Oklahoma Mesonet Research Articles

Quantifying the spatial scale mismatch between satellite-derived solar irradiance and in situ measurements: A case study using CERES synoptic surface shortwave flux and the Oklahoma Mesonet

The spatial scale mismatch between gridded irradiance products and in situ measurements is perhaps the least understood topic in solar resource assessment. However, it has a profound impact on virtually all solar applications that involve satellite-derived or reanalysis irradiance data. This paper investigates spatial scale mismatch through a kriging-based upscaling method. Point-location measurements from a monitoring network are upscaled to the size of a satellite-derived irradiance footprint. Subsequently, satellite-derived irradiance is validated against both the nearest point-location measurements and the upscaled areal averages, and the error reduction can, thus, be used to quantify the amount of spatial scale mismatch. In that, a new measure is proposed. The empirical part of the paper considers a synoptic scale satellite-derived irradiance product, namely, National Aeronautics and Space Administration's Clouds and the Earth's Radiant Energy System synoptic surface shortwave flux, and a mesoscale monitoring network, namely, the Oklahoma Mesonet. Based on two years of hourly data and the proposed measure, the spatial scale mismatch is found to be 45% for the U.S. state of Oklahoma.

Value of composite Normalized Difference Vegetative Index and growing degree days data in Oklahoma, 1999 to 2018

AbstractFor over 25 yr, sensor‐based Normalized Difference Vegetative Index (NDVI) data has been collected from both satellite imagery and near‐plant (3‐m) readings. Because calibrated NDVI data coming from active sensors is still relatively new, limited research has returned to evaluate databases including multiple years and environments. Composite NDVI readings and final grain yield were collected from 1999 to 2018. This included growing degree day (GDD) records for each mid‐season sensor measurement. This was attempted to potentially improve the use of a historical and subjective morphological scale. Using location‐specific‐archived‐data from the Oklahoma Mesonet, the exact number of days from planting to sensing where GDD > 0 for each date and location were compiled. The ensuing relationship between NDVI (for a predetermined GDD > 0 range) and yield was determined. Grain yield prediction was improved between 80 and 115 GDDs. These ranges further targeted a climatologically identifiable metric that precisely determined when to collect sensor readings in future years. Compared with the current composite yield prediction equation for Oklahoma, the new exponential function created from this study was higher in the lower‐yielding environments. Underestimation of fertilizer N rates has been voiced by producers in recent years. This has likely been the product of more current varieties, more efficient farming practices, and increased optimum N rates needed for higher yields. This validates the adoption of a new YP0 equation for OSU's on‐line Sensor Based Nitrogen Rate Calculator, allowing accurate yield prediction between 80 and 115 GDDs.

Constructing retrospective gridded daily weather data for agro‐hydrological applications in Oklahoma

AbstractRegional, automated meteorological networks, such as the Oklahoma Mesonet can potentially provide high quality forcing data for generating gridded surfaces, but proven methods of interpolating weather variables between the station locations are needed. We compared two interpolation methods, ordinary kriging (OK) and empirical Bayesian kriging (EBK), with and without using long‐term climate imprints (CI), for creating spatially continuous, daily weather datasets. Daily meteorological variables (maximum and minimum temperature, solar radiation, and precipitation) from the Oklahoma Mesonet for the period 1997–2014 were interpolated using geoprocessing tools in ArcGIS. Cross‐validation was used for evaluation of interpolation methods, with 90% of sites chosen randomly for the training set and the remaining 10% left for validation. For all interpolation approaches, cross‐validation showed coefficient of determination (R2) values of .99 and .98 for daily maximum and minimum air temperatures, with mean absolute error (MAE) ranging from ±0.45–0.50 °C for maximum temperature and ±0.77–0.80 °C for minimum temperature. Likewise, for daily solar radiation, R2 values of .94 and .93 showed overall good prediction accuracy with MAE values 1.00 and 1.01 MJ m–2 d–1 for EBK and OK, respectively. However, for rainfall, all methods yielded R2 values ≤.67, suggesting a need for more effective interpolation method. Based on its lower computational time and lower input data requirement, OK appears preferable to the other approaches tested here to provide the daily weather data for gridded models in Oklahoma and other regions with similar monitoring networks.

