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Trepte, Qing

Publications and source records attributed to Trepte, Qing.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Validation of Cloud Properties From Multiple Satellites Using CALIOP Data

The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

Yost, Christopher R.↗
NASA NTRS2015

Cloud Properties of CERES-MODIS Edition 4 and CERES-VIIRS Edition 1

The Clouds and Earth's Radiant Energy System (CERES) analyzes MODerate-resolution Imaging Spectroradiometer (MODIS) data and Visible Infrared Imaging Radiometer Suite (VIIRS) to derive cloud properties that are combine with aerosol and CERES broadband flux data to create a multi-parameter data set for climate study. CERES has produced over 15 years of data from Terra and over 13 years of data from Aqua using the CERES-MODIS Edition-2 cloud retrieval algorithm. A recently revised algorithm, CERESMODIS Edition 4, has been developed and is now generating enhanced cloud data for climate research (over 10 years for Terra and 8 years for Aqua). New multispectral retrievals of properties are included along with a multilayer cloud retrieval system. Cloud microphysical properties are reported at 3 wavelengths, 0.65, 1.24, and 2.1 microns to enable better estimates of the vertical profiles of cloud water contents. Cloud properties over snow are retrieved using the 1.24-micron channel. A new CERES-VIIRS cloud retrieval package was developed for the VIIRS spectral complement and is currently producing the CERES-VIIRS Edition 1 cloud dataset. The results from CERES-MODIS Edition 4 and CERES-VIIRS Edition 1 are presented and compared with each other and other datasets, including CALIPSO, CloudSat and the CERES-MODIS Edition-2 results.

Sun-Mack, Sunny↗
NASA NTRS2007

Integrated Cloud-Aerosol-Radiation Product using CERES, MODIS, CALIPSO and CloudSat Data

This paper documents the development of the first integrated data set of global vertical profiles of clouds, aerosols, and radiation using the combined NASA A-Train data from the Aqua Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat. As part of this effort, cloud data from the CALIPSO lidar and the CloudSat radar are merged with the integrated column cloud properties from the CERES-MODIS analyses. The active and passive datasets are compared to determine commonalities and differences in order to facilitate the development of a 3- dimensional cloud and aerosol dataset that will then be integrated into the CERES broadband radiance footprint. Preliminary results from the comparisons for April 2007 reveal that the CERES-MODIS global cloud amounts are, on average, 0.14 less and 0.15 greater than those from CALIPSO and CloudSat, respectively. These new data will provide unprecedented ability to test and improve global cloud and aerosol models, to investigate aerosol direct and indirect radiative forcing, and to validate the accuracy of global aerosol, cloud, and radiation data sets especially in polar regions and for multi-layered cloud conditions.

Sun-Mack, Sunny↗
NASA NTRS2006

Improved Thin Cirrus and Terminator Cloud Detection in CERES Cloud Mask

Thin cirrus clouds account for about 20-30% of the total cloud coverage and affect the global radiation budget by increasing the Earth's albedo and reducing infrared emissions. Thin cirrus, however, are often underestimated by traditional satellite cloud detection algorithms. This difficulty is caused by the lack of spectral contrast between optically thin cirrus and the surface in techniques that use visible (0.65 micron ) and infrared (11 micron ) channels. In the Clouds and the Earth s Radiant Energy System (CERES) Aqua Edition 1 (AEd1) and Terra Edition 3 (TEd3) Cloud Masks, thin cirrus detection is significantly improved over both land and ocean using a technique that combines MODIS high-resolution measurements from the 1.38 and 11 micron channels and brightness temperature differences (BTDs) of 11-12, 8.5-11, and 3.7-11 micron channels. To account for humidity and view angle dependencies, empirical relationships were derived with observations from the 1.38 micron reflectance and the 11-12 and 8.5-11 micron BTDs using 70 granules of MODIS data in 2002 and 2003. Another challenge in global cloud detection algorithms occurs near the day/night terminator where information from the visible 0.65 micron channel and the estimated solar component of 3.7 micron channel becomes less reliable. As a result, clouds are often underestimated or misidentified near the terminator over land and ocean. Comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 N indicate significant amounts of missing clouds from CLAVR-x because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products (MOD06) and GLAS in the same region also show similar difficulties with MODIS cloud retrievals. The consistent detection of clouds through out the day is needed to provide reliable cloud and radiation products for CERES and other research efforts involving the modeling of clouds and their interaction with the radiation budget.

Trepte, Qing↗
NASA NTRS2006

The Continuous Monitoring of Desert Dust using an Infrared-based Dust Detection and Retrieval Method

Airborne dust and sand are significant aerosol sources that can impact the atmospheric and surface radiation budgets. Because airborne dust affects visibility and air quality, it is desirable to monitor the location and concentrations of this aerosol for transportation and public health. Although aerosol retrievals have been derived for many years using visible and near-infrared reflectance measurements from satellites, the detection and quantification of dust from these channels is problematic over bright surfaces, or when dust concentrations are large. In addition, aerosol retrievals from polar orbiting satellites lack the ability to monitor the progression and sources of dust storms. As a complement to current aerosol dust retrieval algorithms, multi-spectral thermal infrared (8-12 micron) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI) are used in the development of a prototype dust detection method and dust property retrieval that can monitor the progress of Saharan dust fields continuously, both night and day. The dust detection method is incorporated into the processing of CERES (Clouds and the Earth s Radiant Energy System) aerosol retrievals to produce dust property retrievals. Both MODIS (from Terra and Aqua) and SEVERI data are used to develop the method.

Duda, David P.↗
NASA NTRS2004

Daytime Cloud Property Retrievals Over the Arctic from Multispectral MODIS Data

Improving climate model predictions over Earth's polar regions requires a complete understanding of polar clouds properties. Passive satellite remote sensing techniques can be used to retrieve macro and microphysical properties of polar cloud systems. However, over the Arctic, there is minimal contrast between clouds and the background snow surface observed in satellite data, especially for visible wavelengths. This makes it difficult to identify clouds and retrieve their properties from space. Variable snow and ice cover, temperature inversions, and the predominance of mixed-phase clouds further complicate cloud property identification. For this study, the operational Clouds and the Earth s Radiant Energy System (CERES) cloud mask is first used to discriminate clouds from the background surface in Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data. A solar-infrared infrared nearinfrared technique (SINT) first used by Platnick et al. (2001) is used here to retrieve cloud properties over snow and ice covered regions.

Spangenberg, Douglas A.↗
NASA NTRS1999

A Grouped Threshold Approach for Scene Identification in AVHRR Imagery

The authors propose a grouped threshold method for scene identification in Advanced Very High Resolution Radiometer imagery that may contain clouds, fire, smoke, or snow. The philosophy of the approach is to build modules that contain groups of spectral threshold tests that are applied concurrently, not sequentially, to each pixel in an image. The purpose of each group of tests is to identify uniquely a specific class in the image, such as smoke. A strength of this approach is that insight into the limits used in the threshold tests may be gained through the use of radiative transfer theory. Methodology and examples are provided for two different scenes, one containing clouds, forest fires, and smoke; and the other containing clouds over snow in the central United States. For both scenes, a limited amount of supporting information is provided by surface observers.

Baum, Bryan A.↗