The NESDIS Center for Satellite Applications and Research (STAR), formerly ORA, Office of Research and Applications, consists of three research and applications divisions that encompass satellite meteorology, oceanography, climatology, and cooperative research with academic institutions. With such a wide background of talent, and a charter to develop operational algorithms and applications, STAR scientists develop satellite-derived land, ice, ocean, and atmospheric environmental data products in support of all of NOAA’s mission goals. In addition, in close association with the Joint Center for Satellite Data Assimilation, STAR scientists actively work with the numerical modeling communities of NOAA, NASA, and DOD to support the development of new methods for assimilation of satellite data. In this new era of observations from many new satellite instruments, STAR aims to effectively integrate these data into multi-platform data products for utilization by the forecast and applications communities. Much of our work is conducted in close partnerships with other agencies, academic institutes, and industry. In order to support the nearly 400 current satellite-derived products for various users on a routine basis from our sister operations office, and to evolve to future systems requires an ongoing strategic planning approach that maps research and development activities from NOAA goals to user requirements. Since R&D accomplishments are not necessarily amenable to precise schedules, appropriate motivators and measures of scientific progress must be developed to assure that the product development cycle remains aligned with the other engineering segments of a satellite program. This article presents the status and results of this comprehensive effort to chart a course from the present set of operational satellites to the future.

The Virtual Laboratory for Education and Training in Satellite Matters (VL) was established to maximize the exploitation of meteorological satellite data across the globe. It is a collaborative effort joining four major operational meteorological satellite operators with six WMO "centers of excellence" for education and training in satellite meteorology. Those "centers of excellence" serve as the satellite-focused training resource for WMO Members. The VL continues to evolve and has been used successfully in a number of WMO sponsored training events since 2000.

Preparing for the next generation polar orbiting environmental satellite system, the NPOESS system is currently undergoing the design phase of the Program. The NPOESS Direct Readout Mission will service users in the field providing regional environmental coverage with two direct broadcast links, high rate data (HRD) at 20 Mbps and low rate data (LRD) at 3.88 Mbps. Contained within these two links are the suite of sensor data in order to generate 50 HRD and 29 LRD weather products via the NPOESS Field Terminal Segment software being developed for distribution to the weather community or by value added resellers.

In earlier work, NOAA’s Coastal Service Center in Charleston, S.C. reviewed current remote sensing and took a broader look at that technology applied to coastal resource management. They found 25 application areas and grouped them into 5 broader categories. In this paper we will explain some background and complexity of remote sensing when imaging in shallow water. This region is more complex than the deep ocean but there is synergy or opportunity to combine remote sensing measurements. Then we will summarize the 25 coastal areas of application with regard to spatial, spectral and temporal remote sensing needs including use of potential hyper-spectral sensors. Finally, we use the example of coral reef observations to explain the difficulty in trying to set remote sensing operational rules.

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.

The handling of satellite or airborne earth observation data for scientific applications minimally requires pre-processing to convert raw digital numbers into scientific units. However depending on sensor characteristics and architecture, additional work may be needed to achieve spatial and/or spectral uniformity. Standard higher level processing also typically involves providing orthorectification and atmospheric correction. Fortunately some of the computational tasks required to perform radiometric and geometric calibration can be decomposed into highly independent subtasks making this processing highly parallelizable. Such "embarrassingly parallel" problems provide the luxury of being able to choose between cluster or grid based solutions to perform these functions. Perhaps the most convenient solutions are grid-based, since most research groups making these kinds of measurements are likely to have access to a LAN whose spare computing resources could be non-obtrusively employed in a grid. However, since many higher level scientific applications of earth observation data might be composed of more highly interdependent subtasks, the parallel computing resources allocated for these tasks might also be made available for low level pre-processing as well. We look at two modules developed for our prototype data calibration processor for APEX, an airborne imaging spectrometer, which have been implemented on both a cluster and a grid leading us to be able to make observations and comparisons of the two approaches.

Instruments on the next generation operational GOES will generate an expected 50 times greater amount of data than their predecessors on today’s GOES. Part of this increase is due to the high-spectral resolution data from the Hyperspectral Environmental Suite (HES). The data increase led to the need to research the impact to the GOES satellite’s end-to-end communication system, and research compression of hyper-spectral sounder sensor data. The compression research, in mathematics includes, but is not limited to, wavelets, principal components, adaptive clustering, empirical mode decomposition. The high data rates require additional RF spectrum, in the congested 8025-8400 MHz band X-band. Congestion in the geo orbit X and L-bands mandate extensive efforts to control out-of-band RF emissions. The need for communication signal data quality with few errors (low BER) is essential for NOAA science use. Limited bandwidth and the need to be efficient in bandwidth use, force the consideration of bandwidth efficient modulations. These communication issues force the use of the latest technologies. The research has shown that GOES will be able to downlink all of the future sensor data, and could continue to be used as a communication satellite to distribute large volumes of its sensor information to U.S. and foreign users.

The compression of three-dimensional hyperspectral sounder data is a challenging task given its unprecedented size and nature. Vector quantization (VQ) is explored for the compression of this hyperspectral sounder data. The high dimensional vectors are partitioned into subvectors to reduce codebook search and storage complexity in coding of the data. The partitions are made by use of statistical properties of the sounder data in the spectral dimension. Moreover, the data is decorrelated at first to make it better suited for vector quantization. Due to the data characteristics, the iterative codebook generation procedure converges much faster and also leads to a better reconstruction of the sounder data. For lossless compression of the hyperspectral sounder data, the residual error and the quantization indices are entropy coded. The independent vector quantizers for different partitions make this scheme practical for compression of the large volume 3D hyperspectral sounder data.

