The tri-agency Integrated Program Office (IPO) manages the development of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NPOESS will replace the Defense Meteorological Satellite Program (DMSP) and Polar-orbiting Operational Environmental Satellites (POES) that have provided global data for weather forecasting and environmental monitoring for over 40 years. Beginning in late 2009, NPOESS spacecraft will be launched into three orbital planes to provide significantly improved operational capabilities and benefits to satisfy critical civil and national security requirements for space-based, remotely sensed environmental data. NPOESS will observe more phenomena simultaneously from space than its operational predecessors and deliver a data volume significantly greater than the POES and DMSP systems with substantially improved delivery of data to users. Higher (spatial, temporal, and spectral) resolution and more accurate imaging and sounding data will enable improvements in short- to medium-range weather forecasts. NPOESS will support the operational needs of meteorological, oceanographic, environmental, climatic, and space environmental remote-sensing programs and provide continuity of data for climate researchers. With the development of NPOESS, we are evolving operational "weather" satellites into integrated global environmental observing systems by expanding our capabilities to observe, assess, and predict the total Earth system-atmosphere, ocean, land, and the space environment.

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) will produce 54 environmental data products serving military and civil operational users and the science community, supporting the atmospheric, cloud, land, ocean/water, earth radiation budget, and space remote sensing disciplines. Data products that are key to operations included imager, atmospheric temperature and moisture profiling, sea surface temperature and wind speed/direction, and soil moisture. NPOESS exploits advanced sensor and data product development on the Earth Observing System (EOS) and other envirosats, including a predecessor mission, the NPOESS Preparatory Project (NPP). The NPOESS data products will be used in weather forecasting, operational decision making, and climate monitoring. The products are delivered with low latency following data acquisition on-orbit by using downlink to a globally distributed network. Synergistic interconnected processing of data products is used to improve quality and reliability. Because the NPOESS system will serve for many years, planning hgas included consideration of product improvements and long term measurement stability for support to climate monitoring.

The overall objective of the NPOESS Preparatory Project (NPP)/National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Joint Government Shared System Performance Responsibility (SSPR) Contractor Calibration Validation (CalVal) Program is to ensure the environmental data products meet the system specification, and satisfy the users and scientific community. Work spans all program phases, from pre-launch sensor characterization and data prduct verification to on-orbit calibration verification/data product validation and long-term data product quality monitoring/maintenance. A cooperative approach is in place to leverage expertisxe throughout the program-developer, government-customer and users, and scientific community. Draft Calibration and Validation Plans are in development and NPP pre-launch activities are under way. This paper provides an NPP/NPOESS Cal Val Program system perspective, describes the cooperative strategy, and summarizes progress and planned activities to ensure a successful NPP mission.

The National Oceanic and Atmospheric Administration (NOAA), Department of Defense (DoD), and National Aeronautics and Space Administration (NASA) are jointly acquiring the next-generation weather and environmental satellite system; the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NPOESS replaces the current Polar-orbiting Operational Environmental Satellites (POES) managed by NOAA and the Defense Meteorological Satellite Program (DMSP) managed by the DoD. The NPOESS satellites carry a suite of up to 14 sensors that collect meteorological, oceanographic, climatological, and solar-geophysical observations of the earth, atmosphere, and space. The ground data processing segment for NPOESS is the Interface Data Processing Segment (IDPS), developed by Raytheon Intelligence and Information Systems. The IDPS processes NPOESS satellite data to provide environmental data products to NOAA and DoD processing centers operated by the United States government. The IDPS will process environmental data products beginning with the NPOESS Preparatory Project (NPP) in late 2006 and continuing through the lifetime of the NPOESS system. The IDPS must process a data volume significantly greater than the current POES and DMSP systems and within significantly reduced processing times. This paper will describe the architecture approach to hardware and software that is necessary to meet these challenging NPOESS IDPS design requirements.

