9.3.1 Data Quality Objectives

Inherent in any measurement program is the need to establish data quality objectives. These relate the quality of measurements obtained to the level of uncertainty that decision makers are willing to accept in the data and results derived from the data [65]. Data quality objectives state how “good” the data need to be to satisfy the program objectives. The stated objectives generally include completeness, systematic difference, and comparability. Operators of the instruments should let the data quality objectives be determined based on instrument performance specifications and modeling and analysis needs. Data quality objectives should be specified for all of the primary variables measured by the instrument.

To check whether or not the data meet the data quality objectives from an instrument performance perspective, a comparison to another sensor that is known to be operating properly is recommended (see Section 9.5). In assessing how well the sensors compare, the systematic difference and the operational comparability can be computed and compared to the data quality objectives that are presented in Table 9-4.

In evaluating the sodar and radar wind profiler data, the primary criteria for comparison are the component data; the vector wind speed and wind direction are secondary. The indicated values for u and v for the sodar and radar wind profiler in Table 9-4 refer to the components along the antenna axes, and for these instruments, the component comparisons should be performed using calculated values along the antenna axes. Values along the meteorological axes (north/south and east/west) should only be used if evaluating a radiosonde. For the sodar and radar wind profiler, the data quality objective for the vector wind speed and wind direction comparisons should be applied when winds are greater than 2 to 3 ms ^-1 . Note that the values presented in Table 9-5 are based on a number of studies and were reviewed by several measurement experts participating in an EPA-sponsored workshop on upper-air measurement systems.

Table 9-4
Suggested Data quality objectives for upper-air measurement systems.

^a Over a WS range from 3 to 21ms^-1
b For wind speeds greater than approximately 2ms^-1

Comparison results in excess of the data quality objectives do not necessarily mean that the data are invalid. In making this assessment, it is important to understand the reasons for the differences. Reasons may include unusual meteorological conditions, differences due to problems in one or both instruments, or differences due to sampling techniques and data reduction protocols. Both the reasons for and the magnitude of the differences, as well as the anticipated uses of the data, should be considered in determining whether the data quality objectives are met. This assessment should be part of the QA protocol.

Data completeness for radiosonde sounding systems is usually not significantly affected by outside environmental conditions such as high winds, precipitation, or atmospheric stability. However, environmental factors can have a significant effect on the rate of data capture for remote sensing systems.

9. UPPER-AIR MONITORING 
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