Hyperspectral sounder data is used for retrieval of useful geophysical parameters which promise better weather prediction. It features two characteristics. First it is huge in size with 2D spatial coverage and high spectral resolution in the infrared region. Second it allows low tolerance of noise and error in retrieving the geophysical parameters where a mathematically ill-posed problem is involved. Therefore compression is better to be lossless or near lossless for data transfer and archive. Meanwhile medical data from X-ray computerized tomography (CT) or magnetic resonance imaging (MRI) techniques also possesses similar characteristics. It provides motivation to apply lossless compression schemes for medical data to the hyperspectral sounder data. In this paper, we explore the use of a wavelet-based lossless data compression scheme for the 3D hyperspectral data which uses in sequence a forward difference scheme, an integer wavelet transform, a Burrows-Wheeler transform and an arithmetic coder. Compared to previous work, our approach is shown to outperform the CALIC and 3D EZW schemes.

In this paper, we consider an application of the Empirical Mode Decomposition (EMD) introduced by Norden E. Huang in 1996 to the compression of 3D hyperspectral sounding data. The EMD is a new data analysis method which is based on expansion of the data in terms of Intrinsic Mode Functions (IMF). These IMFs are based on and derived from the data set. Since EMD adaptively represent the signal as a sum of "well behaved" amplitude/frequency modulated components, we found it very well suited for the whitening part of the compression scheme.

Video quality metrics can be used to optimize design of advanced geosynchronous remote sensors by providing a basis for comparing information content of video at different data rates for given video sequences and compression methods and to optimize operation of future remote sensing systems by testing and monitoring quality of data collected by these systems. This paper examines and compares video quality metrics in three broad categories: distortion-based metrics that provide objective performance measures, perception-based metrics that attempt to quantify differences in images that are visible to the human visual system and utility-based metrics that address video quality for specific applications. Candidate figures of merit for describing effects of data compression on quality of video sequences or animation derived from geosynchronous imagers are presented along with recommendations for future work.

The compression of hyperspectral sounder data is beneficial for more efficient archive and transfer given its large 3-D volume. Moreover, since physical retrieval of geophysical parameters from hyperspectral sounder data is a mathematically ill-posed problem that is sensitive to the error of the data, lossless or near-lossless compression is desired. This paper provides an update into applications of state-of-the-art 2D and 3D lossless compression algorithms such as 3D EZW, 3D SPIHT, 2D JPEG2000, 2D JPEG-LS and 2D CALIC for hyperspectral sounder data. In addition, in order to better explore the correlations between the remote spectral regions affected by the same type of atmospheric absorbing constituents or clouds, the Bias-Adjusted Reordering (BAR) scheme is presented which reorders the data such that the bias-adjusted distance between any two neighboring vectors is minimized. This scheme coupled with any of the state-of-the-art compression algorithms produces significant compression gains.

In the next decade, the volume of data produced by satellite-based remote sensing instruments will increase dramatically. The success of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments validates the decisions of government agencies to seek higher spectral and spatial resolution in the next generation of polar-orbiting and geosynchronous imagers, but transmitting the resulting high amounts of data to the Earth within constraints of available bandwidth will require new approaches to onboard data compression. In particular, the limitations of bandwidth will force greater use of lossy data compression, particularly in spectral channels with high spatial resolution, such as the reflective channels proposed for the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI), which will fly on GOES-R in 2012. In this study, we present analyses of the trade between two candidate lossy data compression algorithms, JPEG and JPEG-2000, for the encoding of reflective channel data from the ABI. These analyses include application to two types of real data: MODIS imagery and MODIS Airborne Simulator (MAS) imagery. The MODIS images are processed directly; the 50-m resolution MAS images are first run through a basic simulation of ABI spatial and radiometric response. In both cases, spectral channels corresponding to those that will be lossily compressed on ABI are available to support the performance trades between JPEG and JPEG-2000. The performance results are expressed in terms of peak signal to noise ratio (PSNR) and correlated noise, and they are placed in context with an assessment of the current technology readiness level (TRL) of the two standards.

During the past 20 years of NOAA operational polar satellites, it has become evident that a growing problem concerning their utilization in Climate and also Numerical Weather Prediction (NWP) applications are the systematic errors and uncertainties inherent in the satellite measurements. Similar arguments can be made for global radiosonde observations. These uncertainties are often larger than the sensitive signals and processes, that satellite and radiosonde measurements are designed to reveal, particularly in the realm of climate. Possible strategies to quantify and compensate for these problems include the analysis of satellite overlap data and/or available collocations of satellite and ground truth (radiosonde) observations. However, overlap observations are typically not available except in extreme polar regions and current sampling strategies for compiling collocated radiosonde and satellite observations are insufficient, further compounding the inherent uncertainties in the ground-truth radiosonde data. A Satellite Upper Air Network is proposed to provide reference radiosonde launches coincident with operational polar satellite(s) overpass. The SUAN consist of 36 global radiosonde stations sub-sampled from the Global Upper Air Network (GUAN), and is designed to provide a robust, global sample of collocated radiosonde and satellite observations conducive to the monitoring and validation of satellite and radiosonde observations. The routine operation of such a network in conjunction with operational polar satellites would provide a long-term of performance for critical observations of particular importance for climate. The following report presents a candidate network of 36 upper-air sites that could comprise a SUAN. Their selection along with the mutual benefit across the satellite, radiosonde, climate, numerical weather prediction (NWP) and radiative transfer (RT) model areas are discussed.

