A-Train data depot - bringing Atmospheric measurements together

Savtchenko, A.; Kummerer, R.; Smith, Paul H.; Kempler, Steven; Leptoukh, G.

doi:10.1109/igarss.2007.4423794

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…files that contain merged OMI and CALIOP data collocated along CALIPSO's orbital track. The OMI level 2 data subset coincident with CALIOP's measurements was produced by the A-Train Data Depot (ATDD) project at the Goddard Earth Sciences Data and Information Services Center to address the differences in spatial, vertical, and horizontal, as well as temporal scales of coverage of different instruments participating in the A-Train (Savtchenko et al, 2008). The ATDD data set was augmented with CALIOP's observations of attenuated backscatter at 532 and 1064 nm.…”

Section: Collocationmentioning

confidence: 99%

Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations

2013

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Abstract. The height of desert dust and carbonaceous aerosols layers and, to a lesser extent, the difficulty in determining the predominant size mode of these absorbing aerosol types, are sources of uncertainty in the retrieval of aerosol properties from near-UV satellite observations. The availability of independent, near-simultaneous measurements of aerosol layer height, and aerosol-type related parameters derived from observations by other A-train sensors, makes possible the use of this information as input to the OMI (ozone monitoring instrument) near-UV aerosol retrieval algorithm (OMAERUV). A monthly climatology of aerosol layer height derived from observations by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) sensor, and real-time AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) observations are used in an upgraded version of the OMAERUV algorithm. AIRS CO measurements are used as an adequate tracer of carbonaceous aerosols, which allows the identification of smoke layers in regions and seasons when the dust-smoke differentiation is difficult in the near-UV. The use of CO measurements also enables the identification of high levels of boundary layer pollution undetectable by near-UV observations alone. In this paper we discuss the combined use of OMI, CALIOP and AIRS observations for the characterization of aerosol properties, and show an improvement in OMI aerosol retrieval capabilities.

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Section: Collocationmentioning

confidence: 99%

Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations

2013

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“…5 clearly demonstrates that CloudSat and AMSR-E aboard Aqua are flying in two close orbits. 1 Spatial separation between the two orbits reaches its maximum over the equator and minimum near the pole [10]. As shown in the figure, φ is equal to the satellite bearing minus Δφ.…”

Section: B Amsr-e and Cloudsatmentioning

confidence: 99%

Parallax Correction in the Analysis of Multiple Satellite Data Sets

Wang

Huang

2014

IEEE Geosci. Remote Sensing Lett.

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This letter describes a general solution to the parallax correction issue concerning the collocation of measurements of the same object from different satellites that use different viewing geometries. Two examples in cloud remote sensing are described with case studies. The applicability of the parallax correction is also discussed. Correct collocation of data collected by multiple satellites is needed in order to avoid introducing incorrect information in the data fusion of multiple sensors on different satellite platforms.

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“…To understand the impact of spatially distinct measurements taken from different orbits of the various ATrain platforms, user requires sophisticated tools to match up the datasets to extract science results properly -more detailed discussions of the type of problems encountered can be found in [3]. To illustrate the spatial differences between A-Train satellite tracks, and considerations that must be accounted for differing horizontal resolutions consider Fig.…”

Section: Geometrical Considerationsmentioning

confidence: 99%

A-Train Data Depot: Integrating and exploring data along the A-Train tracks

Leptoukh

Kempler

Smith

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-The immense potential for new science findings as a result of inter-instrument data analysis has led to the development of a new data portal at GSFC: the A-train Data Depot. The power and utility of this new service to the general public is amplified immensely when the archived data are used in conjunction with online data analysis services like Giovanni. This presentation details some of the challenges of data usage from multiple distinct missions and how the tool sets we have developed can help to overcome these challenges, considerably cut down on analysis overhead and promote science exploration in an otherwise very challenging arena.

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A-Train data depot - bringing Atmospheric measurements together

Cited by 4 publications

References 8 publications

Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations

Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations

Parallax Correction in the Analysis of Multiple Satellite Data Sets

A-Train Data Depot: Integrating and exploring data along the A-Train tracks

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