This PDF file contains the front matter associated with SPIE Proceedings Volume 7811, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Since the 1970s, an extensive series of data impact studies has been performed to evaluate and enhance the impact of satellite surface wind data on ocean surface wind analyses and fluxes, atmospheric and oceanic modeling, and weather prediction. These studies led to the first beneficial impacts of scatterometer winds on numerical weather prediction (NWP), the development of the methodology to assimilate surface wind speeds derived from passive microwave radiometry, and the operational use of satellite surface winds by marine forecasters and NWP models. In recent years, the impact of these data on NWP has decreased as more competing data have become available; however, the results of our recent experiments still show a very significant impact of satellite surface winds on ocean surface wind analyses and on the prediction of selected storms over the oceans.

Infrared (IR) observations from Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)-2 satellite are assimilated into NCEP global data assimilation system (GDAS). The assimilation of current Geostationary Operational Environmental Satellite (GOES) Imager data was also reinvestigated. The Community Radiative Transfer Model (CRTM) was used as observation operator for the assimilation of SEVIRI radiance observations. The CRTM was enhanced with several critical components for IR radiance assimilation, such as a dynamically-updated land surface emissivity data, as well as fast and accurate gaseous absorption modules. The Global Space-Based Inter-Calibration System (GSICS) calibration corrections were applied to improve the simulation of SEVIRI and GOES Imager radiances. Preliminary results show that inclusions of the SEVIRI radiances at water vapor channels (6.25 and 7.35 micron) and CO2 channel (13.4 micron) in Global Forecast System (GFS) produced significant positive impacts on the six-day forecasts. Assimilation of other five SEVIRI IR window channels in GFS reduces the positive impact. By applying GSICS calibration algorithm to correct SEVIRI and GOES-12 Imager observations biases, the impacts on GFS forecast were improved. Further studies are being carried out to improve the effective assimilation of SEVIRI IR window channels radiances.

A one-day workshop was held on December 10, 2009 at the National Institute of Standards and Technology to address the issue of data gaps in the time series of satellite measurements. Such gaps can occur due to launch delay, launch failure, inconsistencies, or data jumps in radiometric scales between satellites. The presence of such gaps limit the ability of using measurements to detect the small changes in key environmental variables that result from climate change. Leading experts in the Earth Observation community from the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Adminstration (NOAA), United States Geological Survey (USGS), and academia attended the meeting to prioritize the calibration strategies for bridging and mitigating satellite data gaps for climate change detection. These strategies include establishing SI traceability for satellite sensor calibration and measurements; continuing improvements in prelaunch, onboard, and vicarious calibrations and transfer standards; establishing celestial standards and procedures for intercomparisons; establishing SI traceability for alternative measurement strategies, such as in-situ networks and airborne sensor campaigns; and leveraging international satellite assets. This paper summarizes the workshop and recommendations.

Stable earth sites are essential for comparing the measurements from different satellite instruments in the visible and near-infrared in order to maintain the consistency of radiometric calibration, and for quantifying the sensor degradation over time. This study focuses on the radiometric and spectral characterization and comparison between two potential calibration sites, Dome C and Sonoran Desert. The long-term TOA reflectance time series analysis using MODIS observation shows that the radiometric stability of Dome C and Sonoran Desert is better than 2% over the period of 8 years. The study also shows that Dome C is much affected by seasonal variation due to bi-directional reflection, compared to the Sonoran Desert, although the BRDF normalization reduced the uncertainty of Dome C observations to less than 2% for both the visible and NIR band. For AVHRR band 2, at Sonoran Desert, a large variability (>6%) is observed compared to that of MODIS (<2%) due to water vapor absorption. The spectral characteristics of these sites studied using EO-1 Hyperion sensor further show the water vapor absorption differences at the two sites. Nevertheless, the operationally calibrated AVHRR TOA reflectance at both Dome C and Sonoran Desert are significantly lower than that of MODIS, primarily due to calibration traceability issues. The study suggests that, both Dome C and Sonoran Desert sites can be used for postlaunch calibration/validation of the visible/near-infrared bands with uncertainty less than 2% excluding channels affected by water vapor.

The collocated measurements in 3.74μm, 11μm, and 12μm channels from Advanced Very High Resolution Radiometer (AVHRR) and corresponding simulated AVHRR measurements using hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) observations are inter-compared. Both of the instruments are placed on MetOp-A satellite launched in October 2006. Because IASI observations did not have complete spectral coverage over AVHRR 3.74μm channel, Line-By-Line Radiative Transfer Model (LBLRTM) simulated IASI spectra were generated to enable complete IASI coverage for this channel. It is shown that the large AVHRR minus IASI negative bias in 3.74μm channel can be explained more or less completely by the part of the AVHRR spectral band not seen by IASI which is an indication of relatively large absorption in that particular portion of the AVHRR spectral band. The near similarity between slopes of bias dependency on scene radiance from the model and those derived from observations with respect to 3.74μm channel indicate that it could be mostly the CO2 absorption in the higher wave-numbers experienced by AVHRR and not experienced by IASI causing the discrepancy between these two observations. Thus the study confirms that AVHRR short wave infrared channel (3.74 μm) is performing very well with no indication, of spectral uncertainties, or of significant radiometric uncertainties. On the other hand, the results suggest that AVHRR 3.74 um channel experiences significant CO₂ absorption which may disqualify it from being recognized as a "window channel." With respect to long wave infrared channels at 11 μm and 12 μm the study reveals that the bias between the two measurements undergo seasonal variations, however, with small magnitudes.

