Cross-track Infrared Sounder Research Articles

A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Improvement in tropospheric moisture retrievals from VIIRS through the use of infrared absorption bands constructed from VIIRS and CrIS data fusion

Abstract. An operational data product available for both the Suomi National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) platforms provides high-spatial-resolution infrared (IR) absorption band radiances for Visible Infrared Imaging Radiometer Suite (VIIRS) based on a VIIRS and Crosstrack Infrared Sounder (CrIS) data fusion method. This study investigates the use of these IR radiances, centered at 4.5, 6.7, 7.3, 9.7, 13.3, 13.6, 13.9, and 14.2 µm, to construct atmospheric moisture products (e.g., total precipitable water and upper tropospheric humidity) and to evaluate their accuracy. Total precipitable water (TPW) and upper tropospheric humidity (UTH) retrieved from hyperspectral sounder CrIS measurements are provided at the associated VIIRS sensor's high spatial resolution (750 m) and are compared subsequently to collocated operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and S-NPP VIIRS moisture products. This study suggests that the use of VIIRS IR absorption band radiances will provide continuity with Aqua MODIS moisture products.

A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.

A Novel Detection Algorithm for Impulse Spike Interferences in Cross-Track Infrared Sounder Short-Wave Spectral Radiance

Due to deficiencies in the operational spike detection, many moderate and strong impulse spike interferences constantly remain nondetected in the cross-track infrared sounder (CrIS) sensor data record (SDR) data at the short-wave infrared (SWIR) band over the South Atlantic Anomaly (SAA) region. This study characterizes imaginary radiance spectrum features in the presence of four intensities of spikes at three different hitting locations through radiative transfer model simulations. It is found that periodic ringing patterns appear in the imaginary spectral radiance. Following it, we establish a novel spike detection algorithm based on the spiky feature of power spectral density (PSD) in detrended and normalized imaginary SWIR CrIS spectral radiances. The performance of the algorithm is assessed by applying it to the multiple years of the SNPP and NOAA-20 CrIS full spectral resolution (FSR) radiance data. Approximate 300 and 500 spike events per day are detected for SNPP and NOAA-20 CrIS individually. The spikes can obviously increase the uncertainty in brightness temperature measurements at SWIR window channels that is determined by hitting location of the interferogram arms and the impulse intensities. Strong impulse spikes can trigger spectral radiance uncertainty by several times in SWIR window channels. Even the weakest impulse event can increase spectral radiance uncertainty by 4% in SWIR window channels. In addition, our simulations and one case study of CrIS data show that the spikes can degrade the quality of the CrIS data at the SWIR band with the largest impact at absorbing channels and the smallest at the window channels. The resultant brightness temperature change are 3 K for absorbing channels and up to 1 K for window channels in the SWIR band, demonstrating the significance flagging the CrIS data that are contaminated by the moderate and strong spikes that are missed in the operational data processing.

Approach to enhance trace gas determinations through multi-satellite data fusion

The imaging instruments on the polar-orbiting S-NPP and NOAA-20 satellite platforms [e.g., Visible Infrared Imaging Radiometer Suite (VIIRS)] and on geostationary GOES and Himawari platforms [e.g., Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI)] have high horizontal spatial resolution but coarse vertical information about tropospheric gases and temperatures. An approach for fusing a high spatial resolution imager (e.g., VIIRS, ABI, and AHI) and a high information content sounder or trace gas monitor [e.g., Cross-track Infrared Sounder (CrIS) and TROPOspheric Monitoring Instrument (TROPOMI)] offers the opportunity to construct retrieval products (e.g., trace gas concentrations of CO, SO2, and NH3) via product fusion at high spatial resolution. Furthermore, these products can also be provided at high temporal resolution when geostationary imager (ABI and AHI) data are used in the product fusion. Promising results from VIIRS/CrIS, VIIRS/TROPOMI, and AHI/TROPOMI fusion are presented.

Probabilistic retrieval of volcanic SO<sub>2</sub> layer height and partial column density using the Cross-track Infrared Sounder (CrIS)