The tri-agency Integrated Program Office (IPO) created Operational Algorithm Teams (OATs) in 1997 to provide scientific advice for managing the development and operation of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The scientific advice focuses on (1) assuring sound science in instrument and systems design in addition to (2) assuring development and implementation of sound scientific algorithms. This paper outlines the role of IPO operational algorithm teams from mission conception, through instrument design and development, algorithm science code development and conversion to operational code, data processing system implementation, calibration, validation, and, finally, operational data and products distribution to a range of users for weather, national security, and climate science. The composition of the algorithm science teams changes substantially as the sensors and algorithms are developed, tested, integrated, launched, become operational, and age on-orbit. The concept of leveraging our heritage scientists has proven successful with many tangible benefits to the government, the contractor teams, and, ultimately, the nation's taxpayers.

The tri-agency Integrated Program Office (IPO) is managing the development of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Later this decade, the IPO, through its prime contractor, Northrop Grumman Space Technology (NGST), will launch NPOESS spacecraft into three orbital planes (1330, 1730, and 2130 equatorial ascending nodal crossing times) to provide global coverage with a data refresh rate of approximately four hours. A globally distributed ground system will deliver 95 percent of the data within 26 minutes from the time of on-orbit collection. With the development of NPOESS, we are evolving the existing “weather” satellites into integrated environmental observing systems. To meet user-validated requirements, NPOESS will deliver global data for 55 Environmental Data Records (EDRs). Performance characteristics and attributes have been defined for each of the 55 parameters, including: horizontal/vertical resolution; mapping accuracy; measurement range; measurement precision and uncertainty; refresh rate; data latency; and geographic coverage. Long-term stability requirements have been defined for key parameters to ensure temporal consistency and continuity of data over the operational life of NPOESS. The actual EDR performances will be a result of the sensor and algorithm performances. In order for NPOESS program to determine estimates of EDR performance based on current design data and to assess potential sensor design changes or algorithm modifications, NGST developed an Integrated Weather Products Test Bed (IWPTB). This system can generate simulated radiances from mission/orbit variable, sensor variables, atmospheric and background conditions, and radiative transfer models. These simulated radiances at aperture are used with sensor models and spacecraft factors to generate simulated radiometric temperatures which are processed by science retrieval code to generate EDRs. This paper presents an assessment of the impact of the VIIRS sensor design modification to correct Modulated Instrument Background in the sensor’s optical train. This assessment, which focuses on the Sea Surface Temperature EDR in particular, was generated by the IWPTB end-to-end performance assessment capability.

Since 1995 the Global Ozone Monitoring Experiment (GOME) is measuring ozone (total column and profile), nitrogen dioxide and other minor trace gases on-board of the European Space Agency (ESA) ERS-2 satellite. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and ESA decided to fly an advanced GOME-2 instrument on the METOP satellites. Within the EUMETSAT Polar System (EPS), the GOME-2 measurements will provide the input for the ozone data record in the timeframe 2005 to 2020. The radiometric calibration of the polarisation sensitive GOME-2 instrument is significantly improved by the simultaneous measurement of s- and p-polarised light at moderate resolution and high temporal resolution. The Polarisation Monitoring Unit (PU) measures the spectral range between 312 and 790 nm in 15 narrow bands. The ground pixel size in the 960 km swath is 40 * 5km². The paper describes in detail the polarisation measurement devices and their technical capabilities.

Ground-based satellite data processing is a major undertaking for any organization or research group. The costs of hardware required to do the processing is declining, but the software development costs are increasing due to the increasing complexity of the satellite sensor data and the increasing demand for on-line data access by the user community. The CloudSat mission has developed a ground data processing center software system, APAPT, that can be used as a generic template for other satellite missions and promises to minimize the software development costs associated with these data processing activities. The ADAPT system includes a web-based software interface management system called the Algorithm Interface Management System (AIMS), the CloudSat Operational and Research Environment (CORE) which uses off-the-shelf PC technology to process all of the CloudSat mission data and includes a module which maps ancillary sensor data to the CloudSat geolocation data, a web-based data distribution system, an automated DVD data storage and logging system, and a comprehensive web-based Operator Control and Monitoring interface for overall system management. The presentation will focus on how the ADAPT end-to-end system can easily tailored to other satellite sensor missions to provide a low-cost and robust solution for science data processing requirements.