Lessons learned from the implementation, operation, and science processing from a number of successful NASA LaRC remote sensing experiments will be presented. Despite good flight heritage, well-known designs, and known data retrieval algorithms these experiments remain challenging. The authors will summarize common lessons learned from different project phases and discuss these lessons in the context of planned and future missions with emphasis on improving Level 1 data quality.

Three semi-autonomous atmospheric sensing systems were installed in the tropical western Pacific region. The first of these Atmospheric Radiation and Cloud Stations (ARCS) began operation in 1996. Each ARCS is configured as a system-of-systems since it comprises an ensemble of independent instrument systems. The ARCS collect, process, and transmit large volumes of cloud, solar and thermal radiation, and meteorological data to support climate studies and climate-modeling improvements as part of the U.S. Department of Energy’s Atmospheric and Radiation Measurement (ARM) Program. Data from these tropical ARCS stations have been used for satellite ground-truth data comparisons and validations, including comparisons for MTI and AQUA satellite data. Our experiences with these systems in the tropics led to modifications in their design. An ongoing international logistics effort is required to keep gigabytes per day of quality-assured data flowing to the ARM program’s archives. Design criteria, performance, communications methods, and the day-to-day logistics required to support long-term operations of ground-based remote atmospheric sensing systems are discussed. End-to-end data flow from the ARCS systems to the ARM Program archives is discussed.

Over the history of remote sensing, the complexity and volume of data sets have increased unremittingly. The complexity of the data sets has led to the need for more descriptive metadata. The volume, resulting from both increased precision and range in the data sets, has led to the need for efficient data and portable handling techniques. Further, the precision calls for greater detail in describing and documenting the manner in which the data was collected and calibrated. This paper describes one view of the optimal product design for NPOESS requirements.

To meet Naval needs for sensing of the global environment, the Office of Naval Research (ONR) and the Naval Research Laboratory (NRL) sponsor or carry out a variety of research programs using hyperspectral sensing. For ocean sensing, airborne and space-borne hyperspectral sensors are used to characterize the littoral environment with the aim of providing specification of ocean optical parameters including water clarity, diver visibility, bathymetry, bottom type and beach characterization. For the atmosphere, the Navy has interest in hyperspectral remote sensing from geosynchronous orbit. ONR interests include improved modeling of radiation transport in the atmosphere to infer high resolution profiles of wind, temperature and minor species and cloud characteristics. With sponsorship from Director Defense Research and Engineering (DDR&E), ONR is managing a Multidisciplinary University Research Initiative (MURI) to provide new models for use with geosynchronous data. In partnership with NASA, NOAA and the Air Force, ONR is promoting the flight of the Geosynchronous Imaging Fourier Transform Spectrometer-Indian Ocean METOC Imager (GIFTS-IOMI) program to obtain hyperspectral atmospheric imagery with high spatial, spectral and temporal resolution. For the space environment, NRL has flown a suite of experimental ultraviolet hyperspectral sensors to determine altitude profiles of the ionospheric electron density and upper atmospheric neutral density. The High Resolution Airglow/Aurora Spectroscopy (HIRAAS) experiment on the ARGOS satellite provided a proof of concept for a future series of hyperspectral ultraviolet space weather sensors the first of which has recently been launch on a DMSP weather satellite. ONR is sponsoring the development of a multispectral ultraviolet imager to take this capability to geosynchronous orbit.

Ice clouds have been identified as one of the most uncertain components in atmospheric research. In recent years, the atmospheric radiative transfer and remote sensing community has made a concerted effort to improve the characterization of cirrus clouds. A number of airborne and balloon-borne observations have demonstrated that cirrus clouds are essentially composed of nonspherical ice crystals with various habits (or shapes) and sizes. In this paper, we report on some recent progresses towards the computation of the single-scattering properties nonspherical ice crystals and the relevant applications to remote sensing and radiative transfer simulations. Specifically, we have developed a database of the optical properties of ice crystals at the infrared wavelengths. In conjunction with the application of the scattering database, we also developed a fast infrared transfer model under cirrus cloudy condition, which is applied to the retrieval of ice clouds from satellite-based infrared measurements.

This paper presents a conceptual approach toward the remote sensing of cirrus cloud particle size and optical depth using the degree of polarization and polarized reflectance associated with the first three Stokes parameters I, Q, and U for the 0.865 and 2.25 μm wavelengths. A vector line-by-line equivalent radiative transfer program including the full Stokes parameters based on the adding method was developed. The retrieval algorithm employs the steepest descent method in the form of a series of numerical iteration procedures to search for the simulated polarization parameters that best match the measured polarization parameters. Sensitivity studies were performed to investigate the behavior of phase matrix elements as functions of scattering angles for three ice crystal size-shape combinations. Overall, each phase matrix element shows some sensitivity toward ice crystal shape, size, and suface roughness due to the various optical effects. Synthetic retrievals reveal that the retrieval algorithm itself is highly accurate, while polarimetric and radiometric error sources cause very small retrieval errors. Finally, an illustrative example of applying the retrieval algorithm to airborne POLDER data during EUCREX is presented.

