This work assesses the impact of uncertainties in atmospheric state knowledge on retrievals of carbon dioxide column amounts (XCO2) from laser differential absorption spectroscopy (LAS) measurements. LAS estimates of XCO2 columns are normally derived not only from differential absorption observations but also from measured or prior knowledge of atmospheric state that includes temperature, moisture, and pressure along the viewing path. In the case of global space-based monitoring systems, it is often difficult if not impossible to provide collocated in situ measurements of atmospheric state for all observations, so retrievals often rely on collocated remote-sensed data or values derived from numerical weather prediction (NWP) models to describe the atmospheric state. A radiative transfer-based simulation framework, combined with representative global upper-air observations and matched NWP profiles, was used to assess the impact of model differences on estimates of column CO2 and O2 concentrations. These analyses focus on characterizing these errors for LAS measurements of CO2 in the 1.57-μm region and of O2 in the 1.27-μm region. The results provide a set of signal-to-noise metrics that characterize the errors in retrieved values associated with uncertainties in atmospheric state and provide a method for selecting optimal differential absorption line pairs to minimize the impact of these noise terms.
Current surveillance systems operate in a highly dynamic environment in which large numbers of sensors on board
multiple platforms must cooperate in order to achieve overall mission success. In an attempt to maximize sensor
performance, today's sensors employ rudimentary or, in some cases, inflexible sensor tasking schemes. These
approaches are highly tuned to a specific scenario and geometry and are inflexible to changes in the mission,
environmental conditions, heterogeneous sensors, and different system architectures. As the complexity of the problem
space increases and new sensors become available, it is critical to have a sensor management scheme that is capable of
incorporating new environmental knowledge, new sensors and different systems approaches with minimal computational
impact on the overall system. Each system should develop an autonomous sensor tasking capability which factors in
global concerns within the complete distributed network of platforms and sensors. Moreover, tasking efficiency can be
improved by a highly developed understanding of sensor performance at each point in time. This can be achieved by
incorporating the impact of problem geometry - sensor location, track object type and view angle - and weather
phenomena, such as clouds, aerosols, turbulence and sun glint.
This paper describes our approach for simultaneously optimizing sensor resource management, surveillance objectives,
and atmospheric transmission of signals while minimizing sensor and environmental noise. Our approach uses a genetic
algorithm to evolve a population of sensor tasking assignments through constantly-updating track locations, weather
conditions, and lighting conditions. Preliminary studies demonstrate encouraging improvements in sensor management
performance. We will present results from our preliminary studies and discuss a path forward for our technology.
Testing MODTRATM5 (MOD5) capabilities against NASA satellite
state-of-the-art radiance and irradiance
measurements has recently been undertaken. New solar data have been acquired from the SORCE satellite team,
providing measurements of variability over solar rotation cycles, plus an ultra-narrow calculation for a new solar
source irradiance, extending over the full MOD5 spectral range. Additionally, a MOD5-AIRS analysis has been
undertaken with appropriate channel response functions. Thus, MOD5 can serve as a surrogate for a variety of
perturbation studies, including two different modes for including variations in the solar source function, Io: (1) ultra-high
spectral resolution and (2) with and without solar rotation. The comparison of AIRS-related MOD5
calculations, against a suite of 'surrogate' data generated by other radiative transfer algorithms, all based upon
simulations supplied by the AIRS community, provide validation in the Long Wave Infrared (LWIR). All ~2400
AIRS instrument spectral response functions (ISRFs) are expected to be supplied with MODTRANTM5. These
validation studies show MOD5 replicates line-by-line (LBL) brightness temperatures (BT) for 30 sets of
atmospheric profiles to approximately -0.02°K average offset and <1.0°K RMS.
The CrIS and ATMS instruments on NPOESS will provide high quality temperature and moisture profiles greatly surpassing the capabilities of current operational satellite sounders. However, performance of these systems continues to be a challenge in cloudy scenes. The VIIRS sensor on NPOESS will provide much higher spatial resolution data than that of CrIS, with some overlap in spectral coverage. This sub-pixel information can be used to enhance the retrieval performance in a number of ways. This paper presents a potential technique to improve performance of the CrIS temperature profile retrievals by incorporating data from VIIRS. Improvements include more accurate retrievals in partly cloudy situations, better effective spatial resolution and more robust quality control diagnostics. We provide an overview of our approach and show examples utilizing data from the EOS-Aqua AIRS, AMSU and MODIS sensors.
