Meteorological Instrument Performance Characteristics

Summary

This lesson summarized the key performance characteristics of instrumentation used for meteorological measurements. Measurements are necessary to obtain quantitative information about the atmosphere. Elements of a good measurement system are those that return a robust, stable, and reliable output that is of a scale and resolution to be useful in quantifying the phenomenon under investigation. Meteorological measurement techniques are a field of steady change and progress largely but not solely driven by enhancements to electronics, microelectronics, and computer resources [Emeis (2010)]. Students and practitioners of instrumentation, measurement, and observing systems need to keep abreast of these changes to maintain timely knowledge and ensure competency and comprehension. The usefulness of a measurement result is largely determined by the quality of the statement of uncertainty that accompanies the measurement.

A research scientist on board the NSF/NCAR C-130 closely monitors her chemistry instrument to ensure its performance characteristics are optimal for quality data

A research scientist on board the NSF/NCAR C-130 aircraft closely monitors her chemistry instrument to ensure its performance characteristics are optimal for quality data. Image from NCAR/EOL.

Static performance characteristics of an instrument include range, span, resolution, static sensitivity, linearity, stability, and sensor threshold. Dynamic performance characteristics include the time constant , sensor time lag, hysteresis, and first-order transfer functions. The selection of site in terms of representativity and homogeneity can affect these performance characteristics.

The importance of calibration was also emphasized as the key step in determining the relationship between a measurand and the output of an instrument. The best calibration process uses a traceable standard that provides values of the measurand covering the measurement range.

Differences between first order dynamic systems and higher order dynamic systems were described. The lesson also covered the principle of superposition and its usefulness in signal analysis, the characterization of uncertainty, the differences between Type A and Type B estimates of uncertainty, and the propagation of uncertainty.

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