The National Aeronautics and Space Administration established the framework for the Science Investigator-led Processing Systems (SIPS) to enable the Earth science data products to be generated by personnel directly associated with the instrument science team and knowledgeable of the science algorithms. One of the first instantiations implemented for NASA was the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) SIPS. The AMSR-E SIPS is a decentralized, geographically distributed ground data processing system composed of two primary components located in California and Alabama. Initial science data processing in the U.S. is conducted at Remote Sensing Systems (RSS) in Santa Rosa, California. RSS ingests antenna temperature orbit data sets from the Japanese Aerospace Exploration Agency and converts them to calibrated, resampled, geolocated brightness temperatures. The brightness temperatures are sent to the Global Hydrology and Climate Center in Huntsville, Alabama, which generates the geophysical science data products (e.g., water vapor, sea surface temperature, sea ice extent, etc.) suitable for climate research and applications usage. These science products are subsequently sent to the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado for archival and dissemination to the at-large science community. This paper describes the organization, coordination and production techniques employed by the AMSR-E SIPS in implementing, automating and operating the distributed data processing system.

Five years of rainfall data from the Tropical Rainfall Measuring Mission are now available for use. This paper surveys comparisons of these rain rate data with buoy-based estimates over the Tropical Pacific and with simulations with the CCM3, a general circulation model of atmospheric circulation. We examine both the seasonal and diurnal cycles in the data. The TRMM data agree reasonably well with the buoy data for both the seasonal and diurnal cycles. While the data generally agree with the simulations, there are some discrepancies which can be attributed to model failures.

The AER algorithm testbed has been applied to instruments measuring in spectra from the ultraviolet to the microwave. The sensor simulation component starts with environmental data from numerical weather prediction models, surface property and terrain databases, and imagers, and simulates the detailed sensor spatial and spectral sampling processes, radiative transfer, polarization, and detector characteristics. This simulation is integrated with algorithm execution, to provide end-to-end capability. The tools allow for simulation of specific sensor errors, and tracing of their impact through the algorithm process to the quality of the retrieved environmental products. A critical component of the testbed is its radiative transfer models, which employ state-of-the-science programs for line-by-line optical properties, for radiative transfer in scattering atmospheres, and for a variety of surfaces. Fast and accurate computational methods are incorporated, such as Optimal Spectral Sampling (OSS). Application examples are shown, including characterization of AMSU sensor data errors and retrieval performance evaluation, in which retrievals from the microwave sounder data and infrared image data contribute to the interpretation of remote sensing phenomena in cloudy environments.

Two methods to estimate aerosol optical depth (AOD) at a relatively low computational cost, using the data of the sun photometers from the Aerosol Robotic Network (AERONET) are presented and compared. One interpolates the data and the other approximates the data. The technique is based on a geometric approach. Assuming that AOD can be represented as a highly continuous surface function of time and position, a height field approximating AOD using the data from the sun photometers is obtained. Both methods use an optimization-subdivision iterative algorithm to create a function that interpolates or approximates the AOD values measured by the sun photometers. The methods begin by constructing a Delaunay triangulation of the location of the sun photometer sites over the region were the AOD value is to be approximated. The algorithm then alternatively optimizes and subdivides the triangular mesh. At each iteration, the optimization step first creates a data dependent triangulation which is then subdivided. The results obtained by the two methods are compared with those obtained from piecewise linear interpolation.

Satellite oceanography within the Center for Satellite Applications and Research (STAR) in National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) focuses on observation retrievals and applications to address the NOAA missions of environmental assessment, prediction, and stewardship. Satellite oceanography within NOAA/NESDIS is an end-to-end process, addressing user requirements, sensor design support, observation retrieval research and development, calibration, applications and product research and development, the transition of research to operations, continuing product validation, and operational user support. The breadth of scientific investigation encompasses three functional areas: satellite ocean sensors, ocean dynamics/data assimilation, and marine ecosystems/climate. A cross-cutting science team from these functional areas has been established for each core subject: sea-surface temperature, sea-surface height, sea-surface roughness, ocean color, ocean surface winds, and sea ice. These science teams pursue the science and issues end to end within the core subject, with the primary objective being the transition of research to operations. Data fusion opportunities between science teams are also pursued. Each science team area addresses the common themes of calibration/validation, data assimilation, climate, and operational oceanography. Experimental and operational products, as well as user support, are provided to the user community via the NOAA OceanWatch/CoastWatch program.

This paper chronicles the end-to-end data procurement, customized algorithm development and automated processing systems, product distribution, and ultimate user application of selected time-critical satellite meteorology applications developed by the Naval Research Laboratory (NRL) in support of Department of Defense (DoD) assets during Operation Iraqi Freedom. In particular, a mechanism for obtaining high spatial/spectral resolution near real-time data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments found aboard the Earth Observing System (EOS) Terra and Aqua research platforms for operational support was developed through an inter-agency collaboration between the DoD, the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA). Value-added (e.g., dust detection, convective cloud heights, snow/cloud and fire detection) products derived from these low-latency data were then hosted on secure Internet bandwidth via the Fleet Numerical Meteorology and Oceanography Center (FNMOC) operational portal. The MODIS products factored significantly into a wide range of operational requirements that included strike briefs, aircraft routing, ship navigation, sensor targeting and weapons selection. Included herein are dramatic excerpts from direct correspondence between NRL scientists and Naval Meteorology/Oceanography (METOC) officers aboard aircraft carriers deployed in the Arabian Gulf who were actively using these products to support their various mission requirements.