The Optimal Spectral Sampling (OSS) method models band averaged radiances as weighted sums of monochromatic radiances. The method is fast and accurate and has the advantage over other existing techniques that it is directly applicable to scattering atmospheres. Other advantages conferred by the method include flexible handling of trace species and ability to select variable species at run time without having to retrain the model, and the possibility of large speed gains by specializing the model for a particular application. The OSS method is used in the CrIS and CMIS retrieval algorithms and it is currently being implemented in the Joint Center for Satellite Assimilation (JCSDA) Community Radiative Transfer Model (CRTM). A version of OSS is currently under development for direct inclusion within MODTRANTM, as an alternative to the current band models. This paper discusses the OSS interface to MODTRANTM, presents model results, and identifies new developments applicable to narrowband and broadband radiative transfer modeling across the spectrum and the training of OSS for scattering atmospheres.
The Airborne Imaging Radiometer (AIR) is a small, low mass and power sensor being developed by Ball Aerospace for studies of atmospheric and surface processes. AIR is designed to be a well calibrated, high spatial resolution multispectral imaging sensor. It has been proposed to be built and flown as part of a larger compliment of instrumentation for the High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) under development by the National Science Foundation. The sensor design as currently envisaged will fit within the wing pod 18-inch diameter cylindrical envelope. The sensor is configured as a pushbroom-imager with an 8-km swath width at the nominal 12.5-km flight altitude of HIAPER. It will provide 50-meter resolution thermal imagery in ten spectral bands for the determination of surface and cloud top temperature, cirrus cloud properties, and layer averaged distributions of atmospheric temperature, water vapor and column ozone. A companion visible camera provides 25-meter imagery to aid in the analysis of the infrared imagery. AIR is designed around a Raytheon 320x240 element, 25 um pitch uncooled microbolometer detector array. This technology has advantages over other infrared detector technologies for airborne applications because it does not require a mechanical cryocooler or liquid nitrogen-filled dewar to achieve the necessary longwave response simplifying optical, thermal and mechanical design.
Polarimetric measurements in the VIS/NIR spectral region improve aerosol microphysical and compositional retrievals. The retrieval approaches exploit the unique polarimetric signatures of aerosols as function of scattering angle, thereby driving the requirement for data collection over a large range of scattering angles. The scattering angle coverage is a function both of the instrument/sun/target geometry and the instrument architectural approach toward acquiring multi-angular data. These two functions are important aspects of a spaceborne, multi-angular polarimetric mission. The instrument design must also consider the impact of retrieval error arising from aerosol spatial variability. For a single-pixel scanning architecture, both the pixel separation as a function of earth rotation beneath the spacecraft and the pixel growth with increasing scan angle can result in significant retrieval errors due to aerosol spatial variability. We have investigated the impact of aerosol spatial inhomogeneity on the performance of a single-pixel, along-track scanning, multi-angular polarimetric instrument operating in a low-earth orbit (LEO) such as the EOS Aqua orbit of 705 km. Possible mitigation strategies to reduce the impact of the spatial inhomogeneity on aerosol property retrieval performance are also reviewed.
The Interactive Algorithm Tool Box (IATB) is a multi-layered architecture designed to aid in the rapid implementation and end-to-end assessment of algorithms for estimating environmental parameters from remote sensing data. This architectural scheme employs a layered approach. The primary layer provides common methods for accessing sensor, ancillary and auxiliary data as well as user configurable parameters. The second layer provides a standard set of tools that can be used in the development of the target remote sensing algorithm and its supporting simulation tools. One of the primary tools provides a self-consistent mechanism for performing radiative transfer calculations over a broad spectral range from the Microwave to the Infrared/Visible. This toolbox also provides standard mechanisms for building first-order sensor models. The final layer provides a platform independent "wrapper" for integrating the target algorithm and its simulation tools with a set of standard and custom analysis tools. This layer provides an end-to-end product that can be used for extended analysis and calibration/validation with either simulated or "real" data.
The testbed architecture has been applied to instruments measuring in spectra from the visible to the microwave. It has been employed during the development of algorithms for existing remote sensing systems (e.g. AMSU and AIRS) as well as sensor suites that will be flown on next generation satellites (e.g. NPOESS and GOES). This paper describes the IATB architecture and presents the implementation for several applications.
