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Jing J.,New Jersey Institute of Technology | Tan C.,IMSG Inc. | Tan C.,The Center for Satellite Applications and Research | Yuan Y.,New Jersey Institute of Technology | And 4 more authors.
Astrophysical Journal | Year: 2010

In this study, the photospheric vector magnetograms, obtained with the Spectro-Polarimeter of the Solar Optical Telescope on board Hinode, are used as the boundary conditions to extrapolate the three-dimensional nonlinear force-free (NLFF) coronal magnetic fields. The observed non-force-free photospheric magnetic fields are preprocessed toward the nearly force-free chromospheric magnetic fields. The performance of the preprocessing procedure is evaluated by comparing with chromospheric magnetic fields obtained by the Vector SpectroMagnetograph instrument located on the Synoptic Optical Long-term Investigations of the Sun Tower. Then, the weighted optimization method is applied to the preprocessed boundary data to extrapolate the NLFF fields with which we are able to estimate the free magnetic energy stored in the active regions. The magnitude scaling correlation between the free magnetic energy and the soft X-ray flare index (FI) of active regions is then studied. The latter quantifies the impending flare production of active regions over the subsequent 1, 2, and 3 day time windows. Based on 75 samples, we find a positive correlation between the free energy and the FI. We also study the temporal variation of free magnetic energy for three active regions, of which two are flare-active and one is flare-quiet during the observation over a period of several days. While the magnitude of free magnetic energy unambiguously differentiates between the flare-active and the flare-quiet regions, the temporal variation of free magnetic energy does not exhibit a clear and consistent pre-flare pattern. This may indicate that the trigger mechanism of flares is as important as the energy storage in active regions. © 2010. The American Astronomical Society. All rights reserved.


Park S.-H.,New Jersey Institute of Technology | Park S.-H.,Korea Astronomy and Space Science Institute | Chae J.,Seoul National University | Jing J.,New Jersey Institute of Technology | And 3 more authors.
Astrophysical Journal | Year: 2010

To study the three-dimensional (3D) magnetic field topology and its long-term evolution associated with the X3.4 flare of 2006 December 13, we investigate the coronal relative magnetic helicity in the flaring active region (AR) NOAA 10930 during the time period of December 8-14. The coronal helicity is calculated based on the 3D nonlinear force-free magnetic fields reconstructed by the weighted optimization method of Wiegelmann, and is compared with the amount of helicity injected through the photospheric surface of the AR. The helicity injection is determined from the magnetic helicity flux density proposed by Pariat et al. using Solar and Heliospheric Observatory/Michelson Doppler Imager magnetograms. The major findings of this study are the following. (1) The time profile of the coronal helicity shows a good correlation with that of the helicity accumulation by injection through the surface. (2) The coronal helicity of the AR is estimated to be-4.3 × 1043 Mx2 just before the X3.4 flare. (3) This flare is preceded not only by a large increase of negative helicity,-3.2 × 1043 Mx2, in the corona over ∼1.5 days but also by noticeable injections of positive helicity through the photospheric surface around the flaring magnetic polarity inversion line during the time period of the channel structure development. We conjecture that the occurrence of the X3.4 flare is involved with the positive helicity injection into an existing system of negative helicity. © 2010. The American Astronomical Society. All rights reserved. Printed in the U.S.A.


Chen Y.,The Interdisciplinary Center | Han Y.,The Center for Satellite Applications and Research | Delst P.V.,IMSG Inc. | Delst P.V.,Joint Center for Satellite Data Assimilation | Weng F.,The Center for Satellite Applications and Research
Journal of Atmospheric and Oceanic Technology | Year: 2013

