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John J. Cahir
,
John M. Norman
, and
Dale A. Lowry

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.

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John R. Mecikalski
,
George R. Diak
,
Martha C. Anderson
, and
John M. Norman

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.

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Steven G. Perry
,
John M. Norman
,
Hans A. Panofsky
, and
J. David Martsolf

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.

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George R. Diak
,
John R. Mecikalski
,
Martha C. Anderson
,
John M. Norman
,
William P. Kustas
,
Ryan D. Torn
, and
Rebecca L. DeWolf

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.

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Frank O. Bryan
,
Robert Tomas
,
John M. Dennis
,
Dudley B. Chelton
,
Norman G. Loeb
, and
Julie L. McClean

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.

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Cezar Kongoli
,
William P. Kustas
,
Martha C. Anderson
,
John M. Norman
,
Joseph G. Alfieri
,
Gerald N. Flerchinger
, and
Danny Marks

Abstract

The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two-source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snow cover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.

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Martha C. Anderson
,
J. M. Norman
,
John R. Mecikalski
,
Ryan D. Torn
,
William P. Kustas
, and
Jeffrey B. Basara

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.

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Nurit Agam
,
William P. Kustas
,
Martha C. Anderson
,
John M. Norman
,
Paul D. Colaizzi
,
Terry A. Howell
,
John H. Prueger
,
Tilden P. Meyers
, and
Tim B. Wilson

Abstract

The Priestley–Taylor (PT) approximation for computing evapotranspiration was initially developed for conditions of a horizontally uniform saturated surface sufficiently extended to obviate any significant advection of energy. Nevertheless, the PT approach has been effectively implemented within the framework of a thermal-based two-source model (TSM) of the surface energy balance, yielding reasonable latent heat flux estimates over a range in vegetative cover and climate conditions. In the TSM, however, the PT approach is applied only to the canopy component of the latent heat flux, which may behave more conservatively than the bulk (soil + canopy) system. The objective of this research is to investigate the response of the canopy and bulk PT parameters to varying leaf area index (LAI) and vapor pressure deficit (VPD) in both natural and agricultural vegetated systems, to better understand the utility and limitations of this approximation within the context of the TSM. Micrometeorological flux measurements collected at multiple sites under a wide range of atmospheric conditions were used to implement an optimization scheme, assessing the value of the PT parameter for best performance of the TSM. Overall, the findings suggest that within the context of the TSM, the optimal canopy PT coefficient for agricultural crops appears to have a fairly conservative value of ∼1.2 except when under very high vapor pressure deficit (VPD) conditions, when its value increases. For natural vegetation (primarily grasslands), the optimal canopy PT coefficient assumed lower values on average (∼0.9) and dropped even further at high values of VPD. This analysis provides some insight as to why the PT approach, initially developed for regional estimates of potential evapotranspiration, can be used successfully in the TSM scheme to yield reliable heat flux estimates over a variety of land cover types.

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Taneil Uttal
,
Sandra Starkweather
,
James R. Drummond
,
Timo Vihma
,
Alexander P. Makshtas
,
Lisa S. Darby
,
John F. Burkhart
,
Christopher J. Cox
,
Lauren N. Schmeisser
,
Thomas Haiden
,
Marion Maturilli
,
Matthew D. Shupe
,
Gijs De Boer
,
Auromeet Saha
,
Andrey A. Grachev
,
Sara M. Crepinsek
,
Lori Bruhwiler
,
Barry Goodison
,
Bruce McArthur
,
Von P. Walden
,
Edward J. Dlugokencky
,
P. Ola G. Persson
,
Glen Lesins
,
Tuomas Laurila
,
John A. Ogren
,
Robert Stone
,
Charles N. Long
,
Sangeeta Sharma
,
Andreas Massling
,
David D. Turner
,
Diane M. Stanitski
,
Eija Asmi
,
Mika Aurela
,
Henrik Skov
,
Konstantinos Eleftheriadis
,
Aki Virkkula
,
Andrew Platt
,
Eirik J. Førland
,
Yoshihiro Iijima
,
Ingeborg E. Nielsen
,
Michael H. Bergin
,
Lauren Candlish
,
Nikita S. Zimov
,
Sergey A. Zimov
,
Norman T. O’Neill
,
Pierre F. Fogal
,
Rigel Kivi
,
Elena A. Konopleva-Akish
,
Johannes Verlinde
,
Vasily Y. Kustov
,
Brian Vasel
,
Viktor M. Ivakhov
,
Yrjö Viisanen
, and
Janet M. Intrieri

