This PDF file contains the front matter associated with SPIE Proceedings Volume 7085, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

GSICS was established in response to the challenge of providing accurate satellite data acquired by instruments of different types, at different times, and operated by different agencies. This paper summarizes the recent activities and achievements of the GSICS Research working Group.

The creation of multi-decadal data sets for climate research requires better than 100 mK absolute calibration accuracy for the full range of spectral temperatures encountered under global conditions. Validation that this accuracy is achieved by the operational hyperspectral sounders from polar orbit is facilitated by comparing data from two instruments. Extreme radiometric calibration stability is critical to allow a long time series of noisy, but presumably long-term accurate truth measurements to be used for the validation of absolute accuracy at the 100 mK level. We use the RTGSST in the tropical oceans as ground truth. The difference between the AIRS derived sst2616 and the RTGSST based on six years of data shows a systematic cold bias of about 250 mK, but better than 4 mK/year stability. The double difference between AIRS and the RTGSST and IASI and the RTGSST with less than one year of data already allows statements at the 100 mK absolute level. It shows a 60 mK difference between the AIRS and the IASI calibration at 2616 cm^-1 and 300 K, with a statistically insignificant 20 mK shift in six months.

There are significant challenges in making the observations from HIRS (High Resolution Infrared Radiation Sounder) on the 13+ satellites consistent for climate change detection. It is well known that for HIRS, the inter-satellite biases are significantly affected by differences in the spectral response functions (SRF) between instruments, since they often lead to observations of the atmosphere at different altitudes. The SRF dependent biases are further mixed with other effects such as the diurnal cycle due to observation time differences and orbital drifts, blackbody emissivity, and calibration algorithms. In this study, the IASI (Infrared Atmospheric Sounding Interferometer) observations are used to calculate the HIRS radiances by convolving the IASI observed radiances with the SRF of each HIRS model across different climate zones in different seasons, which separates the SRF induced intersatellite biases from other factors. It is found that the calculated radiance ratios using IASI observations for the HIRS satellite pairs form bell shaped curves that vary with the HIRS model, channel, as well as climate zones. Understanding the characteristics of these bell curves are essential for resolving the SRF dependent intersatellite biases and the development of fundamental climate data records from HIRS.

NOAA/NESDIS/Center for Satellite Applications and Research has been reprocessing and recalibrating the Microwave Sounding Unit (MSU) observations to generate atmospheric temperature dataset with climate quality. So far, observations from the MSU channels 2, 3, and 4 for NOAA 10, 11, 12, and 14 have been recalibrated using a recently-developed SNO (simultaneously nadir overpasses) sequential nonlinear calibration technique and a 20-year long deep-layer atmospheric temperature dataset from 1987 to 2006 has been generated. However, when using the SNO nonlinear technique to intercalibrate the MSU instrument for satellites before NOAA 10, one has to deal with the short overlap issue for satellites between NOAA 9 and NOAA 10. In this study, by extending the spatial distance criterion for the SNO matchups, we generate more SNO samplings for the short-overlapping satellites. We analyze the error characteristics of the SNO matchups when the spatial distance is extended to as large as 650km. We also generate calibration coefficients using the SNO nonlinear sequential intercalibration technique and then analyze how the intercalibration affects the SNO biases with different separation distances. These analyses will be helpful in determining the final calibration coefficients used for generating consistent MSU long-term temperature time series that will include all available satellites.

Northrop Grumman Corporation (NGC) provides systems and technologies to ensure national security based on technologies - from undersea to outer space, and in cyberspace. With a heritage of developing and integrating science instruments on space platforms and airborne systems, NGC is conducting analysis of alternatives for a global observing system that integrates data collected from geostationary and polar-orbiting satellites with Unmanned Aerial System (UAS) platforms. This enhanced acquisition of environmental data will feed decision support systems such as the TouchTable ® to deliver improved decision making capabilities. Rapidly fusing and displaying multiple types of weather and ocean observations, imagery, and environmental data with geospatial data to create an integrated source of information for end users such as emergency managers and planners will deliver innovative solutions to improve disaster warning, mitigate disaster impacts, and reduce the loss of life and property. We present analysis of alternatives of combinations of sensor platforms that integrate space and airborne systems with ground and ocean observing sensors and form the basis for vertically integrated global observing systems with the capacity to improve measurements associated with hazard and climate-related uncertainties. The analyses include candidate sensors deployed on various configurations of satellites that include NPOESS, GOES R, and future configurations, augmented by UAS vehicles including Global Hawk, configured to deliver innovative environmental data collection capabilities over a range of environmental conditions, including severe hazards, such as hurricanes and extreme wildland fires. Resulting approaches are evaluated based on metrics that include their technical feasibility, capacity to be integrated with evolving Earth science models and relevant decision support tools, and life cycle costs.

