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Soil moisture data from the Oklahoma Mesonet have been used in numerous fields within the Earth sciences, including agriculture, hydrology, and meteorology. Soil matric potentials measured by heat dissipation sensors at Oklahoma Mesonet stations have been converted to soil volumetric water content estimates using soil water retention curve parameters estimated by the Rosetta pedotransfer function. Recently, an improved version of Rosetta, Rosetta3, was released. Informed by this new pedotransfer function and soil sampling at additional locations, an improved version of the Oklahoma Mesonet soil physical property database, MesoSoil v2.0, has been created. This article presents this new soil database, compares soil water retention parameters estimated using Rosetta3 with those derived from the original Rosetta model, and describes the effects of changes in those parameters on estimated volumetric water content. Using the Rosetta3 model led to changes in the estimated water retention parameters, most notably decreases in the parameter α, which is related to the inverse of the air‐entry potential. These changes resulted in volumetric water content estimates that differed from those based on Rosetta1, with mean absolute differences averaging 0.02 cm3 cm–3 across all site‐years and the greatest differences occurring during wet periods. The mean volumetric water content estimated using the Rosetta3 parameters across more than 100 sites was not significantly different from that determined by soil sampling. The updated database is publicly available and may be found here: http://soilphysics.okstate.edu/data. Core Ideas: Rosetta3 pedotransfer function was used to develop an updated Oklahoma Mesonet soil property database.Along with recent recalibration, the new database improves soil moisture estimates.The greatest changes in volumetric water content estimates were observed during wet conditions. [ABSTRACT FROM AUTHOR]

Obtaining accurate soil moisture data from sensors in automated monitoring networks is critical as these data are increasingly used for research in soil hydrology, ecohydrology, and related disciplines. One of the earliest such networks is the Oklahoma Mesonet, which monitors soil matric potential using heat dissipation sensors. Various calibration equations have been proposed for those sensors, and there is a need to compare and validate the performance of those equations, especially for matric potentials < .150 kPa. A laboratory experiment was conducted in silt loam soil using a sand-kaolin box and a pressure plate apparatus with matric potentials ranging from 0 to .1500 kPa. The calibration equations included Starks' equation, Flint et al.'s equation, Schneider et al.'s equation, and a new logistic equation. The upper limit of the sensors was -9 kPa, and the sensors remained responsive at -1500 kPa matric potential. The logistic equation produced the lowest root mean squared error (34 kPa), followed by Flint et al.'s equation (192 kPa), Starks' equation (295 kPa), and Schneider et al.'s equation (463 kPa). After recalibration of the coefficients in the three preexisting equations, their performances improved, with all RMSE values . 251 kPa; however, the logistic equation still provided superior accuracy. The logistic equation effectively removed an ~ 0.02 cm3 cm-3 positive bias in soil water content that resulted from use of the original parameterization of Schneider et al.'s equation. This logistic equation is recommended for use with past and future data from the Oklahoma Mesonet's heat dissipation sensors.

Two datasets of soil temperature observations collected at Norman, Oklahoma, USA, were analysed to study horizontal and vertical variability in their observations. The first dataset comprised 15-min resolution soil temperature observations from 20 September 2011 to 18 November 2013 in seven plots across a 10-m transect. In each plot, sensors were located at depths of 5, 10, and 30 cm. All seven plots observed fairly consistent maximum soil temperature observations during the spring, fall, and winter months. Starting in late May, the observed spread in soil temperatures across the 10-m transect increased significantly until August when the observed spread in temperatures decreased. The range of observed minimum soil temperature was more consistent year-round at the shallower depths, but showed similar patterns to the maximum soil temperature ranges at deeper depths. The second dataset comprised 15-min resolution soil temperature observations from 20 November 2013 to 1 December 2015 in a single plot at the same location as the first dataset. Soil temperature sensors were placed every 2 cm from the surface down to 40 cm deep to study the vertical variability in soil temperature measurements (focusing on a winter and a summer case). Both winter and summer conditions showed that the temperature differences between depths behaved logarithmically with the shallower depths having larger differences than deeper depths. [ABSTRACT FROM AUTHOR]

The Oklahoma City Micronet (OKCNET) is an operational surface observing network designed to improve atmospheric monitoring across the Oklahoma City, Oklahoma, metropolitan area. The 40 station network consists of 4 Oklahoma Mesonet stations and 36 micronet stations mounted on traffic signals at an average station spacing of approximately 3 km. Using several technical innovations as well as existing infrastructure in Oklahoma City, data are collected and quality assured in near real-time at an interval of 1 min for the traffic signal sites and 5 min for the Mesonet sites. Because OKCNET also spans a land use gradient from rural to urban, the spatial and temporal densities of OKCNET observations have shed new insights on atmospheric processes (e.g. the urban heat island, severe thunderstorm evolution) across the Oklahoma City metropolitan area. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Severe hail is a common event in the United States and few studies have been conducted using high-resolution data to determine the spatial and temporal variability of hail occurrence. Given the coverage of Weather Surveillance Radar-88 Doppler radars (WSR-88D; Crum and Alberty 1993 ) over the Southern Plains of the United States, recent technological advancements including hail detection algorithms and powerful new geographic information systems (GIS) tools, a new methodology was developed to quantify the occurrence of severe hail. The methodology was tested using a dataset spanning 2001–2003 from 15 radar sites across eight states. The Hailswath algorithm developed at Weather Decision Technologies, Inc. was used to estimate severe and significant hail for each individual storm during the study period. Due to the discrete nature of the Hailswath algorithm output, it was manually contoured to create a coherent swath of the most likely area of severe (19 mm in diameter; 0.75 inches) and significant hail (51 mm in diameter; 2 inches). Finally, the results of these analyses were compiled monthly, annually, and for the total duration of the study period using a variety of GIS tools. A thorough demonstration of the data analysis process and results is presented, and the benefits and limitations of the method used to investigate the occurrence of severe hail are explained. [ABSTRACT FROM AUTHOR]