text:
ASRC Federal, 7515 Mission Drive, Seabrook, Maryland 20706, United States
ERT Inc. @ NOAA/NESDIS/STAR, 5830 University Research Court, College Park, Maryland 20740, United States
NOAA/NESDIS Office of Satellite Applications and Research, Advanced Satellite Products Branch (ASPB), Madison, Wisconsin 53706, United States
University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, Madison, Wisconsin 53706, United States
NOAA/NESDIS/OSPO, NSOF 1315 East West Highway, Silver Spring, Maryland 20910-3282, United States
Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component.
Co-registration, or the assurance that measurements from multiple channels are from the same geolocation, is an important performance metric for the Imager instrument on the Geostationary Operational Environmental Satellite (GOES). A number of satellite products rely on the difference between spectral measurements to infer the underlying geophysical parameter. For example, water vapor absorption is weaker in the 3.9 μm band (GOES Imager channel 2) than in the 10.7 μm band (channel 4) such that Tb(2), the brightness temperature for channel 2, is typically warmer than Tb(4) during nighttime hours. A brightness temperature (K) is uniquely related to the radiance via the Planck function. On the other hand, the emissivity of water droplets is lower in the 3.9 μm band than in the 10.7 μm band, which can lead to a cooler Tb(2) than Tb(4) in the presence of fog or low clouds. These properties have been used for fog water cloud detection.1 However, if the channels have large co-registration errors, then measurements in the two channels can differ because they are from different locations. For example, the 3.9 μm band could be over water, while the 10.7 μm band could be over land (over vice versa). This can lead to misinterpretation of the measurements, such as false positives, in the fog detection product (Fig. 1).
Map of fog and low clouds based on the brightness temperature difference between Geostationary Operational Environmental Satellite (GOES)-13 Imager band 2 (3.9 μm) and band 4 (10.7 μm) for May 14, 2012, at 10:15 UTC. The colored regions denote where Tb(2) – Tb(4) is sufficiently negative, which is often indicative of the presence of low cloud and/or fog. Note the narrow lines of fog in the four marked regions along the Mississippi River and the shorelines of Lake Michigan and Lake Huron, where surface stations did not report fog or low clouds at the time. These were caused by the misalignment or co-registration error of the Imager measurement, when band 2 instantaneous field of view is a few kilometers east to that of band 4. As such, band 2 views the relatively cold land when band 4 views the relatively warm water in these early morning hours, which led to the negative Tb(2) – Tb(4) and false fog identification.
A study by Cooperative Institute for Meteorological Satellite Studies personnel, as well as Grotenhuis et al.,2 found that the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) could be as large as 1 pixel in the east/west direction (or ∼4 km), which is considerably larger than the instrument specification. Characterization of GOES channel to channel co-registration errors was seen as an important problem to solve.
Unlike most of the image registration problems discussed in the literature, the GOES Imager infrared (IR) channel-to-channel co-registration problem has its own unique characteristics. The images in different IR channels represent different radiometric attributes of the Earth-atmosphere system, and they are not related by simple affine (linear) transformations. Therefore, there is no controlled experiment to determine the accuracy of the algorithm output. The co-registration errors are generally at the subpixel level, which requires that one of the images be resampled in order to evaluate the co-registration error. An accurate resampling algorithm is crucial for a reliable co-registration error characterization algorithm. Furthermore, the co-registration error for GOES images is time dependent, and the determination of the co-registration error as a function of time is critical for the implementation of the algorithm in the GOES ground system. The focus of this paper is to present the GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm, its validation and verification, and its implementation in the GOES ground system.
Although there are numerous algorithms that discuss image registration in medical imaging and remote sensing literature, few attempts have been made to characterize the channel-to-channel co-registration error. An initial attempt for the GOES Imager was made by Wu et al.,3 who used the asymmetry in spatial versus spectral brightness temperature gradient histograms to identify the existence of co-registration errors. Le Moigne et al.4 investigated the GOES channel-to-channel co-registration problem by using wavelets to extract certain features, or control points (CP), such as landmarks on images from different channels, which is an approach that has been widely used in image registration. A major problem for this approach is that it is not always possible to extract features on GOES images due to the presence of clouds, which makes it less reliable for a systematic evaluation of co-registration errors. A recent study by Grotenhuis et al.2 uses an area-based approach that involves calculating the cross-correlation of Earth-viewing pixels in different IR channels. In this approach, one of the two images from different IR channels is resampled using linear interpolation, and the correlation in brightness temperature between the resampled image and the image from the other channel is computed. The shift that yields the maximum correlation is the negative of the co-registration error.
The GII4C algorithm uses the same approach as Grotenhuis et al.2— an area based approach to calculate correlations between images in different channels for Earth-viewing pixels. The key difference between this algorithm and the one in 2 is that fast Fourier transformation resampling (FFTR)5 is used for the image resampling. The FFTR algorithm has been shown to be accurate, reversible, and efficient enough for spacecraft image resampling. It does not have the discontinuity problem for the correlation function seen with the images being resampled using linear interpolation.2 Using the fast Fourier transform (FFT) to resample an image for the purpose of co-registration has been investigated in the literature.6 A common issue with the FFT in subpixel image registration is the aliasing effects that cause the resampled image to be distorted. To avoid this problem, the Fourier spectrum for both images has to be truncated before the estimate of the co-registration error is made. However, the study in 5 shows that the Fourier spectra for GOES images are very well behaved, especially for channel 4 images, in which the Fourier spectrum is dominated by the low-frequency components, while the high-frequency components that cause the aliasing effects are very small. The aliasing effects do appear occasionally in the channel 2 images, in which the presence of fires, solar reflections, volcanoes, and other heat sources, such as cities, causes individual pixels to appear as high-temperature discontinuities in the spatial domain. An attempt to locate pixels containing actively burning fires and treat them separately to avoid the aliasing effects was made in 5. For the GII4C algorithm being presented, the images in channel 4 are resampled so that truncation of the Fourier spectrum is not necessary. The images in channel 2 are unchanged. This makes the algorithm simpler.
The validation and verification of the algorithm presents another challenge in characterizing the channel-to-channel co-registration errors since there is no controlled experiment to determine the accuracy of the algorithm. This has not been investigated in the literature. To verify the output of the GII4C algorithm, the channel 4 images have to be resampled based on the co-registration errors obtained from the GII4C algorithm itself. One can examine local features on the resampled image to determine if the co-registration error has been corrected. However, the study of local features alone is not sufficient to determine the overall image quality improvements, and global measures are needed to provide a quantitative description of such improvements. Because the correlations between images in different channels are driven by features with large temperature gradients, the spatial and spectral gradient approach proposed in 3 is a very useful representation for highlighting the co-registration error effects. The spectral gradient mean and standard deviation as a function of the temperature gradient provide quantitative indicators of co-registration errors. Our study shows that the co-registration error leads to an asymmetric behavior of the spectral gradient mean function that is in agreement with the conclusion in 3. A global spatial-spectral gradient asymmetry parameter is defined in terms of the spectral gradient mean function, which is strongly correlated to the co-registration errors and is a useful quantitative metric for the effectiveness of channel-to-channel co-registration characterization algorithms.
