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Larry L. Stowe
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Larry L. Stowe Jr.

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The effects of particulate matter on the radiance of the emerging terrestrial infrared radiation for six wave-numbers in the wings and centers of the 6.3-µm water vapor and 15-µm carbon dioxide absorption bands have been theoretically evaluated using the mathematical formulation developed by Sekera and Stowe. Atmospheric models based on measured aerosol particle concentration and radiosonde data have been used in the computations, characterizing atmospheric turbidity conditions in low, middle and high latitudes. Empirical formulas by Golubitskiy and Moskalenko were used in the calculation of the molecular absorption parameters, while the scattering and absorption data for the particulates were taken from tables by Deirmendjian.

The changes in radiance of the upward radiation emerging from models of clear atmospheres due to the presence of particulates have been computed for nadir angles 0° ≤ ζ ≤ 70° and found to be rather small, reducing the radiance from a clear atmosphere by less than 1% in most cases. However, certain quantities used in the determination of temperature and humidity vertical profiles from terrestrial radiation measurements by satellite (inversion problems) are subject to larger changes if the effects of particulate matter are disregarded.

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Kenneth R. Knapp and Larry L. Stowe

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In spite of numerous studies on the remote sensing of aerosols from satellites, the magnitude of aerosol climate forcing remains uncertain. However, data from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder-Atmosphere (PATMOS) dataset—a statistical reduction of more than 19 yr of AVHRR data (1981–2000)—could provide nearly 20 yr of aerosol history. PATMOS data have a daily 110 × 110 km2 equal-area grid that contains means and standard deviations of AVHRR observations within each grid cell. This research is a first step toward understanding aerosols over land with PATMOS data. Herein, the aerosol optical depth is retrieved over land at numerous Aerosol Robotic Network (AERONET) sites around the globe using PATMOS cloud-free reflectances. First, the surface bidirectional reflectance distribution function (BRDF) is retrieved using a lookup table created with a radiative transfer model and the Rahman BRDF. Aerosol optical depths are then retrieved using the retrieved BRDF parameters and the PATMOS reflectances assuming a globally constant aerosol model. This method is applied to locations with ground truth measurements, where comparisons show that the best retrievals are made by estimating the surface reflectance using observations grouped by month. Random errors (i.e., correlation coefficients and standard error of estimate) in this case are lower than those where the surface BRDF is allowed year-to-year variations. By grouping the comparison results by land cover type, it was found that less noise is expected over forested regions, with a significant potential for retrieval for 80% of all land surfaces. These results and analyses suggest that the PATMOS data can provide valuable information on aerosols over land.

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Larry L. Stowe, Paul A. Davis, and E. Paul McClain

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An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 × 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real-time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded that the algorithm does well at classifying perfectly clear pixel arrays, except at high latitudes in their winter seasons. It also has difficulties with classifications over some desert and mountainous regions and when viewing regions of ocean specular reflection. Generally, the CLAVR-1 fractional cloud amounts, when computed using a statistically equivalent spatial coherence method, agree to within about 0.05–0.10 of image/analyst estimates on average. There is a tendency for CLAVR-1 to underestimate cloud amount when it is large and to overestimate it when small.

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Yu-Tai Hou, Kenneth A. Campana, Kenneth E. Mitchell, Shi-Keng Yang, and Larry L. Stowe

Abstract

CLAVR [cloud from AVHRR (Advanced Very High Resolution Radiometer)] is a global cloud dataset under development at NOAA/NESDIS (National Environmental Satellite, Data, and Information Service). Total cloud amount from two experimental cases, 9 July 1986 and 9 February 1990, are intercompared with two independent products, the Air Force Real-Time Nephanalysis (RTNEPH), and the International Satellite Cloud Climatology Project (ISCCP). The ISCCP cloud database is a climate product processed retrospectively some years after the data are collected. Thus, only CLAVR and RTNEPH can satisfy the real-time requirements for numerical weather prediction (NWP) models. Compared with RTNEPH and ISCCP, which only use two channels in daytime retrievals and one at night, CLAVR utilizes all five channels in daytime and three at night from AVHRR data. That gives CLAVR a greater ability to detect certain cloud types, such as thin cirrus and low stratus. Designed to be an operational product, CLAVR is also compared with total cloud forecasts from the National Meteorological Center (NMC) Medium Range Forecast (MRF) Model. The datasets are mapped to the orbits of NOAA polar satellites, such that errors from temporal sampling are minimized. A set of statistical scores, histograms, and maps are used to display the characteristics of the datasets. The results show that the CLAVR data can realistically resolve global cloud distributions. The spatial variation is, however, less than that of RTNEPH and ISCCP, due to current constraints in the CLAVR treatment of partial cloudiness. Results suggest that if the satellite cloud data is available in real time, it can be used to improve the cloud parameterization in numerical forecast models and data assimilation systems.

