The Importance of Data Quality on Studies of Short-term and Long-Term Ionospheric Processes

Details The Importance of Data Quality on Studies of Short-term and Long-Term Ionospheric Processes The Importance of Data Quality on Studies of Short-term and Long-Term Ionospheric Processes

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004agufmsa54a..03w&link_type=abstract

American Geophysical Union, Fall Meeting 2004, abstract #SA54A-03

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2427 Ionosphere/Atmosphere Interactions (0335), 2494 Instruments And Techniques, 1694 Instruments And Techniques

Scientific paper

The 50-year record of ionospheric observations from ground-based stations worldwide provides a valuable data set for several applications, including studies of short-term processes and detection of long-term environmental change. The ionospheric region is subject to large natural variability as a result of numerous influences including solar and geomagnetic activity. These variations occur on both short and long timescales and must be well understood before a clear trend or signal can be identified. Previous analyses of the layer heights and critical frequencies have provided conflicting results, and an evaluation of various approaches suggests that data quality has a significant influence on studies of both short- and long-term ionospheric behavior. Exploring the quality of the observations brought to light three main areas that must be addressed before the data are suitable for analysis. First, changes in instrumentation, including the switch from analog to digital technology, have resulted in observable shifts in the data records. Understanding and accounting for these shifts can reduce biases that may result when analyzing the time series of observations. Second, many of the records contained time periods during which no data were reported. These gaps, if not properly addressed in the data analysis, can affect the evaluation of both trends and forcing mechanisms. Third, changes in how parameters (particularly peak layer heights and virtual heights) are inferred can also affect the analysis. These problems have been carefully addressed for four ionosonde stations and have resulted in a corrected, more robust data set for analyzing storms and other short-term processes and for assessing long-term trends. We will summarize some of the issues affecting the data record and will present preliminary results from analyses of the improved data set.

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