This PDF file contains the front matter associated with SPIE Proceedings Volume 7456, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Satellite instrument radiometric stability is critical for climate studies. The Atmospheric Infrared Sounder (AIRS) radiances are of sufficient stability and accuracy to serve as a climate data record as evidenced by comparisons with the global network of buoys. In this paper we examine the sensitivity of derived geophysical products to potential instrument radiometric stability issues due to diurnal, orbital and seasonal variations. Our method is to perturb the AIRS radiances and examine the impact to retrieved parameters. Results show that instability in retrieved temperature products will be on the same order of the brightness temperature error in the radiances and follow the same time dependences. AIRS excellent stability makes it ideal for examining impacts of instabilities of future systems on geophysical parameter performance.

The geostationary meteorological satellites (GEO), such as Geostationary Operational Environmental Satellite (GOES), are susceptible to a calibration anomaly around local midnight of the sub-satellite point. A counter measure, the Midnight Blackbody Calibration Correction (MBCC) currently exists at operational level. In this study, the MBCC performance on GOES-11 satellite is characterized with the help of Global Space-based Inter-Calibration System (GSICS) data sets. Results from the comparison of coincident and collocated GSICS-based GOES-11-AIRS data pairs, corresponding to two and half year period from January 2007 through June 2009, reveal that "mid-night residuals" in brightness temperatures persist in all of the GOES-11 Infra-Red (IR) channels, in spite of MBCC. The GOES-11 split window channels (channels 4 and 5) consistently showed significantly large negative (GOES-11-AIRS) biases often reaching values of -1. 5 K or less while the short wave Infra-Red (SWIR) channel (channel 2) produced relatively smaller negative biases (~ -0.3 K or less). Interestingly, the water vapor IR channel (channel 3) exhibits a different pattern from rest of the channels in which consistently opposite biases with small positive (GOES-11-AIRS) difference values (~ 0.3 K or less) could be observed. The reason for the differential behavior of GOES-11 channel 3 is yet to be understood, while it is hypothesized that this might be linked to the convolution algorithm used for matching the AIRS data spectrally with those from GOES water vapor channel. The amount of midnight residuals is shown to have a consistent seasonal dependency, which gets repeated year after year, for the period considered in the analysis.

The Global Space-based Inter-Calibration System (GSICS) is a critical space component of the Global Earth Observation System of Systems (GEOSS) that provides users with high-quality inter-calibrated satellite radiances. In an early development, GSICS has implemented the inter-calibration of imaging instruments on geostationary (GEO) satellites with hyperspectral sounding instruments AIRS and IASI on Low Earth Orbit (LEO) satellites. This paper summarizes the major components and the theoretical basis of the baseline algorithm that is common to all implementations, and demonstrates the initial impact of the GSICS Correction.

The stability of surface and atmospheric conditions at the Antarctic Dome Concordia (Dome C) area provides a potential calibration and validation opportunity for sensors on-board polar orbiting satellites (POS). In this study, MODIS observed reflectances at the top of the atmosphere (TOA) over the Dome C site during a one-year period early in the mission are used to derive the bi-directional reflectance distribution function (BRDF). The BRDF is determined by combining the TOA reflectances with the surface BRDF obtained from tower measurements above the Antarctic snow. This study examines the agreement between the BRDF-predicted and TOA-observed reflectances over all available viewing and solar zenith angles. Applying the BRDF to MODIS observed reflectance trends from 2002 to 2009 shows that both Terra and Aqua MODIS are in excellent stable conditions with combined long-term drifts and relative biases within 2% in the VIS and NIR bands. The same BRDF is also applied to TOA reflectances observed by other POS sensors, including NOAA-15, 16, 17, 18, and Metop-A AVHRR and ENVISAT AATSR over the Dome C site in the 2008 summer season. Results show that the BRDF normalized reflectances provide a nearly flat trend for each POS sensor while differences among these sensors clearly indicate their calibration biases.

Pseudo-invariant ground targets have been extensively used to monitor the long-term radiometric calibration stability of remote sensing instruments. The NASA MODIS Characterization Support Team (MCST), in collaboration with members from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, has previously demonstrated the use of pseudo-invariant ground sites for the long-term stability monitoring of Terra MODIS and Landsat 7 ETM+ sensors. This paper focuses on the results derived from observations made over the Sonoran Desert. Additionally, Landsat 5 TM data over the Sonoran Desert site were used to evaluate the temporal stability of this site. Top-ofatmosphere (TOA) reflectances were computed for the closely matched TM, ETM+, and MODIS spectral bands over selected regions of interest. The impacts due to different viewing geometries, or the effect of test site Bi-directional Reflectance Distribution Function (BRDF), are also presented.

