Successes and challenges of factor analysis/target transformation application to visible-to-near-infrared hyperspectral data
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J.D. Tarnas↗
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A Nowcasting Approach for Low-Earth-Orbiting Hyperspectral Infrared Soundings within the Convective Environment
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54 ENVIRONMENTAL SCIENCES↗
Impacts of an Early Morning Low Earth Orbit Observing Platform in A Future Global Observing Network Scenario
Significant changes to the global observing network are expected in the coming decades including the launch of a global ring of geostationary satellites and a reduction in the number of low earth orbit (LEO) platforms. It is anticipated that there may be a gap in the LEO coverage between the planned mid-morning and early afternoon orbits. Here, the utility of an early morning LEO orbit for numerical weather prediction is considered using an observing system simulation experiment (OSSE). A global observing network with two LEO platforms including microwave and hyperspectral infrared instruments and three geostationary hyperspectral infrared platforms is considered for a future baseline scenario. Two instruments, a microwave radiometer modeled on the Advanced Technology Microwave Sounder (ATMS) and a hyperspectral infrared radiometer modeled on the Cross-track Infrared Sounder (CrIS), are tested both individually and in conjunction on a new early morning orbit in addition to the future baseline scenario. The microwave instrument is found to have beneficial impacts for up to 4–7 days in the medium range forecast period with beneficial impacts for the infrared instrument for up to 3–5 days. Short-range forecast impacts estimated with Forecast Sensitivity Observation Impacts (FSOI) over the conterminous United States for the early morning orbit are somewhat weaker than for the same instruments in the early afternoon orbit due to the orbital passage being coincident with rawinsondes while the afternoon orbit is coincident with the minima of both rawinsondes and aircraft.
Nikki C Prive↗
Principle Component Analysis of AIRS and CrIS Data
Synthetic Eigen Vectors (EV) used for the statistical analysis of the PC reconstruction residual of large ensembles of data are a novel tool for the analysis of data from hyperspectral infrared sounders like the Atmospheric Infrared Sounder (AIRS) on the EOS Aqua and the Cross-track Infrared Sounder (CrIS) on the SUOMI polar orbiting satellites. Unlike empirical EV, which are derived from the observed spectra, the synthetic EV are derived from a large ensemble of spectra which are calculated assuming that, given a state of the atmosphere, the spectra created by the instrument can be accurately calculated. The synthetic EV are then used to reconstruct the observed spectra. The analysis of the differences between the observed spectra and the reconstructed spectra for Simultaneous Nadir Overpasses of tropical oceans reveals unexpected differences at the more than 200 mK level under relatively clear conditions, particularly in the mid-wave water vapor channels of CrIS. The repeatability of these differences using independently trained SEV and results from different years appears to rule out inconsistencies in the radiative transfer algorithm or the data simulation. The reasons for these discrepancies are under evaluation.
infrared↗
Development of a 4-15 micron Infrared GaAs Hyperspectral QWlP Imager (HQI)
A viewgraph presentation on the development of a 4-15 micron Infrared GaAs Hyperspectral QWIP Imager (HQI) is provided. The topics include: 1) Background and Project Goals; 2) Instrument Overview; 3) Subassembly Description; 4) Operating Conditions; 5) Current System Performance Status; and 6) Near-Term Improvements.
