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Abstract
Real-time computer graphics systems are being introduced into weather stations throughout the United States. A sample of student forecasters used such a system to solve specific specialized forecasting problems. Results suggest that for some types of problems, involving timing, their forecasts were better than those made by forecasters who did not have access to the system.
Examples are given of the diagnostic use of some of the available analyses.
Abstract
Real-time computer graphics systems are being introduced into weather stations throughout the United States. A sample of student forecasters used such a system to solve specific specialized forecasting problems. Results suggest that for some types of problems, involving timing, their forecasts were better than those made by forecasters who did not have access to the system.
Examples are given of the diagnostic use of some of the available analyses.
Objective analyses on vertical cross sections are presented as examples of the type of real-time product available on the Penn State, Department of Meteorology, on-line minicomputer. The analyses are not new, but their real-time availability is. Our experience has been that such products improve forecaster diagnosis and understanding and suggest that the “man-machine mix” concept, extended to other types of analyses and diagnoses, may be as appropriate to small machines as to large ones.
Objective analyses on vertical cross sections are presented as examples of the type of real-time product available on the Penn State, Department of Meteorology, on-line minicomputer. The analyses are not new, but their real-time availability is. Our experience has been that such products improve forecaster diagnosis and understanding and suggest that the “man-machine mix” concept, extended to other types of analyses and diagnoses, may be as appropriate to small machines as to large ones.
Abstract
Abstract
Abstract
A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.
Abstract
A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.
In a NASA-sponsored program entitled “Use of Earth and Space Science Data Over the Internet,” scientists at the University of Wisconsin—Madison have developed a suite of products for agriculture that are based in satellite and conventional observations, as well as state-of-the-art forecast models of the atmosphere and soil–canopy environments. These products include an irrigation scheduling product based in satellite estimates of daily solar energy, a frost protection product that relies on prediction models and satellite estimates of clouds, and a product for the prediction of foliar disease that is based in satellite net radiation, rainfall measured by NEXRAD, and a detailed model of the soil–canopy environment. During the growing season, the first two products are available in near-real time on the Internet. The last product involving foliar disease depends on a decision support system named WISDOM developed by the University of Wisconsin—Extension, which resides locally on growers' home computers. Growers interface WISDOM with a server to obtain the rainfall, meteorological data, surface radiation inputs, and canopy model output required by WISDOM for the blight models.
In a NASA-sponsored program entitled “Use of Earth and Space Science Data Over the Internet,” scientists at the University of Wisconsin—Madison have developed a suite of products for agriculture that are based in satellite and conventional observations, as well as state-of-the-art forecast models of the atmosphere and soil–canopy environments. These products include an irrigation scheduling product based in satellite estimates of daily solar energy, a frost protection product that relies on prediction models and satellite estimates of clouds, and a product for the prediction of foliar disease that is based in satellite net radiation, rainfall measured by NEXRAD, and a detailed model of the soil–canopy environment. During the growing season, the first two products are available in near-real time on the Internet. The last product involving foliar disease depends on a decision support system named WISDOM developed by the University of Wisconsin—Extension, which resides locally on growers' home computers. Growers interface WISDOM with a server to obtain the rainfall, meteorological data, surface radiation inputs, and canopy model output required by WISDOM for the blight models.
Abstract
A surface layer experiment is described which includes measurements of turbulent velocities at 2 m above the surface with an army of newly developed drag anemometers. The experiment site is located in central Pennsylvania where mesoscale topographic irregularities exist. The presence of a low mountain ridge near the site affects the estimated lateral scale of turbulence and the fluctuations of the lateral velocity component. A good correlation has been found between the variance spectrum of the lateral (or crosswind) velocity component and an estimate of the lateral Eulerian integral scale of the longitudinal velocity component. This can provide future estimates of the lateral scale from turbulent velocity measurements at a single location.
A model for the decay of horizontal coherence which accounts for the stability, roughness and instrument separation has been suggested in a previous paper by Panofsky and Mizuno. The present data compare favorably with this model. The effect of stability on coherence decay is found to have a definite site dependence.