To support the verification and implementation of the Visible/Infrared Imaging/Radiometric Suites algorithms used for inferring cloud environmental data records, an inter-comparison effor has been carried out to assess the consistency between the simulated cloudy radiances/relectances from the University of California at Los Angeles line-by-line equivalent radiative transfer model (UCLA-LBLE RTM) and those from the Moderate-Resolution Transmission Model (MODTRAN) with the 16-stream Discrete Ordinate Radiative Transfer Model (DISORT) incorporated. For typical ice and water cloud optical depths and particle sizes, we find discrepancies in the visible and near-infrared reflectances from the two models, presumably due to the difference in phase function (non-spherical vs. Henyey-Greenstein), different numbers of phase function expansion terms (16-term vs. 200-term), and different treatment of forward peak truncation in each model. Using MODTRAN4, we also find substantial differences in the infrared radiances for optically thick clouds. These differences led to the discovery by MODTRAN4 developers of an inconsistency in the MODTRAN4/DISORT interface. MODTRAN4 developers corrected the inconsistency, which provided dramatic reductions in the differences between the two radiative transfer models. The comparison not only impacts the prospective test plan for the VIIRS cloud algorithms, but also leads to improvements in future MODTRAN releases.

Environmental satellites today are designed to meet the most requirements possible within the constraints of budget, reliability, availability, robustness, manufacturability, and the state of the art in affordable technology. As we learn more and more about observing and forecasting, requirements continue to be developed and validated for measurements that can benefit from for advances in technology. The goal is to incorporate new technologies into operational systems as quickly as possible. Technologies that exist or are being developed in response to growing requirements can be categorized as "requirements pull" whereas technologies rooted in basic research and engineering exploration fall in to a "technology push" category. NOAA has begun exploration into technologies for future NOAA satellite systems. Unmet requirements exist that drive the need to locate, explore, exploit, assess, and encourage development in several technologies. Areas needing advanced technologies include: atmospheric aerosols; cloud parameters; precipitation; profiles of temperature, moisture, pressure, and wind; atmospheric radiation; trace gas abundance and distribution; land surface; ocean surface; and space weather components such as neutral density and electron density. One of the more interesting ideas in the technology push category is a constellation of satellites at Medium Earth Orbit (MEO) altitudes, here described as circular orbits near 11,000 km altitude. Consider the vision of being able to observe the environment anywhere on the Earth, at anytime, with any repeat look frequency, and being able to communicate these measurements to anyone, anywhere, anytime, in real time. Studies suggest that a constellation of MEO satellites occupying equatorial and polar orbits (inclination = 90 degrees) could, in principle, accomplish this task. Also new on the horizon is solar sail technology. NOAA has been looking at solar sails as providing a propulsive system that could be used to maintain a satellite in a position closer to the Sun than L1. L1 is that point between the Earth and the sun where the gravitational forces of the Earth and the sun are equal. The sail would allow the increased gravitational force from the Sun to be balanced by the propulsive force of the solar sail. This capability could increase the lead-time for measuring and predicting the impact of solar events. Solar sails could also allow a satellite to be positioned over the Earth's polar regions continuously, filling a critical gap in current orbital observations and services. The combination of these technologies will enable the NOAA Satellites and Information Service to meet important requirements currently unmet and help satisfy NOAA strategic goals.

In order to meet the requirements, documented by the GOES user communities, the instruments designated for the GOES-R notional baseline include an Advanced Baseline Imager (ABI), a Hyperspectral Environmental Suite (HES), a Lightning Mapper, and advanced space and solar observing instruments. These instruments will first be launched in 2012. The Advanced Baseline Imager is a state of the art, 16-channel imager covering 6 visible to near-IR bands (0.47, 0.64, 0.86, 1.38, 1.61, and 2.26 mm), and 10 infrared (IR) bands (3.90 mm to 13.3 mm). Spatial resolutions are band dependent, 0.5 km at nadir for broadband visible, 1.0 km for near IR and 2.0 km for IR. The ABI will scan the Full Disk (FD) in approximately 5 minutes. The HES is a multi channel imager and sounder instrument suite with three threshold tasks. HES will provide high-spectral resolution Hemispheric Disk Soundings (DS) and Severe Weather Mesoscale (SW/M) soundings and Coastal Waters (CW) imaging. HES DS provides 10 km IR resolution from 3.7 mm to 15.4 mm with a one-hour refresh rate of the full disk, 62° local zenith angle. SW/M will cover a 1000 x 1000 km square in 4 minutes, at 4 km resolution for IR. HES CW task will provide at least 14 channels covering 0.4 mm to 1.0 mm, with a 300 m resolution and a 3-hour refresh rate. Coastal Waters are defined as the 400 km zone adjacent to CONUS. Additional capabilities include an improved Space Environment Monitor, a Solar X-Ray Imager, and direct user services, such as Search and Rescue (SAR), and a Data Collection System (DCS). This paper will focus on the planned instrument capabilities of the GOES-R Series, the space system architecture, and how the new capabilities will complement the future Global Observing System to meet the documented user needs.