Since October 2006 EUMETSAT is flying the first operational European meteorological polar orbiting satellite Metop-A as the morning orbit part of the Initial Joint Polar System (IJPS) with the U.S. Metop-A is the first of a series of three in the frame of the EUMETSAT Polar System and carries a payload of eight meteorological instruments which provide inter alia sounding information for numerical weather prediction, ocean surface information, information on ozone and atmospheric chemistry. Most of the planned products are now operational. In addition, so called Day-2 products are developed or have already been developed. Such products include Soil Moisture from the Advanced Scatterometer ASCAT, a Vegetation index from the AVHRR imager and polar cap winds from AVHRR. About two years after the launch the first of these products have become operational: The soil moisture. The paper will discuss the first delivered Day-2 products and outline future development aspects. Future Day-2 products address improved radio occultation with the GRAS instrument and synergistic use of instruments for trace gas observations.

Global Space-based Inter-Calibration System (GSICS) is a critical space component of Global Earth Observation System of Systems (GEOSS) that provided users with high-quality inter-calibrated satellite measurements. As part of the GSICS, imaging instruments on geostationary (GEO) satellites have been inter-calibrated with hyperspectral instrument Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) on Low Earth Orbit (LEO) satellites. This paper reports the GSICS GEO-LEO inter-calibration at NOAA/NESDIS, for GOES-11/12 with AIRS (since January 2007) and IASI (since June 2007), and of METEOSAT-7/8/9, MTSAT-1R, and FY-2C with AIRS and IASI since August 2008. Major components of the operation are reviewed, including algorithm development, data processing, product generation, results dissemination, and selected inter-calibration examples. The preliminary results of the GSICS correction show that the fully functioning GSICS is a powerful tool to monitor instrument performance, to correct sensor bias, and to diagnose the root cause of calibration anomalies.

A combination of CIMEL radiometer and MODIS measurements are used to correct surface albedo models. In particular, we show through an analysis of hyperspectral high resolution Hyperion data that the correlation coefficient assumption underestimates ground albedo resulting in an overestimate of the VIS optical depth and operational collect 5 surface model shows an incorrect trend between the MVI index and the surface correlations. Preliminary radiative transfer calculations based on the same model show that this mechanism can help explain the observed overestimation and the corrected models have been implemented for NYC and Mexico City with significantly improved AOD.

Data from satellites and model simulations is increasing exponentially as observations and model computing power improve rapidly. Not only is technology producing more data, but it often comes from sources all over the world. Researchers and scientists who must collaborate are also located globally. This work presents a software design and technologies which will make it possible for groups of researchers to explore large data sets visually together without the need to download these data sets locally. The design will also make it possible to exploit high performance computing remotely and transparently to analyze and explore large data sets. Computer power, high quality sensing, and data storage capacity have improved at a rate that outstrips our ability to develop software applications that exploit these resources. It is impractical for NOAA scientists to download all of the satellite and model data that may be relevant to a given problem and the computing environments available to a given researcher range from supercomputers to only a web browser. The size and volume of satellite and model data are increasing exponentially. There are at least 50 multisensor satellite platforms collecting Earth science data. On the ground and in the sea there are sensor networks, as well as networks of ground based radar stations, producing a rich real-time stream of data. This new wealth of data would have limited use were it not for the arrival of large-scale high-performance computation provided by parallel computers, clusters, grids, and clouds. With these computational resources and vast archives available, it is now possible to analyze subtle relationships which are global, multi-modal and cut across many data sources. Researchers, educators, and even the general public, need tools to access, discover, and use vast data center archives and high performance computing through a simple yet flexible interface.