Optimal Spectral Sampling (OSS) is a new approach to radiative transfer modeling which addresses the need for algorithm speed, accuracy, and flexibility. The OSS technique allows for the rapid calculation of radiance for any class of multispectral, hyperspectral, or ultraspectral sensors at any spectral resolution operating in any region from microwave through UV wavelengths by selecting and appropriately weighting the monochromatic points that contribute over the sensor bandwidth. This allows for the calculation to be performed at a small number of spectral points while retaining the advantages of a monochromatic calculation such as exact treatment of multiple scattering and/or polarization. The OSS method is well suited for remote sensing applications which require extremely fast and accurate radiative transfer calculations: atmospheric compensation, spectral and spatial feature extraction, multi-sensor data fusion, sub-pixel spectral analysis, qualitative and quantitative spectral analysis, sensor design and data assimilation. The OSS was recently awarded a U.S. Patent (#6,584,405) and is currently used as part of the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) CrIS, CMIS, and OMPS-IR environmental parameter retrieval algorithms. This paper describes the theoretical basis and development of OSS and shows examples of the application and validation of this technique for a variety of different sensor types and applications.
The Cross-track Infrared and Microwave Sounder Suite (CrIMSS) consists of a microwave radiometer and an infrared interferometer and is scheduled to fly on the NPP and NPOESS satellites. The sensors are designed for the accurate measurement of atmospheric pressure, temperature and moisture profiles. This paper presents an overview of the CrIMSS sensors and the retrieval algorithm. Validation of the algorithm with current satellite sounder data will also be presented.
An instrument concept for an Imaging Multi-Order Fabry-Perot Spectrometer (IMOFPS) has been developed for measuring tropospheric carbon monoxide (CO) from space. The concept is based upon a correlation technique similar in nature to multi-order Fabry-Perot (FP) interferometer or gas filter radiometer techniques, which simultaneously measure atmospheric emission from several infrared vibration-rotation lines of CO. Correlation techniques provide a multiplex advantage for increased throughput, high spectral resolution and selectivity necessary for profiling tropospheric CO. Use of unconventional multilayer interference filter designs leads to improvement in CO spectral line correlation compared with the traditional FP multi-order technique, approaching the theoretical performance of gas filter correlation radiometry. In this implementation, however, the gas cell is replaced with a simple, robust solid interference filter. In addition to measuring CO, the correlation filter technique can be applied to measurements of other important gases such as carbon dioxide, nitrous oxide and methane. Imaging the scene onto a 2-D detector array enables a limited range of
spectral sampling owing to the field-angle dependence of the filter transmission function. An innovative anamorphic optical system provides a relatively large instrument field-of-view for imaging along the orthogonal direction across the detector array. An important advantage of the IMOFPS concept is that it is a small, low mass and high spectral resolution spectrometer having no moving parts. A small, correlation spectrometer like IMOFPS would be well suited for global observations of CO2, CO, and CH4 from low Earth or regional observations from Geostationary orbit. A prototype instrument is in development for flight demonstration on an airborne platform with potential applications to atmospheric chemistry, wild fire and biomass burning, and chemical dispersion monitoring.
Spectrally uniform treatment of the atmospheric radiative transfer (RI) problem has been approached through two different techniques - very high resolution line-by-line (LBL) algorithms and lower resolution band models (BM). Each has its advantages and specific applications. However, if commonality and validation of a specific pair of RI approaches is to be mutually maintained, then these codes must be continually reevaluated against both measurements and other models.
A sounder using a high-finesse Fabry-Perot etalon offers substantial potential to extract temperature profiles from the stratosphere atmosphere. The multi-order etalon sounder (MOES) is such an instrument. Its extremely high spectral resolution makes it possible to selectively observe emission at the very line centers and near shoulders of individual CO2 lines. However, the radiances at the centers of these lines can contain large non-LTE contributions originating at much higher altitudes. At high altitudes, the non-equilibrium absorption and re-radiation of solar illumination enhances the vibrational temperature compared to the kinetic temperature. The kinetic temperature profile can only be estimated from the complex line radiance spectrum by relating the observed vibrational temperature to the probable kinetic temperature. The effective separation of layer contributions requires that the instrument be designed so that (1) its dynamic range preserves the much larger range of expected radiances and (2) its spectral response function is very well known so that the line wing radiances can be accurately determined in the presence of large line center radiances. This paper discusses the retrieval of stratospheric temperatures in terms of typical measured radiance covariances, the required solar illumination model and a sensor suitable for routine temperature profile retrieval.
