An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.
Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output truth for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.
A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze
other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base
height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES-
10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to
normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms.
Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties
are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected
domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level
results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the
products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for
aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have
potential for use in many other applications.
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.
Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO<sub>2</sub>-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the <i>Aqua</i> satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.
MODIS aerosol retrievals over ocean from <i>Terra</i> and <i>Aqua</i> platforms are available from the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) datasets generated at NASA Langley Research Center (LaRC). Two aerosol products are reported side by side. The <i>primary</i> M product is generated by subsetting and remapping the multi-spectral (0.44 - 2.1 μm) MOD04 aerosols onto CERES footprints. MOD04 processing uses cloud screening and aerosol algorithms developed by the MODIS science team. The <i>secondary</i> (AVHRR-like) A product is generated in only two MODIS bands: 1 and 6 on <i>Terra</i>, and ` and 7 on <i>Aqua</i>. The A processing uses NASA/LaRC cloud-screening and NOAA/NESDIS single channel aerosol algorthm. The M and
A products have been documented elsewhere and preliminarily compared using two weeks of global <i>Terra</i> CERES SSF (Edition 1A) data in December 2000 and June 2001. In this study, the M and A aerosol optical depths (AOD) in MODIS band 1 and (0.64 μm), τ<sub>1M</sub> and τ<sub>1A</sub>, are further checked for cross-platform consistency using 9 days of global <i>Terra</i> CERES SSF (Edition 2A) and <i>Aqua</i> CERES SSF (Edition 1A) data from 13 - 21 October 2002.
The Clouds and Earth’s Radiant Energy System (CERES) project is using multispectral imagers, the Visible Infrared Scanner (VIRS) on the tropical Rainfall Measuring Mission (<i>TRMM</i>) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on <i>Terra</i>, operating since spring 2000, and <i>Aqua</i>, operating since summer 2002, to provide cloud and clear-sky properties at various wavelengths. This paper presents the preliminary results of an analysis of the CERES clear-sky reflectances to derive a set top-of-atmosphere clear sky albedo for 0.65, 0.86, 1.6, 2.13 μm, for all major surface types using the combined MODIS and VIRS datasets. The variability of snow albedo with surface type is examined using MODIS data. Snow albedo was found to depend on the vertical structure of the vegetation. At visible wavelengths, it is least for forested areas and greatest for smooth desert and tundra surfaces. At 1.6 and 2.1-μm, the snow albedos are relatively insensitive to the underlying surface because snow decreases the reflectance. Additional analyses using all of the MODIS results will provide albedo models that should be valuable for many remote sensing, simulation and radiation budget studies.
The micro- and macrophysical properties of clouds play a crucial role in Earth’s radiation budget. The NASA Clouds and Earth’s Radiant Energy System (CERES) is providing simultaneous measurements of the radiation and cloud fields on a global basis to improve the understanding and modeling of the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. Cloud properties derived for CERES from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the <i>Terra</i> and <i>Aqua</i> satellites are compared to ensure consistency between the products to ensure the reliability of the retrievals from multiple platforms at different times of day. Comparisons of cloud fraction, height, optical depth, phase, effective particle size, and ice and liquid water paths from the two satellites show excellent consistency. Initial calibration comparisons are also very favorable. Differences between the <i>Aqua</i> and <i>Terra</i> results are generally due to diurnally dependent changes in the clouds. Additional algorithm refinement is needed over the polar regions for <i>Aqua</i> and at night over those same areas for <i>Terra</i>. The results should be extremely valuable for model validation and improvement and for improving our understanding of the relationship between clouds and the radiation budget.
Surface emissivity is essential for many remote sensing applications including the retrieval of the surface skin temperature from satellite-based infrared measurements, determining thresholds for cloud detection and for estimating the emission of longwave radiation from the surface, an important component of the energy budget of the surface-atmosphere interface. In this paper, data from the Terra MODIS (MODerate-resolution Imaging Spectroradiometer) taken at 3.7, 8.5, 10.8, 12.0 micron are used to simultaneously derive the skin temperature and the surface emissivities at the same wavelengths. The methodology uses separate measurements of the clear-sky temperatures that are determined by the CERES (Clouds and Earth's Radiant Energy System) scene classification in each channel during the daytime and at night. The relationships between the various channels at night are used during the day when solar reflectance affects the 3.7 micron data. A set of simultaneous equations is then solved to derive the emissivities. Global results are derived from MODIS. Numerical weather analyses are used to provide soundings for correcting the observed radiances for atmospheric absorption. These results are verified and will be available for remote sensing applications.
The NASA CERES Project has developed a combined radiation and cloud property dataset using the CERES scanners and matched spectral data from high-resolution imagers, the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. The diurnal cycle can be well-characterized over most of the globe using the combinations of TRMM, Aqua, and Terra data. The cloud properties are derived from the imagers using state-of-the-art methods and include cloud fraction, height, optical depth, phase, effective particle size, emissivity, and ice or liquid water path. These cloud products are convolved into the matching CERES fields of view to provide simultaneous cloud and radiation data at an unprecedented accuracy. Results are available for at least 3 years of VIRS data and 1 year of Terra MODIS data. The various cloud products are compared with similar quantities from climatological sources and instantaneous active remote sensors. The cloud amounts are very similar to those from surface observer climatologies and are 6-7% less than those from a satellite-based climatology. Optical depths are 2-3 times smaller than those from the satellite climatology, but are within 5% of those from the surface remote sensing. Cloud droplet sizes and liquid water paths are within 10% of the surface results on average for stratus clouds. The VIRS and MODIS retrievals are very consistent with differences that usually can be explained by sampling, calibration, or resolution differences. The results should be extremely valuable for model validation and improvement and for improving our understanding of the relationship between clouds and the radiation budget.