This paper addresses the potential applications of commercial remote sensing (CRS) technologies for bridge monitoring. High resolution optical-photonic images can provide bridge damage information including through-deck collision damages, large permanent deformations, overload cracking and surface erosions, as well as surrounding environmental information. This paper summaries bridge issues that can be detected from high resolution remote sensing imageries based on visual interpretation as guidance for remote sensing imagery based bridge inspection, and the development of future automatic detection methods. A LiDAR based automatic bridge evaluation system LiBE (LiDAR Bridge Evaluation) is introduced in this paper with an application example of a test bridge maintained by the Los Angeles County (CA) Department of Public Works. Laser scanning techniques have also been used for bridge load testing in a new bridge near Charlotte, NC and maintained by the North Carolina DOT. The primary results of these preliminary tests are also presented. Remote sensing techniques are introduced as a supplement to the existing, required visual bridge inspections.

In development of retrieval algorithm for satellite land surface temperature (LST) measurements, it is crucial yet difficult to estimate precision and accuracy of the algorithm using ground measurements. In this effort, we built up a theoretical model for estimating the random error of the satellite measurement. The method requires a series of surface temperature measurements from three independent data sources. In our case, they were collected from the GOES-8, GOES-10 Imagers and the SURFace RADiation (SURFRAD) budget network stations. SURFRAD data at five sites in the year 2001 were processed along with the corresponding GOES-8 and -10 Imager data. A manual cloud filtering procedure was applied to ensure a high quality cloud-free data set. An LST retrieval algorithm developed for the GOES-R mission was applied to the GOES-8 and -10 data, while the SURFRAD data provided the third independent LST estimation. Standard deviation errors of the three measurements were calculated from the theoretical model, and biases of the measurements were estimated with some assumptions. The method was particularly developed for evaluating GOES-R LST algorithm. It may have wider applications in remote sensing development and applications.

NESDIS/Center for Satellite Applications and Research (STAR) has been reprocessing and recalibrating observations from the Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) to generate atmospheric temperature climate data record (CDR). To obtain reliable atmospheric temperature trends from the dataset, diurnal drift errors due to orbital drift must be removed from the time series. This adjustment is especially important for the MSU/AMSU mid-tropospheric temperature product over land where diurnal-drift effect is large. In this study, we applied the diurnal anomalies developed by the Remote Sensing Systems (RSS) to the STAR MSU/AMSU atmospheric temperatures CDR and examined how the correction affects the trend and intersatellite biases over land. A scaling factor was introduced to multiply the RSS diurnal anomalies to account for uncertainties in the dataset. The results show that the diurnal drift has negligible effect on the mid-tropospheric temperature trends over oceans, which is consistent with previous investigations. However, the trend over land is very sensitive to the magnitude of the scaling factor. The final scaling factor was determined by minimizing intersatellite temperature differences over land. The trend values corresponding to such a scaling factor for the 28-year (1979-2006) merged MSU T₂ time series are 0.193 K/Decade over the global land and 0.180 K/Decade over the global ocean. The global mean T₂ trend is 0.183 K/decade.

The next-generation Geostationary Operational Environmental Satellites (GOES), designated the GOES-R Series, will provide continuity and improvement of remotely-sensed environmental data from a geosynchronous orbit in the 2015- 2028 era. The GOES-R Ground Segment (GS) Project will acquire the integrated, distributed GS that will conduct satellite operations and product generation and distribution. This paper describes improvements in GOES-R sensors and measurements over previous GOES satellites and facets of the product generation subsystem capabilities. The GS will be capable of producing several key environmental products at low latency on a continuous basis.

The NOAA GOES-R Advanced Baseline Imager (ABI) will have nearly the same capabilities as NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) to generate multi-wavelength retrievals of aerosol optical depth (AOD) with high temporal and spatial resolution, which can be used as a surrogate of surface particulate measurements such as PM2.5 (particulate matter with diameter less than 2.5 μm). To prepare for the launch of GOES-R and its application in the air quality forecasting, we have transferred and enhanced the Infusing satellite Data into Environmental Applications (IDEA) product from University of Wisconsin to NOAA NESDIS. IDEA was created through a NASA/EPA/NOAA cooperative effort. The enhanced IDEA product provides near-real-time imagery of AOD derived from multiple satellite sensors including MODIS Terra, MODIS Aqua, GOES EAST and GOES WEST imager. Air quality forecast guidance is produced through a trajectory model initiated at locations with high AOD retrievals and/or high aerosol index (AI) from OMI (Ozone Monitoring Instrument). The product is currently running at http://www.star.nesdis.noaa.gov/smcd/spb/aq/. The IDEA system will be tested using the GOES-R ABI proxy dataset, and will be ready to operate with GOES-R aerosol data when GOES-R is launched.