Abstract
A surface layer experiment is described which includes measurements of turbulent velocities at 2 m above the surface with an army of newly developed drag anemometers. The experiment site is located in central Pennsylvania where mesoscale topographic irregularities exist. The presence of a low mountain ridge near the site affects the estimated lateral scale of turbulence and the fluctuations of the lateral velocity component. A good correlation has been found between the variance spectrum of the lateral (or crosswind) velocity component and an estimate of the lateral Eulerian integral scale of the longitudinal velocity component. This can provide future estimates of the lateral scale from turbulent velocity measurements at a single location.
A model for the decay of horizontal coherence which accounts for the stability, roughness and instrument separation has been suggested in a previous paper by Panofsky and Mizuno. The present data compare favorably with this model. The effect of stability on coherence decay is found to have a definite site dependence.
Abstract
The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.
Abstract
The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.
Since the advent of the meteorological satellite, a large research effort within the community of earth scientists has been directed at assessing the components of the land surface energy balance from space. The development of these techniques from first efforts to the present time are reviewed, and the integrated system used to estimate the radiative and turbulent land surface fluxes is described. This system is now running in real time over the continental United States at a resolution of 10 km, producing daily and time-integrated flux components.
Since the advent of the meteorological satellite, a large research effort within the community of earth scientists has been directed at assessing the components of the land surface energy balance from space. The development of these techniques from first efforts to the present time are reviewed, and the integrated system used to estimate the radiative and turbulent land surface fluxes is described. This system is now running in real time over the continental United States at a resolution of 10 km, producing daily and time-integrated flux components.
Abstract
The emerging picture of frontal scale air–sea interaction derived from high-resolution satellite observations of surface winds and sea surface temperature (SST) provides a unique opportunity to test the fidelity of high-resolution coupled climate simulations. Initial analysis of the output of a suite of Community Climate System Model (CCSM) experiments indicates that characteristics of frontal scale ocean–atmosphere interaction, such as the positive correlation between SST and surface wind stress, are realistically captured only when the ocean component is eddy resolving. The strength of the coupling between SST and surface stress is weaker than observed, however, as has been found previously for numerical weather prediction models and other coupled climate models. The results are similar when the atmospheric component model grid resolution is doubled from 0.5° to 0.25°, an indication that shortcomings in the representation of subgrid scale atmospheric planetary boundary layer processes, rather than resolved scale processes, are responsible for the weakness of the coupling. In the coupled model solutions the response to mesoscale SST features is strongest in the atmospheric boundary layer, but there is a deeper reaching response of the atmospheric circulation apparent in free tropospheric clouds. This simulated response is shown to be consistent with satellite estimates of the relationship between mesoscale SST and all-sky albedo.
Abstract
The emerging picture of frontal scale air–sea interaction derived from high-resolution satellite observations of surface winds and sea surface temperature (SST) provides a unique opportunity to test the fidelity of high-resolution coupled climate simulations. Initial analysis of the output of a suite of Community Climate System Model (CCSM) experiments indicates that characteristics of frontal scale ocean–atmosphere interaction, such as the positive correlation between SST and surface wind stress, are realistically captured only when the ocean component is eddy resolving. The strength of the coupling between SST and surface stress is weaker than observed, however, as has been found previously for numerical weather prediction models and other coupled climate models. The results are similar when the atmospheric component model grid resolution is doubled from 0.5° to 0.25°, an indication that shortcomings in the representation of subgrid scale atmospheric planetary boundary layer processes, rather than resolved scale processes, are responsible for the weakness of the coupling. In the coupled model solutions the response to mesoscale SST features is strongest in the atmospheric boundary layer, but there is a deeper reaching response of the atmospheric circulation apparent in free tropospheric clouds. This simulated response is shown to be consistent with satellite estimates of the relationship between mesoscale SST and all-sky albedo.
Abstract
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
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.
