The Cross-track Infrared Sounder (CrIS) on the Joint Polar Satellite System (JPSS) satellites is a Fourier transform spectrometer, providing sounding (temperature and humidity) and trace gas the of the atmosphere with 2221 spectral channels along three infrared bands. This paper presents a Machine Learning (ML) method to retrieve the methane in the middle troposphere from CrIS spectra. Different from traditional physical retrieval methods, the main idea of the ML-based approach is to use a Neural Network (NN) to approximate the complex inverse function that maps the target trace gas concentration to the CrIS radiances measured based on the training datasets. The authors utilize the Thermodynamic Initial Guess Retrieval (TIGR) dataset coupling with a three-dimensional chemical-transport model outputs (Global Greenhouse Gas Reanalysis) to build the training datasets through radiative transfer model calculations. The training dataset covers a large range not only of the concentration of the target trace gas but also of the auxiliary parameters on the state of the atmosphere. A deep residual neural network (ResNet) is trained to determine model parameters (weights for each node). The preliminary results are encouraging and indicate that the AI-based method has the ability to retrieve the tropospheric methane from CrIS data. In proposed future work, we will use the real CrIS spectral as inputs to estimate the tropospheric methene. These newly developed products will be compared with the existing sounding products from physical methods as well as those directly measured from ground and aircraft.
The Suomi NPP VIIRS thermal emissive bands (TEB) have been performing very well since data became available on January 20, 2012. The longwave infrared bands at 11 and 12 um (M15 and M16) are primarily used for sea surface temperature (SST) retrievals. A long standing anomaly has been observed during the quarterly warm-up-cool-down (WUCD) events. During such event daytime SST product becomes anomalous with a warm bias shown as a spike in the SST time series on the order of 0.2 K. A previous study (CAO et al. 2017) suggested that the VIIRS TEB calibration anomaly during WUCD is due to a flawed theoretical assumption in the calibration equation and proposed an Ltrace method to address the issue. This paper complements that study and presents operational implementation and validation of the Ltrace method for M15 and M16. The Ltrace method applies bias correction during WUCD only. It requires a simple code change and one-time calibration parameter look-up-table update. The method was evaluated using colocated CrIS observations and the SST algorithm. Our results indicate that the method can effectively reduce WUCD calibration anomaly in M15, with residual bias of ~0.02 K after the correction. It works less effectively for M16, with residual bias of ~0.04 K. The Ltrace method may over-correct WUCD calibration biases, especially for M16. However, the residual WUCD biases are small in both bands. Evaluation results using the SST algorithm show that the method can effectively remove SST anomaly during WUCD events.
The Cross-track Infrared Sounder (CrIS) on the newly-launched Suomi National Polar-orbiting Partnership (Suomi NPP)
is a Fourier transform spectrometer that provides soundings of the atmosphere with 1305 spectral channels, over 3
wavelength ranges: LWIR (9.14 - 15.38 μm); MWIR (5.71 - 8.26 μm); and SWIR (3.92 - 4.64 μm). An accurate spectral
and radiometric calibration as well as geolocation is fundamental for CrIS radiance Sensor Data Records (SDRs). In this
study, through inter- and intra-satellite calibration efforts, we focus on assessment of NPP/CrIS post-launch
performance. First, we compare CrIS hyperspectral radiance measurements with the Atmospheric Infrared Sounder
(AIRS) on NASA Earth Observing System (EOS) Aqua and Infrared Atmospheric Sounding Interferometer (IASI) on
Metop-A to examine spectral and radiometric consistence and difference among three hyperspectral IR sounders.
Secondly, an accurate collocation algorithm has been developed to collocate high spatial resolution measurements from
the Visible Infrared Imager Radiometer Suite (VIIRS) within each CrIS Field of View (FOV). We compare CrIS
spectrally-averaged radiances with the spatially-averaged and collocated pixels from the VIIRS IR channels. Since CrIS
and VIIRS are onboard on the same satellite platform, the intra-satellite comparison will allow examining the
radiometric difference between CrIS and VIIRS with scene temperatures, scan angles, and orbital position. In addition,
given a high spatial resolution of VIIRS channels, the VIIRS-CrIS comparison results can access geolocation accuracy
of CrIS that have relatively large FOVs (14 km at ndair) using high resolution VIIRS pixel (375m or 750m at nadir).
