Christopher A. Fiebrich

Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet

Abstract 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.

Quality Assurance Procedures in the Oklahoma Mesonetwork

Abstract 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 a...

Mesoscale Monitoring of Soil Moisture across a Statewide Network

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.

Quality assurance procedures for mesoscale meteorological data.

Abstract Mesoscale meteorological data present their own challenges and advantages during the quality assurance (QA) process because of their variability in both space and time. To ensure data quality, it is important to perform quality control at many different stages (e.g., sensor calibrations, automated tests, and manual assessment). As part of an ongoing refinement of quality assurance procedures, meteorologists with the Oklahoma Mesonet continually review advancements and techniques employed by other networks. This article’s aim is to share those reviews and resources with scientists beginning or enhancing their own QA program. General QA considerations, general automated tests, and variable-specific tests and methods are discussed.

The Value of Routine Site Visits in Managing and Maintaining Quality Data from the Oklahoma Mesonet

Abstract The Oklahoma Mesonet, jointly operated by the University of Oklahoma and Oklahoma State University, is a network of 116 environmental monitoring stations across Oklahoma. Technicians at the Oklahoma Mesonet perform three seasonal (i.e., spring, summer, and fall) maintenance passes annually. During each 3-month-long pass, a technician visits every Mesonet site. The Mesonet employs four technicians who each maintain the stations in a given quadrant of the state. The purpose of a maintenance pass is to 1) provide proactive vegetation maintenance, 2) perform sensor rotations, 3) clean and inspect sensors, 4) test the performance of sensors in the field, 5) standardize maintenance procedures at each site, 6) document the site characteristics with digital photographs, and 7) inspect the station’s hardware. The Oklahoma Mesonet has learned that routine and standardized station maintenance has two unique benefits: 1) it allows personnel the ability to manage a large network efficiently, and 2) it provide...

New Soil Property Database Improves Oklahoma Mesonet Soil Moisture Estimates

AbstractSoil 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...

The Oklahoma Mesonet's Skin Temperature Network

Abstract In 1999, the Oklahoma Mesonet deployed infrared temperature (IRT) sensors at 89 of its environmental monitoring stations. A 3-yr dataset collected since that time provides a unique opportunity to analyze longer-term, continuous, mesoscale observations of skin temperature across a large area. Several limitations of the sensor have been identified and include 1) failure of the calibration equation during the cold season, 2) difficulty in keeping the sensors lens clean at remote sites, and 3) limited representativeness of local conditions due to the sensors narrow field of view. Despite these limitations, the Oklahoma Mesonets skin temperature network provides a wealth of information that can be used to better understand many land–atmosphere interactions. Not only can the observations be used to estimate the partitioning of latent and sensible heat flux, they also provide beneficial “ground truth” estimates to validate remotely sensed estimates of skin temperature. This manuscript describes the I...

Upper washita river experimental watersheds: meteorologic and soil climate measurement networks.

Hydrologic, watershed, water resources, and climate-related research conducted by the USDA-ARS Grazinglands Research Laboratory (GRL) are rooted in events dating back to the 1930s. In 1960, the 2927-km Southern Great Plains Research Watershed (SGPRW) was established to study the effectiveness of USDA flood control and soil erosion prevention programs. The size of the SGPRW was scaled back in 1978, leaving only the 610-km Little Washita River Experimental Watershed (LWREW) to be used as an outdoor hydrologic research laboratory. Since 1978, the number of measurement sites and types of instruments used to collect meteorologic and soil climate data have changed on the LWREW. Moreover, a second research watershed, the 786-km Fort Cobb Reservoir Experimental Watershed (FCREW), was added in 2004 to the GRLs outdoor research laboratories to further study the effects of agricultural conservation practices on selected environmental endpoints. We describe the SGPREW, FCREW, and LWREW and the meteorologic measurement network (historic and present) deployed on them, provide descriptions of measurements, including information on accuracy and calibration, quality assurance measures (where known), and data archiving of the present network, give examples of data products and applications, and provide information for the public and research communities regarding access and availability of both the historic and recent data from these watersheds.

Automation: A Step toward Improving the Quality of Daily Temperature Data Produced by Climate Observing Networks*

Abstract The research documented in this manuscript demonstrates that undeniable differences exist between values of daily temperature recorded by the National Weather Service Cooperative Observer Program network and data recorded by the Oklahoma Mesonet. Because of this fact, a transition to automated observations would have the effect of changing the climate record for Oklahoma. However, the change to automated observations would produce an improvement in overall data quality. A sampling of daily data from the two networks was compared for closely spaced station pairs for the period 1 January 2003 through 31 December 2005. As a result, a host of observer errors were discovered (including transcription errors, incorrectly resetting the manual sensors, and delaying the observation time). These errors created large daily differences that sometimes exceeded 5°C between the two datasets. More than 55% of the paired observations were found to differ by more than 1°C.

Upper Washita River Experimental Watersheds: Data Screening Procedure for Data Quality Assurance

The presence of non-stationary conditions in long-term hydrologic observation networks is associated with natural and anthropogenic stressors or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation due to changes in systematic errors that can exacerbate modeling results or bias research conclusions. We applied a data screening procedure to the USDA-ARS experimental watersheds data sets () in Oklahoma. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers to assess the impact of changes in the amount of systematic error. Detection of spurious data, filling in missing data, and data screening procedures were applied to >1000 time series, and processed data were made publicly available. The SPELLmap application was used for data processing and statistical tests on watershed segregated data sets and temporally aggregated data. A test for independency (Anderson test), normality, monotonic trend (Spearman test), detection of change point (Pettitt test), and split record test ( and -tests) were used to assess non-stationary conditions. Statistically significant (95% confidence limit) monotonic trends and changes in mean and variance were detected for annual maximum air temperature, rainfall, relative humidity, and solar radiation and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected as driving the detection of statistically significant trends and changes in mean and variance. We concluded that a data screening procedure that identifies changes in systematic errors and detection of false non-stationary conditions in hydrologic problems is fundamental before any modeling applications.