The Man computer Interactive Data Access System (McIDAS) project began over 30 years ago at the University of Wisconsin-Madison to analyze and visualize data from the first generation of geostationary weather satellites. McIDAS continues to provide a strong data analysis and visualization capability for the current environmental satellites. However, the next generation of operational remote sensing instruments under development for the NPOESS and GOES-R programs require software tools with expanded capability and performance to support innovative techniques for developing algorithms, visualizing data and products, and evaluating results. A project is underway at SSEC to develop the fifth generation of McIDAS, a java-based, open-source system for multispectral and hyperspectral researchers and algorithm developers that will provide powerful new data manipulation and visualization tools to work in this data rich environment. NASA EOS MODIS and AIRS data as well as MSG SEVERI and METOP IASI data are now being used in conjunction with in situ and gridded data to develop new analysis and product validation techniques in the McIDAS-V environment. This new data analysis and visualization system will support both researchers and operational users of the advanced measurement systems on NPOESS and GOES R.

The algorithm for the current Geostationary Operational Environmental Satellite (GOES) Sounders has been adapted to produce atmospheric temperature and moisture legacy profiles from simulated infrared radiances of the Advanced Baseline Imager (ABI) onboard the next generation GOES-R. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) Meteosat-8/9 is used as proxy to test the algorithm because it has many of the same spectral and spatial features as ABI. The impact of radiative transfer model on the algorithm is evaluated by comparing two models: the PFAAST and the RTTOV9.1. It is found that RTTOV9.1 is better than PFAAST. The selection of numerical forecast profiles as first guess in the retrieval is another key factor. We compared the retrievals by using a global model (ECMWF 12H forecast) and a regional (RAM-3H forecast) as first guess, respectively. It is found that the retrieval of low-level water vapor by regional model is better than global model because of the higher spatial/temporal resolution of regional model.

The potential use of the inter-calibration results to optimally integrate and merge data from GOES 11 and 12 imagers to create consistent, seamless global products is explored in this study. There are three steps involved, including 1) limb correction; 2) tying GOES measurements to IASI; and 3) resolving the SRF-difference-induced biases. We first use the IASI hyperspectral measurements on the polar-orbiting MetOp-A satellite to access the calibration accuracy of water vapor channels on the GOES-11 and GOES-12 imagers with one year of match-up data. The simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES Imager SRFs to compare with GOES Imager observations. Assuming that IASI is well calibrated and can be used as a radiometric reference standard, the GOES imager water vapors were found to have an estimated calibration accuracy of less than 0.3 K (with a standard deviation of less than 0.2 K) at the BT range 240-260K relative to IASI, which meets the GOES imager design specification (1.0 K calibration accuracy for infrared channels). In a second step, merging GOES-11 and GOES-12 water vapor channel through IASI is investigated. A linear relationship is proposed. An example of creating water vapor composite image from the GOES-11 and GOES-12 to resolve their observational discrepancy is presented step-by-step. This study further demonstrates the usefulness of employing high spectral resolution radiance measurements to accurately assess broadband radiometer calibration and create the calibration link between instruments. In the future, we will extend this method to other satellites.

Soil Moisture Active/Passive (SMAP) Mission is one of the first satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the moisture present at Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow better estimates of water and energy transfers between Earth's surface and atmosphere, which are primary driving factors for weather and climate. Soil moisture measurements are also of great importance in assessing flooding potential and as input to flood prediction models. Conversely, observations of widespread low soil moisture levels can provide early warning of drought conditions, reduced water supply and crop loss. SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP freeze/thaw timing observations will also reduce a major uncertainty in quantifying the global carbon balance and will help resolve the problem of the missing carbon sink. The SMAP mission concept would utilize L-band radar and radiometry. These instruments will share a rotating 6-meter mesh antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days.

Soil moisture has long been recognized as one of the critical land surface initial conditions for numerical weather, climate hydrological predictions, particularly for transition zones between dry and humid climates. However, none of the currently existing soil moisture products has been used operationally in these models because of their consistency and reliability issues. A consistent and qualitatively reliable global soil moisture product is thus in desire to make good use of observations from different microwave sensors, such as AMSR-E, WindSat and TMI. This study explores the potential of WindSat data for producing such a product using the single channel algorithm (SCA) for soil moisture retrieval in conjunction with field observations for calibrating the algorithm and for validation. The preliminary results show good agreement between the results from WindSat and NASA AMSR-E product both in terms of spatial pattern and magnitude. The validation results show that the differences between the retrieved soil moisture from WindSat data and the ground measurements are below 0.05 (vol/vol) in most cases, meaning a great potential of WindSat data for producing a blended product. Further cross calibration between the brightness temperatures from different sensors might be needed for producing such a blended product.

