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NSIDC Special Report 9
Methods for Regridding AARI Data in SIGRID Format to the EASE-Grid Projection
Raymond F. Kokaly
24 January 1996
National Snow and Ice Data Center
Cooperative Institute for Research in Environmental Sciences
University of Colorado, Boulder, Colorado, USA
1. Introduction
2. Description of Grids
2.1 AARI implementation of SIGRID
2.2 EASE-Grid
2.3 Comparison of Grids
3. Regridding Process
3.1 Gridding Direction
3.1a Forward regridding (AARI to EASE)
3.1b Inverse regridding (EASE to AARI)
3.2 Gridding Concepts (grid points and grid cells)
3.3 Gridding Procedures (determination of "value" to place in EASE)
3.3a Nearest neighbor
3.3b Drop-in-box
3.3c Point min/max
3.3d Interpolation
3.3e Area min/max
3.3f Area weighted averages
4. Comparison of Regridding Approaches
4.1 Effect on Data Content
4.2 Computer Resource Requirements
4.3 Consistency of Results
4.4 Dealing with the Descriptive and Symbolic Nature of AARI Data
5. Adjustments for erroneous starting longitudes in SIGRID
6. Conclusions & Recommendations
7. Future Work Plans
7.1 Further Evaluation of Nearest Neighbor and Area Min/Max Resampling
7.2 Processing (directions for optimizing/rewriting code)
7.3 Subset of Full EASE-Grid
7.4 Quality Control
7.6 Comparisons with NIC and/or SSM/I
7.7 Southern Hemisphere
8. References
Russian sea ice charts digitized by the Arctic and Antarctic Research Institute (AARI) in the Sea Ice Grid (SIGRID) format are being regridded to the Equal Area SSM/I Earth Grid (EASE-Grid). Several methods of regridding are considered: nearest neighbor, drop-in-box, point min/max, interpolation, area min/max and area weighted averages. These methods are evaluated for their effect on data content, computer resource requirements, consistency of results, and how they deal with the descriptive and symbolic nature of SIGRID data. Two of these methods, nearest neighbor and area min/max, are identified as most appropriate to apply for regridding AARI data.
The nearest neighbor method is straightforward and not computationally demanding, but results in data loss and duplication. The area min/max approach is more complex, and requires more computer resources, but results in EASE-Grid images created from all the AARI data points. In testing the methods, errors were found in the digitization of the sea ice charts by AARI. Some of the charts were digitized with a starting longitude that causes data to be recorded at differing locations on the charts. The regridding routines were designed to work with data at consistent locations. However, by oversampling the data at 0.25 degree spacing, the erroneous data points may be transferred to the EASE-Grid projection.
Future work remains to select between the nearest neighbor and area min/max regridding approaches. These methods should be applied to a representative set of AARI data from the east and west sectors and the results evaluated and compared. If necessary, the approach using oversampled AARI data needs to be refined. Furthermore, quality control procedures, subsetting of the full EASE-Grid to polar regions, and code optimization need to be implemented before production of EASE images of the AARI data can be completed. [Ed. note: Subsequently, EASE-Grid nearest neighbor regridding with 12.5 km grid cell size was chosen.] See (NSIDC Special Report 6).
1. Introduction
This document describes the process of regridding sea ice data obtained from Arctic and Antarctic Research Institute (AARI) in the SIGRID format to the Equal Area SSM/I Earth Grid (EASE-Grid) northern hemisphere projection. The many options for the regridding process are presented along with the advantages and disadvantages of these options. In addition, some statistics and examples of regridding SIGRID data are presented. This document is intended to help define the optimum method for the regridding process of AARI data. Although the criteria for selecting the best regridding technique varies from project to project, the general discussion in this document may help in selecting the method of regridding for other data sets at NSIDC.
2. Description of Grids
This section gives a general description of the SIGRID and EASE-Grid grids. Only general information and details relevant to the regridding process is presented. For more detailed descriptions consult the references for SIGRID (1) and EASE-Grid (2-5). The work conducted on this project was with northern hemisphere data.
2.1. AARI Implementation of SIGRID
SIGRID was developed for the World Meteorological Organization. It was designed to place information recorded on operational ice charts into a digitized format more suitable for statistical and climatological use. This was done by assigning numerical values to the ice parameters and recording them at specific grid points on the charts. As shown in Figure 1, the value at each grid point is representative for the ice conditions of the grid cell area around the point. Thus, only the value of the sea ice parameter at the grid point is recorded, even if the majority of the grid cell area has other values.