Land surface albedo is crucial for land surface radiation and energy budgets. In this study we compared the MODIS 16-day albedo product (MCD43A3) with field-measured data in Qinghai-Tibet Plateau. The validation data were used from 4 automatic weather stations(AWS) locations, spanning the year 2002-2008. Results indicate that MCD43A3 albedo product in snow-free seasons is in good agreement with ground-based observations, with a bias of ±0.02-0.05. But in snow season of Qinghai-Tibet Plateau, the MCD43A3 albedo product reaches a high bias. One of the possible reasons is that the amount of bidirectional reflectance observations may not be sufficient for getting the high quality surface albedo retrieval because of cloudy weather during the snowing days. Another reason may be that the heterogeneity of snow surface and complexity of snow grain. It is well known that the snow albedo is influenced by many parameters. However, the accumulated daily maximum temperature is shown to be a good predictor of the snow albedo. And also the snow albedo may effected by snow depth and snow water equivalent. In this paper, we improved a snow albedo retrieval model through daily maximum temperature from AWS and SWE from AMSR-E which can provide time series observations during snowing and snowmelt period. Also the AMSR-E SWE product has a coarse-resolution (25km) and has some uncertainties, the results show better correlation with the field-measured snow surface albedo. The 16-day average value of this algorithm performs well when there is snow in spring.

Spectral absorption properties of total suspended matter (TSM) and colored dissolved organic matter (CDOM) are important for the use of the bio-optical model to estimate water quality parameters. This study aims to investigate the variation in the absorption coefficients of TSM and CDOM of inland waters. A total of 92 water samples were collected from Shitoukoumen Reservoir and Songhua Lake in Northeast China, analyzed for TSM and Chl-a, and measured for the absorption coefficient of TSM, CDOM and total pigments using a laboratory spectrophotometer. The absorption coefficient of TSM has been decomposed for phytoplankton and inorganic sediments. The results show that for Shitoukoumen Reservoir, CDOM has strong absorptions with shallow absorption slopes (i.e., the coefficient S in a(λ)=a(λ₀)exp[-S(λ- λ₀)]) and large absorption at 355 nm; and for Songhua Lake, CDOM follows similar spectral absorption curves but less variation in the S value. The results also show TSM has the average absorption coefficient 5.7 m^-1 at 440 nm and 0.93 m^-1 at 675 nm, and their concentration is well correlated to TSM with R² larger than 0.85 at 440 nm over both Songhu Lake and Shitoukoumen Reservoir. In summer, CDOM was mainly terrigenous and had a high proportion of humic acid derived from the decomposition of phytoplankton and there were no obvious difference of S value. The results indicate that inorganic sediments contributed much more absorption than phytoplankton pigments in Shitoukoumen Reservoir than that in Songhua Lake, and there is strong association of TSM concentration to absorption coefficient at 440 nm.

A set of circulation indices are defined and calculated to characterize monthly mean polar vortex at 10 hPa geopotential height chart in the Northern Hemisphere, including area-(S), intensity-(P) and center position (λ_c, φ_c)-indices by use of 1948-2007 NCEP/NCAR 10 hPa monthly height data. These indices series are used to investigate the seasonal variation and interannual anomaly of polar vortex, along with the relations with global warming, ozone anomaly and Arctic Oscillation (AO). The results show that (1) there is anticyclonic (cyclonic) from Jun. to Aug. (from Sep. to Mar.). The change of spring circulation pattern is slower than that of autumn. (2) S can be replaced by P due to the interannual synchronal variations of the intensity and area for polar vortex. The interannual (interdecadal) variations of P are significant in Jan. (Jul.). (3) The anomalies of system center position in Jan. are more evident than that in Jul. (4) The variations of mean temperature at mid-stratosphere in the vicinity of pole zone in Jan. are different from that in Jul., but they are synchronal with the corresponding P and not significant correlation with the trend of global warming. However, the relationship between P and total O3 in Jul. are obvious. (5) There is so notable correlation between P and AO that P can represent AO.

The land surface temperature is an important parameter to hydrology and meteorology, it affects the exchange of sensible and latent heats between atmosphere, sea and land, and it can not be lack in many research fields. To retrieve land surface temperature exactly and quantificationally will promote the development of research areas such as drought forecasting crop yield estimating numerical weather forecast, global climate change and carbon balance. Therefore, retrieval of land surface temperature using thermal infrared remote sensing becomes one of the most important tasks in quantificational remote sensing study. The TM images are used in this article, which were recorded in June 11, 2001 over Nakchu area in Tibetan Plateau, to calculate the land surface temperature. The natural surface is classified based on information of remote sensing (snow, water and other land surface) and relevant information of geography, then the emissivity can be dealt with by each surface type in different way. Last, the land surface temperature is inversed by mono-window algorithm. The result show that the derived regional distributions of the the land surface temperature for the whole mesoscale area is agreed with the land surface status very well.