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.
The main objective of this work is to describe a research project on fog and visibility, and to summarize the results. The
Fog Remote Sensing and Modeling (FRAM) project was designed to focus on 1) development of microphysical
parameterizations for model applications, 2) development of remote sensing methods for fog nowcasting/forecasting, 3)
understanding of issues related to instrument capabilities and improvement of the analysis, and 4) integration of model
data with observations. The FRAM was conducted over three regions of Canada and US. These locations were: 1)
Center for Atmospheric Research Experiments (CARE), Egbert, Ontario 2005-2006, 2) Lunenburg, Nova Scotia June of
2006 and 2007, and 3) U.S. Department Of Energy (DOE) ARM Climate Research Facility at Barrow, Alaska, US
during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) field program April of 2008; FRAM C, FRAM-L, and
ISDAC-FRAM-B, respectively. FRAM-C was undertaken in a continental fog environment while FRAM-L was in a
marine environment. The FRAM-B was undertaken to study ice fog conditions. During the project, numerous in-situ
measurements were obtained, including droplet and aerosol spectra, precipitation, and visibility. Analysis of satellite
microphysical retrievals and visibility parameterizations suggested that improved scientific understanding of fog
formation can lead to better forecasting/nowcasting skills benefiting both aviation and public forecasting applications.
The NOAA AVHRR program has given the remote sensing community over 25 years of imager radiances to retrieve
global cloud, vegetation, and aerosol properties. This dataset can be used for long-term climate research if the AVHRR
instrument is well calibrated. Unfortunately, the AVHRR instrument does not have onboard visible calibration and does
degrade over time. Vicarious post-launch calibration is necessary to obtain cloud properties that are not biased over
time. The recent AVHRR/3 instrument has a dual gain in the visible channels in order to achieve greater radiance
resolution in the clear-sky. This has made vicarious calibration of the AVHRR/3 more difficult to unravel. Reference
satellite radiances from well-calibrated instruments, usually equipped with solar diffusers, such as MODIS, have been
used to successfully vicariously calibrate other visible instruments. Transfer of calibration from one satellite to another
using co-angled, collocated, coincident radiances has been well validated. Terra or Aqua MODIS and AVHRR
comparisons can only be performed over the poles during summer. However, geostationary satellites offer a transfer
medium that captures both parts of the dual gain. This AVHRR/3 calibration strategy uses Meteosat-8 radiances
(calibrated with MODIS) simultaneously to determine the dual gains using 50km regions. The dual gain coefficients
will be compared with the nominal coefficients. Results will be shown for all visible channels for NOAA-17.
At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce
cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing
aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products
available, summarizes the content of the online database, and details web-based satellite browsers and tools to access
satellite imagery and products.
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.
Remote sensing of cloud and radiation properties from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites requires constant monitoring of the visible sensors. NOAA satellites do not have onboard visible calibration and need to be calibrated vicariously in order to determine the calibration and the degradation rate. Deep convective clouds are extremely bright and cold, are at the tropopause, have nearly a Lambertian reflectance, and provide predictable albedos. The use of deep convective clouds as calibration targets is developed into a calibration technique and applied to NOAA-16 and NOAA-17. The technique computes the relative gain drift over the life-span of the satellite. This technique is validated by comparing the gain drifts derived from inter-calibration of coincident AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) radiances. A ray-matched technique, which uses collocated, coincident, and co-angled pixel satellite radiance pairs is used to inter-calibrate MODIS and AVHRR. The deep convective cloud calibration technique was found to be independent of solar zenith angle, by using well calibrated Visible Infrared Scanner (VIRS) radiances onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which precesses through all solar zenith angles in 23 days.
Rapid and accurate calibrations of satellite imager sensors are critical for remote sensing of surface, cloud and radiative properties. A post-launch technique has been developed to routinely cross calibrate and normalize the imager visible (VIS) channel on-board operational geostationary (GEO) and low-Earth-orbit (LEO) satellites. As a reference calibration source, this simple approach uses the self-calibrating sensor from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner (VIRS) to calibrate other GEO and LEO satellites. The VIRS sensors have been found to be a stable and reliable reference source. This technique uses VIRS to calibrate the eighth Geostationary Operational Environmental Satellite (GOES-8) VIS sensor using collocated data with similar viewing zenith, solar zenith, and relative azimuth angles. GOES-8 is then used as a transfer medium to cross calibrate other GEO and LEO satellites. Post-launch VIS (~0.65 µm) calibration coefficients for GOES-8, -9, -10, -12, Meteosat-7, -8, and NOAA-14 AVHRR satellites are presented. GOES-8 had a non-linear degradation rate of 11% the first year of operational service and 4% in last year before it was decommissioned. GOES-9 degraded linearly at 7.9% per year during 1995-1998. GOES-10 degraded 12% the first year and 1.6% less each year after that. GOES-12 degraded 6% per year. The VIRS visible channel calibration is in good agreement with the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board the Terra and Aqua satellites supporting its use as a reference.
Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA-16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.