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Fangfang Yu and Xiangqian Wu

Abstract

Desert-based vicarious calibration plays an important role in generating long-term reliable satellite radiances for the visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR). Lacking an onboard calibration device, the AVHRR relies on reflected radiances from a target site, for example, a large desert, to calibrate its solar reflective channels. While the radiometric characteristics of the desert may be assumed to be stable, the reflected radiances from the target can occasionally be affected by the presence of clouds, sand storms, vegetation, and wet surfaces. These contaminated pixels must be properly identified and removed to ensure calibration performance. This paper describes an algorithm for removing the contaminated pixels from AVHRR measurements taken over the Libyan Desert based on the characteristics of consistent normalized difference vegetation index (NDVI) land-cover stratification. An NDVI histogram-determined threshold is first applied to screen pixels contaminated with vegetation in each individual AVHRR observation. The resulting analyses show that the vegetation growth inside the desert target has a negligibly small impact on the AVHRR operational calibration results. Two criteria based on the maximum NDVI compositing technique are then employed to remove pixels contaminated with clouds, severe sand storms, and wet sand surfaces. Compared to other cloud-screening methods, this algorithm is capable of not only identifying high-reflectance clouds, but also removing the low reflectance of wet surfaces and the nearly indifferent reflectance of severe dust storms. The use of clear pixels appears to improve AVHRR calibration accuracy in the first 3–4 yr after satellite launch.

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Xiangqian Wu and William L. Smith

Abstract

A method is developed to assimilate satellite data for the purpose of improving the diagnosis of fractional cloud cover within a numerical weather prediction model. The method makes use of a nonlinear programming technique to find a set of parameters for the cloud diagnosis that minimizes the difference between the observed and model-produced outgoing longwave radiation (OLR). The algorithm and theoretical basis of the method are presented.

The method has been applied in two forecast experiments using a numerical weather prediction model. The results from a winter case demonstrate that the root-mean-square (rms) difference between the observed and forecasted OLR can be reduced by 50% when the optimized cloud diagnosis is used, with the remaining rms difference within the background noise. The optimized diagnosis also reduces the rms difference in a summer experiment, but the reduction is inadequate, possibly because of the inability of the current cloud scheme to deal with convective activity.

The optimization procedure is both stable and sensitive. The largest impact of the optimized cloud diagnosis is on the forecast of surface temperature. The impact on the forecast of other model variables is insignificant. This is partly due to the model's highly simplified treatment of cloud and to the short time of model integration compared to the time scale of radiative forcing. Possible applications and limitations of the method are discussed.

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Xiangqian Wu, W. Paul Menzel, and Gary S. Wade

Sea surface temperatures (SSTs) are derived using measurements from the new generation of imaging instruments on the Geostationary Operational Environmental Satellites (GOES). The National Environmental Satellite, Data and Information Service has been producing hourly GOES SST estimates since December 1998. This paper presents the algorithm for cloud detection and atmospheric moisture correction and shows some initial results. Several advantages of GOES SST are evident in comparison with SST from polar orbiting satellites. Frequent sampling by GOES imagers results in a more complete map of SST as clouds move away. Changes in scene temperature over a short period of time help to detect the presence of clouds. The abundance of GOES observations enables stringent screening for cloud-free observations while maintaining good spatial coverage of clear-sky inferences of SST. Diurnal variations of SST over large areas are observed for the first time and their implications for numerical weather prediction and climate monitoring are discussed.

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Alexander Ignatov, Changyong Cao, Jerry Sullivan, Robert Levin, Xiangqian Wu, and Roy Galvin

Abstract

The solar reflectance bands (SRB; centered at λ 1 = 0.63, λ 2 = 0.83, and λ 3A = 1.61 μm) of the Advanced Very High Resolution Radiometers (AVHRR) flown on board NOAA satellites are often referred to as noncalibrated in-flight. In contrast, the Earth emission bands (EEBs; centered at λ 3B = 3.7, λ 4 = 11, and λ 5 = 12 μm) are calibrated using two reference points: deep space and the internal calibration targets. In the SRBs, measurements of space count (SC) are also available; however, historically they are not used to specify the calibration offset [zero count (ZC)], which does not even appear in the calibration equation. A regression calibration formulation is used instead, equivalent to setting the ZC to a constant, whose value is specified from prelaunch measurements.

The analyses below, supported by a review of the instrument design and a wealth of historical SC information, show that the SC varies in-flight and differs from its prelaunch value. It is therefore suggested that 1) the AVHRR calibration equation in the SRBs be reformulated to explicitly use the ZC, consistently with the EEBs; and 2) the value of ZC be specified from the onboard measurements of SC. The ZC formulation of the calibration equation is physically solid, and it minimizes human-induced calibration errors resulting from the use of a regression formulation with an unconstrained intercept. Specifying the calibration offset improves radiances, most notably at the low end of radiometric scale, and subsequently provides for more accurate vicarious determinations of the calibration slope (gain). These calibration improvements are important for the products derived from the AVHRR low radiances, such as aerosol over ocean, and are particularly critical when generating their long-term climate data records (CDRs).

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Likun Wang, Xiangqian Wu, Mitch Goldberg, Changyong Cao, Yaping Li, and Seung-Hee Sohn

Abstract

The Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI), together with the future Cross-track Infrared Sounder, will provide long-term hyperspectral measurements of the earth and its atmosphere at ∼10 km spatial resolution. Quantifying the radiometric difference between AIRS and IASI is crucial for creating fundamental climate data records and establishing the space-based infrared calibration standard. Since AIRS and IASI have different local equator crossing times, a direct comparison of these two instruments over the tropical regions is not feasible. Using the Geostationary Operational Environmental Satellite (GOES) imagers as transfer radiometers, this study compares AIRS and IASI over warm scenes in the tropical regions for a time period of 16 months. The double differences between AIRS and IASI radiance biases relative to the GOES-11 and -12 imagers are used to quantify the radiance differences between AIRS and IASI within the GOES imager spectral channels. The results indicate that, at the 95% confidence level, the mean values of the IASI − AIRS brightness temperature differences for warm scenes are very small, that is, −0.0641 ± 0.0074 K, −0.0432 ± 0.0114 K, and −0.0095 ± 0.0151 K for the GOES-11 6.7-, 10.7-, and 12.0-μm channels, respectively, and −0.0490 ± 0.0100 K, −0.0419 ± 0.0224 K, and −0.0884 ± 0.0160 K for the GOES-12 6.5-, 10.7-, and 13.3-μm channels, respectively. The brightness temperature biases between AIRS and IASI within the GOES imager spectral range are less than 0.1 K although the AIRS measurements are slightly warmer than those of IASI.

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Hung-Lung Huang, William L. Smith, Jun Li, Paolo Antonelli, Xiangqian Wu, Robert O. Knuteson, Bormin Huang, and Brian J. Osborne

Abstract

This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.

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Johannes Schmetz, W. Paul Menzel, Christopher Velden, Xiangqian Wu, Leo van de Berg, Steve Nieman, Christopher Hayden, Kenneth Holmlund, and Carlos Geijo

This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160°W to 50°E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-μ absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations.

Quantitative information on large-scale circulation patterns in the upper troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about −5 × 10−6 and 5 × 10−6 sec−1 when averaged over a scale length of about 1000–2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation.

Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include threedimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.

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