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Tropical Storm Bill produced over 400 mm of rainfall in portions of southern Oklahoma from 16 to 20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes, which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as ZH, ZDR, KDP, and ρhv were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision–coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision–coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky. [ABSTRACT FROM AUTHOR]

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass‐yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county‐level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)‐driven grassland productivity models with and without assimilation of measured FAW into the models' water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county‐level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild‐hay yields with soil moisture data assimilation (Nash–Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs. Core Ideas: In situ soil moisture data were used to estimate grassland biomass productivity.Statistical and mechanistic models were assessed as alternative applications of the data.Soil moisture correlated strongly with productivity in a simple statistical model.The mechanistic model was improved after calibration and with soil moisture.Soil moisture data have good potential for grassland productivity forecasting. [ABSTRACT FROM AUTHOR]

Global "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. [ABSTRACT FROM AUTHOR]

Observations from the Oklahoma Mesonet and high resolution Weather Research and Forecasting model simulations were used to evaluate the effect that the dry line and large-scale atmospheric patterns had on drought evolution during 2011. Mesonet observations showed that a “dry” and “wet” pattern developed across Oklahoma due to anomalous atmospheric patterns. The location of the dry line varied due to this “dry” and “wet” pattern, with the average dry line location around 1.5° longitude further to the east than climatology. Model simulations were used to further quantify the impact of variable surface conditions on dry line evolution and convective initiation (CI) during April and May 2011. Specifically, soil moisture conditions were altered to depict “wet” and “dry” conditions across the domain by replacing the soil moisture values by each soil category’s porosity or wilting point value. Overall, the strength of the dry line boundary, its position, and subsequent CI were dependent on the modification of soil moisture. The simulations demonstrated that modifying soil moisture impacted the nature of the dry line and showed that soil moisture conditions during the first half of the warm season modified the dry line pattern and influenced the evolution and perpetuation of drought over Oklahoma. [ABSTRACT FROM AUTHOR]

On 13 May 2009, 13 surface-based observing stations within central and western Oklahoma recorded maximum wind speeds in excess of 22.5 m s−1, along with gusts exceeding 28 m s−1 in isolated locations, during a localized wind storm that lasted in excess for over an hour. These wind speeds were associated with an enhanced mesoscale pressure gradient that developed during the late evening of 12 May 2009 and early morning hours of 13 May 2009, which then slowly propagated from west to east. Ultimately, the event produced localized wind damage in northern portions of Oklahoma City. Analysis of the synoptic and mesoscale conditions present at each stage of the event revealed that specific parameters associated with heat bursts occurred during the event, but with the inclusion of varying mesoscale and microscale influences. As such, a close examination of the mesoscale pressure field indicated that a dynamically variable mesoscale convective system (MCS) produced a mesohigh/mesolow couplet that ultimately created the strong pressure gradient. The result was a prolonged period (in excess of 1 h) of enhanced wind speed values across a swath approximately 300 km in length and 50 km in width that were not directly associated with the anomalously warmer and drier conditions that occurred due to the heat burst. Copyright © 2011 Royal Meteorological Society [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]

During late July and early August 2008, an intense heat wave occurred in Oklahoma City. To quantify the impact of the urban heat island (UHI) in Oklahoma City on observed and apparent temperature conditions during the heat wave event, this study used observations from 46 locations in and around Oklahoma City. The methodology utilized composite values of atmospheric conditions for three primary categories defined by population and general land use: rural, suburban, and urban. The results of the analyses demonstrated that a consistent UHI existed during the study period whereby the composite temperature values within the urban core were approximately 0.5°?C warmer during the day than the rural areas and over 2°?C warmer at night. Further, when the warmer temperatures were combined with ambient humidity conditions, the composite values consistently revealed even warmer heat-related variables within the urban environment as compared with the rural zone. [ABSTRACT FROM AUTHOR]

Oklahoma is a region that is well known for its high frequency of severe thunderstorms, which vary in activity, mode, and coverage. In particular, this region experiences a significant number of highly organized supercell thunderstorms that pose hazards such as high winds, large hail, and tornadoes. This demonstration study focuses on the development and analysis of a 10-yr sample of supercell storms resulting from organized severe weather events in Oklahoma. Geographic information systems (GIS) were used as the primary tool to develop and analyze the 10-yr supercell dataset. The use of GIS technologies within the field of meteorology has increased dramatically in recent years and will likely continue as additional atmospheric science data formats become available in popular GIS software packages such as the Environmental Systems Research Institute’s ArcGIS series. For this specific study, GIS served as a critical component for developing individual georeferenced storm features and analyzing the life span and characteristics of 943 supercell thunderstorms. The results of a series of spatial storm frequency, initiation, termination, and direction analyses are presented. Results revealed that for the period spanning 1994–2003 supercell storms resulting from organized severe weather events were most frequent across several regions, including east-central Oklahoma, southwest Oklahoma, and west-central through northeast Oklahoma, with an overall mean track from the southwest to northeast. Supercell tracks were predominantly southwesterly during the first 5 months of the year, northwesterly from June through September, and once again southwesterly from October through the end of the year. A final set of analyses and examples illustrate the utility of storm feature–based climatologies. [ABSTRACT FROM AUTHOR]