Abstract. During most volcanic eruptions and many periods of volcanic unrest, detectable quantities of sulfur dioxide (SO2) are injected into the atmosphere at a wide range of altitudes, from ground level to the lower stratosphere. Because the fine ash fraction of a volcanic plume is, at times, colocated with SO2 emissions, global tracking of volcanic SO2 is useful in tracking the hazard long after ash detection becomes dominated by noise. Typically, retrievals of SO2 vertical column density (VCD) have relied heavily on hyperspectral ultraviolet measurements. More recently, infrared sounders have provided additional VCD measurements and estimates of the SO2 layer altitude, adding significant value to real-time monitoring of volcanic emissions and climatological analyses. These methods can provide fast and accurate physics-based retrievals of VCD and altitude without regard to solar irradiance, meaning that they are effective day and night and can observe high-latitude SO2 even in the winter. In this study, we detail a probabilistic enhancement of an infrared SO2 retrieval method, based on a modified trace gas retrieval, to estimate SO2 VCD and altitude probabilistically using the Cross-track Infrared Sounder (CrIS) on the Joint Polar Satellite System (JPSS) series of satellites. The methodology requires the characterization of real SO2-free spectra aggregated seasonally and spatially. The probabilistic approach replaces altitude and VCD estimates with probability density functions for the layer height and the partial VCD at multiple heights, fully quantifying the retrieval uncertainty and allowing the estimation of SO2 partitioning by layer. This framework adds significant value over basic VCD and altitude retrieval because it can be used to assign probabilities of SO2 occurrence to different atmospheric intervals. We highlight analyses of several recent significant eruptions, including the 22 June 2019 eruption of Raikoke volcano, in the Kuril Islands; the mid-December 2016 eruption of Bogoslof volcano, in the Aleutian Islands; and the 26 June 2018 eruption of Sierra Negra volcano, in the Galapagos Islands. This retrieval method is currently being implemented in the VOLcanic Cloud Analysis Toolkit (VOLCAT), where it will be used to generate additional cloud object properties for real-time detection, probabilistic characterization, and tracking of volcanic clouds in support of aviation safety.

Comparison of Tropical Cyclone Center Positions Determined from Satellite Observations at Infrared and Microwave Frequencies

AbstractDetermining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

Infrared Fourier Spectrometer 2 (IKFS‐2) radiance assimilation at ECMWF

AbstractMeteorological satellites launched as part of the Russian space programme have not been widely used in global numerical weather prediction (NWP) to date. There is particular interest in the Meteor‐M series of sun‐synchronous spacecraft, as it is anticipated that future launches will operate in currently unobserved orbital planes complementary to those occupied by Europe, the United States, and China. One of the sensors carried on board these satellites is the Infrared Fourier Spectrometer 2 (IKFS‐2), which observes a spectrum of atmospheric radiation over the wavenumber range 660–2,000.5 cm−1. The IKFS‐2 is thus similar to earlier hyperspectral sensors such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross‐track Infrared Sounder (CrIS), both of which have been used operationally at the European Centre for Medium‐range Weather Forecasts (ECMWF) for many years. This study investigates the quality of IKFS‐2 radiance measurements from the Meteor‐M‐N2 satellite and performs assimilation experiments to assess the impact of these data on the ECMWF analysis and forecasting system. Measured by statistical indicators of the observation fit to short‐range forecasts, the IKFS‐2 performs as well as IASI and CrIS amongst channels sensitive to stratospheric and tropospheric temperature. The assimilation experiments indicate up to 0.5% improvement in headline forecast scores, although the medium‐range impact is mixed and for most parts nonsignificant. One factor that currently limits the impact of IKFS‐2 is poor data timeliness and availability: observations are available for assimilation typically only 75% of the time during the test periods. It is expected, though, that such logistical problems can be overcome, and efforts are already underway to improve data dissemination.

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

Hyperspectral Infrared Sounder Cloud Detection Using Deep Neural Network Model

Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared sounders is essential for numerical weather prediction. A new cloud detection model is developed for the cross-track infrared sounder (CrIS) using the artificial deep neural network (DNN) technique. The truth cloud information used is from another instrument of Visible Infrared Imaging Radiometer Suite (VIIRS) deployed on the same platform of CrIS. The training data set is built from CrIS-VIIRS collocated measurements randomly selected from different months to represent different atmospheric and surface conditions. Then, we use the VIIRS cloud mask collocated within the CrIS footprint to train the CrIS spectra for cloud detection. Specifically, the CrIS spectra were transformed into their principal components (PCs), with only the top 75 PCs used as the predictors rather than the entire CrIS channels, for the purpose of better regression and convergence during the training process and faster prediction. Results were examined globally by the considered truth derived from the VIIRS cloud mask. Generally, the spatial distribution of the proposed CrIS cloud detection result agrees with that from the VIIRS, with a high model accuracy of 93%. Further analysis indicates that the proposed CrIS cloud detection result is slightly better over daytime than nighttime with the accuracy values of 94% versus 91%. The ocean areas have a higher cloud detection accuracy than continental land with accuracy values of 95% versus 88%. In addition, sometimes the DNN model would recognize the thin cloud as clear sky, as their spectra are very similar to each other. False detected pixels are also found over snow- or ice-covered and desert areas. This is possibly due to the VIIRS cloud mask that has a relatively low accuracy over these areas.

Cloud Detection From Paired CrIS Water Vapor and CO₂ Channels Using Machine Learning Techniques

Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> bands.