The SIGRID codes for ice form, stage of development, and concentration are listed in Tables 1-3:
Table 1
Table 2
Table 3
SIGRID data values can indicate an ice form of, say, icebergs (see Table 1) or a stage of ice development of, for instance, old ice (see Table 2). Essentially, all the data values of SIGRID are symbolic. The numbers are codes for values or an range of values for the ice parameters. For example, an AARI ice concentration code of 90 refers to 90% ice concentration and a value of 91 refers to the range of concentrations greater than 90% and less than 100% (see Table 3). The descriptive and symbolic nature of AARI data affects regridding approaches that combine data values (e.g., interpolation or averaging methods).
The SIGRID format uses a "geographical" grid. Essentially, this type of grid defines grid point locations according to latitude and longitude spacing. According to observations of the AARI implementation of SIGRID, the latitude spacing of the grid points is constant at 0.25°. The longitude spacing, in contrast, varies with latitude (Table 4). The longitude spacing in degrees of longitude is determined by multiplying the latitude spacing by a lon/lat ratio. The value of the ratio is different for each of eight defined latitude regions of the grid. As shown in Table 4, grid points at higher latitude regions have greater spacing in longitude. By selecting an initial latitude and longitude, the grid points are determined. If the initial latitude and longitude are not consistently selected to always yield the same grid points, the regridding procedures will give different results for files that have improperly selected initial points. This is discussed in detail in Section 5.
For moving data between these grids, it is important to look at how the grid points are spaced on the surface of the earth. To do so, the surface of the earth as a sphere with radius Re = 6378 km was approximated. Using the information in Table 4, the spacing (in kilometers) of the grid points along meridians (lines of latitude) and parallels (lines of longitude) was determined. The constant 0.25° spacing in latitude translates to a 27.83 km spacing on the Earth's surface.
The longitude spacing, in contrast, is more complex and varies non-linearly with latitude. As shown in Table 4, within each latitude region, the spacing decreases as latitude increases (the spacing varies with the cosine of the latitude). At the boundaries of the regions, where the lon/lat ratio changes, the longitude spacing increases abruptly (i.e., at 50° the spacing is 17.89 km; in the next region at 50.25° latitude, the spacing jumps to 35.59 km). These characteristics are evident in Figure 2 a and b.
Like the spacing on parallels,the area of each SIGRID cell changes with latitude. Table 4 and Figure 3 show this variation.
2.2. EASE-Grid
The EASE-Grid projections were developed under the SSM/I Pathfinder program for use with SSM/I data. Three equal area projections were defined: a global, a northern hemisphere and a southern hemisphere. The EASE-Grid for the northern hemisphere is a Lambert Equal-Area Projection in north polar aspect. It is based on a spherical model of the Earth with radius Re = 6371.228 km. The important aspects of this projection are its equal area, the azimuthal property showing true direction from center of the projection, and its scale at a given distance from the center varies less from scale at the center than any of the other major azimuthal projections. Table 5 shows the change in scale factors with latitude.
The nominal cell size is 25.067525 km. In order to remain equal area, the latitude spacing increases and the longitude spacing decreases from the north pole to the equator to preserve a nominal cell area of 628.37956 km². The projection is shown in Figure 4 with coast lines and a graticule of 15° latitude and 30° longitude overlaid (black pixels). In this projection, the corner pixels are in the southern hemisphere (negative latitude) and are shown in grey. These pixels are not used to represent data and are considered blank or non-valid pixels.
2.3. Comparison of Grids
An important distinction between these grids is that the EASE-Grid is equal area and the SIGRID is not. Also of importance for transferring data from one grid to another is the disparate manner in which they have their grid points placed. The SIGRID has constant 0.25° (27.83 km) spacing along meridians, while the EASE-Grid varies from 0.318° at the equator to 0.225° at the pole (35.45 km at the equator to 25.0 km at the pole). Even more importantly, the SIGRID spacing along parallels decreases as latitude increases within the eight regions and has large step increases across boundaries of these regions. EASE-Grid, in contrast, smoothly increases from 0.159° (17.72 km) at the equator to 51.24091° (25.06746 km) one pixel below the pole (Figure 2).
The grid cell area of SIGRID follows the same pattern as its spacing along parallels, but the EASE-Grid area, of course, remains constant in each cell (Figure 3).
Another important factor in regridding is that the AARI implementation of SIGRID has a total possible number of 375,084 digitized points. The EASE-Grid projection has 405,845 grid cells. Thus, there are 30,761 more grid cells in EASE-Grid. For a resampling method which uses only one SIGRID value to determine a value in EASE (e.g., nearest neighbor) there will be some amount of SIGRID data that must be repeated in order to fill the entire EASE grid.
3. Regridding Process
This section describes several methods for representing the AARI data in the EASE-Grid northern hemisphere projection. The section includes examples of regridding AARI data; however, some gridding methods (e.g., area min/max, interpolation, and area weighted averages) have not been evaluated yet. Any of these options that seem appropriate will be tested in the future before a decision is made on the optimum regridding approach.
