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The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

In this paper, laboratory-based bidirectional reflectance distribution function (BRDF) analysis of vegetation leaves, soil, and leaf-litter samples is presented. The leaf litter and soil samples, numbered 1 and 2, were obtained from a site located in the savanna biome of South Africa (Skukuza: 25.0° S, 31.5° E). A third soil sample, number 3, was obtained from Etosha Pan, Namibia (19.20° S, 15.93° E, altitude of 1100 m). In addition, BRDF of local fresh and dry leaves from tulip polar tree (Liriodendron tulipifera) and black locust tree (Robinia pseudoacacia) were studied. It is shown how the BRDF depends on the incident and scatter angles, sample size (i.e., crushed versus whole leaf), soil samples fraction size, sample status (i.e., fresh versus dry leaves), vegetation species (i.e., poplar versus locust), and the vegetation's biochemical composition. As a demonstration of the application of the results of this paper, airborne BRDF measurements acquired with NASA's Cloud Absorption Radiometer over the same general site where the soil and leaf-litter samples were obtained are compared to the laboratory results. Good agreement between laboratory and airborne-measured BRDF is reported. [ABSTRACT FROM AUTHOR]

Lightning in all corners of the world is monitored by one or more land- or space-based lightning locating systems (LLSs). The applications that have driven these developments are numerous and varied. This paper describes the history leading to modern LLSs that sense lightning radiation fields at multiple remote sensors, focusing on the interactions between enabling technology, scientific discovery, technical development, and uses of the data. An overview of all widely used detection and location methods is provided, including a general discussion of their relative strengths and weaknesses for various applications. The U.S. National Lightning Detection Network (NLDN) is presented as a case study, since this LLS has been providing real-time lightning information since the early 1980s, and has provided continental-scale (U.S.) information to research and operational users since 1989. This network has also undergone a series of improvements during its >20-year life in response to evolving detection technologies and expanding requirements for applications. Recent analyses of modeled and actual performance of the current NLDN are also summarized. The paper concludes with a view of the short- and long-term requirements for improved lightning measurements that are needed to address some open scientific questions and fill the needs of emerging applications. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. OMI is an ultraviolet-visible imaging spectrograph providing daily global coverage with high spatial resolution. This paper discusses the ground data processing software used for Level 0 to Level lb processing of OMI data. In addition, the OMI operations scenario is described together with the data processing concept. This paper is intended to serve as a reference guide for users of OMI (Level 1b) data. [ABSTRACT FROM AUTHOR]

The Multispectral Thermal Imager (MTI) satellite is a research and development project sponsored by the U.S. Department of Energy. The primary mission is to demonstrate advanced multispectral and thermal imaging from a satellite, Including new technologies, data processing, and analysis techniques and validation by reference to ground truth. The MTI builds on the efforts of a number of earlier efforts, Including Landsat, National Aeronautics and Space Administration remote sensing missions, and others, but the MTI incorporates a unique combination of attributes designed to advance the state of the art. The MTI satellite was launched on March 12,2000 into a 580 km × 610 km, sun-syn-chronous orbit with nominal 1 A.M. and 1 P.M. equatorial crossing times. The Air Force Space Test Program provided the Orbital Sciences Taurus launch. The satellite-based sensors obtain radiance data that are subsequently processed into measurements of atmospheric and surface properties such as column water vapor, atmospheric aerosol loading, surface temperatures, material composition, and others. This paper provides an overview of the MTI research objectives, design, operations, data products, and data processing and analysis. Several other papers provide greater detail on selected topics. [ABSTRACT FROM AUTHOR]

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al. [ABSTRACT FROM PUBLISHER]

