and Russian A-22 (below 20 km) radiosonde models. The analysis presented in this paper shows that all of the above radiosondes have small errors in individual radiosonde soundings at night (< ±1°C) and the errors of the monthly averaged data are estimated to be less than ±0.5°C, except for the A-22 (±0.8°C). In addition, temperature data from the Japanese RS-2-80, the Russian A-22 above 20 km, Vaisala RS80 (RSN80 and RSN86 corrections applied), and VIZ can be made suitable for climate analysis if the appropriate temperature correction models are used to correct the data.
Abstract
Ten of the most common radiosondes used throughout the world since 1960 have been evaluated concerning potential use of their temperature data for climate studies. The VIZ; Space Data Corp.; Chinese GZZ; Japanese RS2-80; Russian RKZ, MARS, and A-22; and Vaisala RS80, RS 12/15, and RS18/21 radiosondes were evaluated by modeling the temperature of the sensing element relative to the temperature of the air in which it is immersed. The difference, designated as the temperature error, was calculated under various environmental conditions. Validation and sensitivity analysis studies were performed on each radiosonde model as a means of estimating the environmental parameters that influence the temperature error and the resulting accuracy of the day and nighttime temperature profiles. Environmental parameters to which some sondes were sensitive include cloud cover, surface temperature, solar angle, ambient temperature profile, blackbody temperature, and the ventilation velocity. The ventilation velocity was found to depend strongly on the position of the sensor in the balloon wake. It is believed that the results of these analyses provide the best guidelines available to anyone wishing to perform climate studies using radiosonde data.
The research work presented in this paper indicates that climate trends can currently be estimated with a subset of the worldwide upper-air data. Trends can be calculated for monthly averaged, nighttime soundings with some confidence for the Vaisala RS80 (models not using the RSN80 and RSN86 corrections), Vaisala RS 12/15, Vaisala RS 18/21, Chinese GZZ (below 25 km), Russian RKZ, Russian MARS, and Russian A-22 (below 20 km) radiosonde models. The analysis presented in this paper shows that all of the above radiosondes have small errors in individual radiosonde soundings at night (< ±1°C) and the errors of the monthly averaged data are estimated to be less than ±0.5°C, except for the A-22 (±0.8°C). In addition, temperature data from the Japanese RS-2-80, the Russian A-22 above 20 km, Vaisala RS80 (RSN80 and RSN86 corrections applied), and VIZ can be made suitable for climate analysis if the appropriate temperature correction models are used to correct the data.
Abstract
Chinese radiosonde data from 1970 to 1990 are relatively homogeneous in time and are used to examine the climatology, trends, and variability of China’s atmospheric water vapor content. The climatological distribution of precipitable water (PW) is primarily dependent on surface temperature. Influenced by the east Asia monsoon, China’s precipitable water exhibits very large seasonal variations. Station elevation is also a dominant factor affecting water vapor distribution in China.
An increase (decrease) in precipitable water over China is associated with an increase (decrease) of precipitation in most regions. Increases in the percentage of PW relative to climatology are greater in winter and spring than in summer and autumn.
Interannual variation and trends in precipitable water and surface temperature are closely correlated in China, confirming a positive “greenhouse” feedback. Interannual variations between precipitable water and precipitation are also significantly correlated.
Abstract
Chinese radiosonde data from 1970 to 1990 are relatively homogeneous in time and are used to examine the climatology, trends, and variability of China’s atmospheric water vapor content. The climatological distribution of precipitable water (PW) is primarily dependent on surface temperature. Influenced by the east Asia monsoon, China’s precipitable water exhibits very large seasonal variations. Station elevation is also a dominant factor affecting water vapor distribution in China.
An increase (decrease) in precipitable water over China is associated with an increase (decrease) of precipitation in most regions. Increases in the percentage of PW relative to climatology are greater in winter and spring than in summer and autumn.
Interannual variation and trends in precipitable water and surface temperature are closely correlated in China, confirming a positive “greenhouse” feedback. Interannual variations between precipitable water and precipitation are also significantly correlated.
Abstract
A theory for the velocity deficit in the wake of a moving vehicle in still air is derived from a perturbation analysis of the equations of motion. By suitable assumptions, expressions are found for the turbulent energy fluctuations in the wake. This theory is then applied to predict the velocity deficit and turbulent energy fluctuations on a difference net in the x-z plane across the roadway for the case of the wind speed being much less than the vehicle speed (i.e., the GM experiment). The predictions are then compared to data from the General Motors Sulfate Dispersion Experiment. Comparison of observations to predictions show that the theory predicts the velocity deficit and turbulent fluctuations accurately.
Abstract
A theory for the velocity deficit in the wake of a moving vehicle in still air is derived from a perturbation analysis of the equations of motion. By suitable assumptions, expressions are found for the turbulent energy fluctuations in the wake. This theory is then applied to predict the velocity deficit and turbulent energy fluctuations on a difference net in the x-z plane across the roadway for the case of the wind speed being much less than the vehicle speed (i.e., the GM experiment). The predictions are then compared to data from the General Motors Sulfate Dispersion Experiment. Comparison of observations to predictions show that the theory predicts the velocity deficit and turbulent fluctuations accurately.