3.1. Gridding Direction
Because the origin of the data is the SIGRID format, the process of starting with AARI data points and placing them into EASE grid cells as the "forward" direction is defined. The "inverse" direction is defined as starting with empty EASE grid cells, calculating where these cells lie on the surface of the Earth with respect to the SIGRID grid points, and determining the value to place in the EASE-Grid cell from these surrounding points.
3.1a. Forward regridding (AARI to EASE)
Initially, implementing the regridding process in the forward direction was considered. The AARI grid points were "dropped" onto the EASE-Grid (i.e., using the latitude and longitude of the AARI grid points, it was determined which EASE grid cell contained the point). Because of the disparity in grid point spacing (as discussed in Section 2.3), the AARI grid points did not fall uniformly into the EASE grid (Figure 5). The image demonstrates how the different grids compare. At the start of each of the AARI latitude regions, the AARI grid spacing on parallels is greater than the EASE spacing, thus there are a greater proportion of empty (white) cells. In contrast, at the end of these regions the AARI spacing is less than EASE and there are more AARI grid points and, thus, a greater number of overfilled (blue) cells.
Table 6 shows the number and percentages of AARI points in EASE grid cells. Ideally, the more one to one correspondence, the less data manipulation may have to take place in the regridding process. Over 68% of the EASE grid cells contain exactly 1 SIGRID grid point. However, nearly 20% of the EASE grid cells are un-filled (zero SIGRID points). Furthermore, over 11% of grid cells have 2 or more SIGRID points (over-filled). The empty cells present a problem for the forward regridding since the data presented to the user in the EASE-Grid projection would always contain some empty (un-filled) cells. Likewise, the over-filled cells necessitate a decision making or calculation to decide the value for the grid cell. As shown in the next section, the inverse approach has the advantage of having every grid cell in EASE assigned a value based on the SIGRID data. The method of determining the value presented in EASE-Grid can range from a simple nearest-neighbor approach to a more complicated area weighted average calculation. For these reasons, using inverse regridding is suggested.
3.1b. Inverse regridding (EASE from AARI)
The inverse approach is made up of two steps: 1) locating each EASE grid cell in relation the the digitized SIGRID grid points, and 2) establishing a value for the EASE grid cells based on the values of the surrounding SIGRID grid points. This section discusses the first step and a following section, Section 3.3 Gridding Procedures, addresses the several options for the second part.
The locations of the EASE grid cell centers were determined using the "easeconv.pro" IDL routine. This routine establishes the center latitude and longitude for each pixel in the EASE-Grid northern hemisphere projection. Then, the center locations are compared to the locations of the SIGRID digitized grid points.
In order to visually represent the SIGRID digitized points a rectangular image is used (Figure 6). The image has a y-dimension (rows) of 361 pixels (90/0.25 + 1) and x-dimension (columns) of 1440 pixels (360/0.25). At latitudes greater than 50° (i.e., rows greater than 201), not all of the pixels represent a SIGRID digitized point. This is because the SIGRID format changes spacing from every 0.25° in longitude in the 0-50° latitude region to every 0.50° in longitude in the 50.25-70° latitude region (see Section 2.1 AARI Implementation of SIGRID). Thus, only every other pixel in the x-dimension represents an AARI digitized point. Likewise, at higher latitude regions there are increasingly fewer digitized points (light gray pixels).
3.2 Gridding Concepts (grid points and grid cells)
In dealing with the AARI digitized data the concepts of grid points and grid cells need to be defined. AARI values of sea ice concentration, ice form, etc. are recorded at specifically defined SIGRID grid points. These grid points have exact locations at latitude and longitude coordinates discussed in Section 2.1 and defined in the document proposing SIGRID(1). Furthermore, AARI considers the value at these grid points to apply to the associated grid cells. The area covering half way between the previous and next point in latitude and the previous and next point in longitude is the grid cell (see Figure 1). Thus, during the AARI digitization process, the grid cell on the chart may contain several values for sea ice concentration, but only the value at the grid point is recorded. Subsequently, we regard this value to apply to the entire grid cell area.
Whether the concept of grid points or grid cells is used affects the regridding process. A method which considers both the original (AARI) and target (EASE) grids in terms of grid points is the nearest neighbor resampling (Section 3.3a). This concept is simple and computationally efficient but may not represent the entire original data volume in the final grid. Similarly, the drop-in-box method (Section 3.3b) is simple and efficient but, however, treats the original grid as grid cells and the destination as grid points. Again, this method results in some data loss. The point min/max (Section 3.3c) and interpolation (Section 3.3d) methods perform some computations or decision making that reduces data loss. These methods are more complex and require more computation time. The most complex methods presented here, area min/max (Section 3.3e) and area weighted averages (Section 3.3f), deal with both AARI and EASE grid cells and the analysis of overlapping grid cell areas.
