Friez ML-3A Barograph Accuracy - 4.5 Day Charts
For the month of January 2017 I analyzed the accuracy of my Friez ML-3A microbarograph by comparing its recorded sea level values with calculated sea level values from my Henry J Green observatory mercury barometer. The barograph has the original 4.5 day Friez clock and used Belfort 5-1090 4.5 day charts. Values were determined once-per-day near midday.
Four statistics were calculated:
- The average error was calculated. It is the average difference calculated as the mercury barometer pressure minus the pressure read from the barograph.
- The standard deviation of the individual error values was calculated. By definition, 68% of the error values fall within +/- one standard deviation of the mercury barometer value. The average error can be reduced by recalibration of the barograph, while the standard deviation generally cannot, it is more inherent in the performance of the barograph.
- The average drift was calculated. This is simply the slope of a straight line fitted to the the scatter of error data points plotted against day of the month. It is an indicator of how the error changes with time.
- Finally, the average non-linearity was calculated. This is the slope of a straight line fitted to the scatter of error data points plotted against the mercury barometer sea level pressure, in mb per mb of barometric pressure. It is an indicator of how much the error changes on average with the local pressure.
For the ML-3A with 4.5 day charts, the calculated average error for January 2017 was only 0.064 mb, but the standard deviation of the error values was 1.35 mb. There was significant drift in the error values, amounting to -2.23 mb/month. The calculated nonlinearity was a substantial 0.079 mb per mb of barometric pressure. Plots of the error values showing the scatter, drift, and nonlinearity are presented below.
Drift
Data for January 2017
Nonlinearity
Data for January 2017
The calibration of this ML-3A appears good, it matches the mercury barometer near the middle of the barograph chart as seen in the nonlinearity graph. For the month of March 2017 I will disconnect the dashpots from the barograph mechanism to see if this improves the nonlinearity. I will repeat this evaluation in April using the March data.
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