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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]

A road ice prediction model was developed on the basis of existing data networks with an objective of providing a computationally efficient method of road ice forecasting. Icing risk was separated into three distinct road ice formation mechanisms: hoarfrost, freezing fog, and frozen precipitation. Hoarfrost parameterizations were mostly gathered as presented in previous literature, with modifications incorporated to account for diffusional ice crystal growth-rate complexity. Freezing-fog parameterizations were based on previous fog typological analyses under the assumption that fog formation mechanisms are similar in above- and subfreezing temperatures. Frozen-precipitation parameterizations were primarily unique to the developed model but were also partially based on previous research. Diagnostic analyses use a synthesis of Automated Surface Observing System (ASOS), Automated Weather Observing System (AWOS), and Oklahoma Mesonet data. Prognostic analyses utilize the National Digital Forecast Database (NDFD), a 2.5-km gridded database of forecast meteorological variables output from National Weather Service Weather Forecast Offices. A frequency analysis was performed using the diagnostic parameterizations to determine general road icing risk across the state of Oklahoma. The frequency analyses aligned well with expected temporal maxima and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing-fog-formation parameterizations to capture 89% of fog events. These results suggest that the developed model, identified as the Road-Ice Model (RIM), may be implemented as a robust option for analyzing the potential for road ice development based on the background meteorological environment. [ABSTRACT FROM AUTHOR]

As NWP and climate models continue to evolve toward finer grid spacing, efforts have been undertaken to better represent urban effects. For this study, the single-layer urban canopy model (SLUCM) of the High-Resolution Land Data Assimilation System (HRLDAS) and WRF Model was used to investigate the sensitivity of near-surface air temperatures and energy fluxes to SLUCM parameters in uncoupled (land) and coupled (land-atmosphere) predictions. Output from HRLDAS and WRF was compared with observations from the Oklahoma Mesonet and Joint Urban 2003 experiment. Variations in roof albedo (0.04-0.4) produced 40-135 W m22 changes in net radiation and sensible heat fluxes. Sensible and ground heat fluxes varied by 40-100 W m22 with changes in roof thermal conductivity (0.05-1.4). The urban fraction was found to be the only SLUCM parameter to significantly impact latent heat fluxes. Near-surface air temperatures, particularly during the daytime, did not show significant variations with SLUCM parameters (remaining within the 0.5-K range). Differences in urban air temperatures due to the change in boundary layer scheme were greater than the temperature changes due to SLUCM parameter variations. The sensitivity of near-surface air temperatures to SLUCM parameters depended on the method used to calculate the skin temperature of the impervious surface. For all simulations, predicted 2-m urban air temperatures were consistently higher than observations, with deviations approaching 8K during the day and below 3K at night. These large errors affected the model's skill in reproducing the diurnal cycle of UHI intensity. [ABSTRACT FROM AUTHOR]

Flooding is routinely one of the most deadly weather-related hazards in the United States, which highlights the need for more hydrometeorological research related to forecasting these hazardous events. Building upon previous literature, a synergistic study analyzes hydrometeorological aspects of major urban flood events in the United States from 1977 through 2014 caused by locally heavy precipitation. Primary datasets include upper-air soundings and climatological precipitable water (PW) distributions. A major finding of this work is that major urban flood events are associated with extremely anomalous PW values, many of which exceeded the 99th percentile of the associated climatological dataset and all of which were greater than 150% of the climatological mean values. However, of the 40 cases examined in this study, only 15 had PW values that exceeded 50.4 mm (2 in.), illustrating the importance of including the location-specific PW climatology in a PW analysis relevant to the potential for flash floods. Additionally, these events revealed that, despite geographic location and time of year, most had a warm cloud depth of at least 6 km, which is defined here as the layer between the lifting condensation level and the height of the −10°C level. A 'composite' flood sounding was also calculated and revealed a characteristically tropical structure, despite cases related to tropical cyclones being excluded from the study. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify dipole events (a drought year followed by a pluvial year) for various spatial scales including the nine Oklahoma climate divisions and the author-defined regions of the U.S. Southern Great Plains (SGP), High Plains (HP), and Northern Great Plains (NGP). Analyses revealed that, on average, over twice as many standard deviation (STDEV) dipoles existed in the latter half of the dataset (1955-2013) relative to the first half (1896-1954), suggesting that dramatic increases in precipitation from one year to the next within the Oklahoma climate divisions are increasing with time. For the larger regions within the Great Plains of the United States, the percent chance of a significant pluvial year following a significant drought year was approximately 25% of the time for the SGP and NGP and approximately 16% of the time for the HP. The STDEV dipole analyses further revealed that the frequency of dipoles was consistent between the first and second half of the dataset for the NGP and HP but was increasing with time in the SGP. The temporal periods of anomalous precipitation during relative pluvial years within the STDEV dipole events were unique for each region whereby October occurred most frequently (70%) within the SGP, September occurred most frequently (60%) within the HP, and May occurred most frequently (62%) within the NGP. [ABSTRACT FROM AUTHOR]

Oklahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution of near-storm environments with data currently available to operational forecasters. These data are used to train a complex data-mining algorithm that can analyze the variability of meteorological data in both space and time and produce a probabilistic prediction of tornadogenesis given variables describing the near-storm environment. The algorithm was assessed for utility in four ways. First, its probability forecasts were scored. The algorithm did produce some useful skill in discriminating between tornadic and nontornadic supercells as well as in producing reliable probabilities. Second, its selection of relevant attributes was assessed for physical significance. Surface thermodynamic parameters, instability, and bulk wind shear were among the most significant attributes. Third, the algorithm's skill was compared with the skill of single variables commonly used for tornado prediction. The algorithm did noticeably outperform all of the single variables, including composite parameters. Fourth, the situational variations of the predictions from the algorithm were shown in case studies. They revealed instances both in which the algorithm excelled and in which the algorithm was limited. [ABSTRACT FROM AUTHOR]

A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime. [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]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [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]

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]