An Analysis of the Processes Affecting Rapid Near-Surface Water Vapor Increases during the Afternoon to Evening Transition in Oklahoma

AbstractThis study used 20 years of Oklahoma Mesonet data to investigate the changes of near-surface water vapor mixing ratio qυ during the afternoon to evening transition (AET). Similar to past studies, increases in qυ are found to occur near sunset. However, the location, magnitude, and timing of the qυ maximum occurring during the AET are shown to be dependent on the seasonal growth and harvest of vegetation across Oklahoma in the spring and summer months. Particularly, the late spring harvest of winter wheat grown in Oklahoma appears to modify the relative contribution of local and nonlocal processes on qυ. By analyzing time series of qυ during the AET, it is found that the likelihood of a presunset qυ maximum is strongly dependent upon vegetation, soil moisture, wind speed, and cloud cover. Analysis also reveals that the increase in qυ during the AET can increase the parcel conditional instability despite the surface cooling produced by loss of insolation. Next to known changes in low-level wind shear, these changes in instability and moisture demonstrate new ways the AET can modify the presence of the key ingredients relevant to explaining the climatological increase in severe convective storm hazards around sunset.

Oklahoma's future wind energy resources and their relationship with the Central Plains low-level jet

Previous work has projected increases in wind speed/wind power density (WPD) over the United States' South-Central Plains, which could be linked to future increases in frequency of the Central Plains Low-Level Jet (LLJ). This work serves to both update projections of this region's wind energy resources, and to assess the LLJ's importance for influencing these resources. Projected changes in several wind energy metrics (including ramp event frequency and cut-off frequency) were assessed specifically over the Oklahoma Panhandle. These projections were computed by using a climatological delta method to statistically downscale changes in near-surface wind speed from multiple climate models onto observations from the Oklahoma Mesonet. The results suggest a pattern of overall reductions in wind speed and WPD, slight reductions in ramp event frequency, and little change in cut-off frequency, with projected changes often being larger and of statistical significance at night and during summer and autumn. Highly distinguishable diurnal and seasonal variability in ramp event and cut-off frequency was found in this work, with these frequencies most notably maximizing during the day. This climatological variability is consistent with that seen in observations of these wind energy metrics. Regression of LLJ characteristics against wind energy metrics identified consistent and conceptually understandable links between increased LLJ frequency and decreased ramp event frequency in future decades. Such a result attests to a more stable and easier to manage future wind energy resource over the Oklahoma Panhandle.

Prediction of soil suction using measured climatic data

This paper presents calibration and validation of a one-dimensional soil–atmosphere model created using the SVFlux software package for predicting soil suction changes based on the measured climate data at two Oklahoma Mesonet sites. The parameters for the soil water characteristic curves and the hydraulic conductivity function were obtained from published calibration studies conducted using laboratory test results on soil samples extracted from these sites. The plant cover quality, the root depth and the root uptake distribution function were first calibrated using 1-year data at one site. While a rectangular root uptake distribution function overpredicted suction at deeper layers, the triangular uptake distribution function overpredicted suction at shallower depths. In addition, reducing the root depth to 1 m improved the predictions at surface layers and led to overestimation of the suction at deeper layers. Based on the root mean squares of the differences between the simulated and the measured values and weighting the shallower depths more, a rectangular root uptake distribution function and a root depth of 1 m were selected to predict suction values at the calibration site and the second site for another year. Reasonable predictions of soil suction values at shallower depths were obtained at both sites.

Clarifying the Baroclinic Contribution to the Great Plains Low-Level Jet Frequency Maximum

Abstract The frequency and intensity of the Great Plains nocturnal low-level jet (LLJ) are enhanced by baroclinicity over the sloped terrain of the region. A classical description of baroclinic-induced diurnal wind oscillations over the Great Plains considers differential heating of the slope with respect to air at the same elevation far removed from the slope, but with buoyancy constant along the slope (Holton mechanism). Baroclinicity can also occur due to differential heating of the slope itself, which creates a gradient in buoyancy along the slope. The relative prevalence of the two types of baroclinicity in this region has received scant attention in the literature. The present study uses 19 years of data from the Oklahoma Mesonet to evaluate the characteristics of along-slope buoyancy gradients over the region. A mean negative afternoon along-slope buoyancy gradient (east–west gradient) is found over Oklahoma. The sign of this afternoon buoyancy gradient is favorable for LLJ formation, as it results in the strongest southerly geostrophic wind near the ground around sunset, which is conducive to nocturnal jet formation via the inertial oscillation mechanism. The negative afternoon buoyancy gradient is at least partially created by an east–west gradient in diurnal heating and is stronger and more consistent in the summer months, which is when LLJs are most frequent. The contribution of the along-slope buoyancy gradient to the low-level geostrophic wind was found to be as important as the contribution of the Holton mechanism. Overall, these results indicate that along-slope buoyancy gradients should be accounted for in studies of LLJ dynamics over the Great Plains.

