The infrared Absolute Radiance Interferometer (ARI) instrument - developed at University of Wisconsin-Madison, Space Science and Engineering Center (SSEC) - measures absolute spectrally resolved infrared radiance (200-2000 cm-1 or 5-50 μm at 0.5 cm-1 resolution) with ultra-high accuracy (< 0.1 K 3-sigma brightness temperature at scene temperature). The ARI prototype instrument was deployed for field measurements of clear-sky far infrared (FIR) surface emissivity and radiances on the SSEC rooftop. Currently there are very few measurements available in the FIR spectral region. Our targeted samples include snow and ice surfaces which are important for radiative cooling in the polar regions. We will demonstrate the ARI instrument configuration, capability for ground-based measurements in the FIR region, and the retrieval of infrared emissivity spectra. The ARI ground-based FIR measurements would support scientific applications that involve FIR studies, such as the PREFIRE (Polar Radiant Energy in the Far InfraRed Experiment) and the European FORUM (Far-infrared-outgoing Radiation Understating and Monitoring) missions.
The rain/no-rain threshold value of cloud liquid water (CLW) is important for the microwave precipitation retrieval
algorithms. In our previous study, we proposed a parameterization of rain/no-rain threshold value of CLW as a function
of storm height for Global Satellite Mapping of Precipitation (GSMaP) algorithm. In this study, we determine rain/norain
threshold value of CLW using CloudSat precipitation product and the cloud liquid water derived from Aqua/AMSRE.
The threshold values of CLW from CloudSat precipitation product are lower than 0.5 kg m-2 for GSMaP over all
regions. The threshold value of CLW is found at its peak in the Tropics and decreases poleward. The threshold value of
cloud liquid water contents computed from threshold value of CLW divided by the zonal mean storm height from
PR3A25 is employed on the parameterization of threshold value of CLW. The result shows that GSMaP with new
parameterization can detect the shallow rain observed by CloudSat.
The Spectral Latent Heating (SLH) algorithm was developed to estimate latent heating profiles for the TRMM PR. The
method uses PR information (precipitation top height, precipitation rates at the surface and melting level, and rain type)
to select heating profiles from lookup tables. Lookup tables for the three rain types-convective, shallow stratiform, and
anvil rain (deep stratiform with a melting level)-were derived from numerical simulations of tropical cloud systems
from the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean-Atmosphere Response Experiment (COARE)
utilizing a cloud-resolving model (CRM). The two-dimensional ("2D") CRM was used in the previous studies. The
availability of exponentially increasing computer capabilities has resulted in three-dimensional ("3D") CRM simulations
for multiday periods becoming increasing prevalent. In this study, we compare lookup tables from the 2D and 3D
simulations. The lookup table from 3D simulations results in less agreement between the SLH-retrieved heating and
sounding-based one for the South China Sea Monsoon Experiment (SCSMEX). The level of SLH-estimated maximum
heating is lower than that of the sounding-derived one. This is explained by the fact that the 3D lookup table produces
stronger convective heating and weaker stratiform heating above the melting level that 2D counterpart. Condensate
generated in and carried over from the convective region is larger in 3D than in 2D, and condensate that is produced by
the stratiform region's own upward motion is smaller in 3D than 2D.
Extensive sensitivity and error characteristics of a recently developed optimal estimation retrieval algorithm which simultaneously determines aerosol optical depth (AOD), aerosol single scatter albedo (SSA) and total ozone column (TOC) from ultra-violet irradiances are described. The algorithm inverts measured diffuse and direct irradiances at 7 channels in the UV spectral range obtained from the United States Department of Agriculture's (USDA) UV-B Monitoring and Research Program's (UVMRP) network of 33 ground-based UV-MFRSR instruments to produce aerosol optical properties and TOC at all seven wavelengths. Sensitivity studies of the Tropospheric Ultra-violet/Visible (TUV) radiative transfer model performed for various operating modes (Delta-Eddington versus n-stream Discrete Ordinate) over domains of AOD, SSA, TOC, asymmetry parameter and surface albedo show that the solutions are well constrained. Realistic input error budgets and diagnostic and error outputs from the retrieval are analyzed to demonstrate the atmospheric conditions under which the retrieval provides useful and significant results. After optimizing the algorithm for the USDA site in Panther Junction, Texas the retrieval algorithm was run on a cloud screened set of irradiance measurements for the month of May 2003. Comparisons to independently derived AOD's are favorable with root mean square (RMS) differences of about 3% to 7% at 300nm and less than 1% at 368nm, on May 12 and 22, 2003. This retrieval method will be used to build an aerosol climatology and provide ground-truthing of satellite measurements by running it operationally on the USDA UV network database.
Clouds play an important role in the hydrologic cycle, influence global energy balance, and represent a significant yet poorly understood component of global climate change. As a result, quantitative global observations of liquid and ice cloud microphysical and radiative properties continue to be a focus of a growing number of satellite-based sensors each having an associated suite of retrieval algorithms. While a number of these algorithms have successfully been applied to map clouds, many can only be applied under specific conditions (eg. during the daytime) or over a limited dynamic range (eg. optically thin cirrus) often leading to unphysical discontinuities when one seeks to compile a complete picture of the global distribution of clouds. Furthermore, discrepancies exist between products of different algorithms when they are applied to the same scene by virtue of differences in the information provided by distinct combinations of measurements.
This paper revisits the problem of cloud microphysical property
retrievals from satellite radiance observations at solar and thermal
wavelengths in an effort to quantify their information content with
respect to single layer liquid and ice clouds over an oceanic
background. Using the channels on the Moderate Resolution Imaging
Spectroradiometer (MODIS) as an example, it will be demonstrated that
an entropy-based definition of information content provides a useful
metric for evaluating the utility of a set of observations in a
retrieval problem. This approach is used to objectively determine the
subset of wavelengths that provide the greatest amount of information
for oceanic microphysical property retrievals from the MODIS
instrument. The results show that the combination of a conservative and a non-conservative scattering shortwave channel in concert with a near-infrared channel, an infrared window channel, and one in the wings of the 15 m CO2 band provide the optimal channel combination for the wide variety of liquid and ice clouds examined. With an eye toward developing a coherent representation of the global distribution of cloud microphysical and radiative properties, this combination of channels may be integrated into a suitable multi-channel inversion methodology such as the optimal estimation or Bayesian techniques to provide a means of establishing a common framework for cloud retrievals under varying conditions. Under some circumstances, other channels may provide a small amount of additional information but in most cases the remaining channels only supply redundant information and do not justify the additional computation cost required to integrate them into an algorithm.
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