Errors due to wireless transmission can have an arbitrarily large impact on a compressed file. A single bit error appearing in the compressed file can propagate during a decompression procedure and destroy the entire granule. Such a loss is unacceptable since this data is critical for a range of applications, including weather prediction and emergency response planning. The impact of a bit error in the compressed granule is very sensitive to the error's location in the file. There is a natural hierarchy of compressed data in terms of impact on the final retrieval products. For the considered compression scheme, errors in some parts of the data yield no noticeable degradation in the final products. We formulate a priority scheme for the compressed data and present an error correction approach based on minimizing impact on the retrieval products. Forward error correction codes (e.g., turbo, LDPC) allow the tradeoff between error correction strength and file inflation (bandwidth expansion). We propose segmenting the compressed data based on its priority and applying different-strength FEC codes to different segments. In this paper we demonstrate that this approach can achieve negligible product degradation while maintaining an overall 3-to-1 compression ratio on the final file. We apply this to AIRS sounder data to demonstrate viability for the sounder on the next-generation GOES-R platform.
We present an intercomparison of retrieved dust parameters obtained from
analyzing AIRS and MODIS satellite data. Recent papers have highlighted
using AIRS data to retrieve dust top (layer) height, loading and particle size.
Different methods have been used, such as assuming a fixed particle size
and dust top height before fitting radiance
data from selected AIRS channels, or using lookup tables to retrieve dust
loading, height and particle size. In this paper we use the combination
of dust retrievals from MODIS visible and AIRS thermal infrared channels
to provide information on dust top height by forcing the error term (or
intercept of the linear regression of dust optical depths retrieved from
MODIS and AIRS) to zero. When available, GLAS measurements will be used to
validate dust top height. Collocated ship based M-AERI observations, obtained in March 2004 during the AEROSE campaign will also be analyzed to verify this
Much progress has been made toward modeling the spectral infrared
(IR) emissivity of wind-roughened water surfaces. Existing
emissivity models explicitly calculate the ensemble mean
emissivity of the wavy surface for a given observer zenith angle
and local wind speed. However, field observations of emissivity
spectra obtained by the Marine Atmospheric Emitted Radiance
Interferometer (M-AERI) suggest that emissivity models are
deficient at larger view angles and wind speeds. In this
preliminary work, we attempt to identify and explain the sources
of error in these models using M-AERI data acquired at sea (e.g.,
during AEROSE 2004). Our results demonstrate that proper
accounting for non-unity surface emissivity must ultimately
include appropriate specification of the reflected IR radiation
field, especially in window channels. Atmospheric IR surface
reflectance becomes important for high accuracy applications
(e.g., sea surface skin temperature), that rely on window channel
observations at zenith angles ≳45 deg. Lookup tables of
ensemble mean effective incidence angle, rather than mean
emissivity, are generated using different published mean square
slope PDF models. These results roughly agree with recent
findings. Lookup tables of ensemble mean local zenith incidence
angle are also generated. This new approach to
emissivity/reflection modeling will be refined and validated
against M-AERI field data from several previous oceanographic
cruises, and will be the subject of a forthcoming paper.
Traditional cloud clearing methods utilize a clear estimate of the atmosphere inferred from a microwave sounder to extrapolate cloud cleared radiances (CCR's) from a spatial interpolation of multiple cloudy infrared footprints. Unfortunately, sounders have low information content in the lower atmosphere due to broad weighting functions, interference from surface radiance and the microwave radiances can also suffer from uncorrected side-lobe contamination. Therefore, scenes with low altitude clouds can produce errant CCR's that, in-turn, produce errant sounding products. Radiances computed from the corrupted products can agree with the measurements within the error budget making detection and removal of the errant scenes impractical; typically, a large volume of high quality retrievals are rejected in order to remove a few errant scenes. In this paper we compare and contrast the yield and accuracy of the traditional approach with alternative methods of obtaining CCR's. The goal of this research is three-fold: (1) to have a viable approach if the microwave instruments fail on the EOS-AQUA platform; (2) to improve the accuracy and reliability of infrared products derived from CCR's; and (3) to investigate infrared approaches for geosynchronous platforms where microwave sounding is difficult. The methods discussed are (a) use of assimilation products, (b) use of a statistical regression trained on cloudy radiances, (c) an infrared multi-spectral approach exploiting the non-linearity of the Planck function, and (d) use of clear MODIS measurements in the AIRS sub-pixel space. These approaches can be used independently of the microwave measurements; however, they also enhance the traditional approach in the context of quality control, increased spatial resolution, and increased information content.