This article is organized as follows. Section 2 briefly discusses the FFTR algorithm. Section 3 provides detailed discussions of the GII4C algorithm. Section 4 shows how the algorithm works for a specific set of GOES images. Section 5 presents the mean and standard deviation of the spectral gradient as a function of the spatial gradient, and how these measures can be used as quantitative metrics for the overall improvement of data quality due to co-registration error corrections. The determination of the co-registration error as a function of time, which is crucial to the implementation of the GII4C algorithm in the GOES ground system, is discussed in Sec. 6. Finally, the summary is given in Sec. 7.
As was outlined by Li,5 an image can be characterized by a two-dimensional array, with each element Pi,j specifying a pixel value at a location {i,j}. Image resampling involves finding an array with elements Pi+δi,j+δj at locations {i+δi,j+δj} based on the input array Pi,j, where i and j are both integers and δi and δj are generally both fractional numbers. Generally, resampling in δi and δj directions can be performed separately. Therefore, we will focus our discussion on resampling in one dimension, which can then be easily extended to two dimensions.
For one-dimensional resampling, one can define a global continuous function G(x) with the condition Display Formula
In order to use the FFT algorithm, the dimension of an input array has to meet the size requirement M=2m for the integer m. The global function G(x) in Eq. (3) from the FFT is related to the input array as Display Formula
Therefore, one can use Eq. (4) to obtain the amplitude g(k). The resampled array Pi can be obtained by adding a phase shift δ to the Fourier expansion in Eq. (3). Display Formula
For an integer δ, Eq. (7) becomes Display Formula
The elements at i of the resampled array are equal to the element at i+δ of the input array, which should be generally true for any resampling algorithm.
For a noninteger δ, Eq. (7) cannot be directly used in the FFT because the argument of the sine function no longer has the form of a standard discrete transform, and direct calculation of Eq. (7) has an efficiency of O(N2) for a one-dimensional array. In order to use the FFT, Eq. (7) is rewritten as Display Formula
This global function with a phase shift can be evaluated with two separate FFTs. The steps for FFTR are as follows:
The c routines for the sine and cosine function FFTs come from 7.
Both resampled and original images belong to the same global continuous function with the same set of Fourier spectra. The resampled images from the FFTR algorithm maintain the image quality of the original images, which are supported by independent studies.8 Furthermore, the FFTR algorithm is generally reversible. One could simply treat the resampled image with the shift δ as the original image to derive the global function and regenerate the original image by performing the image resampling with the shift δ. A special test case is the integer shift, as the resampled image data at the position (i,j) correspond directly to the original image data at the position (i+δ,j) for the integer shift δ along the x-direction.
The GOES Imager instrument senses images of Earth with both visible and IR channels. The GOES Imager has one visible and four IR channels.9 The four IR channels for the current generation of GOES have wavelengths 3.9, 6.5, 10.7, and 13.3 μm, which are identified as channels 2, 3, 4, and 6, respectively. Assuming that channel 4 is used as the reference channel, a pixel with coordinates {i,j} on an image corresponds to the geolocation {θ,φ} on Earth, where θ and φ represent latitude and longitude, respectively. The same geolocation {θ,φ} would correspond to {i+δiC,j+δjC} for channels C=2, 3, and 6, where δiC and δjC are the co-registration errors in the east/west and north/south directions, respectively.
The GII4C algorithm uses the same approach as that described by Grotenhuis et al.2 It is an area-based approach based on characteristics of the GOES images that do not depend on a specific set of CP. The interpolation that best correlates brightness temperature between the two channels is used to estimate the co-registration error. Using correlation to evaluate the co-registration errors has been shown to be one of the best approaches.10 This is especially true for GOES images in IR channels, as they are different images not related by the affine transformation, but nonetheless display similar features. The algorithm calculates the correlation line by line or column by column. For a given line j in the images of two IR channels, this correlation function can be written as Display Formula
The value for the co-registration error δjC at line j corresponds to the maximum correlation Rj(δ). Equation (12) shows that the correlation function Rj(δ) depends on the variation of the temperature from the mean value. A large correlation value requires that a line in an image have a large brightness temperature variation, in which the difference between the maximum and minimum brightness temperature in a line is sufficiently large. This is not always true in a satellite image, and it depends on the content of the image. For example, if a line in a satellite image contains a large segment of space pixels, the brightness temperature would be nearly constant, and consequently, the correlation should be small. Thus, it is important to exclude the space segment data from the correlation calculation so that only Earth data are evaluated. The selection of Earth pixels from GOES images is based on navigation information typically provided along with the GOES image. A detailed formulism can be found in 12. The co-registration error for an image is the weighted average over the total number of lines in the image and should be used to obtain the channel-to-channel co-registration errors. Display Formula
The threshold for a line to be included in the weighted mean of the image is 0.8. This ensures that the co-registration errors are determined from the lines with large correlations. The algorithm to measure the east/west channel-to-channel co-registration error is shown in Fig. 2.
The GOES Imager infrared channel-to-channel co-registration characterization algorithm.
To demonstrate how the GII4C works with IR channel images from the GOES Imager, we will show the application of the algorithm to the IR channels 2 and 4 images shown in Figs. 3 and 4. This set of images covers the continental United States (CONUS) and was imaged at 12:15 UTC on ordinal day 136, 2012 (May 15, 2012). The image set dimensions are 3462 (east/west) by 1246 (north/south) pixels for both channels. Most GOES images acquired during normal operations have sizes that are either equal to or larger than the size of these images. The images contain both Earth and space pixels, with the space pixels located in the upper left and right corners of each image. Both images contain similar features; however, they represent different spectral regions of the Earth's atmosphere that are not defined by the affine transformations. Figure 5 shows the channels 2 and 4 brightness temperatures along a given image line in Figs. 3 and 4, and highlights the similarities and differences between these two channels. Both channels show similar features, in which the brightness temperature in both channels increases or decreases at the same time; however, the magnitude of the brightness temperature changes is not identical.
Infrared (IR) channel 2 (3.9 μm) image from the GOES-13 Imager from 1215 UTC, May 15, 2012.
IR channel 4 (10.7 μm) image from the GOES-13 Imager from 1215 UTC, May 15, 2012.