FASE is a line-by-line (LBL) atmospheric radiation code, grounded in the original USAF FASCODE (Fast Atmospheric
Signature Code) line shape decomposition algorithm. The Department of Energy Atmospheric Radiation Measurement
(ARM) Program and the AF/PL Geophysics Directorate jointly supported FASE which now envelops both agencies'
important upgrades. ARM's LBLRTM (LBL Radiative Transfer Model authored by S.A. Clough and P.D. Brown of AER,
Inc.) expanded the FASCODE algorithms to specifically address scientific and coding issues of particular concern to the
climate community including: H20 and C02 continua, lineshape, radiance algorithms, sampling, vectorization, array
parameterization, spectral ranges and inputloutput modes. These features have then been recombined with FASCODE non-
LTE and laser options, plus shared common elements from MODTRAN (Moderate Resolution Transmittance Model, a
2 cm- band model) evolution. These include a new solar irradiance and UV cross sections. Examples of the feedback and
validation between FASE and MODTRAN3 will be presented.
The Optical Physics Division of the Phillips Laboratory with support from the DoE Atmospheric Radiation Measurement (ARM) Program is developing a state-of-the-art line-by- line atmospheric radiative transfer model as the successor by FASCODE. The goal of this project is to create a computationally efficient model which contains the most up-to-date physics. The new model, known as FASCODE for the Environment, or `FASE', will combine the best features of FASCODE and LBLRTM, the DoE's standard radiative transfer model. FASE will also contain new features such as new cross-sections for heavy molecules, an improved solar irradiance model, and improvements to the Schumann-Runge bands and continuum. The code will be optimized for vectorized and/or parallel processing, put under configuration control for easy maintenance, and will be structured into separate modules for each function: atmospheric profiles, layer optical properties, radiative transfer, multiple- scattering, etc. This modular structure will allow for increased flexibility and easy customization of the code for specialized applications, such as a forward model for iterative inversion algorithms. Ease-of-use will be enhanced with improved input control structures and documentation to accommodate the needs of novice and advanced users. This paper addresses changes which have been made to FASCODE and LBLRTM to create FASE, and gives an overview of the modular structure and its capabilities.
Remote sensing of major and minor constituents in the earth's atmosphere is of great importance to the study of climate and global change. Because much of remote sensing involves placing instrumentation in environments that are not easily accessible, such as balloons, spacecraft, or remote field stations, it is usually necessary that the instrumentation be compact, lightweight, and rugged. This paper describes the development of a new type of remote sensing instrument we have chosen to call the multiplex Fabry-Perot interferometer (MFPI). We present atmospheric spectra obtained with our working prototype instrument. The MFPI is a Fabry-Perot interferometer for which the etalon plate separation is changed over a large optical distance during a measurement. When the resulting interferogram is Fourier transformed the multiple reflections within the etalon cavity produce a spectrum analogous to that which would be produced by an array of Michelson interferometers. However, for high spectral resolution measurements the scan distance required by the MFPI is much less than for the comparable Michelson. The MFPI will be ideal for remote sensing applications where weight, size, and mechanical reliability are primary considerations.
We are developing a compact, rugged, high-resolution remote sensing instrument with wide spectral scanning capabilities. This relatively new type of instrument, which we have chosen to call the Fourier-Transform Fabry-Perot Interferometer (FT-FPI), is accomplished by mechanically scanning the etalon plates of a Fabry-Perot interferometer (FPI) through a large optical distance while examining the concomitant signal with a Fourier-transform analysis technique similar to that employed by the Michelson interferometer. The FT-FPI will be used initially as a ground-based instrument to study near-infrared atmospheric absorption lines of trace gases using the techniques of solar absorption spectroscopy. Future plans include modifications to allow for measurements of trace gases in the stratosphere using spectral lines at terahertz frequencies.
A new type of Fabry-Perot Interferometer (FPI) which exploits the multiplex advantage is presented. The Multiplex Fabry-Perot Interferometer (MFPI) has one etalon plate that is fixed while the other is moved over a large optical distance thus producing an interferogram similar to that obtained with a Michelson Interferometer. The result is an instrument which has the ability to examine large spectral regions at high resolution using the inversion techniques normally applied to a Michelson Interferometer while retaining the small size of an FPI. The MFPI is a compact rugged high resolution instrument that will be useful for the remote sensing of minor species.
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