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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]

• 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]