Recent advancements of meteorology and oceanography (METOC) satellite products has resulted from a surge in computing resources and expanded communications via the Internet. Greater tactical demands in military operations are placed on Navy and Marine METOC personnel to provide better atmospheric depictions and forecasts in support of helicopter, fighter jet and ground troop operations, as was experienced in Operation Enduring Freedom and Operation Iraqi Freedom. Unfortunately, US military weather forecasters are often limited in their abilities to provide state of the art products and forecasts. One reason for these inefficiencies are that oftentimes, daily forecasting tasks are left to non-commissioned personnel (e.g., AG’s and ET’s) who receive little or no classroom training in this area, nor are there continuing education/training available. METOC forecast centers vary greatly and might not have access to the appropriate information base to answer ongoing questions. Additionally, the typical tour of duty at a particular forecast center is 2 years, resulting in a stressful environment to bring new forecasters up to speed in demanding forecast operations. The result is that the user is often confined to image looping and basic image enhancements to convey the general environmental conditions over the region of interest. To facilitate the learning process, the Naval Research Laboratory and the Naval Postgraduate School have developed a 3 day intensive laboratory and lecture course in satellite remote sensing, focusing on topics vital to military operations such as smoke and fire detection, coastal maritime layer analysis, snow, fog, haze, tropical cyclones, hazardous wind conditions, etc. A wealth of satellite data is provided from MODIS, AVHRR, DMSP and Geostationary satellite data. Background satellite remote sensing topics such as radiative transfer theory is also presented. This report presents a sample of the material used within the training.

Space-borne passive sensing of the Earth’s surface and atmosphere has an essential and increasing importance in Earth Observation. The impressive progress recently made or shortly expected in weather analysis, warning and forecasts (in particular for dangerous weather phenomena as rain and floods, storms, cyclones, droughts) as well as in the study and prediction of climate change, is mainly attributable to the spaceborne observations. On this basis, economic studies show that meteorological services have a high positive impact on a wide range of economic activities, notwithstanding safety of life and property aspects. Space-borne passive sensing feeds crucial observational data to numerical weather predction models run on the most advanced super-computers that are operated by a few global forecasting centers. All meteorological and environmental satellite organizations operate these crucial remote-sensing missions as part of the GOS of the World Weather Watch and others... Spaceborne passive sensing for meterological applications is performed in frequency bands allocated to the Earth Exploration-Satellite Service. This is named "EESS passive" in the ITU-R Radio Regulations. The appropriate bands are uniquely determined by the physical properties (e.g. molecular resonance) of constituents of the atmosphere, and are therefore one of the unique natural resources (similarly to Radio Astronomy bands). Passive measurements at several frequencies in the microwave spectrum must be made simultaneously in order to extract the individual contribution of the geophysical parameter of interest. Bands below 100 GHz are of particular importance to provide an "all-weather" capability since many clouds are almost transparent at these frequencies. Along this line, the two first figures below about zenithal opacity describes respectively the atmosphere optical thickness due to water vapor and dry components in the frequency range 1 to 275 GHz and 275 GHz to 1000 GHz on which have been based the definition of most of the current allocations to EESS (passive) that are listed, as currently specified in ITU-R Rec. SA.515-3 summarized below. Interference criteria and performance criteria of passive sensors are indicated in ITU-R Rec(s) SA.1028-2 and 1029-2, respectively. A common summary of these two Rec(s) is also available below.

Since October, 2002, NESDIS has provided specially tailored radiance and retrieval products derived from Aqua AIRS and AMSU-A observations operationally (24 hours x 7 days) to a number of Numerical Weather Prediction (NWP) centers, including NCEP, ECMWF and the UK Met. Office. Two types of products are available -- thinned radiance data and full resolution retrieval products consisting of atmospheric temperature, moisture and ozone as well as surface parameters of temperature and emissivity. The radiances are thinned because of current limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: a) spatial and spectral thinning, and b) data compression using principal component analysis (PCA). PCA is used for a) reconstructing radiances with the properties of reduced noise, b) independent instrument noise estimation, c) quality control, and d) deriving the retrieval first guess used in the AIRS processing software. The radiance products also include cloud cleared radiances. The cloud clearing procedure remove the effect of cloud contamination in partial overcast conditions and have been demonstrated to increase the amount of data that can be treated as clear from 5% to 50%. The AIRS temperature and moisture retrieval are significantly more accurate than AMSU-only retrievals in clear, cloud contaminated and cloud-cleared conditions. Most NWP centers are currently assimilating clear radiances, which we believe severely limits the impact of AIRS data. Fortunately, results presented in this paper have stimulated new upcoming experiments to test the impact of cloud-cleared radiances.

A near real-time AIRS processing and distribution system is fully operational at NOAA/NESDIS/ORA. The AIRS system went though three separate production phases: design and development, implementation, and operations. The design and development phase consisted of two years of preparation for the near real-time AIRS data. The approach was to fully emulate the AIRS measurement stream. This was accomplished by using a forecast model to represent the geophysical state and computation of simulated AIRS measurements using the characteristics of the AIRS channels. The preparation included file format development and the creation of a program to subset the radiance and product data. The implementation phase lasted over a year and involved utilizing AIRS/AMSU/HSB simulated data quasi-operationally. This simulated data was placed into deliverable files and distributed to the customers for their pre-launch preparations. The operational phase consisted of switching the simulation system to real data and is the current system status. Details of what went right and wrong at each production phase will be presented. This methodology eased the transition to operations and will be applied to other advanced sounders such as IASI and CrIS.