Under cloud-free conditions during the daytime, global synergistic retrievals of sea surface temperature (SST) and aerosol optical depths (AOD, or ) are made from the AVHRR instruments flown onboard polar-orbiting sun-synchronous NOAA-16 (equator crossing time, EXT~1400) and -17 (EXT~1000) satellites. Validation against buoys and sun-photometers is customarily considered the ultimate check of the quality and accuracy of SST and AOD retrievals. However, ground-truth data are not available globally and their quality is non-uniform. Moreover, the remotely-sensed parameters may not be fully comparable with their counterparts measured from the surface (e.g. skin vs. bulk SST), and the current procedures to merge data in space and time are not fully objective and may themselves introduce additional errors. In this paper, we propose to supplement the traditional validation with another global diagnostic system. The proposed Quality Control/Assurance (QC/QA) system is based on a comprehensive set of statistical self- and cross-consistency checks. Here, it is illustrated with 8 days of global NOAA-16 and -17 data in December 2003. The AODs and SST anomalies have been first aggregated into 1-day, 1-degree boxes, and their global statistics examined. Analyses are best done in anomalies from the expected state (climatology), which is currently available for the SST but not for the AOD. Histograms of NOAA-16 and -17 SST anomalies are highly correlated (R~0.77), both showing an approximately Gaussian shape, with a mean of ~+0.3K and RMS~1K. AODs also show much similarity but reveal significant cross-platform biases. The magnitudes and even the signs of these biases are band-specific, suggesting that they are due to calibration differences between the two AVHRRs flown on the two platforms. Recall that the AVHRR solar reflectance bands used for aerosol retrievals lack on-board calibration, and therefore may be subject to large calibration errors.
The National Polar-orbiting Operational Satellite System (NPOESS) Aircraft Sounder Testbed-Interferometer (NAST-I) is one of two airborne infrared sounder systems currently being used to evaluate future spaceborne advanced sounder designs. The NAST-I instrument is a cross-track scanning Fourier Transform Spectrometer (FTS) that measures the upwelling radiation in the infrared spectrum between 645 - 2700 cm<SUP>-1</SUP> (15.5 - 3.7 micrometer) at a high-spectral resolution of 0.25 cm<SUP>-1</SUP>. Each observation has a spatial resolution of 2.6 km from NASA's ER-2 high-altitude aircraft, which operates 20 km above the surface. Measurements from this instrument in its first year of operation have not only contributed to risk reduction studies for future IR sounders but have also provided valuable datasets from three different climate regimes. The spatial coverage, 40 km swath width, has facilitated evaluation of non-linear retrieval algorithms using high-spectral resolution information content and provided a means for further validation of infrared radiative transfer models. The capabilities of the NAST-I instrument have already been tested under varied field conditions such as tropical, mid-latitude summer, and mid- latitude winter regimes. Preliminary results from these field experiments have demonstrated favorable sounding capability under such conditions, including intensive tropical cyclone environments (Hurricane Bonnie, August 1998 and Hurricane Georges, September 1998). In addition, repeated observations over the same geographic location near Andros Island in the Bahamas have provided additional information on the temporal change and spatial distribution of water vapor responding to complex mesoscale and large-scale dynamic processes. The framework for future spaceborne IR sounders will be well established by current and future observations made by the NAST-I instrument with its capability to remotely sense atmospheric state variables and cloud radiative properties.
It is well known that a globally accurate, non-biased, satellite-derived sea surface temperature (SST) database has important applications in quantitative studies of the earth's atmosphere-ocean system. Current satellite SST retrieval algorithms do not provide the instantaneous accuracy of 0.3 K necessary for useful calculations of surface flux in climate modeling. The present study demonstrates the potential for utilizing high spectral resolution infrared radiance data toward the objective of improving global satellite-derived SSTs. The NPOESS aircraft sounder testbed interferometer (NAST-I), a newly developed experimental Fourier transform spectrometer, is employed here. This instrument is flown experimentally from high-altitude aircraft and is capable of cross-track scanning similar to that found on polar orbiting satellites; this unique scanning feature allows the possibility of accurate SST imaging. NAST-I data collected during the Wallops 1998 field campaign are used in a generalized physical multiwindow retrieval method. The combined technology and algorithm is anticipated to allow clear-sky imaging of SST with accuracy limited primarily by the noise and calibration errors of the instruments.