In addition to data loss and computational efficiency, the symbolic nature of the AARI data has an effect on the results of the various regridding approaches and impacts the selection of the optimum method. Especially affected are the more complex methods that use more than one SIGRID value to determine a value in EASE-Grid. Furthermore, unknown values and empty grid cells can change the implementation and results of interpolation and averaging methods.
3.3 Gridding Procedures (determination of "value" to place in EASE grid cells)
The second step in the inverse regridding, once the locations of EASE grid cells with respect to the digitized SIGRID points are known, is determining the data value for each of the EASE grid cells. The method of determining the value can be as simple as selecting the value of the closest SIGRID point (nearest neighbor) to a more complex averaging of the surrounding values using a weighting based on the amount of SIGRID cell area falling in the EASE grid cell (area weighted averaging). In this section, examples of some of the methods show the starting AARI data values in a SIGRID rectangular image (Figure 6) and the resulting EASE image. Furthermore, the advantages and disadvantages of each approach are discussed.
3.3a Nearest Neighbor
The most straightforward method of determining the value to place in the EASE grid cells is to assign it the value of the closest SIGRID point. An IDL routine was written to establish the nearest neighbors for each EASE grid cell. The routine loops through the EASE grid pixels, first establishing their center latitude and longitude, then computing the distances to surrounding SIGRID points, and finally selecting the closest SIGRID point using distance (arc length) on the surface of the Earth.
Thus, for each EASE grid pixel, the closest SIGRID point is determined and its location (row and column) in the SIGRID rectangular array is stored in a file. In special cases when two or more SIGRID points are equidistant at the minimum distance, the grid point at the lower latitude and longitude is selected as the nearest neighbor. This convention is consistent with the drop-in-box regridding, see Section 3.3b.
The routine is only executed once to establish the nearest neighbors (program execution time is 4 hours). Subsequently, a different routine uses the row and column files to regrid AARI data files to the EASE projection. This is done simply by looping through the EASE pixels, reading the nearest neighbor AARI row and column from the stored files, looking in the AARI data file for the value digitized at this location, and assigning this value to the EASE grid cell.
An example is shown for the AARI file w900904.sigrid. Table 7 shows information extracted about the contents of the SIGRID file. Figure 7 shows the AARI total ice concentration data displayed in the 0.25° spaced rectangular array (all examples in this document are for total ice concentration). A close-up of the region with the most data is shown in Figure 8. Figure 9 shows the resulting full EASE-Grid image using the nearest neighbor resampling. Figure 10 is a close-up of the polar region.
In general, the data seems to transfer well between the grids. Coast lines are followed closely, but, in order to assess this quality control procedures are recommended (see Section 7.4 - Quality Control). As the table and figures show, the SIGRID file contains data from 65° to 90° north latitude. In Figures 7 and 8, the SIGRID data has 100% total ice concentration (red pixels) extending to the pole (top line of the image). However, in the EASE-Grid images, data above 87° is not shown. The reason for this is seen by closely examining the SIGRID figures. In the images, black pixels represent cells that should not contain data (non-digitized points) and the darkest grey pixels represent cells that may contain data for the SIGRID format but do NOT for this particular file. At high latitudes (greater than 87°), there are data values in non-digitized pixels and no data represented where it should be in the digitizable (darkest grey) pixels (top of Figure 8). This is due to improper selection of the initial longitude. Table 7 shows that AARI chose -24° longitude. However, for proper digitization, the starting longitude must be divisible by the longitude spacing of the the highest latitude region in the file. In this case, 90° is the highest latitude; thus, the initial longitude must be a multiple of 20°. If the highest latitude was in the 87.25-89° region, it would have to be a multiple of 5°. Thus, for the 87.25-89° region the proper initial longitude would be -25°. This difference is affirmed by noting the pixels in this region are offset by four pixels, or 1° (Figure 7 and Figure 8). Similarly, above 89.5°, the digitized values are misplaced by 4° (the difference between -20° and -24°).
This error in the digitizing is a problem since the data are no longer consistently located at the same points. Since the regridding was established assuming a proper starting longitude, when data are not recorded in the right locations they do not get regridded to the EASE-Grid projection. Possible ways to work around this problem are addressed in Section 5.
