Greenbelt, MD, United States
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Gladkova I.,The Center for Satellite Applications and Research | Gladkova I.,GST Inc. | Gladkova I.,City College of New York | Kihai Y.,The Center for Satellite Applications and Research | And 6 more authors.
Remote Sensing of Environment | Year: 2015

Discriminating clear-ocean from cloud in the thermal IR imagery is challenging, especially at night. Thresholds in automated cloud detection algorithms are often set conservatively leading to underestimation of the Sea Surface Temperature (SST) domain. Yet an expert user can visually distinguish the cloud patterns from SST. In this study, available pattern recognition methodologies are discussed and an automated SST Pattern Test (SPT) is formulated. Analyses are performed with SSTs retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard S-NPP using the NOAA operational Advanced Clear-Sky Processor for Oceans (ACSPO) system. Based on the analyses of global data, we have identified spatial features potentially useful for discriminating cloud from clear-ocean. The SPT attempts to mimic the visual perception by a human operator such as gradient information, spatial connectivity, and high/low frequency discrimination. It first identifies contiguous areas with similar features, and then makes a decision based on the statistics of the whole region, rather than on a per pixel basis. The initial objective of the SPT was to automatically identify clear sky regions misclassified by ACSPO clear sky mask as cloudy, and improve coverage in dynamic areas of the ocean and in the coastal zones. Future work will be directed towards extending the SPT to also minimize cloud leakages, and redesigning the current ACSPO clear-sky mask making full use of pattern recognition approach. © 2015 Elsevier Inc.


Petrenko B.,The Center for Satellite Applications and Research | Petrenko B.,GST Inc. | Ignatov A.,The Center for Satellite Applications and Research | Kihai Y.,The Center for Satellite Applications and Research | And 4 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2014

Two global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear-Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) for implementation in IDPS and ACSPO. Testing the OSI-SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks. Key Points Regression SST algorithms for VIIRS are selected from existing SST algorithms Algorithm metric is defined as area in which SST specifications are met Selected algorithms emphasize angular dependencies of regression coefficients. © 2014. American Geophysical Union. All Rights Reserved.


Liang X.,National Oceanic and Atmospheric Administration | Liang X.,Cooperative Institute for Research in the Atmospheres CIRA | Ignatov A.,National Oceanic and Atmospheric Administration | Kramar M.,National Oceanic and Atmospheric Administration | And 3 more authors.
Remote Sensing | Year: 2016

Clear-sky brightness temperatures (BT) in five bands of the Advanced Himawari Imager (AHI; flown onboard Himawari-8 satellite) centered at 3.9, 8.6, 10.4, 11.2, and 12.3 μm (denoted by IR37, IR86, IR10, IR11, and IR12, respectively) are used in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) sea surface temperature (SST) retrieval system. Here, AHI BTs are preliminarily evaluated for stability and consistency with the corresponding VIIRS and MODIS BTs, using the sensor observation minus model simulation (O-M) biases and corresponding double differences. The objective is to ensure accurate and consistent SST products from the polar and geo sensors, and to prepare for the launch of the GOES-R satellite in 2016. All five AHI SST bands are found to be largely in-family with their polar counterparts, but biased low relative to the VIIRS and MODIS (which, in turn, were found to be stable and consistent, except for Terra IR86, which is biased high by 1.5 K). The negative biases are larger in IR37 and IR12 (up to ~-0.5 K), followed by the three remaining longwave IR bands IR86, IR10, and IR11 (from -0.3 to -0.4 K). These negative biases may be in part due to the uncertainties in AHI calibration and characterization, although uncertainties in the coefficients of the Community Radiative Transfer Model (CRTM, used to generate the "M" term) may also contribute. Work is underway to add AHI analyses in the NOAA Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) system and improve AHI BTs by collaborating with the sensor calibration and CRTM teams. The Advanced Baseline Imager (ABI) analyses will be also added in MICROS when GOES-R is launched in late 2016 and the ABI IR data become available. © 2016 by the authors.


Petrenko B.,College Park | Petrenko B.,GST Inc. | Ignatov A.,College Park | Kihai Y.,College Park | And 3 more authors.
Journal of Atmospheric and Oceanic Technology | Year: 2016

The formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups. For each segment, local regression coefficients and standard deviations (SDs) of BSST minus in situ SST are calculated from the corresponding subset of matchups. The local regression coefficients are used to generate an auxiliary product-piecewise regression (PWR) SST-and SSES biases are estimated as differences between BSST and PWR SST. Correction of SSES biases, which transforms BSST back into PWR SST, reduces the effects of residual cloud; variations in view zenith angle; and, during the daytime, diurnal surface warming. This results in significant reduction in the global SD of fitting in situ SST, making it comparable with SD for the Canadian Meteorological Centre (CMC) L4 SST. Unlike the foundation CMC SST (which is consistent with in situ SST at night but biased cold during the daytime), the PWR SST is consistent with in situ data during both day and night and thus may be viewed as an estimate of "depth" in situ SST. The PWR SST is expected to be a useful input into L4 SST analyses, especially for foundation SST products, such as the CMC L4. © 2016 American Meteorological Society.


