A time and frequency-domain analysis is made of the effects of averaging and sampling methods used for constructing magnetic-observatory hourly data values. Using 1-min data as a proxy for continuous, geomagnetic variation, we construct synthetic hourly values of two standard types: instantaneous "spot" measurements and simple 1-h "boxcar" averages. We compare these average-sample types with others: 2-h average, Gaussian, and "brick-wall" low-frequency-pass. Hourly spot measurements provide a statistically unbiased representation of the amplitude range of geomagnetic-field variation, but as a representation of continuous field variation over time, they are significantly affected by aliasing, especially at high latitudes. The 1-h, 2-h, and Gaussian average-samples are affected by a combination of amplitude distortion and aliasing. Brick-wall values are not affected by either amplitude distortion or aliasing, but constructing them is, in an operational setting, relatively more difficult than it is for other average-sample types. It is noteworthy that 1-h average-samples, the present standard for observatory hourly data, have properties similar to Gaussian average-samples that have been optimized for a minimum residual sum of amplitude distortion and aliasing. For 1-h average-samples from medium and low-latitude observatories, the average of the combination of amplitude distortion and aliasing is less than the 5.0 nT accuracy standard established by Intermagnet for modern 1-min data. For medium and low-latitude observatories, average differences between monthly means constructed from 1-min data and monthly means constructed from any of the hourly average-sample types considered here are less than the 1.0 nT resolution of standard databases. We recommend that observatories and World Data Centers continue the standard practice of reporting simple 1-h-average hourly values.
|Publication Subtype||Journal Article|
|Title||Averaging and sampling for magnetic-observatory hourly data|
|Series title||Annales Geophysicae|
|Contributing office(s)||Geologic Hazards Science Center|
|Google Analytic Metrics||Metrics page|