Abstract
The second part of this two-part article discusses the differences between observations taken at 0000 and 1200 UTC, particularly in the stratosphere, by the Vaisala RS80-57H radiosondes that are integrated within the National Weather Service's (NWS's) Micro-ART system. There are large maxima in the horizontal distributions of the monthly time means of the 0000/1200 UTC temperature and height differences over the central United States that are absent over Canada. These maxima are as large as 5 K and 150 m at 10 hPa. Data analysis shows that the 0000/1200 UTC differences are largely artificial, especially over the central United States. They originate in the postprocessing software at observing stations, thus confirming the findings in Part I.
Special flight data from the NWS test facility at Sterling, Virginia, have been used to deduce the bias correction applied by Vaisala's postprocessing system. By analyzing the correction data, it has been shown that the inconsistencies with non-U.S. Vaisala RS80 data, as well as most of the large 0000/1200 UTC differences over the United States, can be accounted for by multiplying the reported time since radiosonde launch by a factor of 5/3, which is incorrectly applied by the Vaisala postprocessing software. After being presented with the findings in this paper, Vaisala further isolated the source of the inconsistencies to a software coding error in the radiation bias correction scheme. The error affects only the software installed at NWS stations.
Abstract
The second part of this two-part article discusses the differences between observations taken at 0000 and 1200 UTC, particularly in the stratosphere, by the Vaisala RS80-57H radiosondes that are integrated within the National Weather Service's (NWS's) Micro-ART system. There are large maxima in the horizontal distributions of the monthly time means of the 0000/1200 UTC temperature and height differences over the central United States that are absent over Canada. These maxima are as large as 5 K and 150 m at 10 hPa. Data analysis shows that the 0000/1200 UTC differences are largely artificial, especially over the central United States. They originate in the postprocessing software at observing stations, thus confirming the findings in Part I.
Special flight data from the NWS test facility at Sterling, Virginia, have been used to deduce the bias correction applied by Vaisala's postprocessing system. By analyzing the correction data, it has been shown that the inconsistencies with non-U.S. Vaisala RS80 data, as well as most of the large 0000/1200 UTC differences over the United States, can be accounted for by multiplying the reported time since radiosonde launch by a factor of 5/3, which is incorrectly applied by the Vaisala postprocessing software. After being presented with the findings in this paper, Vaisala further isolated the source of the inconsistencies to a software coding error in the radiation bias correction scheme. The error affects only the software installed at NWS stations.
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.
Abstract
Inhomogeneities in U.S. radiosonde data that used the VIZ and Vaisala RS80 cannot be explained by radiation errors, which can be removed by the heat balance models. WMO intercomparision data, modeling results, temperature time series, and 1200 minus 0000 UTC temperature differences are examined to show that there appears to be an error in the U.S. RS80/RSN93 temperature correction software.
Radiosonde soundings taken at U.S. stations that launch Vaisala RS80 radiosondes, which are integrated within the National Weather Service (NWS) Microcomputer Automatic Radio-Theodolite (Micro-ART) system, should not be used in climate studies since there is a large systematic error of unknown origin in the temperature data. This paper is the first of two and is primarily concerned with the midtroposphere. The second paper discusses the large unexplained 0000 and 1200 UTC differences in the stratosphere.
Abstract
Inhomogeneities in U.S. radiosonde data that used the VIZ and Vaisala RS80 cannot be explained by radiation errors, which can be removed by the heat balance models. WMO intercomparision data, modeling results, temperature time series, and 1200 minus 0000 UTC temperature differences are examined to show that there appears to be an error in the U.S. RS80/RSN93 temperature correction software.
Radiosonde soundings taken at U.S. stations that launch Vaisala RS80 radiosondes, which are integrated within the National Weather Service (NWS) Microcomputer Automatic Radio-Theodolite (Micro-ART) system, should not be used in climate studies since there is a large systematic error of unknown origin in the temperature data. This paper is the first of two and is primarily concerned with the midtroposphere. The second paper discusses the large unexplained 0000 and 1200 UTC differences in the stratosphere.
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.
Abstract
A finite-difference highway model is presented which uses surface layer similarity theory and a vehicle wake theory to determine the atmospheric structure along a roadway. Surface similarity is used to determine the wind profile and eddy diffusion profiles in the ambient atmosphere. The ambient atmosphere is treated as a basic-state atmosphere on which the disturbances due to vehicle wakes are added. A conservation of species equation is then solved using an upstream-flux corrected technique which insures positive concentrations. Simulation results from the highway model are compared with 58 half-hour periods of data (meteorological and SF6 tracer) taken by General Motors. The results show that the predictions of this model are closer to the observations than those of the Gaussian-formulated EPA highway model (HIWAY).
Abstract
A finite-difference highway model is presented which uses surface layer similarity theory and a vehicle wake theory to determine the atmospheric structure along a roadway. Surface similarity is used to determine the wind profile and eddy diffusion profiles in the ambient atmosphere. The ambient atmosphere is treated as a basic-state atmosphere on which the disturbances due to vehicle wakes are added. A conservation of species equation is then solved using an upstream-flux corrected technique which insures positive concentrations. Simulation results from the highway model are compared with 58 half-hour periods of data (meteorological and SF6 tracer) taken by General Motors. The results show that the predictions of this model are closer to the observations than those of the Gaussian-formulated EPA highway model (HIWAY).
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.
