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9.2.1 Definition of Performance Specifications

Accuracy is defined as the degree of agreement of a measurement with an accepted reference or true value [2]. Determining the absolute accuracy of an upper-air instrument through an inter-comparison study is difficult because there is no “reference” instrument that can provide a known or true value of the atmospheric conditions. This is due in part to system uncertainties and inherent uncertainties caused by meteorological variability, spatial and temporal separation of the measurements, external and internal interference, and random noise. The only absolute accuracy check that can be performed is on the system electronics, by processing a simulated signal. Similarly, a true precision, or the standard deviation of a series of measured values about a mean measured reference value, can only be calculated using the system responses to repeated inputs of the same simulated signal.

The performance specifications provided by manufacturers for accuracy, precision, and other data quality objectives are derived in a number of ways, and it is prudent to understand the basis behind the published specifications. Manufacturers' specifications may be derived from the results of inter-comparison studies, from what the instrument system can resolve through the system electronics and processing algorithms, or a combination of these methods. It may not be practical for a user to verify the exact specifications claimed by the manufacturers. What is needed, however, is a means of verifying that the data obtained from an upper-air system compare reasonably to observations obtained from another measurement system. Guidance for system acceptance testing, field testing, auditing, and data comparison is provided in Section 9.6.

To quantify the reasonableness of the data, one compares observations from the upper-air system being evaluated to data provided by another sensor that is known to be operating properly. In assessing how well the sensors compare, two measures are commonly used. The first involves calculating the “systematic difference” between the observed variables measured by the two methods. The second involves calculating a measure of the uncertainty between the measurements, which is referred to as the “operational comparability” (or simply “comparability”), as described in reference [100]. Comparability, for these purposes, is the root-mean- square (rms) of a series of differences between two instruments measuring nearly the same population. The comparability statistic provides a combined measure of both precision and bias, and will express how well the two systems agree.

Using the ASTM notation [100], the systematic difference (or bias) is defined as:


n = number of observations
= ith observation of the sensor being evaluated
= ith observation of the “reference” instrument

Operational comparability (or root-mean-square error) is defined as

Many of the inter-comparison programs discussed in the next section have evaluated instrument performance using the systematic difference and comparability statistics described here. Other statistical measures that can be used include, for example, correlation coefficients and linear regression.

Another important performance specification for upper-air instrument systems is data recovery rate. Data recovery is usually calculated as the ratio of the number of obervations actually reported at a sampling height to the total number of observations that could have been reported so long as the instrument was operating (i.e., downtime is usually not included in data recovery statistics but is treated separately). Data recovery is usually expressed as percent as a function of altitude. Altitude coverage for upper-air data is often characterized in terms of the height up to which data are reported 80 percent of the time, 50 percent of the time, etc.

9.1 Fundamentals  
      9.1.1 Upper-Air Meteorological Variables  
     9.1.2 Radiosonde Sounding System  
     9.1.3 Doppler Sodar 
     9.1.4 Radar Wind Profiler 
     9.1.5 RASS  
 9.2 Performance Characteristics  
     9.2.1 Definition of Performance Specifications  
     9.2.2 Performance Characteristics of Radiosonde Sounding Systems 
     9.2.3 Performance Characteristics of Remote Sensing Systems  
 9.3 Monitoring Objectives and Goals  
     9.3.1 Data Quality Objectives  
 9.4 Siting and Exposure
 9.5 Installation and Acceptance Testing 
9.6 Quality Assurance and Quality Control 
     9.6.1 Calibration Methods  
     9.6.2 System and Performance Audits  
     9.6.3 Standard Operating Procedures 
     9.6.4 Operational Checks and Preventive Maintenance  
     9.6.5 Corrective Action and Reporting  
     9.6.6 Common Problems Encountered in Upper-Air Data Collection 
 9.7 Data Processing and Management (DP&M) 
9.7.1 Overview of Data Products  
     9.7.2 Steps in DP&M 
     9.7.3 Data Archiving  
 9.8 Recommendations for Upper-Air Data Collection 

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