Based on the brightness temperature observations of AMSR-E in 2005 and 2006, this study attempts to find the simplest and best drought index for identifying drought areas. Totally 8 candidate drought indices were tested from the AMSR-E brightness temperature data. Assuming that the NASA AMSR-E soil moisture data obtained from complicated soil moisture retrieval algorithms are the best currently available soil drought indicator, the computed index with the best correlation with these soil moisture data should the best drought index. For each of the selected three stations in Hebei province, the temporal correlations between this drought index and the corresponding precipitation anomalies were computed for the two years. The spatial variations of this drought index and the precipitation anomaly were also compared with each other for Hebei province in March 2006. Based on these comparisons, the selected drought index was found to be useful for monitoring drought. Problems for using satellite microwave observations and future research needs on microwave drought monitoring are discussed.

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.

Evaluation of satellite land surface temperature (LST) is one of the most difficult tasks in LST retrieval algorithm development, because of spatial and temporal variability of land surface temperature and surface emissivity variations. A large number of high quality "match-up" satellite and ground LST data is needed for the evaluation process. In developing a LST algorithm for the GOES-R Advanced Baseline Imager, we produced a set of "match-up" dataset from SURFace RADiation (SURFRAD) budget network ground measurements and GOES-8 and -10 satellite measurements. The dataset covers one-year GOES Imager data over six SURFRAD sites in the United States. A stringent cloud filtering procedure was applied to minimize cloud contamination in the match-up dataset. Each of the SURFRAD sites contains enough match-up data pairs for ensuring significance of statistical analyses of the LST algorithm. The evaluation was performed by directly and indirectly comparing the SURFRAD and satellite LSTs of each site. The direct comparison was illustrated using scatter plots and histogram plots of the ground and the satellite LSTs, while the indirect comparison was performed using a matrix analysis model developed by Flynn (2006)[1]. We demonstrated that LST measurements from the SURFRAD instrument can be used in our evaluation of the GOES-R LST algorithm development and the precision of the GOES-R LST algorithm can be fairly well estimated.

This study aims to analyze impacts of the NESDIS new product of green vegetation fraction (GVF) data on simulated surface air temperature and surface fluxes over the continental United States (CONUS) using the Nonhydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system, i.e. WRF-NMM, coupled with the Noah land surface model (LSM). The new global 0.144 by 0.144 degree GVF dataset is an AVHHR-based, near real-time weekly dataset starting from 1982. It has better quality and a higher temporal resolution than the old monthly GVF dataset that is currently used in the NOAA operational numerical weather prediction models. The new weekly climatology GVF data shows a higher percentage of greenness fraction over most US areas than the old dataset, with the largest differences by 20-40% over the southeast U.S., the northern Middle West, and the west coast of California in summer. We have performed some case studies over CONUS during July 2006. In general, using the new GVF data cools predicted surface temperature over most regions compared to the old data, with the largest cooling over regions with the largest GVF increase. The latent heat increases significantly over most areas while the sensible heat decreases slightly. These results are physically consistent as more of the net radiation is dissipated in form of latent heat via enhanced evapotranspiration in response to increasing vegetation cover. Compared with observations, the new GVF application reduces the WRF-NMM 2-m surface air temperature warm biases, 2-m relative humidity negative biases, and their RMSEs.

An adjoint limited area numerical weather prediction model with multiple nests has been developed. The adjoint modeling system has the capability to pass gradient information from the finer spaced nest to the coarser spaced domain. Therefore, gradients of scalar functions calculated from small scale features can be computed with respect to the large scale model state. Sensitivity experiments were performed to show that the nested adjoint model produces physically meaningful gradients. Results from data assimilation experiments will be presented at the conference. An adjoint model such as the one presented here, could be an important tool for variational assimilation schemes that intend to make use of high resolution remotely sensed data.