Petrenko B.,National Oceanic and Atmospheric Administration | Petrenko B.,GST Inc. | Ignatov A.,National Oceanic and Atmospheric Administration | Kramar M.,National Oceanic and Atmospheric Administration | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Multichannel regression algorithms are widely used to retrieve sea surface temperature (SST) from infrared observations with satellite radiometers. Their theoretical foundations were laid in the 1980s-1990s, during the era of the Advanced Very High Resolution Radiometers which have been flown onboard NOAA satellites since 1981. Consequently, the multi-channel and non-linear SST algorithms employ the bands centered at 3.7, 11 and 12 μm, similar to available in AVHRR. More recent radiometers carry new bands located in the windows near 4 μm, 8.5 μm and 10 μm, which may also be used for SST. Involving these bands in SST retrieval requires modifications to the regression SST equations. The paper describes a general approach to constructing SST regression equations for an arbitrary number of radiometric bands and explores the benefits of using extended sets of bands available with the Visible Infrared Imager Radiometer Suite (VIIRS) flown onboard the Suomi National Polar-orbiting Partnership (SNPP) and to be flown onboard the follow-on Joint Polar Satellite System (JPSS) satellites, J1-J4, to be launched from 2017-2031; Moderate Resolution Imaging Spectroradiometers (MODIS) flown onboard Aqua and Terra satellites; and the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 satellite (which in turn is a close proxy of the Advanced Baseline Imager (ABI) to be flown onboard the future Geostationary Operational Environmental Satellites-R Series (GOES-R) planned for launch in October 2016. © SPIE.


Li X.,College Park | Li X.,EMC | Zheng W.,GST Inc. | Zheng W.,National Oceanic and Atmospheric Administration | And 4 more authors.
Journal of the Atmospheric Sciences | Year: 2013

Both atmospheric gravity waves (AGW) and marine atmospheric boundary layer (MABL) rolls are simultaneously observed on an Environmental Satellite (Envisat) advanced synthetic aperture radar (ASAR) image acquired along the China coast on 22 May 2005. The synthetic aperture radar (SAR) image covers about 400 km × 400 km of a coastal area of the Yellow Sea. The sea surface imprints of AGW show the patterns of both a transverse wave along the coastal plain and a diverging wave in the lee of Mount Laoshan (1133-m peak), which indicate that terrain forcing affects the formation of AGW. The AGW have a wavelength of 8-10 km and extend about 100 km offshore. Model simulation shows that these waves have an amplitude over 3 km. Finer-scale (~2 km) brushlike roughness features perpendicular to the coast are also observed, and they are interpreted as MABL rolls. The FFT analysis shows that the roll wavelengths vary spatially. The two-way interactive, triply nested grid (9-3-1 km) Weather Research and Forecasting Model (WRF) simulation reproduces AGW-generated wind perturbations that are in phase at all levels, reaching up to the 700-hPa level for the diverging AGW and the 900-hPa level for the transverse AGW. The WRF simulation also reveals that dynamic instability, rather than thermodynamic instability, is the cause for the MABL roll generation. Differences in atmospheric inflection-point level and instability at different locations are reasons why the roll wavelengths vary spatially. © 2013 American Meteorological Society.


Petrenko B.,National Oceanic and Atmospheric Administration | Petrenko B.,GST Inc. | Ignatov A.,National Oceanic and Atmospheric Administration | Kihai Y.,National Oceanic and Atmospheric Administration | Kihai Y.,GST Inc.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

Multichannel regression algorithms are widely used in retrievals of sea surface temperature (SST) from infrared brightness temperatures (BTs) observed from satellites. The SST equations typically include terms dependent on the difference between BTs observed in spectral bands with different atmospheric absorption. Such terms do account for variations in the variable atmospheric attenuation, but may introduce additional noise in the retrieved SST due to amplification of the radiometric noise. Some processing systems (e.g., the EUMETSAT OSI-SAF) incorporate noise suppression algorithms, based on spatial smoothing of the differential terms in the SST equations. A similar algorithm is being tested for the potential use in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO). The ACSPO smoothing algorithm aims to preserve natural variations in SST field, while minimizing distortions in the original SST imagery, at a minimal processing time. This presentation describes the ACSPO smoothing algorithm and results of its evaluation with the SST imagery, and with the in situ matchups for NOAA and Metop AVHRRs, Terra and Aqua MODISs, and SNPP/JPSS VIIRS. © 2015 SPIE.


A system, apparatus, device, tools, kit and method is provided for the preparation of the jawbone and insertion of dental implants. The apparatus includes a universal and reusable clamping device and removable components for the clamp which allow for precision surgical preparation and implantation of dental implants into the jawbone. According to some embodiments, an apparatus is provided that includes a platform; one or more frames connected to the platform, wherein the frames include a clamp arm that extend to opposing sides of the bone; and one or more fixation cleats on each arm; wherein the apparatus includes one or more features for positioning the platform, for changing a position of the platform, for changing an angle of a component of the platform or any combination thereof, following the securing of the clamp arms to the bone and prior to the procedure.


A system, apparatus, device, tools, kit and method is provided for the preparation of the jawbone and insertion of dental implants. The apparatus (1) includes a universal and reusable compact clamping device and removable components for the damp which allow for precision surgical preparation and implantation of dental implants into the jawbone. According to some embodiments, an apparatus is provided that includes a swivel platform (3) having a guide member for receiving a tool for preparing an osteotomy and/or for implanting a dental implant into a jaw bone; an orientation member for positioning of the platform and one or more frames (2,12) connectable to the platform.

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