Channel 2 (see Fig. 3) and 4 (see Fig. 4) brightness temperature for image line 739. The red data represent the brightness temperature in channel 4 and the blue data represent the brightness temperature in channel 2.
Figure 6 shows the Fourier spectrum of the channel 4 brightness temperatures in Fig. 4, where the x axis is defined as k/M, as shown in Eq. (9), and has a range from 0 to 1. The Fourier spectrum is dominated by the very low-frequency components, and it decreases very quickly to 0 as the frequency increases. The magnitude for the component k=1 is ∼3, while the magnitude for k=M is ∼10−4. This makes frequency truncation in the resampling4 unnecessary, as aliasing is not a concern for the channel 4 data. As a result, the GII4C algorithm becomes less complex since a Fourier transformation for the data in channel 2 is not needed.
The Fourier spectrum for the channel 4 brightness temperatures shown in Fig. 5.
Figure 7 shows the correlation as a function of δ for line 739. The correlation is essentially a statistical approach that requires a large data set to become mathematically stable. The size of the data set in this case is >1000 data points. The maximum value of the correlation is 0.89 at δ=1.0, which is considerably larger than the results presented in 4 using the feature-based approach with a much smaller data sample size.
Brightness temperature correlation as a function of δ (delta) at line 739. The brightness temperature data for channels 2 and 4 are shown in Fig. 5.
Unlike the approach in 2 that uses linear interpolation for the image resampling, the GII4C algorithm uses the FFTR for the resampling. Figure 7 shows that the correlation function is continuous as a function of δ (delta); therefore, it does not require the scramble procedure described in 2.
Figure 8 shows the maximum correlation values as a function of line number for the channels 2 and 4 images shown in Figs. 3 and 4. The correlation is small for the top area of the images (far north latitudes), with a range from 0.4 to 0.6. The small correlation is due to the fact that the number of Earth pixels is relatively small because of the presence of space segments on either side of the Earth. The maximum correlation values become more stable when the image line number is larger than ∼400. The data show that as much as 80% of the lines in the images have a maximum correlation larger than the threshold (0.8) and are included in the weighted average in Eq. (13). The percentage of the number of lines above the 0.8 threshold is even larger for typical GOES-13 images, as the percentage of far northern and southern pixels is smaller than the CONUS image shown in Figs. 3 and 4. The largest value found for the maximum correlation is ∼0.95, which is a very strong correlation. The reasons for such a strong correlation in this approach are twofold. First, the size of the data set is much larger than that in the feature-based approach. More importantly, this approach includes many more image elements than the feature-based approach, which only selects a limited set of CP. In addition to coast lines and landmarks, clouds in the images also enhance the correlation. Figure 5 highlights the components included in the correlation calculation. There are many maximum and minimum peaks for a given line, and each peak contributes to the correlation between the two data sets. Figure 9 shows the corresponding co-registration error for each line. There is a relatively large fluctuation at the top of the images, where the maximum correlation value is small. As the maximum correlation value in Fig. 8 increases, the corresponding co-registration error becomes relatively stable with less fluctuation. Adopting a 0.8 threshold for the weighted average in Eq. (13) ensures a more stable outcome.
Maximum correlation value as a function of line number for the channels 2 and 4 images shown in Figs. 2 and 3, respectively.
Co-registration error corresponding to the maximum correlation as a function of line number.
After using the data represented in Figs. 8 and 9 for the weighted average in Eq. (13), the final co-registration error for the image set is 1.17 pixels.
There are two main steps needed for algorithm updates. The first is the verification step, i.e., whether the update meets the specified requirement(s). The second step is the validation, where the deliverable is deemed suitable in the intended environment. Unlike algorithms in image registration, where the accuracy of an algorithm can be determined with a set of images with known image registration errors, there is no controlled experiment to determine the accuracy of the channel-to-channel co-registration characterization algorithm. To verify the GII4C algorithm, the channel 4 image has to be resampled with the shift determined by the GII4C algorithm, so that the features in different IR channels can be evaluated in terms of alignment. One possible approach is to examine local features, such as landmarks, to see if the co-registration error has been corrected. Unfortunately, this type of approach only provides qualitative answers. More desirable are quantitative measures that are sensitive to co-registration errors on a global scale. In this section, we present such quantitative measures related to brightness temperature differences between different channels that can be used as metrics for channel-to-channel co-registration characterization algorithms.
Since the channel 2 to 4 co-registration errors are also very sensitive to the brightness temperature difference (also called the spectral gradient), one can calculate the spectral gradient mean (m) and standard deviation (σ) values between channels, respectively, as Display Formula
Table 1 shows that the changes to the σ value for the image as a whole are relatively small. This is due to the fact that the differences between the resampled and original images are only significant where the features (such as landmarks or coast lines) are located. Mathematically, the difference between the original and resampled images at a given location is proportional to the temperature gradient. This is defined as Display Formula
The summations in Eqs. (18) and (19) are over those pixels with the same temperature gradient value G, and G is the rounded integer of the temperature gradient for the channel 2 image. The variable NG in Eqs. (18) and (19) is the number of pixels with the temperature gradient G. The functions m(G) and σ(G) are equivalent to the spectral gradient functions in the spatial-spectral gradient representation discussed in Wu et al.3
Figure 10 shows the functions σ(G) and m(G) for the original and resampled channel 4 images. The function σ(G) is essentially the same when the magnitude of temperature gradient G is <2 K, which is due to the fact that changes to the resampled pixels are very small. The changes to both mean and σ functions are more significant as the temperature gradient increases. Since the magnitude of the temperature gradient for the vast majority of the pixels in the images is <2 K, the changes to the overall mean and σ value for the Earth-viewing pixels are small. There are also larger fluctuations as the temperature gradient becomes larger, due to the fact that sample size decreases exponentially as the magnitude of the temperature gradient increases. The number of pixels with a gradient of 0 K is 2.4×106 for the channel 2 image, and it decreases exponentially as the magnitude of the gradient increases. The number of pixels with a gradient >16 K is <100, which makes it more dependent on the content of specific images and less meaningful statistically. As the temperature gradient increases, the differences in both the σ(G) and m(G) functions between the original and resampled images become much more pronounced.
σ (on the top) and mean (on the bottom) values of the brightness temperature difference between channels 2 and 4 images as a function of the temperature gradient in channel 2. The blue line represents the original channel 4 image shown in Fig. 4, and the red line represents the resampled channel 4 image. The co-registration error is 1.17.
Figure 10 shows that there are overall reductions in the σ values for the resampled image by as much as 2 K, especially for the positive temperature gradient region, which suggests that the resampled image improves the noise level in the temperature difference significantly. The high noise level is directly related to the co-registration error between the images in two channels; the larger the misalignment, the bigger the noise level. This shows that using σ as a function of the temperature gradient is an important measure that can be used to verify the output of the channel-to-channel co-registration characterization algorithm.
