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Over the decades, significant progress has been made in satellite precipitation product development. In particular, temporal resolution and timely availability have been improved by blended techniques. The resulting products, including near-real-time precipitation products, are widely used in various research and applications. However, the lack of support for user-defined areas or points of interest poses a major obstacle to quickly gaining knowledge of product quality and behavior on a local or regional scale. Current online operational intercomparison and validation services have not addressed this issue adequately. This paper describes an ongoing work to develop an online information system prototype for global satellite precipitation algorithm validation and intercomparison, to overcome current shortcomings by providing dynamic and customized information to users on the expected bias and accuracy of the products, and to give algorithm developers a better understanding of the strengths and weaknesses of different algorithmic approaches and data sources. An example is provided to illustrate the functionality and capabilities of the system, and future plans are discussed. The system being developed complements and accelerates the existing and ongoing validation activities in the community and contributes to the current NASA Tropical Rainfall Measuring Mission and the future NASA Global Precipitation Mission. [ABSTRACT FROM AUTHOR]

The Tropospheric Emission Spectrometer (TES) is one of four instruments onboard the Earth Observing System Aura satellite. The TES Science Investigator-led Processing System (SIPS) Facility performs production processing of all TES science data. When the TES project was proposed in 1988, its science algorithms were still evolving, but it was already understood to be at least two orders of magnitude more complex than its NASA predecessor, the Atmospheric Trace Molecule Spectroscopy (ATMOS) instrument. In addition, the expected data volume of the TES instrument would be more than 1000 times greater than that of ATMOS. Development of the TES SIPS faced a number of technical challenges. It also would have been impractical and prohibitively costly to develop the TES SIPS facility without carefully deploying the computing technologies that have recently become available. This paper describes how the challenges were met in the development of the facility by making use of evolving hardware technology and software refinement. The process revealed that the architecture of the hardware implemented was highly dependent upon the processing algorithm, and a stable algorithm was needed early in the hardware development process for performance analysis and benchmarking. [ABSTRACT FROM AUTHOR]

Abstract: Time series of the Normalized Difference Vegetation Index (NDVI) derived from satellite observations provide important information on the state of terrestrial vegetation over a wide range of spatiotemporal scales. For understanding long-term changes in terrestrial ecosystems (post-1981), data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the satellites of National Oceanic and Atmospheric Administration (NOAA) series is a unique source of information. In this paper, we describe a new processing methodology for a comprehensive AVHRR data set at 1km spatial resolution acquired over Canada, the northern United States and Greenland post-1981. The methodology incorporates a pre-processing algorithm, Canadian AVHRR Processing System (CAPS), recently developed by the Canada Centre of Remote Sensing (CCRS), which enables highly accurate geolocation and ortho-rectification at efficiency rates of >90%. Once image navigation is completed, our approach consists of five key steps: first, two clear-sky composites for each 10day interval are generated from the forward or backward scattering hemisphere; second, AVHRR Channel 1 and 2 reflectances are normalized to the AVHRR/3 on board NOAA-17 to account for differences in the spectral response function among the AVHRR sensors; third, atmospheric correction is performed using the Simplified Method for Atmospheric correction (SMAC) algorithm, using standard meteorological data sets (water vapor, surface level air pressure, ozone); fourth, NDVI is calculated based on atmospherically corrected Channel 1 and 2 reflectances; and finally, the NDVI is adjusted for directional effects based on the Ross-Thick Li-Sparse Bidirectional Reflectance Distribution Function (BRDF) model. The processed NDVI data are compared to an equivalent spatially and temporally overlapping MODIS NDVI data set from 2001 to 2005 for validation. Results at continental scale indicate that time series of MODIS and AVHRR were similar for a wide range of biomes and generalized ecoregions. Analysis stratified by land cover indicated that the correlation was strongest for homogeneous land cover types, such as cropland, when compared to structurally more diverse classes, such as deciduous broadleaf forests. The comparison of the NDVI at the local scale at seven sites of the Fluxnet Canada Research Network resulted in the correlation coefficient r=0.95. Given confidence in the processing approach, this NDVI data set can be a valuable source of information for climate and vegetation-related studies over Canada and the northern United States. [Copyright &y& Elsevier]

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. [ABSTRACT FROM AUTHOR]

The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999-2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products. [ABSTRACT FROM AUTHOR]

NASA/GEWEX (National Aeronautics and Space Administration/Global Energy and Water Cycle Experiment) Surface Radiation Budget (SRB) has released its latest radiation dataset, version 3.0. We examine the accuracy of the monthly mean global radiation in China using surface-observed radiation (SOR) data at 42 stations during the period 1984-2004. Overall comparison shows a general overestimation of satellite retrieval radiation data with a bias of 14.6 W m and a root mean square error of 25.9 W m. Differences at individual stations suggested satellite data are consistently higher than surface measurements over eastern China (110°E), but occasional underestimation occurs in Western China, especially Southwest China. Intra-annual variation analysis indicates that SRB satellite radiation can capture the annual cycle well. For trend of global radiations, there are evident discrepancies between satellite retrievals and surface measurements for both the entire period and segmental terms. For the entire period from 1984 to 2004, most stations show a positive trend based on surface measurements, while the majority of collocated pixels show a negative trend. Segmental trends demonstrated that the principal difference occurred during the first period of 1981-1994. After 1994, the two datasets change similarly. Therefore, trend analysis in terms of detecting global dimming/brightening remains very difficult as surface measurements and satellite products do not agree yet. In addition, some proposals are made towards better understanding of the bias of satellite products and to improve further the satellite retrieval algorithm with better representation of both cloud and aerosol properties. [ABSTRACT FROM AUTHOR]