This paper discusses activities related to mesoscale product development in preparation for the GOES-R satellite to be launched in 2012. These new image products will feature improved spatial, temporal, spectral, and radiometric resolution compared to current GOES imagery. Emphasis in this paper is on simulations of GOES-R date using observations from existing operational and experimental satellites.

In preparation for future satellites, we have developed a method to simulate satellite observations using a cloud-scale numerical model and radiative transfer models. In short, a numerical cloud model was used to simulate mesoscale weather events, including severe storms and tropical cyclones. A second model was used to calculate the brightness temperatures of the clear and cloud sky scenes from the model simulations. This procedure allows for advanced product development for severe weather (precipitation estimation, updraft diagnosis products) and tropical cyclones (intensity estimation). The development of products in advance of the satellite launch extends the useful life of the satellite system. Examples of this method for a severe storm case will be presented.

A solar ultraviolet detector prototype for the GOES spacecraft has been calibrated using the X24C beamline at the Brookhaven NSLS. Similar in design to the 3-channel SOHO CELIAS SEM, the GOES EUV uses a combination of transmission gratings and silicon photodiodes with thin-film metal overcoats to provide the required bandpasses. Four of the channels position the photodiodes at the first to fourth orders of 2500 and 5000 L/mm transmission gratings to provide spectral information over four wavelength bands from approximately 5-80 nm. The fifth channel positions the photodiode at first order of a 1667 L/mm transmission grating in combination with a bandpass filter centered at approximately 120 nm to provide coverage in the Lyman alpha region of teh solar spectrum. The GOES EUV will provid continuous monitoring of solar EUV in bandpasses that are known to have a large variability in the amount of energy deposition in the earth's ionosphere over a solar cycle. Prototype detector design and calibration procedure are discussed. Absolute responses of the design model and synchrotron beamline properties relevant to calibration are presented.

One of the important applications of satellite surface wind observations is to increase the accuracy of weather analyses and forecasts. The first satellite to measure surface wind over the oceans was Seasat in 1978. On board was a scatterometer, which measured radar backscatter from centimeter-scale capillary waves, from which surface wind speed and direction could be deduced. In more recent years, passive microwave remote sensing of the ocean surface has provided extensive observations of surface wind speed, and advanced scatterometers have been providing surface wind velocity data over the oceans. The initial impact of satellite surface wind data on weather analysis and forecasting was very small, but extensive research has been conducted since the early days of Seasat to improve the data accuracy and the utilization of these data in atmospheric models. Current satellite surface wind data are used to improve the detection of intense storms over the ocean, as well as to improve the overall representation of the wind field in numerical weather prediction models. As a result, these data are contributing to improved warnings for ships at sea and to improved global weather forecasts. Recent experiments conducted with data from the SeaWinds scatterometers aboard both Quikscat and ADEOS 2 indicate that increased coverage of scatterometer data can lead to even larger impacts than are routinely obtained now.

Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA-16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.

Microwave remote sensing over land has lagged behind remote sensing over oceans. This is due to the larger land emissivity values and their changes on daily to seasonal timescales. The lack of surface emissivity knowledge has hindered full exploitation of the capabilities of operational satellite sensors such as AMSU and SSM/I in remote sensing and data assimilation. Microwave land surface models that can be used to predict the emissivity have been developed and show promise, but independent global measurements of emissivity are needed for comparison. We have developed an optimal estimation retrieval, demonstrated with AMSU data, which retrieves global emissivity at frequencies from 23 to 183 GHz. A simultaneous retrieval of the temperature and water vapor profiles as well as cloud liquid water is performed. The simultaneous retrieval allows the masking effects of water vapor and some clouds to be reduced. The method does not currently use infrared data as a surface temperature constraint in clear sky regions. Initial comparisons with the NOAA NESDIS Microwave Emissivity Model are encouraging. The retrieval provides an independent estimate of emissivity, which is especially useful over difficult surface types such as snow and ice. These early results point the way to the creation of dynamic global emissivity fields which have applications to satellite microwave data assimilation, remote sensing of soil moisture, and future microwave sensors.

