It is well known that the varying geometrical relationships between the Sun and the Earth throughout the year affect to
some degree the performance of the instruments onboard Earth orbiting satellites. Following the commissioning of
MetOp-A, EUMETSAT and NOAA have continued monitoring the long term trends in in-orbit performance of AVHRR,
HIRS and AMSU-A. The data acquired since the launch of the satellite has allowed studying how the yearly seasonal
variations, as well as aging, have affected the instrument performance. This paper presents the evolution of the
performance of the AVHRR, HIRS and AMSU-A for more than four years since the launch of the MetOp-A satellite.
KEYWORDS: Signal to noise ratio, Satellites, Black bodies, Long wavelength infrared, Calibration, Short wave infrared radiation, Sensors, Electronics, Near infrared, Infrared radiation
The Advanced Very High Resolution Radiometer (AVHRR) instruments on board NOAA-18, MetOp-A and NOAA-19
satellites are key components of the current operational NOAA-EUMETSAT Initial Joint Polar System (IJPS) and are
routinely monitored. Overall, the results of trending analysis show that the AVHRR instruments on NOAA-18/19 and
MetOp-A are functioning well outperforming the channel noise specification limits. The backup NOAA-17 AVHRR
functioned well for the on-orbit period prior to the onset of scan motor failure around April 11, 2010. The sun-earthsatellite
geometry driven seasonality is exhibited by temperature measurements from thermistors on various instrument
housing components including blackbody with the exception of patch temperature which is typically maintained stable.
The only electrical measurement which exhibits seasonality is patch power. It is shown that the seasonality has no
significant adverse impact on AVHRR radiometric performance. On the other hand the space view is adversely affected
by intermittent periodic lunar signals and ubiquitous low frequency variability presumably connected to space clamping
mechanism. Based on this it is suggested that the AVHRR channel noise estimation should be based on blackbody view.
Finally, the temporal stability of the monitored parameters and the smaller or comparable magnitudes of seasonal
variability in most of the instrument housekeeping measurements as compared to their orbital variability confirm the
good health of AVHRR instruments on-board NOAA-18/19 and MetOp-A.
It is well known that the varying geometrical relationships between the Sun and the Earth throughout the year affect to
some degree the performance of the instruments onboard Earth orbiting satellites. Following the commissioning of
MetOp-A, EUMETSAT and NOAA have continued monitoring the long term trends in in-orbit performance of AVHRR,
HIRS and AMSU-A. The data acquired since the launch of the satellite has allowed studying how the yearly seasonal
variations, as well as aging, have affected the instrument performance. This paper presents the evolution of the
performance of the AVHRR, HIRS and AMSU-A for more than three years since the launch of the Metop-A satellite.
The collocated measurements in 3.74μm, 11μm, and 12μm channels from Advanced Very High Resolution Radiometer
(AVHRR) and corresponding simulated AVHRR measurements using hyper-spectral Infrared Atmospheric Sounding
Interferometer (IASI) observations are inter-compared. Both of the instruments are placed on MetOp-A satellite
launched in October 2006. Because IASI observations did not have complete spectral coverage over AVHRR 3.74μm channel, Line-By-Line Radiative Transfer Model (LBLRTM) simulated IASI spectra were generated to enable complete
IASI coverage for this channel. It is shown that the large AVHRR minus IASI negative bias in 3.74μm channel can be explained more or less completely by the part of the AVHRR spectral band not seen by IASI which is an indication of
relatively large absorption in that particular portion of the AVHRR spectral band. The near similarity between slopes of
bias dependency on scene radiance from the model and those derived from observations with respect to 3.74μm channel indicate that it could be mostly the CO2 absorption in the higher wave-numbers experienced by AVHRR and not experienced by IASI causing the discrepancy between these two observations. Thus the study confirms that AVHRR short wave infrared channel (3.74 μm) is performing very well with no indication, of spectral uncertainties, or of significant radiometric uncertainties. On the other hand, the results suggest that AVHRR 3.74 um channel experiences
significant CO2 absorption which may disqualify it from being recognized as a "window channel." With respect to long
wave infrared channels at 11 μm and 12 μm the study reveals that the bias between the two measurements undergo
seasonal variations, however, with small magnitudes.