The Reprocessed Suomi NPP Satellite Observations

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.

Improving the Understanding of CrIS Full Spectral Resolution Nonlocal Thermodynamic Equilibrium Radiances Using Spectral Correlation

AbstractIn recent years, significant progress has been made in fast radiative transfer model (RTM) simulation of daytime nonlocal thermodynamic equilibrium (NLTE) emission. However, NLTE remains as one important reason that prevents the hyperspectral shortwave infrared (SWIR) radiance observations from being assimilated into numerical weather prediction (NWP) models. To better understand the limitations of existing RTM‐based NLTE simulation, this study introduces a new statistical method, called Spectral Correlations to Estimate Non‐Local Thermal Equilibrium (SCENTE), to estimate the NLTE radiances in the Cross‐track Infrared Sounder (CrIS) SWIR radiance observations. SCENTE is applied to four typical season days, including spring equinox, summer solstice, fall equinox, and winter solstice. By analyzing calculation/background minus observation (BMO) of CrIS SWIR brightness temperature (BT), results show that SCENTE characterizes the NLTE well with standard deviation of differences (STD) comparable to observation noise for both daytime and nighttime, while the community RTM (CRTM) has substantially larger STD at night, mainly due to the lack of daytime NLTE just beyond the day/night terminator and the lack of aurora‐related NLTE. Detailed investigation of the biases of BMO shows that CRTM underestimates daytime SWIR NLTE effects by 0.76 K, while SCENTE overestimates SWIR NLTE effects by 0.70 K. The overestimation is because SCENTE uses CRTM‐simulated SWIR local thermodynamic equilibrium (LTE) radiances in the training, which is underestimated by 0.70 K in BT. SCENTE, complementary to RTM‐based simulations, can be used for quality control of SWIR radiances for assimilation and retrieval of atmospheric soundings.

Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion

Abstract. Retrieval of semitransparent ice cloud properties from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 platforms is challenging due to the absence of infrared (IR) water vapor and CO2 absorption channels. However, on these platforms, there is a companion sensor called the Crosstrack Infrared Sounder (CrIS) that provides these spectral measurements but at a lower spatial resolution (∼15 km at nadir). To mitigate the lack of VIIRS spectral measurements in these IR absorption channels, recent studies suggest an approach to supplement VIIRS measurements by fusion of the imager and sounder data. In particular, Weisz et al. (2017) demonstrate a method to construct IR water vapor and CO2 absorption channel radiances for VIIRS at 750 m spatial resolution. Based on these constructed channels for both S-NPP and NOAA-20, this study evaluates three cloud properties – cloud mask, cloud thermodynamic phase, and cloud top height – through comparison to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP) V4-20 cloud layer products and Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud top products. Each of these cloud properties shows improvement with the use of these constructed channel radiances. The major improvement for the cloud mask is found over polar regions, where the correct cloud detection percentage increases due to a decrease in missed clouds and/or false detection. For cloud thermodynamic phase, the ice cloud fraction increases over non-polar regions and the combined liquid water and ice cloud discrimination improves in comparison with CALIPSO. The retrieved cloud top height for semitransparent ice clouds increases over non-polar regions and tends to be closer to the true CALIPSO/CALIOP cloud top height. Moreover, the uncertainty of cloud top height retrievals decreases globally for these clouds.

The development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) greenhouse gas profiling system and application for satellite retrieval validation

Abstract. We report in this paper the development of an embedded ultralight spectrometer (&lt;3 kg) based on tuneable diode laser absorption spectroscopy (with a sampling rate of 24 Hz) in the mid-infrared spectral region. This instrument is dedicated to in situ measurements of the vertical profile concentrations of three main greenhouse gases – carbon dioxide (CO2), methane (CH4) and water vapour (H2O) – via standard weather and tethered balloons. The plug and play instrument is compact, robust, cost-effective, and autonomous. The instrument also has low power consumption and is non-intrusive. It was first calibrated during an in situ experiment on an ICOS (Integrated Carbon Observation System) site for several days, then used in two experiments with several balloon flights of up to 30 km altitude in the Reims region of France in 2017–2018 in collaboration with Météo-France CNRM (Centre National de Recherches Météorologiques). This paper shows the valuable interest of the data measured by the AMULSE (Atmospheric Measurements by Ultra-Light Spectrometer) instrument during the APOGEE (Atmospheric Profiles of Greenhouse Gases) measurement experiment, specifically for the vertical profiles of CO2 and CH4, measurements of which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: the validation of simulations of infrared satellite observations, evaluating the quality of chemical profiles from chemistry transport models (CTMs) and evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) instruments performed in operational mode for numerical weather prediction (NWP) by the radiative transfer model (RTM) RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) are of good quality. We also show that the MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) and CAMS (Copernicus Atmospheric Monitoring Service) CTMs modelled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to MOCAGE. Finally, the measured in situ ozone profiles allowed us to show the good quality of the retrieved ozone profiles by assimilating ozone-sensitive infrared spectral radiances from the IASI and CrIS.