Recalibration of Sensors in One of The World's Longest Running Automated Soil Moisture Monitoring Networks

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.Core Ideas Soil matric potential data from the Oklahoma Mesonet are widely used. Those data are estimated using a calibration equation shown here to be inaccurate. The logistic equation developed here produces more accurate matric potential estimates.

A Modified Framework for Quantifying Land–Atmosphere Covariability during Hydrometeorological and Soil Wetness Extremes in Oklahoma

AbstractGlobal “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.

Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging

AbstractSoil moisture spatial patterns with length scales of 1‐100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite‐based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large‐scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5‐, 25‐, and 60‐cm depths at 800‐m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth‐specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of ≤0.0576 cm3/cm3 across all three depths.

The relationships between urban-rural temperature difference and vegetation in eight cities of the Great Plains

Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHI. The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas. Due to the limited number of paired meteorological stations, many studies have used remotely sensed land surface temperature, but these time-series land surface temperature data are often heavily affected by cloud cover and other factors. These factors, together with the algorithm for inversion of land surface temperature, lead to accuracy problems in detecting the UHII, especially in cities with weak UHII. Based on meteorological observations from the Oklahoma Mesonet, a world-class network, we quantified the UHII and trends in eight cities of the Great Plains, USA, where data from at least one pair of urban and rural meteorological stations were available. We examined the changes and variability in urban temperature, UHII, vegetation condition (as measured by enhanced vegetation index, EVI), and evapotranspiration (ET). We found that both UHI and urban cold islands (UCI) occurred among the eight cities during 2000–2014 (as measured by impervious surface area). Unlike what is generally considered, UHII in only three cities significantly decreased as EVI and ET increased (p<0.1), indicating that the UHI or UCI cannot be completely explained simply from the perspective of the underlying surface. Increased vegetative cover (signaled by EVI) can increase ET, and thereby effectively mitigate the UHI. Each study station clearly showed that the underlying surface or vegetation affects urban-rural temperature, and that these factors should be considered during analysis of the UHI effect over time.

The Evolution and Impact of a Meteorological Outreach Program for Public Safety Officials: An Update on the Oklahoma Mesonet’s OK-First Program

AbstractSince 1997, the Oklahoma Mesonet (the state’s automated mesoscale weather station network) has served a community of more than 1,400 public safety officials (emergency managers, fire officials, law enforcement, etc.) across Oklahoma through a weather data and training program called Oklahoma’s First-Response Information Resource System using Telecommunications (OK-First). OK-First provides free weather and radar data interpretation classes to eligible public safety officials and, following successful completion of training, password-protected access to weather data tools including a website and software. The objective of OK-First when it began was to fill significant gaps in weather product training and data access for Oklahoma’s public safety community. Though the core mission remains the same 20 years later, many aspects of OK-First have evolved over time, including participant membership, training curriculum, formats of training, training requirements, website and software technology, and program feedback. The purpose of this paper is to provide an update on the Mesonet’s OK-First program, with a particular focus on training, tools, and the impact it has had on the public safety community.

Modeling transient soil moisture dichotomies in landscapes with intermixed land covers

Large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, but existing networks feature almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations are unlikely to adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. Here we demonstrate the severity of the problem for one particular landscape and introduce a flexible new method for solving the problem. The specific objectives of this study were (i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); (ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as an observation operator; and (iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 83 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 83 locations using a calibrated and validated soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only six inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9 × 9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSD = 21 mm and normalized RMSD = 0.17, using observed PAW under grassland as an input. This new method for estimating soil moisture under adjacent, contrasting land covers at a relatively low computational cost could be employed for any region and land cover pairing with training data available, and it may significantly enhance the applications of existing large-scale soil moisture monitoring networks.

Environmental effects on water intake and water intake prediction in growing beef cattle.