The nadir-viewing satellite radiances at shortwave infrared channels from 3.5 to 4.6 mm are not currently assimilated in operational numerical weather prediction data assimilation systems and are not adequately corrected for applications of temperature retrieval at daytime. For satellite observations over the ocean during the daytime, the radiance in the surface-sensitive shortwave infrared is strongly affected by the reflected solar radiance, which can contribute as much as 20.0K to the measured brightness temperatures (BT). The nonlocal thermodynamic equilibrium (NLTE) emission in the 4.3-μm CO2 band can add a further 10K to the measured BT. In this study, a bidirectional reflectance distribution function (BRDF) is developed for the ocean surface and an NLTE radiance correction scheme is investigated for the hyperspectral sensors. Both effects are implemented in the Community Radiative Transfer Model (CRTM). The biases of CRTM simulations to Infrared Atmospheric Sounding Interferometer (IASI) observations and the standard deviations of the biases are greatly improved during daytime (about a 1.5-K bias for NLTE channels and a 0.3-Kbias for surface-sensitive shortwave channels) and are very close to the values obtained during the night. These improved capabilities in CRTM allow for effective uses of satellite data at short infrared wavelengths in data assimilation systems and in atmospheric soundings throughout the day and night. © 2013 American Meteorological Society.


Zhan X.,The Center for Satellite Applications and Research | Anderson M.,U.S. Department of Agriculture | Liu J.,The Center for Satellite Applications and Research | Liu J.,IMSG Inc.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

Based on Bayesian theory, the merged soil moisture data product should have smaller errors than the errors of each of the individual soil moisture products to be merged. From the application point of view, if the accurate coarser resolution microwave soil moisture observations can constrain the errors of the less accurate high resolution thermal soil moisture estimates using the merging approach, then the merged product will broaden the applications of the satellite soil moisture products. This study examines how the selected merging approaches realize the advantages of the merged soil moisture data products. © 2010 IEEE.


Petrenko B.,The Center for Satellite Applications and Research | Petrenko B.,IMSG Inc. | Ignatov A.,The Center for Satellite Applications and Research | Shabanov N.,The Center for Satellite Applications and Research | And 3 more authors.
Remote Sensing of Environment | Year: 2011

Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incremental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms. © 2011 Elsevier Inc.


Vargas M.,The Center for Satellite Applications and Research | Kogan F.,The Center for Satellite Applications and Research | Guo W.,IMSG Inc.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard The National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites have been measuring electromagnetic radiation emitted by the Earth in the visible (VIS), Near-Infrared (NIR) and Infrared (IR) portions of the electromagnetic spectrum for nearly 30 years. The Global Vegetation Index Vegetation Health product (GVI-x VH) developed from the AVHRR dataset includes the Brightness Temperature (BT) variable calculated from the IR channels, which in turn is used to estimate other environmental variables such as Sea Surface Temperature (SST), Land Surface Temperature (LST), Temperature Condition Index (TCI), and Vegetation Health Index (VTI) among others. However, the satellite measured IR radiances need to be corrected with sufficient accuracy to minimize the uncertainty introduced by a host of sources such as the atmosphere, stratospheric aerosols, and satellite orbital drift before being input into any algorithm to generate remotely sensed products. In this research we have applied a statistical technique based on Empirical Distribution Functions (EDF) to normalize the NOAA GVI-x BT records for the combined effect of the sources of uncertainty mentioned above, avoiding the need for physics based corrections. The normalized results are tested to verify that the normalization improves the data. © 2011 SPIE.


Levinson D.H.,National Oceanic and Atmospheric Administration | Diamond H.J.,National Oceanic and Atmospheric Administration | Knapp K.R.,National Oceanic and Atmospheric Administration | Kruk M.C.,STG Inc | Gibney E.J.,IMSG Inc.
Bulletin of the American Meteorological Society | Year: 2010

The International Best Track Archive for Climate Stewardship (IBTrACS) project was formed under the leadership of the World Data Center for Meteorology-Asheville (WDC-Asheville) to address the best-track accessibility issue by combining TC best-track data from all agencies into an integrated dataset readily available to the user community. The new IBTrACS dataset provides the original TC intensities in a uniform format and catalogs the full range of reported values for pressure, wind speed, and position for each 6-hour time step from each agency. The final dataset is available in a varity of formats in use by the TC community, which includes the original unedited data as received from each of the RSMCs (Regional Specialized Meteorological Centers). The purpose of the IBTrACS workshop was to begin a dialog with representatives from the RSMCs and TCWCs, as well as from other organizations to accurately combine the disparate data to better understand the global climatology of TCs.