The AIRS instrument was launched on the Aqua satellite in May of 2002. In addition to the core level 2 products, that include cloud cleared radiances; temperature, moisture, and ozone profiles; surface skin temperature; NDVI (from AIRS visible channels); surface spectral emissivity and reflectivity; and cloud products, the AIRS science team is also developing research algorithms for the retrieval of carbon monoxide (CO), methane (CH₄), and carbon dioxide (CO₂). These algorithms are being tested by the National Oceanographic and Atmosphere Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) in simulation and applied to real AIRS radiances. The trace gas retrievals require cloud free infrared radiances. In practice, we observe that AIRS measurements without cloud contamination occur less than 5% of the time. A key feature of the AIRS algorithm is the utilization of cloud cleared radiances that removes the effects of clouds and increases the yield of trace gas products to 50-60%. The increased yield should allow a better assessment of sources and sinks of these gases. Determination of sources and sinks of these trace gas requires an unprecedented precision for a remote sounding measurement. In addition, both the variability and errors in the trace gas products tend to be correlated with variability and errors in other products (e.g., clouds, temperature, moisture, and ozone profile). Multi-spectral, high-resolution measurements can minimize the effects of this correlation. Currently, for the AIRS products, we estimate a precision of 15% for CO, 0.5% for CO₂ and 1% for CH₄. The remote sounding methodology for these trace gases is discussed in detail. The METOP IASI and NPOESS CrIS instruments can extend the continuity of these trace gas products over the next two decades. Simulation experiments are used to assess the relative performance of the trace gas retrievals from these sounders.

The Atmospheric Infrared Sounder (AIRS) is a grating array spectrometer on the EOS Aqua satellite. AIRS covers the thermal infrared spectral range between 640 and 2700 cm^-1. In order to retain the maximum radiometric accuracy of the AIRS data, the effects of cloud contamination have to be minimized. We discuss cloud filtering which uses the high spectral resolution of AIRS to identify about 100,000 of 500,000 non-polar ocean spectra per day as relatively free of clouds. The comparison of AIRS surface sensitive channels with the NCEP provided global real time sst (rtg.sst) shows a cold bias of the order of 0.5K in daily global averages, which is traceable to cloud contamination. During the day the cloud contamination is due to a 2-3% broken cloud cover at 1-2 km altitude, characteristic of low stratus clouds. Cloud contamination can be reduced to 0.2K, but not eliminated, by combining the spectral filter with a spatial coherence threshold, but the yield drops to 16,000 spectra per day. AIRS was launched in May 2002 on the Earth Observing System (EOS) Aqua satellite. Since September 2002 it has returned 4 million spectra of the globe each day.

Development and testing of the IASI processing and distribution system is currently ongoing at NOAA/NESDIS/ORA. Level 1C data for 8461 channels will be available to NESDIS/NOAA from EUMETSAT shortly after MetOp 1 launch (currently scheduled for October 2005). Prior to launch, a simulation system will provide pseudo near-real time data for system testing and refinement. This will allow for a smooth and immediate system transition to the actual data processing when it becomes available. The ingested EUMETSAT level1C data will be subset both spectrally and spatially and then placed into BUFR format for a number of products including: (1) Level 1C (calibrated, apodized, and navigated) brightness temperatures, (2) cloud-cleared radiances, and (3) PCA reconstructed radiances. The subset level 1C data will be delivered within three hours of observation. System validation will consist of comparing the products to collocated radiosonde observations and model forecasts.

The USU global ionospheric data assimilation model is part of the Global Assimilation of Ionospheric Measurements (GAIM). This model uses a physics-based ionosphere-plasmasphere-polar wind model and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. Some of the data that are assimilated include in situ electron density measurements from the DMSP satellites, bottomside electron density profiles from the Air Force network of digisondes, GPS-TEC data from a network of more than 900 stations, and occultation data. GAIM provides specifications and forecasts on a spatial grid that can be global, regional, or local. The primary GAIM output is in the form of 3-dimensional electron density distributions from 90 km to the geosynchronous altitude. GAIM also provides auxiliary parameters (N_mF₂, h_mF₂, N_mE, h_mE, slant and vertical TEC) and global distributions of the self-consistent ionospheric drivers (neutral winds and densities, electric fields, and particle precipitation). In its specification mode, GAIM provides quantitative estimates for the accuracy of the reconstructed ionospheric densities. In addition to the physics-based, Kalman filter model, we have also developed a Gauss-Markov Kalman filter model. The status of the models and the relevant applications are discussed.

As Space Weather makes the transition from a research to an operational mode, the need for reliable and relevant datasets will be of paramount importance. Good research data will not necessarily be good operational data, we will discuss some reasons why. The first order data inputs required for the developing suite of Space Weather Forecast models (GAIM, CISM, EPPIM, etc) need to be identified at an early stage so that adequate observational networks (space and ground based) can be put into place. Lessons learned from decades of operational meteorological forecast systems should be applied. Ranking, relevance and cost-benefit decisions need to be performed on each component of the Operational Space Weather system to insure that the maximum amount of capability is obtained at a reasonable cost. We will examine a range of proposed space weather data inputs and attempt to apply these criteria to each of them.