Land cover information is important for the study of physical, chemical, biological and anthropological process on the surface of earth. Remote sensing data has been used to produce the land cover map by visual interpretation or automatic classification method in the past years. IGBP DISCover land cover dataset is a global land cover dataset based on remote sensing method in recent years. Firstly, we present a method to compare different land cover dataset based on invariant reliable land unit. Secondly, we compare IGBP Discover land cover dataset with Chinese land cover dataset. Finally, we analyze the possible reasons impacting the differences among the land cover classifications. The comparison results show that most of the land surface in China was identified as different types in those two datasets. For example, 63.7% of the deciduous needleleaf forest units in CLCD are mapped to the mixed forest by IDLCD. The different classification scheme and method used in these datasets are most likely the reasons to explain the differences between them.

Information about the surface bi-directional reflectance distribution function (BRDF) and albedo is required as a boundary condition for radiative transfer modeling, aerosol retrievals, cloud retrievals, and atmospheric modeling. The typical spatial resolution provided by MODIS and MISR standard surface products (~1km) is insufficient to measure the BRDF of the pure surface types, because most pixels at this scale correspond to mixed classes. We present an approach for the retrieval of the basic surface BRDFs from the observations of MODIS/Terra and MISR using an angular unmixing method. Our analysis is focused on the Atmospheric Radiation Measurement (ARM) Program area in the Southern Great Planes (SGP) region, which is a predominantly agricultural area with a few major crop types. Pure surface classes were identified using high-resolution (30m) Landsat imagery and results of a ground survey. Assuming that the reflectance for each coarse pixel is a linear superposition of reflectances of basic surface types, it is possible to estimate the original BRDF parameters for each landcover type. In our case, three dominant classes were selected: wheat, grass, and baresoil. In the case of wheat and grass, the dispersion of the results is smaller than in the case of soil. This can be explained by the relatively low fractional coverage of the soil class within large pixels and by the significant variability of soil reflectance depending on wetness, soil type (sand, clay, etc.), and other factors. The correlation between the BRDF shape factors and the normalized difference vegetation index (NDVI) has also been analyzed. There is a high degree of correlation between the NDVI and BRDF isotropic factor (r0 in the case of MISR), while the correlation with other BRDF parameters was found to be smaller. In general, the NDVI can be used as a crude proxy for the BRDF shape.

Vegetation phenology is an important variable in a wide variety of Earth and atmospheric science applications. The role of remote sensing in phenological studies is increasingly regarded as a key to understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest phenology analysis. The phenology of forest covering Northeast China and its spatial characteristics were investigated using MODIS normalized difference vegetation index (NDVI) data. Threshold-based method was used to estimate three key forest phenological variables: start of growing season (SOS), end of growing season (EOS) and the growing season length (GSL). The spatial pattern of key phenological stages were mapped and analyzed. The derived phenological variables were validated by referring to previous research achievements in this study area. The phenological pattern of Changbaishan Reserve was compared with the distribution of forest types. Results indicate that spatial characteristics of vegetation phenology are corresponding with the distribution of vegetation types and the phenology information can be used to improve vegetation classification accuracy as an auxiliary variable.

Surface bi-directional reflectance distribution function (BRDF) and albedo properties are retrieved over the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) area. A landcover-based fitting approach is employed by using a newly developed landcover classification map and the MODIS 10-day surface reflectance product (MOD09). The surface albedo derived by this method is validated against other satellite systems (e.g. Landsat-7 and MISR) and ground measurements made by an ASD spectroradiometer. Our results show good agreements between the datasets in general. The advantages of this method include the ability to capture rapid changes in surface properties and an improved performance over other methods under a frequent presence of clouds. Results indicate that the developed landcover-based fitting methodology is valuable for generating spatially and temporally complete surface albedo and BRDF maps using MODIS observations.