For more than a decade, the U.S. government has been developing laser-based sensors for detecting, locating, and classifying aerosols in the atmosphere at safe standoff ranges. The motivation for this work is the need to discriminate aerosols of biological origin from interferent materials such as smoke and dust using the backscatter from multiple wavelengths in the long wave infrared (LWIR) spectral region. Through previous work, algorithms have been developed for estimating the aerosol spectral dependence and concentration range dependence from these data. The range dependence is required for locating and tracking the aerosol plumes, and the backscatter spectral dependence is used for discrimination by a support vector machine classifier. Substantial progress has been made in these algorithms for the case of a single aerosol present in the lidar line-of-sight (LOS). Often, however, mixtures of aerosols are present along the same LOS overlapped in range and time. Analysis of these mixtures of aerosols presents a difficult inverse problem that cannot be successfully treated by the methods used for single aerosols. Fortunately, recent advances have been made in the analysis of inverse problems using shrinkage-based L1-regularization techniques. Of the several L1-regularization methods currently known, the split Bregman algorithm is straightforward to implement, converges rapidly, and is applicable to a broad range of inverse problems including our aerosol unmixing. In this paper, we show how the split Bregman algorithm can successfully resolve LWIR lidar data containing mixtures of bioaerosol simulants and interferents into their separate components. The individual components then can be classified as bio- or nonbioaerosol by our SVM classifier. We illustrate the approach through data collected in field tests over the past several years using the U.S. Army FAL sensor in testing at Dugway Proving Ground, UT. [ABSTRACT FROM PUBLISHER]

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve. [ABSTRACT FROM AUTHOR]

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products. [ABSTRACT FROM AUTHOR]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]

This paper investigates the improvements to the quality of ocean color data using an appropriate choice of ancillary data for National Oceanic and Atmospheric Administration (NOAA) operational ocean color data processing, which requires routine ocean color product production in near real time. The ancillary data, such as the total column ozone amount, sea surface wind speed, atmospheric pressure, and total column water-vapor amount, are required for satellite ocean color data processing for deriving ocean color products, e.g., normalized water-leaving radiance spectra data, chlorophyll-a concentration, water diffuse attenuation coefficient, etc. Currently, NOAA's CoastWatch program uses the climatology ancillary data for the near-real-time ocean color data processing. Alternative ancillary data sets that can replace the climatology data for the near-real-time ocean color data processing have been investigated and studied. The studies were carried out from four selected NOAA CoastWatch regions covering the U.S. coastal and Hawaii regions and for four months (January, April, July, and October), representing the four seasons. Based on the evaluation results, we propose to use the ancillary data produced from the Global Forecast System (GFS) model for the NOAA operational ocean color data processing, as well as for any other near-real-time data processing that requires ancillary data inputs. The effects of using the GFS model data on the accuracy of the derived ocean color products are also investigated and discussed. [ABSTRACT FROM AUTHOR]

A tropospheric ozone Differential Absorption Lidar system, developed jointly by The University of Alabama in Huntsville and the National Aeronautics and Space Administration, is making regular observations of ozone vertical distributions between 1 and 8 km with two receivers under both daytime and nighttime conditions using lasers at 285 and 291 nm. This paper describes the lidar system and analysis technique with some measurement examples. An iterative aerosol correction procedure reduces the retrieval error arising from differential aerosol backscatter in the lower troposphere. Lidar observations with coincident ozonesonde flights demonstrate that the retrieval accuracy ranges from better than 10% below 4 km to better than 20% below 8 km with 750-m vertical resolution and 10-min temporal integration. [ABSTRACT FROM AUTHOR]

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 \m^3/\m^3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation. [ABSTRACT FROM AUTHOR]

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging–interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging–interpolation procedure can produce complete spatial fields with acceptable errors. [ABSTRACT FROM AUTHOR]

Physically based passive microwave precipitation retrievals are difficult to develop over land because high nonuniform land emissivity values are difficult to distinguish from those of clouds. This paper uses an empirical approach to determine the covariance of emissivity at different microwave window channels and relies on this covariance to estimate the portion of the observed brightness temperatures that may be attributable to rainfall. One year (2006) of global cloud-free surface emissivity values were retrieved using data sets from multiple instruments on NASA's Aqua satellite. Correlations between the emissivities at different channels were developed for use in an empirical model within an optimal estimation retrieval. The optimal estimation retrieves surface temperature, total column water vapor, cloud water, and the emissivity at the 10.7-GHz horizontally polarized channel. From this retrieval and the covariance of emissivities, the 89.0-GHz brightness temperature at both polarizations can be estimated. Significant differences between the observed and retrieved high-resolution brightness temperatures are used to screen for precipitation, and results are compared to ground-based radar data for several study regions representing a variety of land surface types in the U.S. The Heidke Skill Score is used to determine the robustness of this methodology and, in all cases, demonstrates at least some increase in skill relative to random chance. [ABSTRACT FROM AUTHOR]