Similar to the analysis of how data points transfer in forward regridding (Section 3.1a), the number of times AARI grid points were selected as nearest neighbors to the EASE grid cell centers was considered. Table 8 shows this for each latitude region, for the entire grid, and for the polar region above 50° latitude. The percentages change in each region because the AARI and EASE grid spacings vary. For the full grid, approximately 10% of the possible digitized points are never selected as nearest neighbors. This means the data at these points does not get represented in the EASE projection. Thus, there is a 10% loss of data. Considering only the region above 50°, the data loss is 11.34%. In general, the data loss increases as the latitude increases because the AARI grid spacing is sometimes less than EASE (Figure 2). Thus, in these areas, when transferring data from SIGRID to EASE using nearest neighbor resampling, not all of the greater number of SIGRID points transfer to the fewer number of EASE grid cells.
In addition to the data loss, there is also an amount of data replication in the regridding. In Table 8, this is when an AARI point is used two or more times. This occurs when the EASE spacing is less than AARI. The amount of data replication in the full EASE-Grid is 18% for the nearest neighbor resampling.
In summary, the nearest neighbor regridding is conceptually simple, easily implemented, and computationally fast. However, the results do suffer from a certain amount of data loss and duplication in the final EASE-Grid image. Lastly, the errors in the starting longitude corrupt the regridding results at higher latitudes.
3.3b Drop-in-box
The drop-in-box method is another simple approach to regridding the AARI data. The value of the EASE grid cell is equal to the value of the SIGRID cell that contains the center position (latitude and longitude) of the EASE cell. As with the nearest neighbor resampling, the center position of the EASE cells are determined and an IDL routine determines in which SIGRID cells the centers fall. Once again, the SIGRID cell locations, rows and columns, are saved in files. Subsequently, another routine regrids the AARI file to the EASE-Grid.
Comparing the drop-in-box row and column files to those of the nearest neighbor regridding shows almost identical results. The only discrepancies are for 112 pixels along the edges of the eight latitude regions. At these points, the situation depicted in Figure 11 is encountered. When a new latitude region is started the SIGRID spacing along longitude increases (sometimes doubling the previous value) and an EASE grid center may fall near the edge of SIGRID cell. It's coordinates may, however, be closer to the SIGRID point below. Thus, for the 112 points where they differ, the drop-in-box method selects the SIGRID value one row above the nearest neighbor resampling.
In addition to giving results similar to the nearest neighbor approach, the drop-in-box resampling has the same characteristics with respect to errors in initial longitude, data loss and redundancy, and computational efficiency.
3.3c Point min/max
This method is similar to the drop-in-box method but in the forward gridding direction. The concept of the point min/max resampling is applied to forward regridding since more than one SIGRID point may fall into an EASE grid cell, as discussed in Section 3.1a. The point min/max regridding generates two EASE grid images for each AARI file: one with the maximum values and one with the minimum values. For grid cells where only one SIGRID point is present, the same information would be shown in both grids. However, the EASE images would still contain a significant number of empty cells, nearly 20% of the images (see Figure 5).
The two images combined would contain almost all of the information in an AARI file. The only exceptions would be for 16 EASE grid pixels which have three SIGRID point falling into them (see Table 6). In these 16 pixels one of the SIGRID points would not be presented in either the min or max image.
Another important aspect of this approach is that each image will not consistently show values that come from the same digitized location in SIGRID. For example, one time the max image may show the first of two SIGRID points that fall into an EASE grid cell. On another date, the second SIGRID point may have the highest value and be selected for the max image. Since the points are at different locations, the images from different dates may not be spatially consistent.
3.3d Interpolation
The interpolation resampling computes a value for the EASE grid cell by interpolating from the values of the four surrounding SIGRID points. This type of resampling makes use of all of the SIGRID data to construct the EASE images; thus, it avoids the data loss problem of the nearest neighbor and drop-in-box methods. The implementation would consist of saving lat/lon coordinates for the four associated interpolation points rather than the one nearest neighbor. In addition, the weighting factors would also be stored in files. These files would be generated one time to establish the details of the regridding. Subsequently, a program could be run to apply these to an AARI file in order to create an EASE image.
Despite the elimination of data loss by this method, there are two major disadvantages in applying this technique to AARI data. The first problem is that many AARI points in each file have missing and unknown values. Thus, instead of interpolating an EASE grid cell value from four points, the value may be calculated from three, two or one AARI value. This requires additional code in the regridding routine to handle each case and causes an increase in computation time. For instance, in dealing with AARI values of 70%, 60%, 80% and 100% sea ice concentration the interpolated value may be 80%. However, if one or more of these values is unknown, the routine must determine that fact and adjust weighting factors to compensate. If the 100% value is replaced by an unknown value, the result will come out differently.
The second problem in dealing with AARI data is more difficult to resolve. This is the descriptive and symbolic nature of some of the AARI data values. For example, AARI data on ice form has non-numerical information represented by numerical values (Table 1). These data do not work with the interpolation resampling. For example, what is the interpolated value between an ice form of strips and patches and an ice form of pancake ice? For this reason, no examples of this method have been generated and it is not recommended for application to the AARI data set.
