Oklahoma Mesonet Research Articles

Traditional matric potential thresholds underestimate soil moisture at field capacity across Oklahoma

AbstractField capacity is a dubious soil physical property, but its use continues because of its perceived value for representing a soil's capacity to store water. Appropriate field capacity estimates can be useful for interpreting data from soil moisture sensors, including those in large‐scale monitoring networks, but suitable methods for defining field capacity in this context are unclear. Motivated by the desire to determine optimal field capacity values for the Oklahoma Mesonet, our objectives were (1) to develop and apply an automated time series analysis algorithm to estimate volumetric soil water content at field capacity and corresponding matric potential and (2) to compare the resulting water contents to those calculated from traditional matric potential thresholds (−33 and −10 kPa). Across 118 Oklahoma Mesonet sites and three soil depths (5, 25, and 60 cm), a matric potential threshold of −10 kPa underestimated field capacity water content by 0.010–0.014 cm cm−3 (3–4%) on average, and a threshold of −33 kPa underestimated it for every site and depth by 0.055–0.078 cm cm−3 (16%−22%) on average. Median matric potentials corresponding to field capacity were −7.6 kPa at the 5‐cm depth, −7.2 kPa at the 25‐cm depth, and −7.3 kPa at the 60‐cm depth. The algorithm developed here can be used to estimate field capacity wherever adequate data are available, and for sites where soil water retention properties are known, matric potentials at field capacity can also be estimated. Using a matric potential of −33 kPa as a standard threshold to represent field capacity is not scientifically justified and should be discontinued.

Climatology of Wet-Bulb Globe Temperature and Associated Heat Waves in the U.S. Great Plains

Abstract Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.

Regional switchgrass carbon sequestration estimates from high‐frequency eddy covariance and Mesonet observations

AbstractMonitoring net ecosystem carbon dioxide (CO2) exchange (NEE) using eddy covariance (EC) flux towers is quite common, but the measurements are valid at the scale of tower footprints. Alternative ways to quantify and extrapolate EC‐measured NEE across potential production areas have not been explored in detail. To address this need, we used NEE measurements from a switchgrass (Panicum virgatum L.) ecosystem and detailed meteorological measurements from the Oklahoma Mesonet and developed empirical relationships for quantifying seasonal (April to October) sums of NEE across potential switchgrass establishment landscapes in Oklahoma, USA. We identified ensemble areas for potential switchgrass expansion regions and created thematic maps of switchgrass productivity using geostatistics and geographic information systems (GIS) routines. The purpose of this study was to explore if model parametrizations based on high temporal frequency meteorological forcing can be used for reliable estimates of NEE for evaluating the source–sink status of carbon. Rectangular hyperbolic light‐response curve and temperature response functions were fitted using EC measurements to estimate gross primary production (GPP) and ecosystem respiration (ER), respectively, on a seasonal scale. Model performance validated by comparing EC‐measured seasonal NEE for 3 yr showed good‐to‐strong agreement (.29 < R2 < .91; p < .05). Additionally, total seasonal NEE estimates were validated with measured biomass data in three additional locations. The estimated seasonal average net ecosystem production (NEP = −NEE) was 3.97 ± 1.92 (SD) Mg C ha−1. However, results based on a simple linear model suggested significant differences in NEP between contrasting climatic years. Overall, the results from this study indicate that this new scaling‐up approach involving high temporal resolution meteorological data may be a helpful tool for assessing spatiotemporal heterogeneity of switchgrass production and the potential of switchgrass fields to sequester carbon in the Southern Great Plains of the United States.

Station Aridity in Weather Monitoring Networks: Evidence from the Oklahoma Mesonet

HighlightsStation aridity can cause overestimation of ETref at weather monitoring networks in irrigated areas.Station aridity was demonstrated in a mesoscale weather monitoring network.Station aridity is amplified in water-scarce irrigated areas during droughts.Station aridity should be accounted for to achieve water conservation through weather-informed irrigation.Abstract. Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. Keywords: Evapotranspiration, Irrigation requirement, Reference condition, Station aridity, Weather station.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma—Part I: Individual Product Assessment

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m2 to 102 km2) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m3⁄m3. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products.

