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Tom X-P. Zhao, Larry L. Stowe, Alexander Smirnov, David Crosby, John Sapper, and Charles R. McClain


In this paper, a global validation package for satellite aerosol optical thickness retrieval using the Aerosol Robotic Network (AERONET) observations as ground truth is described. To standardize the validation procedure, the optimum time–space match-up window, the ensemble statistical analysis method, the best selection of AERONET channels, and the numerical scheme used to interpolate/extrapolate these observations to satellite channels have been identified through sensitivity studies. The package is shown to be a unique tool for more objective validation and intercomparison of satellite aerosol retrievals, helping to satisfy an increasingly important requirement of the satellite aerosol remote sensing community. Results of applying the package to the second-generation operational aerosol observational data (AEROBS) from the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) in 1998 and to the same year aerosol observation data [Clouds and the Earth's Radiant Energy System-Single Scanner Foodprint version 4 (CERES-SSF4)] from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner (VIRS) are presented as examples of global validation. The usefulness of the package for identifying improvements to the aerosol optical thickness τ retrieval algorithm is also demonstrated.

The principal causes of systematic errors in the current National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS) operational aerosol optical thickness retrieval algorithm have been identified and can be reduced significantly, if the correction and adjustment suggested from the global validation are adopted. Random error in the τ retrieval is identified to be a major source of error on deriving the effective Ångström wavelength exponent α and may be associated with regional differences in aerosol particles, which are not accounted for in the current second-generation operational algorithm. Adjustments to the nonaerosol and aerosol radiative transfer model parameters that reduce systematic errors in τ retrievals are suggested for consideration in the next-generation algorithm. Basic features that should be included in the next-generation algorithm to reduce random error in τ retrievals and the resulting error in the effective Ångström wavelength exponent have also been discussed.

Compared to the AERONET observation, the NOAA-14 AVHRR (AEROBS) τ values for mean conditions are biased high by 0.05 and 0.08, with random errors of 0.08 and 0.05, at 0.63 and 0.83 μm, respectively. Correspondingly, the TRMM VIRS (CERES-SSF4) values for mean conditions are biased high by 0.06 and 0.02, with random errors of 0.06 and 0.04 at 0.63 and 1.61 μm, respectively. After corrections and adjustments to the retrieval algorithm, the biases in both channels of AVHRR and VIRS are reduced significantly to values close to zero, although random error is almost unchanged. The α exponent derived directly from the aerosol optical thicknesses (τs) has been shown to be poorly correlated both before and after adjustments, indicating that random error in the τ measurement (possibly related to aerosol model parameter variations or cloud–surface reflectance contamination) needs to be reduced.

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Paul H. Hwang, Larry L. Stowe, H. Y. Michael Yeh, H. Lee Kyle, and the Cloud Data Processing Team

A total of six years (April 1979 to March 1985) of continuous measurements from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS), both on the Nimbus-7 satellite, have been processed to form the Nimbus-7 Global Cloud Climatology (N7GCC). The cloud-estimation algorithms utilize THIR “11.5-micron” radiances, TOMS-derived “0.37-micron” reflectivities, climatological temperature lapse rates and concurrent surface temperatures, and snow-ice information. (The last two items are taken from the Air Force three-dimensional nephanalysis archive.) This cloud climatology gives, near local noon and midnight, the fractional area covered by high-level clouds middle-level clouds and low-altitude clouds, and the total fractional area covered by all clouds (total cloud). Statistics are also given for the special cloud types: cirrus, deep convective, and warm low-altitude clouds. The cloud and clear-sky radiances, together with correlative surface temperatures, are included. These products have the same spatial resolution and temporal (daily and monthly) resolution as the independently derived concurrent Nimbus-7 Earth Radiation Budget data set. The N7GCC has been compared with preliminary results from the International Satellite Cloud Climatology Project (ISCCP) and with other cloud data sets. For July 1983, the mean global cover was estimated to be 49 percent by N7GCC and 63 percent by ISCCP. Older cloud climatologies showed average July global cloud cover in the 50 percent to 60 percent range.

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Larry L. Stowe, H. Y. Michael Yeh, Thomas F. Eck, Charlie G. Wellemeyer, H. Lee Kyle, and The Nimbus-7 Cloud Data Processing Team


Regional and seasonal variations in global cloud cover observed by the Nimbus-7 satellite over 1 year are analyzed by examining the 4 midseason months—April, July and October 1979 and January 1980. The Nimbus-7 data set is generated from the Temperature Humidity Infrared Radiometer (THIR) 11.5 micron radiances together with Total Ozone Mapping Spectometer (TOMS)-derived UV reflectivities, climatological atmospheric temperature lapse rates, and concurrent surface temperature and snow/ice information from the Air Force three-dimensional-nephanalysis (3DN) archive. The analysis presented here includes total cloud amount, cloud amounts at high, middle and low altitudes, cirrus and deep convective clouds and cloud and cloud-sky 11.5 micron-derived radiances. Also, noon versus midnight cloud amounts are examined and the Nimbus-7 data are compared to three previously published cloud climatologies.

The Nimbus-7 bispectral algorithm gives a monthly mean global noontime cloud cover of 51%, averaged over the 4 months. When only the IR is used, this cloud cover is 49% at noontime and 56% at midnight, indicating that the Earth's cloud cover has a substantial diurnal cycle. Each hemisphere shows a cloud cover maximum in its summer and a minimum in its winter. The Southern Hemisphere shows more clouds than the Northern Hemisphere except for the month of July.

The difference between the cloud-top and clear-scene radiance has maxima in the equatorial cloud belt and minima in the polar regions. Because of thew polar minima and the frequent presence of snow, Nimbus-7 cloud traction estimates are less reliable in the polar regions. In the tropics the data show more clouds at midnight than at noon. Over the tropical ocean, overcast regions show lower cloud top radiation temperatures at noon than at midnight, but over land the reverse occurs.

In July, cloud amounts in the intertropical convergence zone (ITCZ) peak at about 10°N latitude with local maxima greater than 70% around the west coasts of Africa and Central America, and from India east to the dateline. Cloud-top radiances indicate that mid- and high-level clouds predominate in the ITCZ, with 5% to 15% each of cirrus and deep convective clouds, respectively. In January, the peak of the ITCZ shifts to 10°S with local cloud maxima greater than 90% over Brazil and to the north and northwest of Australia. Comparison is made with several other cloud data sets, including a look at the new preliminary International Satellite Cloud Climatology Project (ISCCP) results. There are considerable differences among the several data sets examined.

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