This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (Land Surface Temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance. [ABSTRACT FROM AUTHOR]

Mapping shallow-water bathymetry with acoustic techniques is complicated and expensive. The environmental parameters in shallow-water (< 2 m) areas become more variable and greatly impact the depth extraction from the survey measurements. Current airborne light detection and ranging (LIDAR) bathymetry surveying in shallow-water depths uses green-channel waveforms to measure the water depth. Unfortunately, due to difficulties in distinguishing between the surface and bottom return of the water column, the timing of the bottom return is often ambiguous. Furthermore, the water often becomes optically ‘dirty’ due to turbulence at these shallow depths. Therefore, it is common to find coastal areas that lack any measured depths. This paper presents a novel approach for measuring water depths in these shallow coastal waters with airborne LIDAR. Observations of the red-channel waveforms show that the waveforms are divided into two groups, namely: 1) waveforms in deep waters (> 2 m) whose shape is invariant with respect to the water depth and 2) waveforms in shallow-water depths that show a change in shape as a function of the depth in the water column. The data for this study are from the US Geological Survey LIDAR surveys of Lake Tahoe, CA, and Lake Michigan, using a SHOALS-400 LIDAR system. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) protoflight model onboard the National Aeronautics and Space Administration's Earth Observing System Terra spacecraft has been in operation for over five years since its launch in December 1999. It makes measurements using 36 spectral bands with wavelengths from 0.41 to 14.5 μm. Bands 1–19 and 26 with wavelengths below 2.2 μm, the reflective solar bands (RSBs), collect daytime reflected solar radiance at three nadir spatial resolutions: 0.25 km (bands 1–2), 0.5 km (bands 3–7), and 1 km (bands 8–19 and 26). Bands 20–25 and 27–36, the thermal emissive bands, collect both daytime and nighttime thermal emissions, at 1-km nadir spatial resolution. The MODIS spectral characterization was performed prelaunch at the system level. One of the MODIS onboard calibrators, the Spectroradiometric Calibration Assembly (SRCA), was designed to perform on-orbit spectral characterization of the MODIS RSB. This paper provides a brief overview of MODIS prelaunch spectral characterization, but focuses primarily on the algorithms and results of using the SRCA for on-orbit spectral characterization. Discussions are provided on the RSB center wavelength measurements and their relative spectral response retrievals, comparisons of on-orbit results with those from prelaunch measurements, and the dependence of center wavelength shifts on instrument temperature. For Terra MODIS, the center wavelength shifts over the past five years are less than 0.5 nm for most RSBs, indicating excellent stability of the instrument's spectral characteristics. Similar spectral performance has also been obtained from the Aqua MODIS (launched in May 2002) SRCA measurements. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) on EOS/Aura offers unprecedented spatial and spectral resolution, coupled with global coverage, for space-based UV measurements of sulfur dioxide (SO2). This paper describes an OMI SO2 algorithm (the band residual difference) that uses calibrated residuals at SO2 absorption band centers produced by the NASA operational ozone algorithm (OMTO3). By using optimum wavelengths for retrieval of SO2, the retrieval sensitivity is improved over NASA predecessor Total Ozone Mapping Spectrometer (TOMS) by factors of 10 to 20, depending on location. The ground footprint of OMI is eight times smaller than TOMS. These factors produce two orders of magnitude improvement in the minimum detectable mass of SO2. Thus, the diffuse boundaries of volcanic clouds can be imaged better and the clouds can be tracked longer. More significantly, the improved sensitivity now permits daily global measurement of passive volcanic degassing of SO2 and of heavy anthropogenic SO2 pollution to provide new information on the relative importance of these sources for climate studies. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. The OMI instrument is an ultraviolet-visible imaging spectrograph that uses two-dimensional charge-coupled device detectors to register both the spectrum and the swath perpendicular to the flight direction with a 115° wide swath, which enables global daily ground coverage with high spatial resolution. This paper presents the OMI design and discusses the main performance and calibration features and results. [ABSTRACT FROM AUTHOR]