Economic Value of a Weather Decision Support System for Oklahoma Public Safety Officials

The Oklahoma Mesonet’s Public Safety outreach program, OK-First, has provided weather education and data delivery to the public safety community for the past twenty-five years. By delivering high-quality weather data tools, regular classes, and continued follow-up support to its trained members, the OK-First program has empowered more than 1,800 public safety officials. Testimonials from OK-First users indicate that there is an immense value in providing nonscientific audiences with meteorological information and training. OK-First users have saved lives and property using the the program. There has been no quantitative analysis, however, that evaluates the value of the OK-First program. This study fills a gap in providing quantitative analysis for the economic value of the OK-First program. This research used a modified travel cost method to measure the value of information provided by the OK-First program to the users of the program. Results suggest that the average willingness to pay for OK-First is $1,122 per training. The OK-First program in Oklahoma is valued at an estimated $254,000, with a collective surplus of $80,000 per training for OK-First users. This conservative estimate suggests the importance of programs such as the OK-First training, especially for public safety officials in Oklahoma.

A Multi-Platform In Situ Kinematic and Microphysical Analysis of a Hybrid Parallel–Trailing Stratiform Mesoscale Convective System

Abstract Parallel stratiform–type mesoscale convective systems (MCS) have been found to comprise less than 20% of the central U.S. MCSs, but parallel stratiform MCSs have been reviewed by few studies and only a handful have been sampled using airborne platforms. This study conducts a detailed review of the 11 May 2011 MCS that was observed during the Midlatitude Continental Convective Clouds Experiment. In situ data from the University of North Dakota Cessna Citation II Weather Research Aircraft and the Oklahoma Mesonet are used in conjunction with radar reflectivity data and a multiple-Doppler wind retrieval to present an in-depth analysis of the kinematic and microphysical processes within this hybrid parallel–trailing stratiform MCS. Results suggest the MCS started with parallel stratiform characteristics and then developed trailing stratiform features over the course of a couple of hours such as leading line convection and the presence of rear inflow, which is a feature not observed in previous studies of parallel stratiform MCSs. Based on surface observations and multi-Doppler wind field retrievals, the cold pool influenced the 11 May 2011 MCS and may have helped it to transition toward the trailing stratiform mode. Within the stratiform region, in situ measurements show hydrometeor aggregation and sublimation occurring above the melting layer via a faster rate of decline in total hydrometer concentration compared to the total water content, the presence of a bimodal droplet size spectra, and increasing dispersion and decreasing slope, with respect to increasing temperature in both the unimodal and bimodal gamma distributions. Significance Statement The purpose of this study is to investigate the wind pattern of a nontypical type of thunderstorm complex. The wind pattern is found to be a combination of features from both a common type of thunderstorm complex and another less common type. The behavior of snowflakes as they fall through this thunderstorm was also investigated. The snowflakes in this storm grew bigger by linking up with other snowflakes as they fell down, and then melted before they reached the ground. It is hoped that results from this study are useful to future work to improve weather prediction.

Assessing the Impacts of a Weather Decision Support System for Oklahoma Public Safety Officials

Abstract Oklahoma’s First-response Information Resource System using Telecommunications (OK-First) has been used for over 25 years to provide education, training, connections, and follow-up support for public safety officials with emergency management responsibilities in Oklahoma. Public safety officials use OK-First training and Oklahoma Mesonet tools to plan and make decisions to save lives and property. However, like most public systems, little is known about user interactions with tools, decisions made, and estimated savings using a weather decision support system. This study used a mixed approach to collect and analyze data from three key sources to assess the perceptions, beneficiaries, and applications of weather support systems for public safety officials. Results showed that a diverse set of tools were needed and used by public safety officials to make decisions in hazardous weather conditions. OK-First tools resulted in estimated self-reported cost savings of over $1.2 million for 12 months. This study provides a crucial step in determining user interactions with tools, training, and services to better understand weather decision support systems used during hazardous weather.

Wheat nitrogen response conditional on past yield and rainfall: A step in improving optimal nitrogen applications

AbstractMaking precision nitrogen (N) application clearly profitable will likely require incorporating multiple sources of information. Low‐cost sources of information include rainfall and the previous year's yield. This paper uses data from a long‐term experiment on the response of winter wheat (Triticum aestivum L.) to N fertilizer, as well as rainfall data from the University of Oklahoma's Mesonet weather station. The goal was to determine optimal topdress levels of N. A regression was used to determine the previous year's yield and rainfall on the marginal product of farmer‐applied N. Information from lagged yield and rainfall increases profit by $2.79 ha–1. Therefore, lagged yield and rainfall could be a low‐cost information source to add to other sources of information to achieve the goal of widespread adoption of precision N application.

Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution

This article focuses on developing a data assimilation system that combines the modeled surface moisture estimates and satellite observations. Specifically, model states simulated by the Noah-MP land surface model are updated using an Ensemble Kalman Filter with products from the NASA SMAP (Soil Moisture Active Passive) satellite mission. The land surface model is run on two different regular grids, one at 12.5 km and the other at 500 m to produce surface and root zone soil moisture estimates across Oklahoma during April-July 2015. In the first case, the model was forced with the NLDAS-2 (North America Land Data Assimilation System) dataset and in the second with a downscaled version of the same dataset. Ground observations from the Oklahoma Mesonet network are compared to surface and root zone soil moisture output simulated by three different Noah-MP model runs i) an open loop simulation (in which no satellite data are assimilated); ii) assimilation of the 36 km SMAP radiometer-only product, and iii) assimilation of the 9 km SMAP radiometer-radar combined product. Results show that SMAP soil moisture retrievals improve the model performance (i.e., with respect to the open loop run) and that forcing the land surface model with higher resolution atmospheric forcings yields higher correlations and smaller errors in soil moisture simulations with respect to the original NLDAS-2 dataset. Although root zone soil moisture is not directly assimilated (since satellite observations are limited to the top 5 cm of the soil column), the assimilation of SMAP products at the surface is transferred to lower layers by the modeled physical processes and is shown to improve root zone soil moisture estimates as well.

Evaluation of an experimental Warn‐on‐Forecast 3DVAR analysis and forecast system on quasi‐real‐time short‐term forecasts of high‐impact weather events

AbstractThe purpose of this study is to demonstrate the capability of an experimental, weather‐adaptive, high‐resolution, deterministic Warn‐on‐Forecast (WoF) analysis and forecast system (WoF3DVAR‐AFS) for predicting high‐impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR‐AFS uses a three‐dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF‐ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15‐min frequency over a target domain determined by the “Day 1” Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn‐on‐Forecast System (WoFS) with a higher‐resolution deterministic member to aid forecasters' decision‐making. We performed both qualitative and quantitative evaluations on 0–6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR‐AFS and provides possible directions for further improvements, including the development of a weather‐adaptive, dual‐resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn‐on‐Forecast system.

Retrieving gap-free daily root zone soil moisture using surface flux equilibrium theory

Root zone soil moisture (RZSM) is a dominant control on crop productivity, land-atmosphere feedbacks, and the hydrologic response of watersheds. Despite its importance, obtaining gap-free daily moisture data remains challenging. For example, remote sensing-based soil moisture products often have gaps arising from limits posed by the presence of clouds and satellite revisit period. Here, we retrieve a proxy of daily RZSM using the actual evapotranspiration (ETa) estimates from Surface Flux Equilibrium Theory (SFET). Our method is calibration-less, parsimonious, and only needs widely available meteorological data and standard land-surface parameters. Evaluation of the retrievals at Oklahoma Mesonet sites shows that our method, overall, matches or outperforms widely available RZSM estimates from three markedly different approaches, viz. remote sensing data based Atmosphere-Land EXchange Inversion (ALEXI) model, the Variable Infiltration Capacity (VIC) model, and the Soil Moisture Active Passive (SMAP) mission RZSM data product. When compared with in-situ observations, unbiased root mean square difference of retrieved RZSM were 0.03 (m3 m−3), 0.06 (m3 m−3), and 0.05 (m3 m−3) for our method, the ALEXI model, and the VIC model, respectively. Better performance of our method is attributed to the use of both SFET for the estimation of ETa and non-parametric kernel-based method used to relate the RZSM with ETa. RZSM from our method may serve as a more accurate and temporally-complete alternative for a variety of applications including mapping of agricultural droughts, assimilation of RZSM for hydrometeorological forecasting, and design of optimal irrigation schedules.

MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet

AbstractSoil 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.