The El Reno Oklahoma Mesonet (ELRE) site is one of a few Oklahoma Mesonet sites that has measured inversions of 10°C or greater between 1.5 and 9 m. Historical analyses revealed that strong inversions at ELRE have occurred because of rapid cooling near the surface shortly after sunset when conditions are calm, clear, and dry. In addition, ELRE is a very well sited station and is located on very slightly sloped terrain with no obstructions nearby. Four Portable Automated Research Micrometeorological Stations (PARMS) were deployed along a transect orthogonal to ELRE for 3 months in the spring of 2005 to quantify the micrometeorological processes that caused rapid cooling and subsequent strong inversions to form. One-minute data collected from the PARMS and ELRE during the study verified the variability and duration of strong inversion events. Analyses from the field study also revealed that significant horizontal and vertical differences in air temperature and wind speed existed during periods of differential wind speeds between the PARMS and ELRE. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research (NCAR) and the U.S. Army Test and Evaluation Command have developed a multiscale, rapid-cycling, real-time, four-dimensional data-assimilation and forecasting system that has been in operational use at five Army test ranges since 2001. This system was employed to provide operational modeling support for the Joint Urban 2003 (JU2003) Dispersion Experiment, conducted in Oklahoma City, Oklahoma, during July 2003. To better support this mission, modifications were made to the nonlocal boundary layer (BL) parameterization (known as the Medium Range Forecast scheme) of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model, in order to improve BL forecasts. The NCEP–Oregon State University–Air Force–Hydrologic Research Laboratory land surface model was also improved to better represent urban forcing. Verification of the operational model runs and retrospectively simulated cases show 1) a significantly reduced low bias in the forecast surface wind speed and 2) more realistic daytime BL heights. During JU2003, the forecast urban heat island, urban dry bubble, and urban BL height agree reasonably well with observations and conceptual models. An analysis of three-dimensional atmospheric structures, based on model analyses for eight clear-sky days during the field program, reveals some interesting features of the Oklahoma City urban BL, including complex thermally induced circulations and associated convergence/divergence zones, a nocturnal thermal shadow downwind of the urban area, and the reduction of low-level jet wind speeds by more vigorous nocturnal mixing over the city. [ABSTRACT FROM AUTHOR]

Disaggregation of regional-scale (103 m) flux estimates to micrometeorological scales (101–102 m) facilitates direct comparison between land surface models and ground-based observations. Inversely, it also provides a means for upscaling flux-tower information into a regional context. The utility of the Atmosphere–Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) in effecting regional to local downscaling is demonstrated in an application to thermal imagery collected with the Geostationary Operational Environmental Satellite (GOES) (5-km resolution) and Landsat (60-m resolution) over the state of Oklahoma on 4 days during 2000–01. A related algorithm (DisTrad) sharpens thermal imagery to resolutions associated with visible–near-infrared bands (30 m on Landsat), extending the range in scales achievable through disaggregation. The accuracy and utility of this combined multiscale modeling system is evaluated quantitatively in comparison with measurements made with flux towers in the Oklahoma Mesonet and qualitatively in terms of enhanced information content that emerges at high resolution where flux patterns can be identified with recognizable surface phenomena. Disaggregated flux fields at 30-m resolution were reaggregated over an area approximating the tower flux footprint and agreed with observed fluxes to within 10%. In contrast, 5-km flux predictions from ALEXI showed a higher relative error of 17% because of the gross mismatch in scale between model and measurement, highlighting the efficacy of disaggregation as a means for validating regional-scale flux predictions over heterogeneous landscapes. Sharpening the thermal inputs to DisALEXI with DisTrad did not improve agreement with observations in comparison with a simple bilinear interpolation technique because the sharpening interval associated with Landsat (60–30 m) was much smaller than the dominant scale of heterogeneity (200–500 m) in the scenes studied. Greater benefit is expected in application to Moderate Resolution Imaging Spectroradiometer (MODIS) data, where the potential sharpening interval (1 km to 250 m) brackets the typical agricultural field scale. Thermal sharpening did, however, significantly improve output in terms of visual information content and model convergence rate. [ABSTRACT FROM AUTHOR]

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available. [ABSTRACT FROM AUTHOR]

Abstract Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO 2) and water vapor (H 2 O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO 2 and H 2 O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO 2 exchange (NEE) reached seasonal peaks of −9.4 and −8.8 g C m−2 in 2015 and −6.2 and −7.5 g C m−2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ± 13 and 33 ± 9 g C m−2 year−1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (−128 ± 69 and −119 ± 53 g C m−2 year−1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day−1 in 2015, and 7.2 and 8.2 mm day−1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets. Graphical abstract Unlabelled Image Highlights • Carbon and water fluxes measured in two ecosystems located at 2.7 km apart are compared. • Winter wheat and tallgrass prairie are both carbon sinks during the growing season. • Summer fallow caused the wheat ecosystem to be a source of carbon at annual scale. • Management practices caused changes in the fluxes of prairie and wheat ecosystems. [ABSTRACT FROM AUTHOR]

• First study to evaluate the impacts of MODIS vs. LS2 on GPP VPM and T VTM estimates. • Combined Landsat/Sentinel-2 (LS2) tracked phenology of prairie, alfalfa, and wheat. • LS2-enhanced vegetation index accurately identified site level optimal temperature. • LS2 data produced better gross primary production (GPP) estimates than MODIS. • The vegetation transpiration model is a simpler approach to estimating transpiration (T). Although agroecosystems have a significant potential to offset carbon dioxide (CO 2), the amount of CO 2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPP VPM) and T (T VTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPP VPM and T VTM estimates at different spatial scales was evaluated using GPP (GPP EC) and evapotranspiration (ET EC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO 2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices. [ABSTRACT FROM AUTHOR]

• Managed pasture showed higher sensitivity to drought than native pasture. • Spring temperature and fall rainfall were critical for managed pasture. • Management activities modulated the effects of environmental variables on productivity. Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models. [ABSTRACT FROM AUTHOR]

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2  = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.