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major component—the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on NASA's Earth Observing System AURA satellite, launched in July 2004. OMI is an ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day, with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OCIO. In addition OMI measures aerosol characteristics, cloud top heights and cloud coverage, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with daily global coverage and a small footprint will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change along with Aura's other three instruments. OMI's high spatial resolution enables detection of air pollution at urban scales. Total Ozone Mapping Spectrometer and differential optical absorption spectroscopy heritage algorithms, as well as new ones developed by the international (Dutch, Finnish, and U.S.) OMI science team, are used to derive OMI's advanced backscatter data products. In addition to providing data for Aura's prime objectives, OMI will provide near-real-time data for operational agencies in Europe and the U.S. Examples of OMI's unique capabilities are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper describes the optics design and field-of-view (FOV) calibration for five radiometers covering 114–660 GHz which share a common antenna in the Microwave Limb Sounder instrument on the National Aeronautics and Space Administration's Aura satellite. Details of near-field pattern measurements are presented. Estimated systematic scaling uncertainties (3σ) on calibrated limb emissions, due to FOV calibration uncertainties, are below 0.4%. 3σ uncertainties in beamwidth and relative pointing of radiometer boresights are 0.006° and 0.003°, respectively. The uncertainty in modeled instrument response, due to the scan dependence of FOV patterns, is less than ±0.24 K equivalent blackbody temperature. Refinements to the calibration using in-flight data are presented. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on the National Aeronautics and Space Adminsitration's Earth Observing System Aura satellite launched in July 2004. OMI is a ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OClO. In addition, OMI will measure aerosol characteristics, cloud top heights, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage will be a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI's high spatial resolution is unprecedented and will enable detection of air pollution on urban scale resolution. In this paper, the instrument and its performance will be discussed. [ABSTRACT FROM AUTHOR]

Aura, the last of the large Earth Observing System observatories, was launched on July 15, 2004. Aura is designed to make comprehensive stratospheric and tropospheric composition measurements from its four instruments, the High Resolution Dynamics Limb Sounder (HIRDLS), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). With the exception of HIRDLS, all of the instruments are performing as expected, and HIRDLS will likely be able to deliver most of their planned data products. We summarize the mission, instruments, and synergies in this paper. [ABSTRACT FROM AUTHOR]

Transmissions from ground-based systems in C- and X-bands present a significant challenge to the use of these bands for passive microwave remote sensing from aircraft and satellites. Because future missions plan to continue to use these frequencies, it is important to characterize and understand the nature of interference in as much of the candidate spectrum as possible. This paper presents a statistical analysis of interference observed in the continental U.S. using six months of data collected from the C- and X-band channels of the WindSat microwave radiometer. Our findings are consistent with those of previous studies by Li et a!. and Njoku et aL, which are based on data obtained from the Advanced Microwave Scanning Radiometer-EOS using somewhat similar center frequencies and bandwidths. Results show significant radio-frequency interference (RFI) at C-band, including brightnesses in horizontal and vertical polarizations in excess of 330 K, while X-band RFI is less obvious through direct examination of measured linearly polarized brightnesses. Evidence of lower levels of RFI is provided through use of the spectral and polarization indexes of Li et a!., which reveal likely RFI contributions at X-band as well. Further confirmation of X-band RFI is obtained through analysis of the polarimetric channels, which are shown to provide direct evidence of RFI in contrast to the linearly polarized channels. A temporal analysis of the largest C-band RFI sources is also provided in an attempt to further understand their properties. [ABSTRACT FROM AUTHOR]