Jhabvala, M.↗
Channel alignment and radiometry in hyperspectral atmospheric infrared sounders
The Atmospheric Infrared Sounder (AIRS) is a hyper-spectral infrared sounder which covers the 3.7 to 15,4 micron region with 2378 spectral channels. The AIRS instrument specification called for spatial co-registration of all channels to better than 2% of the field of view. Pre-launch testing confirmed that this requirement was met, since the standard deviations in the centroids was about 1% of the 13.5 km IFOV in scan and 3% in track. Detailed analysis of global AIRS data show that the typical scene gradient in 10 micron window channels is about I .3K/km rms. The way these gradients, which are predominantly caused by clouds, manifest themselves in the data depends on the details of the instrument design and the way the spectral channels are used in the data analysis, AIRS temperature and moisture retrievals use 328 of the 2378 channels from 17 independent arrays. As a result, the effect of the boresight misalignment averages to zero mean. Any increase in the effective noise is less than 0.2K. Also, there is no discernable performance degradation of products at the 45 km spatial resolution in the presence of partially cloudy scenes with up to 80% cloudiness. Single pixel radiometric differences between channels with boresight alignment differences can be appreciable and can affect scientific investigations on a single 15km footprint scale, particularly near coastlines, thunderstorms and surface emissivity inhomogeneities.
sounders↗
Ambient Performance Testing of the CubeSat Infrared Atmospheric Sounder (CIRAS)
Hyperspectral infrared measurements of Earth’s atmosphere from space have proven their value for weather forecasting, climate science and atmospheric composition. The CubeSat Infrared Atmospheric Sounder (CIRAS) instrument will demonstrate a fully functional infrared temperature, water vapor and carbon monoxide sounder in a CubeSat sized volume for at least an order of magnitude lower cost than legacy systems. Design for a CubeSat significantly reduces cost of access to space and enables flight in a constellation to reduce revisit time and enable new measurements including 3D winds. A technology demonstration of CIRAS is currently under development at JPL. The effort has completed integration and ambient testing of a high fidelity brassboard, complete with the flight configured optics assembly developed by Ball Aerospace with a JPL Immersion Grating and Black Silicon Entrance Slit. The brassboard includes a flight-configured High Operating Temperature Barrier Infrared Detector (HOT-BIRD) mounted in an Integrated Dewar Cryocooler Assembly (IDCA), enabling testing in the ambient environment. Ambient testing included radiometric testing of the system to characterize the instrument operability and NEdT. Spatial testing was performed to characterize the system line spread function (LSF) in two axes and report FWHM of the LSF. Spectral testing involved an air path test to characterize the spectral/spatial transformation matrix, and an etalon was used to measure the Spectral Response Functions (SRFs). Results of the testing show the CIRAS performs exceptionally well and meets the key performance required of the system. The end result of testing is the CIRAS instrument now meets TRL 4 with confidence in a brassboard configuration ready for thermal vacuum (TVac) testing necessary to achieve TRL 5 for the system.
Wilson, Robert C.↗
Using OSSEs to Evaluate the Impacts of Geostationary Infrared Sounders
An observing system simulation experiment (OSSE) was performed to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary orbit on numerical weather prediction, with a focus on the proposed sounder on board the Geostationary Extended Observations (GeoXO) program’s central satellite. Infrared sounders on a geostationary platform would fill several gaps left by IR sounders on polar-orbiting satellites, and the increased temporal resolution would allow the observation of weather phenomena evolution. The framework for this OSSE was the Global Modeling and Assimilation Office (GMAO) OSSE system, which includes a full suite of meteorological observations. The experiment additionally assimilated four identical IR sounders from geostationary orbit to create a “ring” of vertical profiling observations. Based on the experimentation, assimilation of the IR sounders provided a beneficial impact on the analyzed mass and wind fields, particularly in the tropics, and produced an error reduction in the initial 24–48 h of the subsequent forecasts. Specific attention was paid to the impact of the GeoXO Sounder (GXS) over the contiguous United States (CONUS) as this is a region that is well-observed and as such difficult to improve. The forecast sensitivity to observation impact (FSOI) metric, computed across all four synoptic times over the CONUS, reveals that the GXS had the largest impact on the 24-h forecast error of the assimilated hyperspectral infrared satellite radiances as measured using a moist energy error norm. Based on this analysis, the proposed GXS has the potential to improve numerical weather prediction globally and over the CONUS.