3.3e Area min/max
The area min/max concept is explained by visualizing the SIGRID grid cells on the surface of the Earth and then overlaying the EASE grid cells on top of them (Figure 12). Next, for each EASE grid cell, determine which SIGRID cells have some portion of the area overlapping the EASE cell. Finally, as with the point min/max (Section 3.3c), place the minimum of these AARI values into one EASE image and the maximum value into another. For example, in Figure 12 the EASE grid cell with SIGRID values of 60%, 80% and 100% sea ice concentration would have a value of 60% in the minimum image and 100% in the maximum image.
The advantage of the area over the point methods (i.e., dealing with purely grid cells as opposed to EASE grid cells and SIGRID points) is that all EASE grid cells are filled. Instead of establishing which SIGRID points fall in the EASE cells, the SIGRID cells that have area overlapping EASE cells are determined. Once this is established, each AARI file can be regridded to the min/max images. Similarly to the point min/max and interpolation approaches, empty cells and unknown values must be handled. However, in contrast to the interpolation method, no mathematical operations are done to combine individual SIGRID values and there is no problem with the descriptive nature of some of the AARI data.
In a sense, all data is used in the creation of the area min/max EASE images; thus, there is no data loss. However, the results are presented in two images and the previously discussed problem with spatial consistency remains.
3.3f Area weighted averages
The final proposed regridding method, area weighted averages, is the most computer intensive. This method is similar to that of area min/max, however, only one EASE image is created. The EASE grid cell values are weighted averages of the SIGRID cell values that have some area falling into the EASE cells. For example, consider Figure 12 where the first SIGRID cell has a value of 100% sea ice concentration and covers one sixteenth of the EASE cell. The second SIGRID cell also has 100% concentration and covers three sixteenths of the cell. The third has 80% ice concentration and covers three sixteenths of the EASE grid cell area. And the final SIGRID value of 60% covers the remaining nine sixteenths of area. The EASE grid cell value is computed from the sum of these concentrations, weighted by the fraction of the total EASE grid cell area that they cover (i.e., an area weighted average):
EASE value = 100%*(1/16) + 100%*(3/16) + 80%*(3/16) + 60%*(9/16) = 73.75%
If one of the SIGRID cells covering the EASE cell is empty or unknown, the weightings are adjusted to be fractions of the area covered by the valid values rather than the total area. Thus, if the 60% ice concentration value was instead digitized as the unknown value, the calculation (with total area = 7/16 of the original total area) now becomes:
EASE value= 100%*(1/16)/(7/16) + 100%*(3/16)/(7/16) + 80%*(3/16)/(7/16) =91.43%
Thus, the area weighted average approach can handle situations when unknown or empty cells are present.
Unfortunately, like the interpolation approach, this method can not handle combining descriptive data values such as those for ice form and some values of stage of development.
4. Comparison of Regridding Approaches
The objective of this section is to evaluate the six regridding methods in four categories: 1) the effects on data content in going from the AARI digitized format to the EASE grid images, 2) computer resource requirements, that is, the estimated amount of time to regrid an AARI file and the required storage, 3) the consistency of results with respect to spatial representation of the data, and 4) how each method deals with descriptive data such as ice form or symbolic data representing a range of values. Based on the evaluations in this section, a recommendation is made in Section 6 as to the optimum regridding method to apply to the AARI data.
4.1 Effect on Data Content
Transforming the data from the AARI digitized format to a gridded EASE image alters the data content. The most obvious example of this is for the nearest neighbor and drop-in-box approaches where approximately 10% of the AARI data is not represented in the EASE image and approximately 18% of the AARI grid points are duplicated in the final EASE image. The data loss and redundancy are consistent for every AARI file. In other words, the same SIGRID points are left out and repeated for each file.
For the point min/max approach, the two resulting images contain all but 16 of the original SIGRID values; however, the EASE images are presented to the user with 20% of the pixels empty. This approach is unsuited for regridding the AARI data.
The interpolation method, on the other hand, has the advantage of filling all the EASE grid cells and, in doing so, uses all SIGRID values to derive the EASE values. Ignoring the problems of combining non-numerical data, the effects of regridding on the EASE image are more subtle than the three previously discussed methods. Consider a completely digitized AARI file without unknown values, the interpolated EASE-Grid image would be a smoothed representation. At sharp boundaries in the AARI data where values change greatly from one pixel to the next (e.g., from a 100% to a 20% ice concentration), the EASE interpolated values would be intermediate (e.g., a smoothed value of 60%). The effect of unknown values on the data represented in EASE is another consideration. The most simple handling of unknown values is to ignore them. Thus, if three of the four SIGRID points surrounding an EASE grid center are unknown, the EASE grid is assigned the value of the fourth point, even though that point may be the furthest away. It is possible to construct more sophisticated methods to deal with unknown data that require an increase in processing time.