Key J.R.,National Oceanic and Atmospheric Administration | Mahoney R.,Northrop Grumman | Liu Y.,University of Wisconsin - Madison | Romanov P.,City University of New York | And 6 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2013

The Visible Infrared Imager Radiometer Suite (VIIRS) instrument was launched in October 2011 on the satellite now known as the Suomi National Polar-orbiting Partnership. VIIRS was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). VIIRS snow and ice products include sea ice surface temperature, sea ice concentration, sea ice characterization, a binary snow map, and fractional snow cover. Validation results with these "provisional" level maturity products show that ice surface temperature has a root-mean-square error of 0.6-1.0 K when compared to aircraft data and a similar MODIS product, the measurement accuracy and precision of ice concentration are approximately 5% and 15% when compared to passive microwave retrievals, and the accuracy of the binary snow cover (snow/no-snow) maps is generally above 90% when compared to station data. The ice surface temperature and snow cover products meet their accuracy requirements with respect to the Joint Polar Satellite System Level 1 Requirements Document. Sea Ice Characterization, which consists of two age categories, has not been observed to meet the 70% accuracy requirements of ice classification. Given their current performance, the ice surface temperature, snow cover, and sea ice concentration products should be useful for both research and operational applications, while improvements to the sea ice characterization product are needed before it can be used for these applications. Key Points VIIRS snow and ice products generally meet accuracy requirements ©2013. American Geophysical Union. All Rights Reserved.


MacWilliams B.A.,Shriners Hospitals for Children | MacWilliams B.A.,University of Utah | Shuckra A.L.,Shriners Hospitals for Children | Mavor T.P.,National Oceanic and Atmospheric Administration | Mavor T.P.,IMSG Inc.
Gait and Posture | Year: 2010

A method to estimate means and variance of strength based on anthropometric data is presented. The method is applied using a database of 10 lower extremity strength measures recorded from 48 typically developing children with a handheld dynamometer. Seven anthropometric variables are considered, and the combination of height and BMI is determined as a set of variables best suited to model these muscle groups. This regression scheme accounts for 45-58% of the observed variance. A clinical example illustrating the utility of the method is presented. © 2010 Elsevier B.V.


Kogan F.,National Oceanic and Atmospheric Administration | Vargas M.,National Oceanic and Atmospheric Administration | Guo W.,IMSG Inc
NATO Science for Peace and Security Series C: Environmental Security | Year: 2011

Several global data sets have been developed from the AVHRR instrument measuring reflectance/emission of the Earth since the early 1980s. The longest datasets currently available for users are NOAA's Global Vegetation Health (GVH), NASA's Global Inventory Modeling and Mapping Studies (GIMMS) and Land Long Term data Records (LTDR). The GVH has 30-year records (1981-2010), GIMMS - 26 (1981-2006) and LTDR - 19 (1981-1999). These datasets have different spatial and temporal resolutions, processing methods (sampling, calibration, noise removal, mapping, gap treatment etc.), applicability, availability, distribution etc. They have been used frequently for monitoring earth surface, atmosphere near the ground and analysis of climate related land surface trends. Since one of the common features of these datasets is the Normalized Difference Vegetation Index (NDVI) this paper is focusing on comparison of NDVI time series, specifically comparing time series dynamics and trends. It is shown that GIMMS NDVI is two to three times higher and has steeper long-term trend compared to GVH and LTDR. © Springer Science+Business Media B.V. 2011.

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