Although the co-registration error does not change the overall spectral gradient mean values, Fig. 10 shows that the spectral gradient function m(G) for the original imagery is asymmetric between the positive and negative temperature gradients G, in which the mean value for the positive gradient is larger than that for the negative gradient. This is the same asymmetric behavior shown on the spatial-spectral gradient histograms in Refs. 3 and 2, and is caused by the co-registration errors between the channels 2 and 4 images. To show the quantitative relationship between the asymmetry in the spatial-spectral gradient representation and the channel-to-channel co-registration error, one can define a global spatial-spectral gradient asymmetry as Display Formula
The asymmetry α versus co-registration error δ. The blue data points represent the spatial-spectral gradient asymmetry between the channel 2 and original channel 4 images, and the red data represent the spatial-spectral gradient asymmetry between the channel 2 and resampled channel 4 images. The asymmetry α has the unit K.
The effects of the spectral gradient functions m(G) and σ(G) are also shown in local features. Figure 12 shows the effects of the reduction of the σ value on the temperature difference surface. The image on the top represents the temperature difference between the channel 2 and original channel 4 image, while the image on the bottom shows the temperature difference between the channel 2 and the resampled channel 4 image. The surface of the temperature difference between the channel 2 and resampled channel 4 images is smoother. The maximum value of the temperature difference in like regions is reduced by ∼5 K, while the minimum value is increased by ∼2.6 K. The variations on the temperature difference surface with the original channel 4 image result from noises caused by the co-registration error.
Temperature difference between the channel 2 and 4 images. The Gulf of California can be seen in the lower left hand corner of each image. The top image is the difference between the channel 2 and original channel 4 images, while the bottom image corresponds to the difference between the channel 2 and resampled channel 4 images.
Figure 13 highlights how the asymmetric behavior shown in Fig. 10 is related to the co-registration error. The figure displays the temperature difference between channels 2 and 4 in the Great Lakes region, which is a subset of the images shown in Figs. 3 and 4. The lake boundaries in Fig. 13 have large temperature gradients, where the changes made by the image resampling are most significant. The temperature gradient on the west side of the lake boundary is negative, so Fig. 13 suggests a lower than normal temperature difference, which is manifested as a darker edge along the lake boundary. On the east side of the lake boundary, the temperature gradient along the west/east direction is positive, and the temperature differences for the misaligned channels 2 and 4 images are higher than the normal value, which is manifested as the whiter edge along the lake boundary on the east side. These are artifacts caused by large co-registration errors and lead to false positives in fog detection, for example. The artifacts along the lake boundary are largely removed after resampling, in which the resampled channel 4 image is shifted west by 1.17 pixels.
Temperature difference between channels 2 and 4 in the vicinity of lakes Michigan, Huron, and Erie.
Figures 12 and 13 verify the veracity of the GII4C algorithm based on local features, and they also provide important empirical evidence in support of using both the spatial-spectral gradient asymmetry α and spectral gradient function σ(G) as global metrics showing image co-registration errors.
There are two components involved in implementing the GII4C algorithm in the GOES ground system. The first is an offline program that implements the GII4C algorithm to determine the (time-dependent) co-registration error; the second is a real-time program to correct the co-registration errors in GOES images based on the output from the offline program.
The offline program of the GII4C algorithm evaluates every image during a five-day period (∼400 images). Figure 14 shows the co-registration errors for the IR channel-to-channel co-registration as a function of time for both the east/west and north/south directions for GOES-13. The data have a pattern that repeats every 24 h. The maximum co-registration error is ∼1.2 pixels for the co-registration between channels 2 and 4. The results in Fig. 14 show that the co-registration error in the north-south direction is generally small and that the dependence on time is not as strong as that observed in the east-west direction. The magnitude of the co-registration error in the east-west direction between channels 2 and 4 is >0.5 pixels during several periods each day, which requires image resampling in either channel 2 or channel 4 for corrective purposes. This is in good agreement with the results from the approach presented in 2.
The east-west (in red) and north-south (in blue) channel-to-channel co-registration error for GOES-13 as a function of time.
There are still fluctuations in co-registration errors from one image to another, due to the fact that the brightness temperature correlation is essentially a statistical approach. The final co-registration error values are obtained through a least squares fit to the time-dependent function. Display Formula
GOES-13 IR co-registration table for east/west co-registration error values after the least squares fitting. The time axis is half-hourly through the day.
The CORT is an array with 48 elements, in which each element represents the co-registration error during a half-hourly period. The GOES ground system corrects co-registration errors for GOES imagery based on the CORT. There are seasonal changes for the CORT; therefore, it is updated on a monthly basis.
The GII4C algorithm presented in this paper provides a systematic approach to characterize the co-registration errors for the GOES Imager IR channels. The algorithm is an area-based approach, and it evaluates the correlation of the brightness temperatures between different IR channels. The algorithm does not depend on a few CP, so it is not impacted by the presence of clouds. In fact, clouds are one of the features in IR channel imagery that enhance the correlation. An FFTR algorithm is used for the image resampling in order to obtain the co-registration errors at a subpixel level. The results presented have shown very high correlation values, a continuous correlation as a function of the imagery shifts, and a stable co-registration error output, which ensures the stability and reliability of the algorithms.
Because the images in different IR channels are not related by an affine transformation, there is no controlled experiment to determine the accuracy of the algorithm. Validation of the algorithms is a combination of global measures and their related local features. Since changes by the image resampling are most significant in regions where the temperature gradients are large, we show that the spectral gradient mean and σ function between the IR channels can be used to highlight the effects of the co-registration errors. Larger co-registration errors lead to larger σ values, especially in those areas where temperature gradients are large. The co-registration error does not change the overall spectral gradient mean value; however, it causes an asymmetric behavior in the function m(G) [Eq. (18)], which is the same as the asymmetry in the spatial-spectral histogram(s) shown in 3. We propose that the spatial-spectral gradient asymmetry α and the spectral gradient function σ(G) are quantitative global measures to verify the channel-to-channel co-registration errors, which can be useful metrics to evaluate channel-to-channel co-registration characterization algorithms. We showed that the spatial-spectral gradient asymmetry α and the co-registration error δ are strongly correlated. Further investigation is needed to determine the quantitative relationship between α and δ.
Both global measures and local features have shown that the co-registration errors between GOES-13 Imager 3.9 and 10.7 μm data are largely removed by the image resampling based on the output of the GII4C algorithm. The quality of the associated weather products should be improved significantly.