Active optical sensor measurements and weather variables for predicting winter wheat yield

AbstractAccurate winter wheat (Triticum aestivum L.) grain yield prediction is vital for improving N management decisions. Currently, most N optimization algorithms use in‐season estimated yield (INSEY) as a sole variable for predicting grain yield potential (YP). Although evidence suggests that this works, the yield prediction accuracy could be further improved by including other predictors in the model. The objective of this work was to evaluate INSEY, pre‐plant N rate, total rainfall, and average air temperature from September to December as predictors of winter wheat YP. An 8‐yr (2012–2019) data set for grain yield was obtained from Experiment 502, Lahoma, OK. The experiment was designed as a randomized complete block with four replications and N applied at 0, 45, 67, 90, and 112 kg ha–1. Weather data was obtained from the Oklahoma Mesonet (http://mesonet.org). The data were analyzed using R statistical computing platform. The best model was selected using least absolute shrinkage and selection operator. Root mean square error (RMSE) was obtained using k‐fold cross‐validation. The model selection algorithm produced the full model as the best model for yield prediction with an R2 of .79 and RMSE of 0.54 Mg ha–1. The best one‐variable model – as expected – used INSEY as the predictor and had the highest RMSE of 0.72 Mg ha–1 and an R2 of .62. Mid‐season YP prediction accuracy could be improved by including pre‐plant N rate, mean air temperature, and total rainfall from September to December in a model already containing INSEY.

Downscaling SMAP Soil Moisture Products With Convolutional Neural Network

Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on a convolutional neural network (CNN) is proposed to solve the problem. Furthermore, a weight layer is designed for the input, and residual SM is treated as the output of the CNN to improve the accuracy. This method is applied to downscale Soil Moisture Active Passive (SMAP) SM products (i.e., 36-km $\mathbf {L3{\_}SM{\_}P}$ and 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ) from January 1, 2018 to December 30, 2018. Compared with 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ , the 9-km downscaling result is satisfactory with obtained correlation coefficient ( $R$ ), root mean square error (RMSE), and unbiased RMSE (ubRMSE) values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are validated by the in situ data, which are collected by the 109 stations of the Oklahoma Mesonet SM monitoring network. Mean $R$ , RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km $\mathbf {L3{\_}SM{\_}P}$ ; 67.78%, 8.35%, and 4.95% for 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ; 67.28%, 8.34%, and 4.97% for 3-km downscaling SM; 65.90%, 8.40%, and 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ while preserving its accuracy. However, error will remarkably increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.

168 The effects of fly control strategies on fly abundance, fly avoidance behaviors, and performance in feedlot bulls and steers

Abstract The objective of this experiment was to examine the effects of fly control strategies on fly abundance, fly avoidance behaviors, and animal performance in a feedlot setting. Angus bulls (n = 64; BW = 281 ± 36.2 kg) and steers (n = 36; BW = 475 ± 40.4 kg) were blocked by sex and BW and assigned to 1 of 4 experimental treatments in a randomized complete block design (7 pens/treatment; 4 bulls/pen; 3 steers/pen). Treatments included: control (CON; no fly control), abamectin, piperonyl butoxide, and zetacypermethrin insecticide tags (FT; Tri-Zap®, Y-Tex Corporation, Cody, WY), permethrin and piperonyl butoxide pour on (PO; Permectrin® CDS, Bayer Animal Health, Shawnee Mission, KS), or a garlic-powder top dress (GR) administered at 0.28 g •-1 animal •-1d. The experiment was conducted between June and September. Horn and stable fly abundance (FA) and fly avoidance behaviors (FAB; i.e. tail-flicks, stomping, head-tossing) were recorded by trained personnel. Both FA and FAB were recorded once/wk on days with similar temperatures, humidity, wind speed, and without precipitation based on Oklahoma Mesonet data (Stillwater site). There was a tendency for a treatment × time interaction for horn FA with differences in treatments occurring at wk 1, 2, 8, and 14. There was no treatment × time interaction for stable FA or FAB, however both were affected by time (P < 0.001). There were no differences in final BW (P = 0.34), overall ADG (P = 0.30), overall DMI (P = 0.53), or overall G:F (P = 0.39). There was a tendency (P ≤ 0.10) for GR to have decreased BW on d 28 and decreased ADG from d 0 to 28 compared to FT and PO. There was also a tendency (P = 0.09) for decreased DMI from d 0 to 56 for GR compared to CON and FT. Overall FA was less than expected (mean = 1 to 17 flies/animal) throughout the experiment and overall animal performance was unaffected by fly control strategy.