In the preceding studies by many authors, in particular they found that moist processes were responsible for the strong initial error growth in meso-scale. In the present study they take a more systematic look at the processes by means of the initial introduced mall errors and found that the errors first grow as small-scale differences associated with moist convection, then spread upscale as their growth begin to slow. In the context, we use vastly different initial perturbation methodologies to investigate the initial error growth in the storm scale with open boundary conditions. Comparison of the perturbation methodologies indicates that the ensuing patterns of ensemble spread converge within only a few minutes, irrespective of the initial perturbations employed. In the vertical direction, the largest errors in different variable fields concentrated in different layers (e.g., the largest errors in the temperature field concentrated in the upper tropopause, but in the horizontal wind field, the largest errors converged in the troposphere.). The error growth in the first and middle time contact with the storm tightly, but at last, the error growth goes their ways very slow and flat. The growth of the uncertainties is limited by the saturation effects, which in turn is controlled by the larger-scale atmospheric environment.

The EUMETSAT Satellite Application Facility in support to Hydrology (H-SAF) focuses on development of new geophysical products on precipitation, soil moisture and snow parameters and the utilisation of these parameters in hydrological models, NWP models and water management. The development phase of the H-SAF started in September 2005 under the leadership of Italian Meteorological Service. The "Centro Nazionale di Meteorologia e Climatologia Aeronautica (C.N.M.C.A.)", the Italian National Weather Centre, that physically hosts the generation chain of precipitation products, carried on activities to reach the final target: development of algorithms, validation of results, implementation of operative procedure to supply the service and to monitor the service performances. The paper shows the architectural status of the H-SAF precipitation group and stress the component of operations. It is shown the full correspondence with the EUMETSAT approved H-SAF documents, in particular the Algorithm Theoretical Design Document (ATDD), where products characteristics are referenced. Are also reported the first results, produced during the first H-SAF Workshop, held in Rome in October 2007, of validation activities performed on version 1 products, and last results of products distribution to beta-users in preparation of distributing version 2.

Hyperspectral infrared (IR) sounder from low earth orbit (LEO) provides temperature and moisture soundings with high accuracy and high vertical resolution, however, due to its low temporal coverage rate (twice every day for one sounder instrument), data are usually missing during short range convective storm development. The Advanced Baseline Imager (ABI) onboard the next generation of geostationary (GEO) satellite, on the other hand, provides very fast coverage rate but lower vertical resolution and less accurate profiles. Combination of GEO ABI measurements and LEO hyperspectral IR sounder data may provide atmospheric evolution with high temporal resolution and fairly vertical structure. An algorithm is developed for monitoring the sounding evolution from combined GEO imager and LEO IR sounder data. The collocated geolocation of GEO imager and LEO sounder systems can (1) provide LEO sounder sub-pixel cloud characterization (mask, amount, phase, layer information, etc.) within the large sounder footprint; (2) be used for LEO sounder cloud-clearing for partly cloudy footprints; (3) provide background information in variational retrieval of cloud properties with sounder cloudy radiances; (4) provide real-time background information for GEO imager instantly without Numerical Weather Prediction (NWP) data. The Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System's (EOS) Aqua satellite provide the opportunity to study the synergistic use of advanced imager and sounder measurements. The combined MODIS and AIRS data for various scenes are analyzed to study the utility of synergistic use of ABI products and LEO sounder radiances for better retrieving atmospheric soundings and cloud properties.

Remote Sensing technology has been used in agricultural statistics since early 1970s in developed countries and since late 1970s in China. It has greatly improved the efficiency with its accurate, timingly and credible information. But agricultural monitoring using remote sensing has not yet been assessed with credible data in China and its accuracy seems not consistent and reliable to many users. The paper reviews different methods and the corresponding assessments of agricultural monitoring using remote sensing in developed countries and China, then assesses the crop area estimating method using Landsat TM remotely sensed data as sampling area in Northeast China. The ground truth is ga-thered with global positioning system and 40 sampling areas are used to assess the classification accu-racy. The error matrix is constructed from which the accuracy is calculated. The producer accuracy, the user accuracy and total accuracy are 89.53%, 95.37% and 87.02% respectively and the correlation coefficient between the ground truth and classification results is 0.96. A new error index δ is introduced and the average δ of rice area estimation to the truth data is 0.084. δ measures how much the RS classification result is positive or negative apart from the truth data.

Based on NCEP/NCAR daily reanalysis data and precipitation data, the circulation background of the extremely heavy rain causing severe floods in Huaihe River valley (HHRV) in 2003 and its relationships to the apparent heating were analyzed. The results showed that the El Nino events with middling intensity during 2002~2003 was the previous background of this extremely heavy rain. The abnormal apparent heating source (Q₁) and vapor sink (Q₂) may be one of the important causes for subtropical high maintaining southward than usual. Compared with summer in 2003, the positive abnormal (Q₁) and (Q₂) were located to the HHRV during June 21~July 22. The centers of high value bands of (Q₁) and (Q₂) were in agreement with those of rainfall. But the negative abnormal (Q₁) and (Q₂) were situated in South China and most of South China Sea areas. The abnormal heating source over the Bay of Bengal forced an abnormal anticyclonic circulation over its northwest high level leading to the South Asian High enhancing and maintaining over the Tibetan Plateau, the south of Yangtze River valley and South China, so HHRV areas just located the updraft areas which was in the south of the high-level jet, making for heavy rain and severe floods.