The GII4C algorithm has been implemented in the operational GOES ground system. The same approach could also be applicable to the next generation of GOES.
The contents of this document are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. Government.
Zhenping Li received a PhD degree in physics at the University of Tennessee, and a master's degree in computer science at Johns Hopkins University. Currently he is working at ASRC Federal as the systems engineer. His research area is satellite image processing and its implementation in the satellite ground system.
Michael Grotenhuis is a physical scientist and an ERT, Inc. contractor at the NOAA/NESDIS Center for Satellite Applications and Research (STAR). Currently, he supports calibration/validation efforts to monitor the instrument performance of the Ozone Mapping Profiler Suite using the Satellite Integrated Calibration/Validation System (ICVS). He received a BA in physics from Cornell University in 2003 and an MS in physics from the University of Minnesota—Twin Cities in 2010.
Xiangqian Wu has been a physical scientist with the National Oceanic and Atmospheric Administration (NOAA) in Maryland, USA, since 2002, where he leads a team to provide calibration support for NOAA's operations of Imager and Sounder on GOES and AVHRR on POES. From 2010 to 2014, he led a team for the instrument calibration and validation of OMPS on Suomi-NPP. Currently, he leads a team for the instrument calibration and validation of ABI on GOES-R.
Timothy J. Schmit works at the Advanced Satellite Products Branch within NOAA's NESDIS Center for Satellite Applications and Research located in Madison, Wisconsin. His experience with satellite processing includes calibration, simulations and algorithms for converting satellite data into meteorological/environmental information. He has extensive experience with deriving products from the GOES imager and sounder data. He was involved in the on-orbit science check-out of GOES-8 through 15. He received his master's degree from the University of Wisconsin—Madison.
Chris Schmidt: Biography is not available.
Anthony J. Schreiner works at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies at Madison, Wisconsin. His experience with satellite information includes developing algorithms for converting satellite data into meteorological/environmental information and product assurance for both the GOES imager and sounder. He was involved in the development and implementation of the algorithm for generating cloud information from both the GOES sounder and imager in order to enhance the National Weather Service Automated Surface Observing System. Anthony received his bachelor's and master's degree in meteorology from the University of Wisconsin—Madison.
James P. Nelson III works at the Cooperative Institute for Meteorological Satellite Studies, located in Madison, Wisconsin. He has extensive experience working with geostationary satellite data and other types of surface and upper air-based meteorological data, mainly in the realm of diagnostic and value-added software development. He has contributed to the on-orbit science check-out of numerous GOES satellites, and focuses on data quality-control. He received his master's degree from the University of Wisconsin—Madison.
Fangfang Yu received a BS degree and MS degree from Peking University, Beijing, China, in 1991 and 1994, respectively, and a PhD degree in geography from the University of Kansas, Lawrence, in 2002. She has been supporting the GOES weather instrument calibration and validation at NOAA/NESDIS since 2007.
Hyre Bysal is Geostationary Environmental Satellite (GOES) payload operations lead engineer at NOAA/NESDIS/OSPO in Suitland, Maryland, since 2002. After four years in nuclear industry, Hyre worked at Honeywell, and Motorola as Six-Sigma process specialist. He then moved to Motorola-Iridium project for satellite manufacturing, integration and launch optimization, which led him to NOAA GOES satellites. He received his bachelor's degree from Bosphorus University in Istanbul, Turkey, and his master's degree from the University of Kentucky.
Zhenping Li ; Michael Grotenhuis ; Xiangqian Wu ; Timothy J. Schmit ; Chris Schmidt, et al."Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system", J. Appl. Remote Sens. 8(1), 083530 (Nov 06, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083530
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-Primarily Brown/Green color mix, with hints of Blue
Olivia Pope
Effective - Staying out of Trouble
Consultant / Problem Solver
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Journalist & husband of Cyrus Beene
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President of the United States
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An Illustration of the End Justifies the Means
Jillian Celich, Jennifer Cramer, Alyssa DePlonty, Steve DeWalt, Courtney Dill
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Sprechen Sie mit einem Experten
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After announcing the Android M in May lots of rumours and speculations started about what M stands for? Finally we have the answer for it, as Google has announced the name for its Android M and the M stands for ……………….. Marshmallow.
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Hi all,
I am planning on moving all our clients and servers from Symantec Corporated Edition 10 to Symantec Endpoint Protection (v11.2).
Now, I've been doing migrations of Symantec Corporate Edition since version 7.6 all the way up to version 10.1 pretty successfully but this version looks very different and it's making me nervous!!
So I was wondering what the best practice would be? The manuals are very thorough and detailed and I am reading through them at the moment however it would be good to get some ideas and pointers off any of you who have actually done it.
Currently I've got one v10 Parent Server looking after all the clients (it was originally two), the SSC is installed locally on that server. My plan is to install SEP Server and Console onto another server spec PC, run the two versions concurrently and then install SEP client on all the workstations and move them to the SEP server, once I've got all the workstations moved I will migrate the v10 parent server to a SEP Server, then I'll have two parent servers sharing the work.
Done that way, it will mean protection across the network is maintained during migration, sound like a fairly solid plan to me... can anyone think of anything to add/change, things I should be aware of, slips/ups etc.?
Architecture is 30 managed clients and 1 server, all Windows XP and 2000.
5 Replies
Aug 8, 2008 at 6:19 UTC
Speaking from personal experience I wouldn't do it at all!
I recently tried to migrate when Endpoint was released and after some trials getting the management console installed I started rolling clients out, that was easy and I was fairly impressed but people reported that it slowed their PC's down (Symantec claim it has a smaller footprint than coroporate edition did), one user found that it blocked opening attachments in Outlook, Symantec released a decomposer update which broke the decomposer engine and it took them 8 days to release a patch to fix it, I uninstalled it from my machine and kept seeing remote connection errors in my event log because Symantec had changed some keys in the registry and neglected to change them back when I uninstalled, similarly on another PC it left a networking component installed which disabled the network and this was all through uninstalling using the Symantec uninstaller in add/remove programs. I've not had one uninstall go smoothly even when using uninstall tools supplied by Symantec themselves.
We have now moved to Kaspersky and I've now got to uninstall Endpoint from around 35 client PC's knowing that it is going to cause problems for me, if you have any doubts about how complicated it is search symantecs site for the manual uninstall instructions for end point.
Aug 11, 2008 at 7:19 UTC
If you install all of the options, it is indeed slow. I recommend installing the first server, and then from there create the customized installation files (I forget exactly where this is, but it's from the server admin console) to install just the AV stuff, unless you have a need for the firewall or other options. If you just install the AV, then it is a smaller footprint than v10, but everything slows down even our newest machines. From there, I used Group Policy to deploy the client to the workstations - that way I have an easy uninstall point should I need it, and it is deployed to new clients without my thinking about it. The v11 installation will automatically remove v10 from the system when it installs.