167 The effects of fly control strategies on fly abundance, fly avoidance behaviors, and performance in feedlot bulls and steers

Abstract The objective of this experiment was to examine the effects of fly control strategies on fly abundance, fly avoidance behaviors, and animal performance in a feedlot setting. Angus bulls (n = 64; BW = 281 ± 36.2 kg) and steers (n = 36; BW = 475 ± 40.4 kg) were blocked by sex and BW and assigned to 1 of 4 experimental treatments in a randomized complete block design (7 pens/treatment; 4 bulls/pen; 3 steers/pen). Treatments included: control (CON; no fly control), abamectin and piperonyl butoxide insecticide tags (FT; XP820®, Y-Tex Corporation, Cody, WY), permethrin and piperonyl butoxide pour on (PO; Permectrin® CDS, Bayer Animal Health, Shawnee Mission, KS), or a garlic-powder top dress (GR) administered at 0.28 g • -1 animal • -1d. The experiment was conducted between June and September. Horn and stable fly abundance (FA) and fly avoidance behaviors (FAB; i.e. tail-flicks, stomping, head-tossing) were recorded by trained personnel. Both FA and FAB were recorded once/wk on days with similar temperatures, humidity, wind speed, and without precipitation based on Oklahoma Mesonet data (Stillwater site). There was a tendency for a treatment × time interaction for horn FA with differences in treatments occurring at wk 1, 2, 8, and 14. There was no treatment × time interaction for stable FA or FAB, however both were affected by time (P < 0.001). There were no differences in final BW (P = 0.34), overall ADG (P = 0.30), overall DMI (P = 0.53), or overall G:F (P = 0.39). There was a tendency (P ≤ 0.10) for GR to have decreased BW on d 28 and decreased ADG from d 0 to 28 compared with FT and PO. There was also a tendency (P = 0.09) for decreased DMI from d 0 to 56 for GR compared to CON and FT. Overall FA was less than expected (mean = 1 to 17 flies/animal) throughout the experiment and overall animal performance was unaffected by fly control strategy.

Severe weather observations from a deployable four sensor infrasound array

Infrasound is sound at frequencies below 20 Hz. It has been observed to be emitted by tornado-producing storms up to two hours before tornadogenesis. Due to the low atmospheric attenuation of sound at these low frequencies, infrasound may be detected several hundreds of kilometers away under ideal atmospheric conditions. Therefore, passive infrasound monitoring has potential for the study and prediction of tornadoes and other severe weather if the received infrasound signals can be correlated with thermodynamic and flow field properties of the storms and/or tornadoes. Previous work accomplished at Oklahoma State University has focused on observations from a single stationary array located in Stillwater, OK. However, the large distance between this array and the nearest NEXRAD II radar stations makes low-level storm characterization challenging to accomplish in tandem with infrasound observation of severe weather. Therefore, a second array was designed to be deployable at various sites in Oklahoma within 50 km of the radar stations, in order to ensure low-altitude radar resolution. Containing four microphones, the new array was deployed during the summer of 2020 at several sites affiliated with the Oklahoma Mesonet. Details of the array's design and deployment will be presented, as well as preliminary data collected during non-tornadic thunderstorms in 2020. [This work was supported by NOAA Grant NA19OAR4590340 and NOAA Grant NA18OAR4590307.]

More science with less: evaluation of a 3D-printed weather station

Abstract. A weather station built using 3D-printed parts and low-cost sensors, based on plans and guidance provided by the University Corporation for Atmospheric Research 3D-Printed Automatic Weather Station Initiative, was deployed alongside an Oklahoma Mesonet station to compare its performance against standard commercial sensors and determine the longevity and durability of the system. Temperature, relative humidity, atmospheric pressure, wind speed and direction, solar radiation, and precipitation measurements were collected over an 8-month field deployment in Norman, Oklahoma. Measurements were comparable to the commercial sensors except for wind direction, which proved to be problematic. Longevity and durability of the system varied, as some sensors and 3D-printed components failed during the deployment. Overall, results show that these low-cost sensors are comparable to the more expensive commercial counterparts and could serve as viable alternatives for researchers and educators with limited resources for short-term deployments. Long-term deployments are feasible with proper maintenance and regular replacement of sensors and 3D-printed components.