A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS

Hyperspectral data have important research and application value in the fields of meteorology and remote sensing. With the goal of improving retrievals of atmospheric temperature profiles, this paper outlines a novel temperature channel selection method based on singular spectrum analysis (SSA) for the Geostationary Interferometric Infrared Sounder (GIIRS), which is the first infrared sounder operating in geostationary orbit. The method possesses not only the simplicity and rapidity of the principal component analysis method, but also the interpretability of the conventional channel selection method. The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum (700–1130 cm−1), and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence. At 700–780 cm−1, the channels selected using our method perform better than IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) temperature channels when used as inputs to the neural network retrieval model.

Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

Characterization of the Observational Covariance Matrix of Hyper-Spectral Infrared Satellite Sensors Directly from Measured Earth Views

The observational covariance matrix, whose diagonal square root is currently named radiometric noise, is one of the most important elements to characterize a given instrument. It determines the precision of measurements and their possible spectral inter-correlation. The characterization of this matrix is currently performed with blackbody targets of known temperature and is, therefore, an output of the calibration unit of the instrument system. We developed a methodology that can estimate the observational covariance matrix directly from calibrated Earth-scene observations. The technique can complement the usual analysis based on onboard blackbody calibration and is, therefore, a useful back up to check the overall quality of the calibration unit. The methodology was exemplified by application to three satellite Fourier transform spectrometers: IASI (Infrared Atmospheric Sounder Interferometer), CrIS (Cross-Track Infrared Sounder), and HIRAS (Hyperspectral Infrared Atmospheric Sounder). It was shown that these three instruments are working as expected based on the pre-flight and in-flight characterization of the radiometric noise. However, for all instruments, the analysis of the covariance matrix reveals extra correlation among channels, especially in the short wave spectral regions.

Ammonia measurements from space with the Cross-track Infrared Sounder: characteristics and applications

Abstract. Despite its clear importance, the monitoring of atmospheric ammonia, including its sources, sinks, and links to the greater nitrogen cycle, remains limited. Satellite data are helping to fill the gap in monitoring from sporadic conventional ground- and aircraft-based observations to better inform policymakers and assess the impact of any ammonia-related policies. Presented is a description and survey that demonstrate the capabilities of the Cross-track Infrared Sounder (CrIS) ammonia product for monitoring, air quality forecast model evaluation, dry deposition estimates, and emission estimates from an agricultural hotspot. For model evaluation, while there is a general agreement in the spatial allocation of known major agricultural ammonia hotspots across North America, the satellite observations show some high-latitude regions during peak forest fire activity often have ammonia concentrations approaching those in agricultural hotspots. The CrIS annual ammonia dry depositions in Canada (excluding the territories) and the US have average and annual variability values of ∼0.8±0.08 and ∼1.23±0.09 Tg N yr−1, respectively. These satellite-derived dry depositions of reactive nitrogen from NH3 with NO2 show an annual ratio of NH3 compared to their sum (NH3+NO2) of ∼82 % and ∼55 % in Canada and the US, respectively. Furthermore, we show the use of CrIS satellite observations to estimate annual and seasonal emissions near Lethbridge, Alberta, Canada, a region dominated by high-emission concentrated animal feeding operations (CAFOs); the satellite annual emission estimate of 37.1±6.3 kt yr−1 is at least double the value reported in current bottom-up emission inventories for this region.

Comparison of the MODIS and VIIRS Thermal Emissive Band Radiometric Calibration

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) are major instruments for Earth science observations. Nearly 40 MODIS scientific products and a wide range of VIIRS environmental data records are produced using their global observations. The consistency of the MODIS and VIIRS calibrated data is important for the study of Earth science. This article assesses the calibration consistency of the Aqua MODIS and VIIRS thermal emissive band (TEB) data. To remove the impact of the mismatched relative spectral response (RSR) on the comparisons, the simultaneous nadir observation data from the cross-track infrared sounder (CrIS) and the infrared atmospheric sounding interferometer (IASI) are used as references in two different methods to independently verify the consistency. The comparisons of the MODIS and VIIRS TEB calibrated data show that the brightness temperature (BT) differences for comparable bands between MODIS and VIIRS are in general within 0.2 K for BT larger than 230 K. The differences of all comparable MODIS and VIIRS TEBs are consistent over time. MODIS measurements agree better with N20 VIIRS than with Suomi National Polar-orbiting Partnership (S-NPP) VIIRS. The S-NPP VIIRS measurements are higher than N20 VIIRS, within 0.1 K for long-wave infrared (LWIR) bands and 0.3 K for band M13.