Aug 11, 2008 at 8:45 UTC
I agree with people saying it is very slow. We tried upgrading in September and ended up coming back to 10.2 less than a month later. Later this year when our contract runs out, we will be switching to another vendor. SEP11 is a pig!
Aug 11, 2008 at 8:54 UTC
Well, like I mentioned earlier, in my testing with just installing the AV part of SEP11, it was less of a hog than v10 was. It's all of the intrustion prevention and firewall stuff that makes the machine run so poorly. From what I've seen it's a smarter product too - whenever I've seen a problem with v11, it seems to rectify itself fairly quickly, I've even seen it reinstall itself on restart to fix a problem. v10 was a pain whenever it had an issue of any kind.
Aug 13, 2008 at 11:35 UTC
Some good advice, thanks people... I think I'll study it a bit more, the v10.1 infrastructure's running well at the moment, 'if it ain't broke...' 'n all that!!
This discussion has been inactive for over a year.
You may get a better answer to your question by starting a new discussion.


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Glossary Term – Place
Angel Island
Located in San Francisco Bay and called the "Ellis Island of the West," Angel Island was a check point and detention center for immigrants in the early twentieth century. Asian and European immigrants were processed at the Angel Island Immigration Station between 1910 and 1940, and an estimated 300,000 were detained there. The detainees were primarily Chinese, and while some were eventually permitted to enter the United States, many more were deported.


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Evans & Saunders Receive SEC Weekly Honors
May 6, 2014
LEXINGTON, Ky. – A pair of Kentucky Wildcats, Andrew Evans and Sha'Keela Saunders, earned weekly awards from the Southeastern Conference on Tuesday.
Evans – who was named National Athlete of the Week by the United States Track & Field and Cross Country Coaches Association (USTFCCCA) on Monday – picked up SEC Men's Field Athlete of the Week. Saunders was named SEC Freshman of the Week.
Evans won the Tennessee Challenge discus with a throw of 66.37m/217-9 – No. 3 in the world for 2014 – to move past defending NCAA discus champion Julian Wruck of UCLA both on the 2014 and all-time performers list.
The senior's mark is more than 18 feet farther than any of his preceding meets in this year and almost 12 feet better than his former PR of 206-0 (62.78m) in a Bronze Medal performance at the 2013 NCAA Championships.
Evans' 217-9 bettered Wruck's official collegiate best of 217-7 (66.32m) to jump to No. 4 on the all-time collegiate performers list with the fifth-best throw in collegiate history (Wruck has thrown further in a non-competitive exhibition).
He added another all-time top-10 throw at 216-8 (66.06m) to take the No. 9 spot. He joins Wruck and Nevada's Kamy Keshmiri as the only three men with two all-time top-10 throws.
His 58-10 (17.93m) shot put effort to finish sixth was also a career best.
Saunders had a career-best performance in the long jump with a mark of 6.38m/20-11.25 to finish as the top collegian, and second overall at the Longhorn Invitational in Austin, Texas.
She extended her UK freshman record by 4.25 inches, coming up just .75 inches short of the UK school record.
Saunders leads all SEC freshmen this season, ranks fourth in the conference overall and eight nationally.
She also ran a wind-aided PR 24.03 to claim 19th in the 200m.
With Evans' honor UK has had the SEC Field Athlete of the Week two of the last three times it was awarded as Raymond Dykstra picked up the honor two weeks ago.
Kentucky will host the SEC Championships from May 15-18.


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The spooky sights of Lichfield will be on offer at a ghost walk in the city.
Visit Lichfield's Gruesome and Ghostly tours return during October, November and December.
Led by Green Badge guides, the tours take in the Guildhall prison cells, the history of the Market Square burnings, the ghosts sighted at Dam Street, The Close and other locations.
Councillor Helen Fisher, Lichfield District Council's Cabinet member for tourism, said: "As the nights draw in and the temperature drops, this is the perfect time to discover the dark and mysterious side of Lichfield."
The walks are rounded off with a visit to a local pub for a glass of mulled wine and a hot sausage roll.
The tours will run from 7pm to 8.30pm on October 26 and 27, November 10, 15, 16, 23 and 29, and December 7, 8, 12 and 15.
Each costs £6.25 and can be booked by calling 01543 256611.


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Reviews & Stories
Over the Moon Happy - Penrith, AU
Dr Tim Papadopolous changed my life. Being over weight and miserable i decided to lose 25 kilos. And with that came alot of lose skin on my stomach. So i went and got a tummy tuck by another surgeon. I was not happy at all. I came across Dr Tim and he did a revision tummy tuck. OMG! AMAZING! So professional and extremely caring. Maticulous in his work. A perfectionist. A miracle worker! And... READ MORE


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(The Hosting News) – It's that time of year again. Time to start Christmas shopping (if you haven't already), or time stock up on items you may want for yourself, thanks to the many discounts and sales going on.
If you're going about doing your shopping online, beware: Counterfeiters are out there selling products.
According to the U.S. government, illegal sales of pirated products can weaken the American economy, stealing profit from legitimate businesses.
But as of yesterday, the internet now has less piracy. And it's thanks to an ongoing investigation launched by the Federal government in June of last year.
Specifically, ICE (The U.S. Immigration & Customs Enforcement), working with other government organizations (such as the Justice Department & FBI), has succeeded in recently seizing 150 more sites allegedly selling counterfeit products such as sports handbags, DVD sets, sports jerseys, and a whole lot more.
Commenting on the matter in a Government release, U.S. Attorney General Eric Holder focused on the threat of counterfeit operations, stating, "Intellectual property crimes harm businesses and consumers, alike, threatening economic opportunity and financial stability, and today we have sent a clear message that the Department will remain ever vigilant in protecting the public's economic welfare and public safety through robust intellectual property enforcement."
Since beginning the operation earlier this year, the U.S. Federal government has succeeded in stopping a total of 350 websites from selling counterfeit items by taking them into Federal custody.
Stopping piracy this time of year is especially important, considering it's the holiday shopping season.
The announcement by ICE was made yesterday, coinciding with Cyber Monday, a day in which online retailers offer discounts on products. To find the violators, authorities made purchases while posing as online shoppers. For more information on the matter, view ICE's news report here (ice.gov).


