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  • Author or Editor: Eva E. Borbas x
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Eva E. Borbas, W. Paul Menzel, Elisabeth Weisz, and Dezso Devenyi

Abstract

Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS–AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.

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W. Paul Menzel, Richard A. Frey, Eva E. Borbas, Bryan A. Baum, Geoff Cureton, and Nick Bearson

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This paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (εf, or cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.

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Elisabeth Weisz, W. Paul Menzel, Nadia Smith, Richard Frey, Eva E. Borbas, and Bryan A. Baum

Abstract

The next-generation Visible and Infrared Imaging Radiometer Suite (VIIRS) offers infrared (IR)-window measurements with a horizontal spatial resolution of at least 1 km, but it lacks IR spectral bands that are sensitive to absorption by carbon dioxide (CO2) or water vapor (H2O). The CO2 and H2O absorption bands have high sensitivity for the inference of cloud-top pressure (CTP), especially for semitransparent ice clouds. To account for the lack of vertical resolution, the “merging gradient” (MG) approach is introduced, wherein the high spatial resolution of an imager is combined with the high vertical resolution of a sounder for improved CTP retrievals. The Cross-Track Infrared Sounder (CrIS) is on the same payload as VIIRS. In this paper Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data are used as proxies for VIIRS and CrIS, respectively, although the approach can be applied to any imager–sounder pair. The MG method establishes a regression relationship between gradients in both the sounder radiances convolved to imager bands and the sounder CTP retrievals. This relationship is then applied to the imager radiance measurements to obtain CTP retrievals at imager spatial resolution. Comparisons with Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud altitudes are presented for a variety of cloud scenes. Results demonstrate the ability of the MG algorithm to add spatial definition to the sounder retrievals with a higher accuracy and precision than those obtained solely from the imager.

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Suzanne W. Seemann, Eva E. Borbas, Robert O. Knuteson, Gordon R. Stephenson, and Hung-Lung Huang

Abstract

A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.

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William L. Smith Sr., Elisabeth Weisz, Stanislav V. Kireev, Daniel K. Zhou, Zhenglong Li, and Eva E. Borbas

Abstract

A fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression “clear trained” and “cloud trained” retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retrieval system. The improvement results from the fact that the nonlinear dependence of spectral radiance on the atmospheric variables, which is due to cloud altitude and associated atmospheric moisture concentration variations, is minimized as a result of the cloud-height-classification process. The detailed method and results from example applications of the DR retrieval algorithm are presented. The new DR method will be used to retrieve atmospheric profiles from Aqua AIRS, MetOp Infrared Atmospheric Sounding Interferometer, and the forthcoming Joint Polar Satellite System ultraspectral radiance data.

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