Established as a multipurpose network, the Oklahoma Mesonet operates more than 110 surface observing stations that send data every 5 min to an operations center for data quality assurance, product generation, and dissemination. Quality-assured data are available within 5 min of the observation time. Since 1994, the Oklahoma Mesonet has collected 3.5 billion weather and soil observations and produced millions of decision-making products for its customers.

High quality data sources are critical to scientists, engineers, and decision makers alike. The models that scientists develop and test with quality-assured data eventually become used by a wider community, from policy makers' long-term strategies based upon weather and climate predictions to emergency managers' decisions to deploy response crews. The process of developing high quality data in one network, the Oklahoma Mesonetwork (Mesonet) is detailed in this manuscript. The Oklahoma Mesonet quality-assurance procedures consist of four principal components: an instrument laboratory, field visits, automated computer routines, and manual inspection. The instrument laboratory ensures that all sensors that are deployed in the network measure up to high standards established by the Mesonet Steering Committee. Routine and emergency field visits provide a manual inspection of the performance of the sensors and replacement as necessary. Automated computer routines monitor data each day, set data flags as appropriate, and alert personnel of potential errors in the data. Manual inspection provides human judgment to the process, catching subtle errors that automated techniques may miss. The quality-assurance (QA) process is tied together through efficient communication links. A QA manager serves as the conduit through whom all questions concerning data quality flow. The QA manager receives daily reports from the automated system, issues trouble tickets to guide the technicians in the field, and issues summary reports to the broader community of data users. Technicians and other Mesonet staff remain in contact through cellular communications, pagers, and the World Wide Web. Together, these means of communication provide a seamless system: from identifying suspicious data, to field investigations, to feedback on action taken by the technician.

Soil moisture is an important component in many hydrologic and land–atmosphere interactions. Understanding the spatial and temporal nature of soil moisture on the mesoscale is vital to determine the influence that land surface processes have on the atmosphere. Recognizing the need for improved in situ soil moisture measurements, the Oklahoma Mesonet, an automated network of 116 remote meteorological stations across Oklahoma, installed Campbell Scientific 229-L devices to measure soil moisture conditions. Herein, background information on the soil moisture measurements, the technical design of the soil moisture network embedded within the Oklahoma Mesonet, and the quality assurance (QA) techniques applied to the observations are provided. This project also demonstrated the importance of operational QA regarding the data collected, whereby the percentage of observations that passed the QA procedures increased significantly once daily QA was applied.

Soil moisture data from the Oklahoma Mesonet are widely used in research efforts spanning many disciplines within Earth sciences. These soil moisture estimates are derived by translating measurements of matric potential into volumetric water content through site- and depth-specific water retention curves. The objective of this research was to increase the accuracy of the Oklahoma Mesonet soil moisture data through improved estimates of the water retention curve parameters. A comprehensive field sampling and laboratory measurement effort was conducted that resulted in new measurements of the percent of sand, silt, and clay; bulk density; and volumetric water content at −33 and −1500 kPa. These inputs were provided to the Rosetta pedotransfer function, and parameters for the water retention curve and hydraulic conductivity functions were obtained. The resulting soil property database, MesoSoil, includes 13 soil physical properties for 545 individual soil layers across 117 Oklahoma Mesonet sites. The root-mean-square difference (RMSD) between the resulting soil moisture estimates and those obtained by direct sampling was reduced from 0.078 to 0.053 cm3 cm−3 by use of the new water retention curve parameters, a 32% improvement. A >0.15 cm3 cm−3 high bias on the dry end was also largely eliminated by using the new parameters. Reanalysis of prior studies that used Oklahoma Mesonet soil moisture data may be warranted given these improvements. No other large-scale soil moisture monitoring network has a comparable published soil property database or has undergone such comprehensive in situ validation.