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Gang Luo, Paul A. Davis, Larry L. Stowe, and E. Paul McClain

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An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.

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Gennady K. Korotaev, Sergey M. Sakerin, Aleksandr M. Ignatov, Larry L. Stowe, and E. Paul McClain

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This paper deals with the problem of aerosol optical thickness (τA) retrieval using sun-photometer measurements. The results of the theoretical analysis and computer processing of the dataset collected during the 40th cruise of the R/V Akademik Vernadsky are presented. Accuracy of retrieved τA is investigated in detail. It is concluded that 1) the τA measurements from the three shortest wavelength channels are sufficiently accurate (0.02–0.03) for evaluation of the NOAA Advanced Very High Resolution Radiometer aerosol optical thickness operational product; 2) serious discrepancies exist between observation and theory for the two longest wavelength channels, which preclude their use in aerosol optical property studies. Further investigations are required, with emphasis on the computation of atmospheric gaseous absorption, before these channels can be used. Shipboard τA will be compared with satellite data from the NOAA/National Environment Satellite Data and Information Service in a subsequent paper.

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Philip E. Ardanuy, Larry L. Stowe, Arnold Gruber, Mitchell Weiss, and Craig S. Long

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Collocated and coincident cloud and outgoing longwave radiation observations taken by experiments on board the Nimbus-7 satellite have been used to infer the daytime longwave cloud-radiative forcing. Through the specification of a time-series of daily values of cloud amount, cloud-top temperature, surface temperature, and outgoing longwave radiation, the clear-sky flux is obtained for both the summer (June, July, and August 1979) and winter (December 1979. January and February 1980) seasons. The longwave component of the cloud-radiative forcing is then computed by subtracting the observed outgoing longwave flux from the inferred clear-sky longwave flux. The results are compared to independent cloud forcing estimates produced using high spatial resolution radiometers and found to agree closely.

The resultant cloud forcing is analyzed regionally, zonally, and globally for each season to quantify, through observation, the role that clouds play in modulating the outgoing longwave radiation. The largest cloud forcing is found over regions of tropical convection, and reaches peak values of about 80 W m−2 in the vicinity of the summer and winter monsoon. Cloud forcing values of less than 10 W m−2 are evident over the deserts the subtropical oceans, and in the polar latitudes, Zonally, the cloud forcing reaches maxima over the Intertropical Convergence Zone (40 to 50 W m−2) and over the polar frontal zones of both hemispheres (25 to 30 W m−2), and minima in the subtropical belts and at the poles. Globally, the cloud forcing is found to be 24 W m−2. The globally averaged cloud cover for the same period is 50%.

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Herbert Jacobowitz, Larry L. Stowe, George Ohring, Andrew Heidinger, Kenneth Knapp, and Nicholas R. Nalli

As part of the joint National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder program, the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) has created a research-quality global atmospheric dataset through the reprocessing of Advanced Very High Resolution Radiometer (AVHRR) observations since 1981. The AVHRR is an imaging radiometer that flies on NOAA polar-orbiting operational environmental satellites (POES) measuring radiation reflected and emitted by the earth in five spectral channels. Raw AVHRR observations were recalibrated using a vicarious calibration technique for the reflectance channels and an appropriate treatment of the nonlinearity of the infrared channels. The observations are analyzed in the Pathfinder Atmosphere (PATMOS) project to obtain statistics of channel radiances, cloud amount, top of the atmosphere radiation budget, and aerosol optical thickness over ocean. The radiances and radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equalarea grid with an approximate 110 km × 110 km spatial resolution and twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. PATMOS data span the period from September 1981 through June 2001. Analyses show that the PATMOS data in their current archived form are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals (e.g., the annual cycle, monsoons, ENSOs, or major volcanic eruptions). Global maps of the annual average of selected products are displayed to illustrate the capability of the dataset to depict the climatological fields and the spatial detail and relationships between the fields, further demonstrating how PATMOS is a unique resource for climate studies. Smaller climate signals, such as those associated with global warming, may be more difficult to detect due to the presence of artifacts in the time series of the products. Principally, these are caused by the drift of each satellite's observation time over its mission. A statistical method, which removes most of these artifacts, is briefly discussed. Quality of the products is assessed by comparing the adjusted monthly mean time series for each product with those derived from independent satellite observations. The PATMOS dataset for the monthly means is accessible at www.saa.noaa.gov/.

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

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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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