OSSE↗
Status and Progress of All-Sky Hyperspectral IR Radiance Assimilation in GEOS
The majority of hyperspectral infrared radiance observations that peak in the lower and mid-troposphere have been excluded from the assimilation in the operational GEOS by the cloud detection scheme. The significance and challenges of assimilating these cloud-affected observations have attracted the attention of the research community, encouraged by the progress in utilizing all-sky microwave radiances affected by clouds and precipitation in many data assimilation systems, including GEOS. While the Global Modeling and Assimilation Office (GMAO) has made significant progress on the assimilation of cloud-cleared infrared radiances, this parallel study marks the first effort to directly assimilate cloud-affected hyperspectral infrared radiances in the GEOS. This initial study is focused on CrIS-FSR wa:wqter-vapor channels. The capability of the GEOS model and CRTM in simulating infrared radiances affected by clouds has been evaluated, and the all-sky brightness temperature Jacobians with respect to temperature, specific humidity, and hydrometeors are examined closely in varying cloud conditions. With hydrometeor control variables in the GEOS, several important aspects of all-sky IR radiance assimilation are investigated. The symmetric cloud effects are assessed for their robustness when used as the cloud proxy in observation error modeling that incorporates the inter-channel correlations with error situation dependency on the amount of cloud. The effective radius, bias correction and quality control processes will also be adapted to accommodate modifications necessary for all-sky infrared assimilation before comprehensive four-dimensional ensemble-variational(4dEnVar) experiments are conducted to assess their impacts on the analysis and forecast performance of GEOS.
Wei Gu↗
A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data
Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.
54 ENVIRONMENTAL SCIENCES↗
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data
Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.
47 OTHER INSTRUMENTATION↗
In-Scene Atmospheric Characterization and Compensation in Hyperspectral Thermal Infrared Images
Compensation of thermal infrared radiometric measurements for atmospheric absorption and emission is one of the main factors limiting the accurate estimation of land surface temperatures and emissivities today.
Compensation thermal infrared radiometric measurem↗
Global Daily Atmospheric State Profiles from the Atmospheric Infrared Sounder
The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the EOS Aqua Spacecraft, launched on May 4, 2002. AIRS has 2378 infrared channels ranging from 3.7 (micro)m to 15.4 (micro)m and a 13.5 km footprint. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles, clouds, dust and trace gas amounts for CO2, CO, SO2, O3 and CH4.[1] AIRS data are used for weather forecasting and studies of global climate change. The AIRS is a 'facility' instrument developed by NASA as an experimental demonstration of advanced technology for remote sensing and the benefits of high resolution infrared spectra to science investigations.
sounder↗
Hyperspectral Sounder Products: SiFSAP and ClimFiSP
Infrared hyperspectral sounders have demonstrated to provide very high value information for a range of weather and climate applications. Traditional retrieval algorithms of hyperspectral sounders use the ‘cloud clearing’ methodology in the retrieval process due to the lack of means to account for cloud scattering effect. The corresponding Level-2 data products are therefore limited by two major factors: the degradation of spatial resolution as compared with the native resolution of the instruments and the lack of ‘radiance closure’ that is critically needed for climate trend studies. The single field-of-view (FOV) sounder atmospheric products (SiFSAP) algorithm has been developed to supplement existing operational products. The SiFSAP algorithm uses the principal component based radiative transfer model (PCRTM) that is ultra-fast and has been extensively validated for the cloud scattering simulation capability. The cloud properties are physically retrieved along with profiles of temperature, moisture, and trace gases of interest, surface properties simultaneously by fitting single FOV spectral measurements. Therefore, SiFSAP ensure the radiometric consistency between the retrieved geophysical properties and the top-of-atmosphere (TOA) spectral measurements. This presentation provides a general introduction of SiFSAP and details about the physical retrieval algorithm. Also introduced here is a derivative data product of SiFSAP, the climate fingerprinting Sounder Products (ClimFiSP). ClimFiSP include the space-time averaged properties of key climate variables that are derived from the space-time averaged radiances via the spectral fingerprinting scheme. The spectral fingerprinting method allows a low latency data update on ClimFiSP following any potential calibration update on satellite measurements. ClimFiSP also include the rigorously defined uncertainty characterization for trends or anomalies of climate variables, being critical for the construction of long-term climate data record. Both SiFSAP and ClimFiSP will be available to users through NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).
