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Andrew K. Heidinger, Istvan Laszlo, Christine C. Molling, and Dan Tarpley

components (vegetation, soil, elevated surfaces, etc.) that comprise the surface. The radiance measured by a satellite sensor integrates all of these components into a single flux or irradiance from which a single LST value is inferred. In addition to these components, satellite-measured radiance also includes the complicating factors of atmospheric emission, transmission and scattering through gases, aerosols, and clouds. While near-surface air temperature is an important climate parameter, large

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Grant Matthews

1. Introduction To validate model predictions of future changes to the earth’s climate, it is important to maintain measurements of the earth radiation budget (ERB) from space. ERB parameters are the emitted thermal or longwave (LW; 5 μ m < λ < 100 μ m) and scattered solar or shortwave (SW; 0.2 μ m < λ < 5 μ m) radiative fluxes leaving Earth. Such measurements, when combined with knowledge of the solar constant, tell of the net heat engine energy input that drives all weather and

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Benjamin D. Reineman, Luc Lenain, and W. Kendall Melville

1. Introduction Measurements of sea state and air–sea fluxes have historically been made from ships, buoys, and other platforms, but these essentially fixed-point measurements, over the time scales of surface wave and atmospheric processes, provide no observations of the spatial evolution and distribution of surface fluxes and the wave field. Aircraft-based measurements are an effective means to sample atmospheric and oceanic phenomena over a wide range of conditions and locations, and are also

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David R. Doelling, Norman G. Loeb, Dennis F. Keyes, Michele L. Nordeen, Daniel Morstad, Cathy Nguyen, Bruce A. Wielicki, David F. Young, and Moguo Sun

longwave (LW), and WN radiances using the approach described in Loeb et al. (2001) . The radiances are then converted to a radiative flux using empirical angular directional models (ADMs) ( Loeb et al. 2003 , 2005 ; Kato and Loeb 2005 ; Loeb et al. 2007 ), which are defined according to various surface, cloud, and atmospheric properties. Cloud property retrievals are determined from coincident Moderate Resolution Imaging Spectroradiometer (MODIS) imager radiances from Terra and Aqua using the

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Reina Nakamura and L. Mahrt

assessed for correctly interpreting air temperature data collected in a naturally ventilated shield. For example, if sensor–shield systems within a network of air temperature measurements experience different radiative forcing because of variable cloudiness or exposure differences, the horizontal temperature gradient may be incorrectly estimated because of the spatial variation of errors. In addition, when air temperature measurements are used to evaluate the sensible heat flux from the surface with

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J. C. H. van der Hage and S. R. de Roode

isotropic flux density in the spectral range of 400–750 nm. A standard actinic flux sensor does not exist; therefore, the linearity of the sensor and the sensitivity for the visible part of solar radiation was tested by a comparison with the Royal Netherlands Meteorological Office version of the monochromatic radiative transfer model TUV ( Madronich 1998 ). We used 60 wavelengths between 400 and 740 nm and a nonequidistant wavelength interval. The ground albedo in the model was 0.85, which is a typical

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T. A. Tarasova and B. A. Fomin

gaseous absorption with Rayleigh scattering, and with scattering and absorption by aerosol and cloud particles. During the integration, atmospheric numerical models use the variables provided by the solar radiation scheme such as solar radiative fluxes at the surface and at the top of the atmosphere (TOA) as well as heating rate profiles in the atmosphere. Here, we perform the comparison of these variables. Table 2 shows the difference between solar radiative fluxes calculated with CLIRAD( FC05 )-SW

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David R. Doelling, Moguo Sun, Le Trang Nguyen, Michele L. Nordeen, Conor O. Haney, Dennis F. Keyes, and Pamela E. Mlynczak

response functions for Aqua MODIS, Met-7 , and Met-9 for the (a) WV and (b) WIN channels. 3. GEO LW narrowband radiance to broadband flux The estimation of LW fluxes from GEO IR imager radiances has been an ongoing effort since the first GEO satellites. Gube (1982) converted the Met WIN and WV imager radiances into LW fluxes using third-order polynomial coefficients stratified by VZA based on radiative transfer (RT) model radiances from a database of atmospheric profiles. Schmetz and Liu (1988

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L. A. Sromovsky, J. R. Anderson, F. A. Best, J. P. Boyle, C. A. Sisko, and V. E. Suomi

: and, for unstable conditions ( S > 0), The ratio for heat transport coefficients at z = 10 m is shown as a function of Richardson number in Fig. 1b . It is noteworthy that corrections increase dramatically for highly unstable conditions seen most often at low wind speeds (note the U −2 dependence in S and Ri). Figure 1c displays sample bulk aerodynamic fluxes for typical tropical Pacific conditions as a function of wind speed. Also shown is a typical net radiative flux under these

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Dean Vickers and L. Mahrt

. Instead, we use a bulk Richardson number calculated as with z = 15 m and where θ sfc is the 1-h average surface radiative temperature based on measurements made at the six stations in the ATD CASES99 network. Using surface radiative temperature measurements over land is often problematic due to irregularities in surface cover and differences between the radiometer footprint and the flux footprint. However, in CASES99, these problems are reduced by averaging the radiative temperature estimates

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