The area min/max approach also uses all SIGRID values to fill EASE grid cells. By creating minimum and maximum images, this approach does not smooth the data like the interpolation approach. This approach seems to have the least problems with regard to altering data content; however, the user must deal with twice the volume of data.
The area weighted averages method has effects on data very similar to the interpolation approach.
4.2 Computer Resource Requirements
In terms of processing time, the simple methods of nearest neighbor and drop-in-box are the quickest, followed by area min/max and, finally, the area weighted averages. Currently, nearest neighbor resampling of an single AARI file (w900904.sigrid) takes approximately one minute to generate an EASE image of total ice concentration. After reviewing the regridding methods for the most likely candidates, future tests should be performed to regrid all information in a test set of AARI files.
In terms of computer storage space requirements, the area min/max method needs twice the amount of space as other methods. The storage per individual EASE-Grid image is 519,841 bytes, uncompressed. Since each AARI file contains information on more than one data type this number should be multiplied by the number of data types each file contains. For example, an AARI file may have data on total sea ice concentration, and sea ice concentration, form and stage of development for the 1st, 2nd, and 3rd thickest ice types, ten data types in total. The uncompressed storage requirement is 5,198,410 bytes. For area min/max the required space is doubled, or 10,396,820 bytes. The total storage requirement for the full data set depends on the total number of files and the number of data types per file. The total number of files is being assessed. Because the total storage volume is expected to be quite large, and because each AARI file contains data for only the polar region of the northern hemisphere, a subset of the full EASE-Grid covering the polar regions is being considered (see Section 7.3).
4.3 Consistency of Results
This section seeks to evaluate each method based on whether the same SIGRID points are used consistently to derive the EASE-Grid values. For the nearest neighbor and drop-in-box approaches this consistency is present. The area min/max and area weighted average methods, in contrast, are more difficult to evaluate. For each AARI file, the methods do consider the same SIGRID cells in determining an EASE grid cell value. However, an AARI file may not have any value or have an unknown value recorded in one or more of the SIGRID cells. This alters the calculation and this fact should be made clear to the data user. Furthermore, for the area min/max approach, it should be made clear that the value in EASE grid cells of the minimum and maximum images do not always come from the same SIGRID cell (see the discussion in Section 3.3e).
Furthermore, with the area min/max or area weighted average methods, the regridding algorithm must deal with situations when part of the EASE-Grid cells are covered by land. Should there be a cutoff at 50% such that when more than half the cell is land covered, the pixel reported in EASE is specified as land? Is this the best value for the cutoff? Or, should the decision be made to always display the ice information?
4.4 Dealing with the Descriptive and Symbolic Nature of AARI Data
This section assesses how the regridding schemes handle combining AARI data values. Specifically, how are AARI data such as ice form or ice stage of development which are descriptive rather than numerical in nature (such as an ice form of iceberg or a stage of development of old ice) combined. Also, reference is made to the many instances in which the AARI data values refer to a range of values rather than an exact value. For example, an AARI data value of 83 for ice stage of development refers to young ice of 10-30 cm thickness. Or, a data value of 05 for ice form which refers to an ice form of big floe of 500 meters to 2 kilometers across. Symbolic data of these types can cause problems for averaging or for combining data values in another way.
The simple techniques of nearest neighbor and drop-in-box do not combine SIGRID values to determine the values for EASE-Grid images; therefore, they have no problems dealing with this type of data. Likewise, the area min/max approach merely assesses the magnitude of the data values in relation to one another and does not perform any mathematical operations on the SIGRID values.
The interpolation and area weighted averages approaches do need to deal with this type of data. For example, when averaging or interpolating SIGRID data values of ice form the following data values (from Table 1) might be encountered:
04 medium floe (100-500 m across)
05 big floe (500m - 2km across)
08 fast ice
How do you combine the numerical ranges for medium floe and big floe with the descriptive ice form of fast ice? More simply, if we just consider the medium floe and big floe data values what would be the intermediate interpolated or averaged data value? A simple numerical average would yield 4.5, but this does not reference an ice form in the AARI table on ice forms (Table 1). Perhaps we could consider averaging the ranges the data values refer to (i.e., the dimensions of the ice floes: 100-500m and 500m-2km) but again, the result does not fit within the AARI list of valid values for this data type.
5. Adjustments for erroneous starting longitudes in SIGRID
As demonstrated in an example of regridding an AARI file using nearest neighbor (Section 3.3a), some AARI sea ice charts were digitized using an improper initial longitude. This caused errors in the locations of SIGRID grid points at higher latitudes. The file tested in this example was for the AARI designated "western" sector. We have not looked at all "western" sector files yet to determine if the improper selection is consistent throughout. Furthermore, we have not gone through the "eastern" sector files to see if an appropriate initial longitude was consistently chosen in these data.