Water is an essential nutrient, but there are few recent studies that evaluate how much water individual beef cattle consume and how environmental factors affect an individual's water intake (WI). Most studies have focused on WI of whole pens rather than WI of individual animals. Thus, the objective of this study was to evaluate the impact of environmental parameters on individual-animal WI across different seasons and develop prediction equations to estimate WI, including within different environments and management protocols. Individual daily feed intake and WI records were collected on 579 crossbred steers for a 70-d period following a 21-d acclimation period for feed and water bunk training. Steers were fed in 5 separate groups over a 3-yr period from May 2014 to March 2017. Individual weights were collected every 14 d and weather data were retrieved from the Oklahoma Mesonet's Stillwater station. Differences in WI as a percent of body weight (WI%) were analyzed accounting for average temperature (TAVG), relative humidity (HAVG), solar radiation (SRAD), and wind speed (WSPD). Seasonal (summer vs. winter) and management differences (ad libitum vs. slick bunk) were examined. Regression analysis was utilized to generate 5 WI prediction equations (overall, summer, winter, slick, and ad libitum). There were significant (P < 0.05) differences in WI between all groups when no environmental parameters were included in the model. Although performance was more similar after accounting for all differences in weather variables, significant (P < 0.05) seasonal and feed management differences were still observed for WI%, but were less than 0.75% of steer body weight. The best linear predictors of daily WI (DWI) were dry mater intake (DMI), metabolic body weights (MWTS), TAVG, SRAD, HAVG, and WSPD. Slight differences in the coefficient of determinations for the various models were observed for the summer (0.34), winter (0.39), ad libitum (0.385), slick bunk (0.41), and overall models (0.40). Based on the moderate R2 values for the WI prediction equations, individual DWI can be predicted with reasonable accuracy based on the environmental conditions that are present, MWTS, and DMI consumed, but substantial variation exists in individual animal WI that is not accounted for by these models.

Economic value of environmental and weather information for agricultural decisions – A case study for Oklahoma Mesonet

For the past 24 years, weather information and monitoring provided by the Oklahoma Mesonet (the state’s largest weather and environmental monitoring network) has been used by many farmers across the state to optimize their farm operations and improve profitability of agricultural production.Several research studies delineated advantages of Mesonet weather information for efficient agricultural decision-making. However, these studies are mainly qualitative or conceptual, while comprehensive quantitative research in this field is still missing. This paper aims at filling this gap by quantifying the value of weather information for Oklahoma farmers in all climate regions, based on profitability analysis, USDA NASS and agricultural output statistics, as well as crop budgets from the Oklahoma Extension Center.The results show that the cumulative economic benefits for agricultural production in Oklahoma from utilizing Mesonet information (assuming the profitability factor of 3.7% and with 73% of farmers applying Mesonet) amounted to $183.1 million in 2006–2014 (combining generated profits and prevented losses in agricultural production). Mesonet information was found to provide higher annual economic benefits in terms of prevented losses than additional production profits, especially in drought years. A regional analysis for Oklahoma climate regions revealed the highest cumulative benefits of Mesonet applications in the Southwest ($45.9 million), North Central region ($38.2 million), and in Panhandle ($34.5 million) in 2006–2014. Also, high temporal variations were found for all Oklahoma climate regions.This research is focused on the agricultural sector, while it also sets the baseline for a more holistic understanding of the economic value and benefits of weather information provided by the Oklahoma Mesonet that can further be applied to other sectors in the state.

Importance of weather monitoring for agricultural decision-making - an exploratory behavioral study for Oklahoma Mesonet.

The Oklahoma Mesonet (the statewide environmental and weather monitoring network) has monitored changes in weather patterns since 1994 to provide accurate and timely mesoscale weather information to farmers and other groups. Studies are still scarce that would quantitatively assess farmers' perceptions about the value of the Oklahoma Mesonet contributions to agricultural operations, profitability of land management, and decision making. This paper aims to analyze those questions by means of an exploratory empirical study in Oklahoma for two groups of Mesonet users and non-users. Familiarity with and application of Mesonet information determines farmers' profitability assessments and decision making. Farmers' perceptions are also influenced by the degree of previous exposure to weather-related losses. The median estimate of the economic value of Mesonet information is $1000 per year. Mesonet users perceive higher profitability from the application of Mesonet data at 7.6/10, whereas Mesonet non-users provided an average assessment of 2.6/10. Consistent use of Mesonet information results in a higher assessment of the importance of Mesonet. This research provides some initial insights into farmers' perceptions about the value of Oklahoma Mesonet information, which could guide stakeholders in developing measures to better serve farmers with environmental monitoring data for improved farm decisions. © 2018 Society of Chemical Industry.