Traditionally the ionosphere and thermosphere are considered "data-starved" regions of the atmosphere but recent experimental and operational optical satellite instruments and new techniques to exploit GPS and orbiting radio beacons are dramatically improving the situation. Similarly, new classes of assimilating ionospheric models are under development to exploit these data and provide a significantly improved capability to specify and forecast weather of the upper atmosphere and ionosphere. The Advanced Research and Global Observation Satellite (ARGOS), launched by the DoD Space Test Program in 1999 demonstrated ultraviolet remote sensing of the neutral atmosphere and ionosphere from limb sensors in low Earth orbit (LEO). Soon after, the Global Ultraviolet Imager (GUVI) on the NASA TIMED satellite employed a similar capability using cross track ultraviolet remote sensing. The first of five sets of operational versions of both of these instruments, the Special Sensor Ultraviolet Limb Imager (SSULI) and Special Sensor Ultraviolet Spectroscopic Imager (SSUSI), respectively, where launched aboard the first of the DMSP Block 5D3 weather satellites in October of 2003. Radio frequency techniques for ionospheric remote sensing using ground-based dual frequency GPS receivers and GPS occultation receivers on LEO satellites are making a significant contribution to the global ionospheric database. In 2005, the launch of a constellation of six satellites called the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) will provide ionospheric measurements using GPS occultation receivers ultraviolet photometers and dual frequency UHF radio beacons. The latter will allow tomographic ionospheric reconstructions using ground-based receivers. These and other data will be used to drive the Global Assimilations of Ionospheric Measurements (GAIM) model to dramatically improved current modeling and forecast capabilities for the global thermosphere and ionosphere. More innovations are under development in the form of long-dwell, high temporal and spatial resolution measurements of the ionosphere from geosynchronous orbit using the Ionospheric Mapping and Geocoronal Experiment (IMAGER). IMAGER will provide GOES-like monitoring of the ionosphere and neutral atmosphere.

Quite frequently visible and uv imagery of the ionosphere and upper atmosphere is used to describe the global scale characteristics of ion density and composition. This invaluable data can be obtained from satellites at very high altitude, providing a complete global picture at modest scale sizes, or from low Earth orbit, where a more restricted view with higher spatial resolution is possible. Here we describe how in-situ data obtained from low Earth orbit can be visualized in a manner similar to optical emission data. With this approach global scale variations of key parameters like plasma temperature and ion velocity can be added to those of composition and density to reveal the evolution of the system in response to external drivers. During times of high magnetic activity the links between key parameters over large temporal and spatial scales can be easily visualized and cuts through the images reveal details that can be used in more quantitative descriptions.

The purpose of the Communication/Navigation Outage Forecasting System (C/NOFS) is to detect and forecast ionospheric irregularities that adversely impact communication, navigation and surveillance systems. The C/NOFS consists of ground sites that monitor the radio link to geosynchronous and GPS spacecraft, a fully-instrumented monitoring satellite scheduled for launch in 2005 and a central data collection facility that creates scintillation warnings in near real-time. The C/NOFS spacecraft will be launched into a low inclination (13°), elliptical (~ 375 x 710 km) orbit. Sensors on the C/NOFS spacecraft measure the ambient ionosphere near the equator and detect density fluctuations which cause scintillation. Ionospheric irregularities often occur after sunset within ±20° of the magnetic equator. These irregularities can be fairly benign, causing a small amount of radio scintillation and perturbing only UHF/VHF communications, or they can be very severe, causing L-band scintillation that interfere with the functioning of GPS receivers. In these severe cases, the ionospheric irregularities can extend beyond 1000 km altitude. One of the challenges of this project is to go beyond detecting irregularities and to predict scintillation-producing irregularities up to 8 hours into the future and estimating changes in the climatology 24 hours or more into the future.

The development of the National Polar-orbiting Operational Environmental Satellite (NPOESS) Visible Infrared Imager Radiometer Suite (VIIRS) design demonstrated the value of an end-to-end simulation of remote sensing systems. To arrive at a truly optimized system, Raytheon developed and refined a system simulation capability that can be applied to a number of other programs. This capability supports both instrument and algorithm design trades. The VIIRS effort included spectral modeling of various surface types, radiative transfer modeling of the atmosphere (sometimes incorporating observed atmospheric profiles), instrument spectral, spatial, and radiometric response, and a chain of environmental product retrieval algorithms. In this paper, we present a system simulation tool that has evolved from the VIIRS capability into a more general software package that runs both quickly and accurately. This tool can be used to simulate the behavior of any system that includes a scanning visible/infrared imager or radiometer and associated product retrieval algorithms. The tool has been validated against actual Moderate-resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) outputs (both instrument-level and product-level) to illustrate the realism of the instrument modeling and environmental algorithm implementation. The package has also been reapplied to the VIIRS system design to deliver a snapshot of what can be expected when the VIIRS prototype is launched aboard the NPOESS Preparatory Project (NPP) in 2006. As an example, we present the system level characteristics of the red and near infrared (NIR) reflectances, as well as the Normalized Difference Vegetation Index (NDVI) product that can be derived from these reflectances.

Traditional methods for deriving wind vectors from sequential geostationary satellite imagery involve the tracking of coherent clouds and moisture features in single channels (spectral bands). While this data source has proven to be important to global wind analyses, the approach is limited in two major ways: 1) The heights assigned to the vectors are not precise, leading to problems in data assimilation, and 2) Vertical profiles of the wind at a given geo-location are not provided, adding further stress to objective data assimilation (difficulty with single-level observations). A new approach to deriving winds from sequential satellite observations is being advanced at CIMSS. The method utilizes the same basic automated tracking code developed at CIMSS, however the input to the algorithm is in the form of constant-level moisture analyses derived from hyperspectral sounding information. Since the altitude of the features being tracked are already determined by the soundings/analyses, the height assignment ambiguities associated with the traditional approaches are ameliorated. Furthermore, the hyperspectral infrared (IR) information provides detailed vertical profiles of moisture where there are no clouds. This allows analyses of moisture at multiple vertical levels, which can then be used in an attempt to retrieve vertical profiles of wind. To date, the new scheme has been trialed on simulated data from GIFTS, and on one case of real data from airborne observations provided by the NAST-I instrument. From these first attempts, the "proof of concept" is successfully illustrated, and will be shown in the presentation.