The Crosstrack Infrared Sounder (CrIS) is a Michelson type Fourier Transform Spectrometer flying
on-board the SUOMI NPP satellite that was launched into orbit on October 28th 2011. CrIS measures
the Top of Atmosphere (TOA) infrared radiance. Calibration and validation activities at NOAA-STAR
includes: 1) The double difference of CrIS field of view (FOV) intercomparison using the
Community Radiative Transform Model (CRTM) where the FOV are consistent to 0.05K or better,
2) Simultaneous nadir overpass (SNO) radiance comparison of CrIS with IASI with 0.2K agreement
over the window channels,3) Top of atmosphere radiance comparison of the measured with the
CRTM with an agreement of 0.4K or better over the window channels, 4) Double difference of CrIS
vs IASI with an agreement of 0.3K over the window channels., 5) Long term monitoring and
trending of 55 parameters, 6) Geolocation assessment using VIIRS where CrIS now has an estimated
accuracy of 1 Km. Calibration and validation of the CrIS SDR is essential because its radiance
product is assimilated by the NWP algorithm leading to weather forecasting.
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.
There are significant challenges in making the observations from HIRS (High Resolution Infrared Radiation Sounder) on the 13+ satellites consistent for climate change detection. It is well known that for HIRS, the inter-satellite biases are significantly affected by differences in the spectral response functions (SRF) between instruments, since they often lead to observations of the atmosphere at different altitudes. The SRF dependent biases are further mixed with other effects such as the diurnal cycle due to observation time differences and orbital drifts, blackbody emissivity, and calibration algorithms. In this study, the IASI (Infrared Atmospheric Sounding Interferometer) observations are used to calculate the HIRS radiances by convolving the IASI observed radiances with the SRF of each HIRS model across different climate zones in different seasons, which separates the SRF induced intersatellite biases from other factors. It is found that the calculated radiance ratios using IASI observations for the HIRS satellite pairs form bell shaped curves that vary with the HIRS model, channel, as well as climate zones. Understanding the characteristics of these bell curves are essential for resolving the SRF dependent intersatellite biases and the development of fundamental climate data records from HIRS.
The potential use of the inter-calibration results to optimally integrate and merge data from GOES 11 and 12 imagers to create consistent, seamless global products is explored in this study. There are three steps involved, including 1) limb correction; 2) tying GOES measurements to IASI; and 3) resolving the SRF-difference-induced biases. We first use the IASI hyperspectral measurements on the polar-orbiting MetOp-A satellite to access the calibration accuracy of water
vapor channels on the GOES-11 and GOES-12 imagers with one year of match-up data. The simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES Imager SRFs to compare with GOES Imager observations. Assuming that IASI is well calibrated and can be used as a radiometric reference standard, the GOES imager water vapors were found to have an estimated calibration accuracy of less than 0.3 K (with a standard deviation of less than 0.2 K) at the BT range 240-260K relative to IASI, which meets the GOES imager design specification (1.0 K calibration accuracy for infrared channels).
In a second step, merging GOES-11 and GOES-12 water vapor channel through IASI is investigated. A linear relationship is proposed. An example of creating water vapor composite image from the GOES-11 and GOES-12 to resolve their observational discrepancy is presented step-by-step. This study further demonstrates the usefulness of employing high spectral resolution radiance measurements to accurately assess broadband radiometer calibration and create the calibration link between instruments. In the future, we will extend this method to other satellites.
Accurate and precise satellite radiance measurements are important for data assimilations in numerical weather
prediction models and climate change detection. Given the high-resolution spectral IASI measurements with high data
quality, it allows us to assess the radiance measurements of 'heritage' instruments that share the same spectral region
with IASI. In this study, we demonstrate the utility of the IASI radiances to evaluate the AVHRR IR channel
observations. The IASI spectral radiances are convolved with the AVHRR SRFs (Spectral Response Functions) to
produce the IASI-convolved AVHRR radiances. The co-registered AVHRR pixels inside each IASI pixel are averaged
to compare with IASI. We analyzed one-orbit data on 21 June 2007, and preliminary comparison has been performed.
Statistically, the temperature observed from AVHRR channels 4 and 5 is slightly warmer than IASI. The mean BT
difference (IASI minus AVHRR) is -0.35K for channels 4 and -0.16 K for channel 5 with a standard deviation of 0.5K.
The BT difference between IASI and AVHRR IR channels is scene-temperature dependent for both channels 4 and 5,
possibly caused by the nonlinearity of detector. Both AVHRR Channel 4 and channel 5 show slightly symmetric
dependence on scan angle with maximum differences of approximately ~0.2 K (AVHRR warmer than IASI) at both ends
of the scan (with respect to the difference at nadir). However, the root cause of the bias still needs to be investigated by
analyzing more datasets.
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