text: 3. As of noon trading, Mohawk Industries ( MHK) is up $2.09 (1.82) to $116.80 on average volume Thus far, 361,081 shares of Mohawk Industries exchanged hands as compared to its average daily volume of 742,200 shares. The stock has ranged in price between $114.57-$116.90 after having opened the day at $114.57 as compared to the previous trading day's close of $114.71. Mohawk Industries, Inc., together with its subsidiaries, produces floor covering products for residential and commercial applications in the United States and for residential applications in Europe. The company operates through three segments: Mohawk, Dal-Tile, and Unilin. Mohawk Industries has a market cap of $8.2 billion and is part of the industrial goods sector. The company has a P/E ratio of 30.2, above the S&P 500 P/E ratio of 17.7. Shares are up 26.8% year to date as of the close of trading on Tuesday. Currently there are 6 analysts that rate Mohawk Industries a buy, no analysts rate it a sell, and 4 rate it a hold. TheStreet Ratings rates Mohawk Industries as a buy. The company's strengths can be seen in multiple areas, such as its revenue growth, largely solid financial position with reasonable debt levels by most measures, impressive record of earnings per share growth, good cash flow from operations and solid stock price performance. We feel these strengths outweigh the fact that the company shows low profit margins. Get the full Mohawk Industries Ratings Report now. 3x UPSIDE POTENTIAL: TheStreet Quant Ratings has identified a handful of stocks that can potentially TRIPLE in the next 12-months. Learn more.


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Pay me a coffee:
The complete changelog is inside the patch file.
I have created a combined patch including the latest versions of several commonly-used qmail patches:
[Follow the patch details here]
Other patches:
Today Erwin Hoffmann has released his ucspi-tcp6 v. 0.99. The current version includes an hack by Manvendra Bhangui from indimail.org
which gets tcpserver and qmail's spfcheck to be IPv4-mapped IPv6
addresses compliant, provided that you use his fix to the qmail-spf
patch (my combined patch already has this adjustment to spf).
those interested, a few days ago Manvendra Bhangui
released a package of patches including now not only DKIM and SURBL but also substancial modifications which get SPF and the entire qmail totally IPv6 compliant. The upgrade for me is not so
straightforward, but I'm planning to have it in my big patch soon or
later. For the moment you can play with it downloading from http://sourceforge.net/projects/indimail/files/netqmail-addons/qmail-dkim-1.0/


text: How do we create a new help page with menu item? I looked in the templates, and sure... adding a menu link is in the template help wrapper, but how do we get it to use a custom created template, ie. help_custompage? Not node pages... help pages (just to avoid confusion) Is there an easy method to make this work via the ACP?


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Timing means everything in most sports and high school volleyball is no exception.
After two weekends packed with tournament action, Tara's Slammin' Volleyball Classic stands alone in the high school sports weekend spotlight in the Baton Rouge area.
The two-time tourney features 16 teams and begins at 3:30 p.m. Friday with pool play at Tara. Action resumes at 8 a.m. Saturday. The title contest is set for 4:15 p.m. Saturday.
Silliman Institute heads to Liberty, Mississippi, for the Mississippi Association of Independent Schools' Class South State tournament.
"I really like the group of teams we have," Tara coach Barry Jackson said. "At this point it would be hard for me pick a favorite.
"I got to see Plaquemine last week at Brusly's tournament and they seem to be coming around.
"Belaire is always tall and athletic and (Belaire coach) Ella Reado does a good job getting everything she can out of them. Baton Rouge High is another team I like and so is Walker."
The Tara gym will be divided into two courts for pool play and the elimination rounds. Ten pool matches are set for Friday, with 14 more scheduled Saturday in the traditional best of three-sets tourney format.
Third-place teams in each of the four pools meet in consolation playoffs at 1:15 p.m. The semifinals are scheduled for 3:30 p.m.
Silliman seeks another title
The two-day MAIS South State tourney starts Saturday and concludes with Monday games at Ethel Vance Park.
Silliman (36-8) won its sixth straight district title, going 6-0 in MAIS District 6-2A. The Wildcats seek their fifth straight South State title. Coach Trae Weaver's team is the top seed from 6-2A. The top four teams advance to the MAIS overall 2A tourney the following weekend.
Kennedy Carruth is 24-6 on the mound with a 1.46 earned run average for the Wildcats. Shortstop Callie Venable leads Silliman with a .524 batting average. Center fielder Kayla McNabb (.429) and Carruth (.393) are the other top hitters for the Wildcats.


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The more do-it-yourself homeowners accomplish themselves, the less money they spend getting others to do things for them. That said, check local building codes before attempting to work on your furnace yourself to see if this repair requires a professional. When working with gas, always err on the side of caution. Although the odor in natural gas is man-made, it is possible for it to dissipate before the gas is completely gone. Provide plenty of ventilation.
Things You'll Need
- Spray bottle with soapy water in it
- Phillips head screwdriver
- Wrench
- Pilot tubing
- Tubing cutter
- 2 compression nuts
Locate the breaker that controls the flow of electricity to the furnace and switch it to the off position. Close the gas valve located next to the furnace. Remove or extinguish any open flames in the area of the furnace.
Open a window close by to be certain all the gas fumes escape the area you are working in. Remove the cover to the furnace's gas regulator with a screwdriver to expose the thermocouple and pilot tube. They both connect to the gas regulator.
Spray some soapy water on the connection of the furnace pilot tube and check for bubbles that indicate a leak. If leaks occur, wait until it stops before proceeding to allow the rest of the gas in the tube to escape.
Back out the nut where the pilot tube meets the regulator with a wrench. Follow the pilot tube to its other end and repeat the process there as well. Pull the pilot tube free.
Measure the length of the tube and cut another piece with the tube cutter. Bend the tube using the old one as a guide so it will fit correctly. Do not make any sharp bends to prevent kinks in the line that could cut the gas flow to the burner.
Push the compression nuts on the ends of the tubing. Check the ends of the tubing for burrs and make sure they are square. Tubes that are not square on the ends won't seat properly inside the gas regulator or the burner.
Insert one end of the tube into the gas regulator until you feel it stop. Hold the tube in place and tighten the compression nut with your other hand until it stays. Tighten it with a wrench one more turn. Repeat this at the other end of the pilot tube.
Turn the gas on. Hold the pilot light button on the regulator down and spray the new connections with the soapy water to check for leaks. Adjust if necessary. Light the pilot light according to the instructions on your furnace. Turn the breaker back on.
Tips & Warnings
- If gas leaks occur where the pilot tube meets the regulator, replace the tubing. Once the compression seal is compromised, it cannot be resealed by tightening the compression nut.
- Never work on gas lines with gas in them.
Related Searches
- Photo Credit gas shut-off valve image by enens from Fotolia.com
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A timeline by Nekromans.