For the next-generation of GOES-R instruments to meet stated performance requirements, state-of-the-art algorithms will be needed to convert raw instrument data to calibrated radiances and derived geophysical parameters (atmosphere, land, ocean, and space weather). The GOES-R Program Office (GPO) assigned the NOAA/NESDIS Center for Satellite Research and Applications (STAR) the responsibility for technical leadership and management of GOES-R algorithm development and calibration/validation. STAR responded with the creation of the GOES-R Algorithm Working Group (AWG) to manage and coordinate development and calibration/validation activities for GOES-R proxy data and geophysical product algorithms. The AWG consists of 15 application teams that bring expertise in product algorithms that span atmospheric, land, oceanic, and space weather disciplines. Each AWG teams will develop new scientific Level- 2 algorithms for GOES-R and will also leverage science developments from other communities (other government agencies, universities and industry), and heritage approaches from current operational GOES and POES product systems. All algorithms will be demonstrated and validated in a scalable operational demonstration environment. All software developed by the AWG will adhere to new standards established within NOAA/NESDIS. The AWG Algorithm Integration Team (AIT) has the responsibility for establishing the system framework, integrating the product software from each team into this framework, enforcing the established software development standards, and preparing system deliveries. The AWG will deliver an Algorithm Theoretical Basis Document (ATBD) for each GOES-R geophysical product as well as Delivered Algorithm Packages (DAPs) to the GPO.

The radiometric intercomparison of instruments is a key element in developing climate-quality data records. In this study we compare data from the first two years of the Infrared Atmospheric Sounding Interferometer (IASI) with the matching data from the Atmospheric Infrared Sounder (AIRS). We compare observed spectra in cloud-free areas of the tropical oceans at night to spectra calculated using data from the European Centre for Medium-Range Weather Forecasts (ECMWF). We use five frequencies-three window channels, one mid-tropospheric sounding channel, and one lower stratospheric sounding channel. The use of ECMWF data as a transfer standard permits comparisons of many more points distributed more widely over the globe than is possible with the traditional simultaneous nadir overpass (SNO) technique. The analysis shows that AIRS and IASI daily mean brightness temperatures track each other within 100 mK, in spite of the fact that the instruments are in different orbits. AIRS was launched into polar orbit on the EOS Aqua spacecraft on May 4, 2002. It is a grating spectrometer with 2378 channels in the range 3.7 to 15.4 microns. IASI was launched into polar orbit in October 2006 on the METOP-A spacecraft. IASI is a Fourier transform spectrometer covering 3.7 to 15.5 microns in three bands with a total of 8461 channels.

With the availability of very accurate six hour forecasts, the metric of accuracy alone for the evaluation of the performance of a retrieval system can produce misleading results: the retrievals may be statistically accurate, but be of little value compared to the accurate forecast. A useful characterization of the quality of a retrieval system and its potential to contribute to an improved weather forecast is its skill, which we define as the ability to make retrievals of geophysical parameters which are closer to the truth than the six hour forecast. We illustrate retrieval skill using one day of AMSU-A and AIRS data with three different retrieval algorithms. In the spirit of achieving global retrievals under clear and cloudy conditions, we evaluated retrieval accuracy and skill for 90% of the covered area. Two of the three algorithms meet the 1 K/1 km "RAOB quality" accuracy requirement and have skill between 900 and 150 hPa, but none have skill between the surface and 900 hPa. AIRS was launched on the EOS Aqua spacecraft in May 2002 into a 705 km polar sun-synchronous orbit with accurately maintained 1:30 PM ascending node. Essentially un-interrupted data are freely available since September 2002.

In this paper, a practical scheme for assimilation of multi-temporal and multi-polarization ENVISAT ASAR data in rice crop model to map rice yield has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the temporal single-polarized rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000. The assimilation method consists in re-initializing the model with optimal input parameters allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e. the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced finally. The scheme was applied over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production situation, with an accuracy of 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.