The Tibetan long-term monthly mean rainfall exhibits a SE to NW decrease, showing strong regionality. The summertime vigorous rainfall centers are roughly coincident with those of heat sources <Q₁> averaged throughout the atmospheric extent, with latent heating making the greatest contribution to <Q₁>. In the heat source stronger (weaker) year than normal, the western Pacific subtropical high amplifies (falls off), making westward extension (eastward withdrawal); the South-Asian high intensifies (weakening), eastward expanding (westward extending); summer monsoon becomes intense (enfeebled). In that case, the precipitation is more (less) in the Jiang-Huai valley compared to normal in relation to the rainfall lower (higher) than mean over the littoral provinces of South China, and two parallel anomalously deep wavetrains (just one wavetrain) of cyclones alternate with anti-cyclones over the Pacific.

The advent of hyperspectral sounders, such as the Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI), makes it possible for us to independently assess the radiance measurements of GOES Infrared (IR) imagers, which also provide an effective way to evaluate the GOES IR on-board calibration. In this study, we demonstrate the utility of the AIRS and IASI radiances to evaluate the GOES-11 IR channel measurements. We use the AIRS and IASI measurements to inter-calibrate the IR channels on GOES-11. The collocated GOES pixels inside each AIRS (IASI) pixel are averaged spatially. Then the spatially-averaged radiance from GOES IR channels are compared to AIRS (IASI) observations by convolving the AIRS (IASI) measured spectra with the GOES imager spectral response functions. This study demonstrates that the high-spectral resolution radiance measurements can serve as a relative reference for the inter-calibration of operational GOES imagers for the Global Space-based Inter-Calibration System (GSICS).

The ERA-40 reanalysis ozone data sets provided by ECMWF are most widely used at present, in order to use the data more effectively and reasonably, a comparison analyses are done and the features are discussed between the ERA-40 reanalysis and Halogen Occultation Experiment (HALOE) Observations on Upper Atmosphere Research Satellite (UARS). The spatial and temporal variations of the differences between two ozone data are analyzed in detail. Signal-to-Noise Ratios are estimated firstly by HALOE observational ozone concentration in seasonal and interannual time-scales, respectively. The results indicate that there are significant seasonal and interannual variations in a large part of stratosphere. The comparison analysis suggests that seasonal variation in ozone from ERA-40 is similar to HALOE observations and interannual variation in ozone from ERA-40 is greatly weaker than that of HALOE observations in the tropical middle stratosphere, but is enhanced at low latitudes in the lower stratosphere.

Using the humidity profiles from Atmospheric Infrared Sounder (AIRS) data set, rainfall from TRMM GPI and winds from QSCAT as well as SST from Aqua/AMSR_E, We analyze the structure of summer quasi biweekly mode (QBM) over western Pacific region in 2003-2004. We find that the signal of 10-20d oscillation in western Pacific originates from Philippine Sea, which propagates northwestward to south of China. The AIRS data reveal much larger tropospheric moisture perturbations than those depicted in ECMWF analysis. It also reveals that the boundary-layer moisture leads the mid-troposphere moisture during the QBM propagation. The positive SST anomaly may play an important role to moistening the boundary-layer, which preconditions the QBM propagation. Therefore, the 10-20d SST anomaly could positively feed back to the atmosphere through moistening the boundary layer, destabilizing the troposphere, and contributing to the northwestward propagation of the QBM in western North Pacific. On the other hand, the salient feature that the boundary-layer moisture anomaly leads mid-troposphere moisture does not exist in ECMWF analysis.

The water vapor data by HALOE from 1993 to 2002 were analyzed to study the major variance of interannual variability, Quasi-Biennial Oscillation (QBO). Three centers of water vapor QBO located in upper, middle and lower stratosphere were found. The simulation results from model SOCRATES3 indicated that the QBO forcing of tropical zonal wind would induce three pairs of residual circulations, the transportation of which played an important role in the cause of water vapor QBO's formation. The QBO of temperature could control the chemical production of water vapor in the middle stratosphere. On the other hand, the temperature's QBO had an important effect on the water vapor condensation in the lower stratosphere.