The "Kingdom and a Horse" TL focuses on an alternative outcome to the Battle of Bosworth, with Richard III winning and Henry Tudor dying in battle. This event butterflies a number of other events into being, including a personal union of Iberia under the Portuguese throne, Columbus sailing for England, and the drawing of England into the various Habsburg personal unions.
The TL currently runs from the 1480s to the 1530s, though it is progressing (semi-)quickly.
The timeline can be found on the template to your left.


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24"W x 60"L High Quality S/s Flat Top Work Table
Green World by Turbo Air | Model: TSW-2460S
Green World by Turbo Air TSW-2460S Specifications
Low Price Guarantee On All Green World by Turbo Air Products!
This high quality 24"W x 60"L work table features 18/304 stainless steel flat top with turned down edges, adjustable galvanized undershelf & legs with adjustable ABS bullet feet.
Manufacturer Information
68 lbs.
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The owl represents the school's mascot, a theme which is repeated in the school's media center. The board also accepted an anonymous donation of two exterior electrified carriage lanterns to be placed by the statue. The electrical wiring for the lanterns has already been installed.
On Monday, Dec. 17, 62 music students from the school went to the West Caldwell Care Center, West Caldwell, where they presented a holiday program to the residents. Parents drove the students to the center and provided cookies for the residents.
The grades K-3 holiday program was presented at the school on Friday, Dec. 14, and grades 4-6 presented their holiday program at the school on Monday, Dec. 17.
Outdated playground equipment was recently removed from school grounds by a group of volunteers headed by parent Jim Blake.
The board approved postponement of an overnight field trip to the Liberty Science Center from Friday, Jan. 4 to Saturday, Jan. 5, to Friday, Feb. 8 to Saturday, Feb. 9, because the center had insufficient overnight guests to justify staying open on the original date.
In other matters the board approved the school's 2002-2003 Professional Development Plan, which provides two additional contract days for teachers to meet professional development requirements during the school year.
Teachers are required to take 100 hours of professional development every five years. Superintendent Catherine Duncan said the school's teachers "averaged 50 hours of professional development per year, so meeting the 100 hour requirement would be no problem."
The board hired Katherine Pollard as a resource room teacher at an annual salary of $42,141 and also appointed Megan Keenan as a special education aide. The board reported that it was also able to add five substitute teachers to its list following a campaign to expand the pool of available substitute teachers.
The next meeting of the board will be on Wednesday, Jan. 30.


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When was the last time you had to book a flight and opted to go directly through the airline itself?
Probably has been a few years if not decades since the inception of travel related sites such as Orbitz, Travelocity, Priceline and others of that ilk.
Truthfully, there are at least a few dozen sites that center on not only flights but hotels and car rentals that tout their services as being the best in a crowded field of online booking players.
Some have even gone out and hired celebrities spokespersons such as William Shatner to promote their products.
But do these sites really differ from each other enough to crown a realistic winner?
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After promoting "Quantico" at Good Morning America and making the crew groove to Bollywood dance moves on 'Bang Bang', Priyanka Chopra is all set to appear on Jimmy Kimmel Live!
Priyanka who plays an FBI agent in her international debut TV show "Quantico" has been promoting the ABC's FBI thriller drama on various shows, and will now be seen on late-night talk show "Jimmy Kimmel Live!"
The 33-year-old actress, whose debut American series "Quantico" premiered in the US on Sunday, took to Twitter to share the news with her fans and well-wishers.
"Have a 6 am morning so will leave u all now. Loved spending the day with u all! @extratv and @jimmykimmel Tom! #Quantico #QuanticoTakeover," she tweeted.
A few days ago, Priyanka Chopra appeared on Good Morning America where she not only promoted "Quantico" but also made the talk show hosts and the crew groove to Nicki Minaj, Ariana Grande and Jessie J's 'Bang Bang' in Bollywood style.
Images Courtesy of Twitter
Priyanka Chopra who plays Alex Parrish, an FBI agent also hinted that will make an appearance on "Extra". A few days ago, the former Miss World, 2000 was on 'Live with Kelly and Michael' with her mother and discussed about her character in the thrilling ABC's drama.
The show also features actors Jake McLaughlin, Tate Ellington and Graham Rogers, who all play fellow recruits. "Quantico" will narrate their adventures and experiences as they train on Quantico base in Virginia, and secrets of their past start to emerge and will premiere in India on October 3.
Quantico has been getting a good response and lots of accolades of Priyanka Chopra who is telling us the story from the past when she was at the FBI base training, and to the present when she is trying to find out the culprit behind a terrorist blast.


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Junglee Rummy | Wheel of Fortunes
Junglee Rummy brings another bag of surprises! This time it's in the form of a spin wheel! That's right! Play your ace at Junglee Rummy and win spin the wheel to win Rs.25,000 in prizes!
This new and innovative promotion is launched to reward the best players of the game. If you want to get lucky, join this exciting contest and grab great prizes with your skills!
In order to join the contest, you need to make a minimum deposit of Rs.100 during 23 and 24 August and win maximum amount to win a prize.
If you play your cards right, you could win a cool cash prize in the wheel of fortunes. Prizes for this promotion range from Rs.50 to Rs.5000!
The first winner of this contest will bag Rs.5000 and the player at the second position will win Rs.3000. The third and fourth prizes are Rs.2500 and Rs.2000. The fifth prize is Rs.1500; the sixth is Rs.1000. The seventh prize is Rs.500 and it will be given to 10 players each. Rs.100 as the eight prize will go to 30 players. The last prize is Rs.50 and it will be given to 40 players.
As you can see, there are many prizes for this promotion. Make sure to deposit Rs.100 during the promo days and play your best to guarantee yourself a prize.
The wheel of fortune will appear on Junglee Rummy at the end of the promotion. You will have to spin it to claim your prize. So, play hard and get lucky at Junglee Rummy – Spin and Win Big!
JULGET2017 Bonus Offer at Ace2Three Grand Welcome Bonus at Classic Rummy Junglee Rummy Bonus | Get Rs.1500 Welcome Bonus Junglee Rummy Bonus | Midweek Masters Promotion Junglee Rummy Bonus | New Moon Eid-al-Fitr Offer Sunday Passion Rummy Bonus from RummyPassion


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Irwin Mitchell has helped a steel worker obtain compensation after he suffered facial injuries in an accident at work.
Our client was working for a steel company as a steel hand, working in a team of four making steel components.
He suffered facial injuries when part of a tool sheared off and flew up, like shrapnel, hitting him and becoming embedded in his cheek. He was wearing safety goggles at the time but these did not cover his face.
Our client underwent surgery on the day of the accident and a piece of the tool approximately 2cm long and 1cm wide was removed from his cheek, with 12 stitches being put in.
Irwin Mitchell secured £8,500 in compensation for his injuries.
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