Observing the May 2015 Record Rainfall at Norman, Oklahoma, Using Various Methods

Abstract The May 2015 record rainfall that occurred across Oklahoma was the result of a large number of high-intensity rain events. A unique set of observations from gauges in the Oklahoma Mesonet, the NWS Cooperative Observer (COOP) network, the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network, an experimental pit gauge system, and NWS radar was available that covered an area in and around Norman, Oklahoma. This paper documents the performance of the various gauges throughout the course of the month. Key findings are 1) observations from all methods significantly exceeded the 200-yr return interval; 2) a weighing-bucket gauge at ground level recorded amounts up to 4.5% higher than a similarly located ground-level tipping-bucket gauge and up to 8.2% higher than a nearby aboveground tipping-bucket gauge; 3) a manual COOP gauge recorded nearly identical (within 1.2%) observations as compared to an automated tipping-bucket gauge at a collocated Mesonet station; and 4) observations from 26 CoCoRaHS gauges yielded an average rainfall within 1% of the aerially averaged radar rainfall derived from the Multisensor Precipitation Estimator.

The Oklahoma Meso-Scale Integrated Socio-Geographic Network: A Technical Overview

AbstractEstablished as a social companion to the Oklahoma Mesonet, the Oklahoma Meso-Scale Integrated Socio-Geographic Network (M-SISNet) is a network of approximately 1500 “social monitoring stations” (geolocated households) across the state of Oklahoma that provide data on household perceptions and responses to signals that are sent from agricultural, hydrological, and meteorological systems. This paper outlines the purpose and nature of the M-SISNet, with specific focus on the sample frame, protocols for recruitment, retention, and survey implementation. It concludes with example survey questions, analyses, and directions for accessing the data.

The Oklahoma Mesonet: A Pilot Study of Environmental Sensor Data Citations

This pilot study of 110 scientific papers utilizing environmental sensor data from the Oklahoma Mesonet during its first two decades of operations demonstrates the diversity of potential purposes in scientific research for a robust, rigorously maintained, accessible source of environmental sensor data, as well as the challenges involved in identifying uses of that data within scientific papers. The study authors selected three publication years (1995, 2005, 2015) from an extensive corpus of peer-reviewed journal publications, identified each paper’s specific citation of and uses of the Mesonet’s environmental sensor data, and derived a typology of those usages (assimilation, experimentation, observation, simulation, utilization, validation) found to be most common. The rapid increase in data assimilation research projects today is discussed in terms of the difficulty and importance of correct attribution to individual data sources in these complex research projects. The study examines the possible role played by highly-cited papers that describe the quality assurance procedures in sensor data sources, which may serve as surrogates to signal the quality of the data provided by such sources, and which may also provide a useful contribution towards understanding data citation as a special form of scholarly citation.

Comparison of Three Methods for Vertical Extrapolation of Soil Moisture in Oklahoma

Core Ideas Exponential filter performs better than LR and ANN for SWI vertical extrapolation. Adding meteorological variables does not improve model accuracy. Soil properties can improve the linear regression model. ExpF and general LR are two potential general methods for SWI extrapolation. Soil moisture monitoring networks can provide real‐time and accurate soil moisture measurements; however, missing values and the lack of unified measurement depths across different networks impedes soil moisture applications at regional and national scales. Therefore, methods for vertical extrapolation of soil moisture, i.e., using shallow soil moisture measurements to estimate deeper soil moisture, are needed for standardizing measurements to a set of common depths. This study compared three methods, artificial neural network (ANN), linear regression (LR), and exponential filter (ExpF), for vertical extrapolation of soil moisture using data from the Oklahoma Mesonet. Based on our analysis of intra‐annual variations in soil moisture, we divided each year into two seasons, warm and cool. Our results demonstrate that all methods perform better in the warm season than in the cool season, especially at deeper depths. The Kling–Gupta efficiency was used to assess the performance of each method. All methods had similar performance for near‐surface extrapolation of soil moisture (top 25 cm). Although the accuracy of all models tended to decrease with depth, the ExpF outperformed the other methods at deeper depths. The soil water index (SWI) is preferred over volumetric water content as input to the ExpF. Incorporating air temperature and an antecedent precipitation index into the ANN and LR methods did not significantly improve their accuracy. We demonstrated that both ExpF and general LR can be used for SWI extrapolation at sites where only surface soil moisture data are available. Soil properties may be useful for further improving the accuracy of the general LR method.