Hyperspectral image possesses incomparable advantage over spaceborne multispectral image when it is employed to quantitatively retrieve these parameters such as vegetation type, coverage, biomass, bare soil moisture, etc. This paper focuses on crucial issues present in the pre-processing of hyperspectral image: band selection, edge radiant correction, tangent correction and spectral reflectivity conversion, exemplified by a case study in which modular airborne OMIS-I imaging spectrometer data are employed to evaluate desertification. The author gives comprehensive consideration to the statistic characteristics of each spectral band, diagnostic spectral reflection of different targets and the purpose of practical application, and fixed upon 41 applicable bands after trying different bands. In the course of edge radiant correction, one correction method based on histogram matching was used, and its result was satisfactory. In addition, tangent correction directing against tangent distortion was carried out, which enriched the normal geometric rectification. Lastly, during the process of surface feature spectral reflectivity conversion, the author converted symbolic model into statistic model by employing some necessary theoretical inference and parameter-setting. The result suggests the quality of OMIS-I data get better improved after these processing and basically can meet the requirements of quantitative retrieval for desertification evaluation.

In this paper we present regional high spatial resolution sounding products from MODIS and AIRS data processed by both stand-alone and synergistic retrieval approaches. These unique regional high spatial resolution (at sensor’s single field of view) atmospheric sounding products such as temperature, total precipitable water and surface temperature can provide valuable information for regional near-real time weather monitoring and meso-scale synoptic analysis, and other local environmental applications. Performance comparison between stand-alone and fusion products will assist validation effort and lead to the refinement of processing algorithms. Synergistic utilization of MODIS and AIRS data will also demonstrate the complementary information content provided by two different sensors and will pave the way for future optimal amalgamation of imaging and sounding data.

Measurements of aerosol optical depth (AOD) are important indicators of aerosol particle behavior. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as DDV (Dense Dark Vegetation) based inversion algorithms which yield AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. We attempt to fill gaps in spatio-temporal AOD measurements using a new assimilation methodology that links AOD measurements and the predictions of a particulate matter Transport Model. This modelling package (AODSEM V2.0 for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution may be tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important and robust parameter. We applied this methodology to a significant smoke event that occurred over the eastern part of North America in July 2002.

Hyperspectral data and products derived from instrumentation such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the GOES-R Hyperspectral Environmental Suite (HES) will impose storage and data retrieval requirements that far exceed the demands of earlier generation remote sensing instrumentation used for atmospheric science research. A new architecture designed to address projected real time and research needs is undergoing prototype design and development. The system is designed using proven aspects of distributed data storage networks, descriptive metadata associated with stored files, data cataloging and database search schemes, and a data delivery approach that obeys accepted standards. Preliminary implementation and testing of some components of this architecture indicate that the design approach shows promise of an improved method for storage and library functionality for the data volumes associated with operational hyperspectral instrumentation.

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the Hyperspectral Environmental Suite (HES) instruments are geostationary infrared spectrometers. Geostationary orbit provides observations with very good time resolution, but also increases the effect of diffraction. There can be significant differences in emitted radiances due to clouds and changes in surface characteristics. High, thick clouds in particular are much cooler than clear areas. Diffraction causes radiation that originates from cloudy areas outside of the detector field of view to contaminate the clear pixels. GIFTS will also have two detector arrays on different focal planes, which may not be perfectly aligned. This can cause spatial misalignment between the data for the two spectral regions. High spatial resolution numerical models run at the University of Wisconsin - Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) provide data for examining the diffraction and misalignment effects. The model data represents a variable cloud case during the IHOP field experiment at 1.3-km resolution. This paper outlines the production of high spatial resolution simulated data, characterization of the far field diffraction effects on radiances, and analysis of misalignment effects on temperature and moisture profile retrievals.

Future meteorological sounding instrumentation for aircraft and satellite platforms will include hyperspectral imaging infrared spectrometers with high time and space resolution, capable of providing terabytes of raw data per day. In tandem with the development of the instruments themselves, corresponding software must be architected to be capable of timely, efficient and accurate processing of the raw data produced. Design candidates for such a software architecture must respond to use cases including deployment in large-scale distributed production environments with stringent reliability specifications; phasing of research algorithms through testing and validation into production use; marshalling of data product views to metadata-aware analysis and archival systems; maintenance of software supporting multiple similar instrument systems over the course of decades; and most importantly, delivery of fully annotated datasets to end-users with real-time latencies. Consistent techniques in the specification and propagation of metadata for both algorithm software and data content are of paramount concern in manipulating large quantities of data over long stretches of time. Further, long-term maintainability and cost-effectiveness of the system can be assured by improving reusability of both systems software and science software, through defining well-specified interfaces for software components and implementing automated mechanisms for integration and testing. We illustrate current design work, avenues of research and lessons learned on a software component architecture and corresponding development practices addressing the aforementioned concerns.