Abstract

International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007–09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is on coordinating intensive measurements of the Arctic atmosphere collected in the United States, Canada, Russia, Norway, Finland, and Greenland to create synthesis science that leads to an understanding of why and not just how the Arctic atmosphere is evolving. The IASOA premise is that there are limitations with Arctic modeling and satellite observations that can only be addressed with boots-on-the-ground, in situ observations and that the potential of combining individual station and network measurements into an integrated observing system is tremendous. The IASOA vision is that by further integrating with other network observing programs focusing on hydrology, glaciology, oceanography, terrestrial, and biological systems it will be possible to understand the mechanisms of the entire Arctic system, perhaps well enough for humans to mitigate undesirable variations and adapt to inevitable change.

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William L. Smith Jr.
,
Christy Hansen
,
Anthony Bucholtz
,
Bruce E. Anderson
,
Matthew Beckley
,
Joseph G. Corbett
,
Richard I. Cullather
,
Keith M. Hines
,
Michelle Hofton
,
Seiji Kato
,
Dan Lubin
,
Richard H. Moore
,
Michal Segal Rosenhaimer
,
Jens Redemann
,
Sebastian Schmidt
,
Ryan Scott
,
Shi Song
,
John D. Barrick
,
J. Bryan Blair
,
David H. Bromwich
,
Colleen Brooks
,
Gao Chen
,
Helen Cornejo
,
Chelsea A. Corr
,
Seung-Hee Ham
,
A. Scott Kittelman
,
Scott Knappmiller
,
Samuel LeBlanc
,
Norman G. Loeb
,
Colin Miller
,
Louis Nguyen
,
Rabindra Palikonda
,
David Rabine
,
Elizabeth A. Reid
,
Jacqueline A. Richter-Menge
,
Peter Pilewskie
,
Yohei Shinozuka
,
Douglas Spangenberg
,
Paul Stackhouse
,
Patrick Taylor
,
K. Lee Thornhill
,
David van Gilst
, and
Edward Winstead

Abstract

The National Aeronautics and Space Administration (NASA)’s Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) acquired unique aircraft data on atmospheric radiation and sea ice properties during the critical late summer to autumn sea ice minimum and commencement of refreezing. The C-130 aircraft flew 15 missions over the Beaufort Sea between 4 and 24 September 2014. ARISE deployed a shortwave and longwave broadband radiometer (BBR) system from the Naval Research Laboratory; a Solar Spectral Flux Radiometer (SSFR) from the University of Colorado Boulder; the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) from the NASA Ames Research Center; cloud microprobes from the NASA Langley Research Center; and the Land, Vegetation and Ice Sensor (LVIS) laser altimeter system from the NASA Goddard Space Flight Center. These instruments sampled the radiant energy exchange between clouds and a variety of sea ice scenarios, including prior to and after refreezing began. The most critical and unique aspect of ARISE mission planning was to coordinate the flight tracks with NASA Cloud and the Earth’s Radiant Energy System (CERES) satellite sensor observations in such a way that satellite sensor angular dependence models and derived top-of-atmosphere fluxes could be validated against the aircraft data over large gridbox domains of order 100–200 km. This was accomplished over open ocean, over the marginal ice zone (MIZ), and over a region of heavy sea ice concentration, in cloudy and clear skies. ARISE data will be valuable to the community for providing better interpretation of satellite energy budget measurements in the Arctic and for process studies involving ice–cloud–atmosphere energy exchange during the sea ice transition period.

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