By using the NCEP/NCAR daily reanalysis data, CMAP precipitation data , daily precipitation data of 740 stations in China and some remote sensing data, features of the short-term position variation of the west Pacific subtropical high(WPSH) during the torrential rain in Yangtze-Huaihe river valley and its possible cause are analyzed. Results show that the short-term position variation of WPSH is closely associated with the diabatic heating. During the torrential rain period, the apparent heating source and apparent moisture sink are exceptionally strong over Yangtze-Huaihe river valley( on the northwest side of WPSH )and the Bay of Bengal (to the west of WPSH). Based on the complete form of vertical vorticity tendency equation, it is found that the heating field over Yangtze-Huaihe river valley during the torrential rain period, which is in favor of the increase of cyclonic vorticity on the north side of WPSH, is unfavorable to the WPSH moving northward. And the heat source over the Bay of Bengal ,which is in favor of the increase of anti-cyclonic vorticity on the west of WPSH, may induce the westward extension of WPSH.

In terms of monthly NCEP/NCAR and 160 site temperature data from NCC (National Climate Center), the main modes of January surface air temperature in 1979-2008 over China and possible mechanism of typical cold/warm episodes are investigated. Results show that the first mode for January temperature is characterized by consist variation in China, which is closely related to circulation anomalies in stratosphere. From the wave source over East Asian in stratosphere wave fluxes propagate downward and westward, and in upper troposphere over North Atlantic there is a remarkable convergent area of wave flux leading to the ridge enhanced with stronger heat transforming to the North and front zone moving to more northerly. Thereby jet stream becomes strong and expands to East Atlantic with positive (negative) NAO anomaly pattern and higher pressure occurs south to Baikal indicating stronger (weaker) than normal cold air, which is helpful for lower (higher) temperature appearing over China

By using GEOS data assimilation system, we investigate the influence of TRMM precipitation products on the structure and underlying physics of ISO in a GCM assimilation system. We get the following conclusion: 1) In 1998 summer, the strong ISO is apparently in Asia monsoon region off equator and and east equatorial Pacific region .2) 20- 30-day eastward propagation is the dominant mode of ISO near equator. 30-60-day northeastward propagation is the dominant mode of ISO north of equator averaged over 10°N-25°N. 3) For 20-30-day eastward propagation near equator, the wavenumver 1 intensity of control run is usually weaker than that of assimilation run and observation. For 30-60-day northward propagation averaged over 60°E-140°E, the wavenumber 1 intensity of control run is usually stronger than that of assimilation run and observation. 4)By comparing the correlation coefficients and RMS of Kelvin wave structure for geopotential height and wind vector at 150hPa between control run and observation with assimilation run with observation, we can conclude that the assimilation run are more resemble observation, especially near equator. 5) For assimilation run, the convection, which occurred between high and low geopotential height at 150hPa or between easterly and westerly and the two anticyclone at 150hPa off equator, are more organized than that for control run compared with observation. The better Kelvin wave features for the eastward-propagating MJO in the tropic from the assimilation with TRMM precipitation imply that latent heating is very important in exciting equatorial MJO.

By using a hybrid model tier-1 that is coupled in the Indo-Pacific tropical ocean, we perform a set of 10 ensemble runs with different initial condition for two-season period that starts from November 1st and May 1st respectively in 1982 though 2004. The tier-1.5 that is only coupled from India Ocean to dateline and prescribes model monthly climatology plus observed SSTA in east tropical Pacific is also used to perform another suite of 10 ensemble runs with same initial conditions. The sensitivity of Asia monsoon and Australian monsoon to SST in central and eastern tropical Pacific has been investigated. The results of experiments suggest that, 1) the climatology precipitation bias has close relationship with climatology SST bias which possibly cause overabundant rainfall over Arab Sea and Bay of Bengal, northward shift rain belt over West Pacific in summer, deficient rainfall of ITCZ over tropical Pacific in summer, too long south branch of ITCZ over southern Pacific in winter. 2) To east of dateline, the precipitation external variance and internal variance in tier-1.5 are much larger than those in tier-1.5. 3) The similar signal to noise ratio or error to external ratio as well as anomaly correlation over Asia monsoon region between in tier-1 and tier-1.5 suggests that precipitation predictability over Asia monsoon region has little relationship with the improvement of central and east Pacific SST. 4) The improvement spatial correlation in winter by tier-1.5 over El Nino region during La Nina year, especially when the cold SST over Nino3.4 turn into warm SST suggest that the winter precipitation predictability over El Nino region can possible be improved by SST predictability.