Temporal Homogenization of Monthly Radiosonde Temperature Data. Part I: Methodology
نویسندگان
چکیده
Historical changes in instrumentation and recording practices have severely compromised the temporal homogeneity of radiosonde data, a crucial issue for the determination of long-term trends. Methods developed to deal with these homogeneity problems have been applied to a near–globally distributed network of 87 stations using monthly temperature data at mandatory pressure levels, covering the period 1948–97. The homogenization process begins with the identification of artificial discontinuities through visual examination of graphical and textual materials, including temperature time series, transformations of the temperature data, and independent indicators of climate variability, as well as ancillary information such as station history metadata. To ameliorate each problem encountered, a modification was applied in the form of data adjustment or data deletion. A companion paper (Part II) reports on various analyses, particularly trend related, based on the modified data resulting from the method presented here. Application of the procedures to the 87-station network revealed a number of systematic problems. The effects of the 1957 global 3-h shift of standard observation times (from 0300/1500 to 0000/1200 UTC) are seen at many stations, especially near the surface and in the stratosphere. Temperatures from Australian and former Soviet stations have been plagued by numerous serious problems throughout their history. Some stations, especially Soviet ones up until ;1970, show a tendency for episodic drops in temperature that produce spurious downward trends. Stations from Africa and neighboring regions are found to be the most problematic; in some cases even the character of the interannual variability is unreliable. It is also found that temporal variations in observation time can lead to inhomogeneities as serious as the worst instrument-related problems.
منابع مشابه
Temporal Homogenization of Monthly Radiosonde Temperature Data. Part II: Trends, Sensitivities, and MSU Comparison
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