Wan Wu↗
Tropical SNO Comparisons of AIRS and CrIS Calibration for Windows
AIRS (Atmospheric Infrared Sounder) on EOS (Earth Observing System)-Aqua and CrIS (Cross-track Infrared Sounder) on Suomi NPP (National Polar-orbiting Partnership) are two hyperspectral infrared sounders with similar capabilities and orbits, so there is a great opportunity to compare their absolute calibration while they are both in orbit. This insures that long-term climate record can be created by concatenating the two instrument records. There are significant differences in instrument architecture which may lead to subtle differences and complicate attempts to combine the records. We use Tropical Simultaneous Nadir Observations (TSNOs), cases where both instruments are looking nearly at the same place at the same time, to explore the differences. Due to the presence of cold clouds and clear hot desert surface, the data cover a brightness temperature range from 190 degrees Kelvin to 340 degrees Kelvin. We concentrate on the differences between the mean of the two instruments using atmospheric window channels as function of brightness temperature in 20-degree-Kelvin wide bins. With the currently available AIRS and CrIS official calibrated data, radiometric differences as large as 0.3 degrees Kelvin are seen at the extreme temperatures. These differences may be reduced in future releases of the AIRS and CrIS calibration.
CrIS↗
Global and Regional Seasonal Variability of Mid-Tropospheric CO2 as Measured by the Atmospheric Infrared Sounder (AIRS)
The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the Earth Observing System (EOS) Aqua Spacecraft, launched on May 4, 2002 into a near polar sun-synchronous orbit. AIRS has 2378 infrared channels ranging from 3.7 ?m to 15.4 ?m and a 13.5 km footprint at nadir. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts for CO2, CO, SO2, O3 and CH4. AIRS CO2 climatologies have been shown to be useful for identifying anomalies associated with geophysical events such as El Nino-Southern Oscillation or Madden-Julian oscillation. In this study, monthly representations of mid-tropospheric CO2 are constructed from 10 years of AIRS Version 5 monthly Level 3 data. We compare the AIRS mid-tropospheric CO2 representations to ground-based measurements from the Scripps and National Oceanic and Atmospheric Administration Climate Modeling and Diagnostics Laboratory (NOAA CMDL) ground networks to better understand the phase lag of the CO2 seasonal cycle between the surface and middle troposphere. Results show only a small phase lag in the tropics that grows to approximately two months in the northern latitudes.
satellite↗
Large Scale Variability of Mid-Tropospheric Carbon Dioxide as Observed by the Atmospheric Infrared Sounder (AIRS) on the NASA EOS Aqua Platform
The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the EOS Aqua Spacecraft, launched on May 4, 2002. AIRS has 2378 infrared channels ranging from 3.7 microns to 15.4 microns and a 13.5 km footprint. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy, water vapor profiles (20%/2km), infrared cloud height and fraction, and trace gas amounts for CO2, CO, SO2, O3 and CH4 in the mid to upper troposphere. AIRS wide swath(cedilla) +/-49.5 deg , enables daily global daily coverage for over 95% of the Earth's surface. AIRS data are used for weather forecasting, validating climate model distribution and processes, and observing long-range transport of greenhouse gases. In this study, we examine the large scale and regional horizontal variability in the AIRS Mid-tropospheric Carbon Dioxide product as a function of season and associate the observed variability with known atmospheric transport processes, and sources and sinks of CO2.
carbon dioxide↗
Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison
The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.
AIRS↗