Global Space-based Inter-Calibration System (GSICS) is a critical space component of Global
Earth Observation System of Systems (GEOSS) that provided users with high-quality inter-calibrated
satellite measurements. As part of the GSICS, imaging instruments on geostationary (GEO) satellites have
been inter-calibrated with hyperspectral instrument Atmospheric Infrared Sounder (AIRS) and Infrared
Atmospheric Sounding Interferometer (IASI) on Low Earth Orbit (LEO) satellites. This paper reports the
GSICS GEO-LEO inter-calibration at NOAA/NESDIS, for GOES-11/12 with AIRS (since January 2007)
and IASI (since June 2007), and of METEOSAT-7/8/9, MTSAT-1R, and FY-2C with AIRS and IASI since
August 2008. Major components of the operation are reviewed, including algorithm development, data
processing, product generation, results dissemination, and selected inter-calibration examples. The
preliminary results of the GSICS correction show that the fully functioning GSICS is a powerful tool to
monitor instrument performance, to correct sensor bias, and to diagnose the root cause of calibration
anomalies.
The geostationary meteorological satellites (GEO), such as Geostationary Operational Environmental Satellite (GOES),
are susceptible to a calibration anomaly around local midnight of the sub-satellite point. A counter measure, the
Midnight Blackbody Calibration Correction (MBCC) currently exists at operational level. In this study, the MBCC
performance on GOES-11 satellite is characterized with the help of Global Space-based Inter-Calibration System
(GSICS) data sets. Results from the comparison of coincident and collocated GSICS-based GOES-11-AIRS data pairs,
corresponding to two and half year period from January 2007 through June 2009, reveal that "mid-night residuals" in
brightness temperatures persist in all of the GOES-11 Infra-Red (IR) channels, in spite of MBCC. The GOES-11 split
window channels (channels 4 and 5) consistently showed significantly large negative (GOES-11-AIRS) biases often
reaching values of -1. 5 K or less while the short wave Infra-Red (SWIR) channel (channel 2) produced relatively
smaller negative biases (~ -0.3 K or less). Interestingly, the water vapor IR channel (channel 3) exhibits a different
pattern from rest of the channels in which consistently opposite biases with small positive (GOES-11-AIRS) difference
values (~ 0.3 K or less) could be observed. The reason for the differential behavior of GOES-11 channel 3 is yet to be
understood, while it is hypothesized that this might be linked to the convolution algorithm used for matching the AIRS
data spectrally with those from GOES water vapor channel. The amount of midnight residuals is shown to have a
consistent seasonal dependency, which gets repeated year after year, for the period considered in the analysis.
Evaluation of satellite land surface temperature (LST) is one of the most difficult tasks in LST retrieval algorithm
development, because of spatial and temporal variability of land surface temperature and surface emissivity
variations. A large number of high quality "match-up" satellite and ground LST data is needed for the evaluation
process. In developing a LST algorithm for the GOES-R Advanced Baseline Imager, we produced a set of
"match-up" dataset from SURFace RADiation (SURFRAD) budget network ground measurements and GOES-8
and -10 satellite measurements. The dataset covers one-year GOES Imager data over six SURFRAD sites in the
United States. A stringent cloud filtering procedure was applied to minimize cloud contamination in the match-up
dataset. Each of the SURFRAD sites contains enough match-up data pairs for ensuring significance of statistical
analyses of the LST algorithm. The evaluation was performed by directly and indirectly comparing the
SURFRAD and satellite LSTs of each site. The direct comparison was illustrated using scatter plots and histogram
plots of the ground and the satellite LSTs, while the indirect comparison was performed using a matrix analysis
model developed by Flynn (2006)[1]. We demonstrated that LST measurements from the SURFRAD instrument
can be used in our evaluation of the GOES-R LST algorithm development and the precision of the GOES-R LST
algorithm can be fairly well estimated.
The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the
Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014.
Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as
well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of
the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polarorbiting
satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not
only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study
we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor
data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the
LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and
adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from
nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well
as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure
the algorithm performance.
A field campaign featuring three collocated Doppler wind lidars was conducted over ten days during September 2000 at the GroundWinds Observatory in New Hampshire. The lidars were dissimilar in wavelength and Doppler detection method. The GroundWinds lidar operated at 532 nm and used fringe-imaging direct detection, while the Goddard Lidar Observatory for Winds (GLOW) ran at 355 nm and employed double-edge filter direct detection, and the NOAA mini-MOPA operated at 10 microns and used heterodyne detection. The objectives of the campaign were (1) to demonstrate the capability of the GroundWinds lidar to measure winds while employing several novel components, and (2) to compare directly the radial wind velocities measured by the three lidars for as wide a variety of conditions as possible. Baseline wind profiles and ancillary meteorological data (temperature and humidity profiles) were obtained by launching GPS radiosondes from the observatory as frequently as every 90 minutes. During the final week of the campaign the lidars collected data along common lines-of-sight for several extended periods. The wind speed varied from light to jet stream values, and sky conditions ranged from clear to thick clouds. Intercomparisons of overlapping lidar and radiosonde observations show that all three lidars were able to measure wind given sufficient backscatter. At ranged volumes containing thicker clouds, and those beyond, the wind sensing capability of the direct detection lidars was adversely affected.
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