If there is a consistent choice of initial longitude in both "eastern" and "western" sectors and if they align the grid points exactly, then the regridding routines could be tailored to these starting points. Then, the regridding to EASE-Grid could be performed and produce consistent results. The EASE images would, however, differ from regridding of National Ice Center (NIC) or other implementations of the SIGRID format which use correct starting points. Thus, comparisons of two such data sets would be complicated (see Section 7.6).
If, however, the initial longitude changes in the AARI files, a different approach must be taken to regrid the data to the EASE-Grid. We must consider that we want the EASE image to be created in a consistent manner. So, although we could generate a regridding transfer for each initial longitude that occurs, the EASE images would be representing different SIGRID points. This spatial inconsistency in the regridding is undesirable.
The revised implementation of the SIGRID format, SIGRID-2 (Section 7.5), should address this problem. In case SIGRID-2 may still contain errors or that implementation of the format by a different group (NIC or other agency) might differ from AARI, we are working to devise a regridding scheme that gets around this problem. We have considered replacing the rectangular SIGRID image that shows the grid centers (Figure 6) in a grid with grid cell dimensions of 0.25° latitude by 0.25° longitude. Instead, we have proposed a rectangular grid that uses these grid cell dimension but shows the AARI values over the full grid cell area. For example, in the second AARI latitude region, the lon/lat ration is two; thus, the dimensions of the grid cells are 0.25° latitude by 0.50° longitude. Instead of representing the data value of this grid cell with one pixel at the center position, we represent the total grid cell area with grid cells each having dimensions of 0.25° latitude by 0.25° longitude. The centers of the smaller replicated grid cells would have to adjusted to cover the same area as the original:
lon of 1st replicated cell = original lon - 1/2 * (grid spacing)
lon of 2nd replicated cell = original lon + 1/2 * (grid spacing)
where the grid spacing is 0.25°. Similarly, SIGRID cells at higher latitudes would be represented by a number of 0.25° cells equal in number to the lon/lat ratio (see Table 4) and with the center of the first grid cell at:
lon of 1st replicated cell = original lon - 1/2*(grid spacing)*(ratio-1)
and the centers of the rest of the cells replicated at subsequent longitude intervals of 0.25°. The first and following 0.25° grid cells would have the same value of the SIGRID grid point repeated in them.
The 0.25° grid is essentially an oversampled version of the original. Once this oversampled grid has been established, the different regridding methods can be applied to define the transfer between the 0.25° repeated grid and the EASE grid. In contrast to the original nearest neighbor resampling, the nearest neighbor of an EASE grid center will be one of these repeated 0.25° cells. Also, the locations of the cells are independent of the initial longitude. The placement of the replicated cells never changes.
The values in the AARI files need to be placed into this grid. Although the initial longitude may have been erroneous (as seen in Figure 8), the misplaced grid cell will still cover the same amount of area but will be represented by filling an appropriate number of 0.25° grid cells to the left and right of the digitized grid center. For the test file, "w900904.sigrid," the full AARI image analogous to Figure 7 is shown in Figure 13. Similarly, a zoom in on the left side of the grid is shown in Figure 14 (this one is analogous to Figure 8). The results of regridding to EASE Figure 16) show that AARI values at improper locations at higher latitudes are successfully regridded to the EASE projection.
We are in the process of comparing nearest neighbor results using this approach to the results of the original. It is suspected that they will differ and what that difference is and how it occurs needs to be defined. This approach would work for a problematic data set in which the initial longitude changes from file to file. It would also work for comparing data from sources that use different starting longitudes.
6. Conclusions and Recommendations
This section reviews the application of the six regridding schemes to AARI data and rejects those that do not work well. Of those that perform well, two techniques are recommended for further evaluation to determine which is the optimum approach. Problems that still remain in conducting the regridding are identified. Future work plans are described in the next section, Section 7.
Of the six methods examined, the point min/max method can be rejected immediately because 20% of the EASE images created are empty cells. This artifact of the forward regridding does not create a desirable image to give the data user despite the fact that all data is regridded to the two images.
The remaining methods are evaluated in the four categories of the previous section. These evaluations are summarized in Table 9. Using either the interpolation or area weighted average methods is not recommended since they have difficulty dealing with the symbolic nature of the AARI data. Two of the remaining three options, nearest neighbor and drop-in-box, have minimal differences with regard to their resulting EASE-Grid images. Using the nearest neighbor approach is recommended since it seems to be the closest to sampling the original sea ice charts. The sea ice charts had values digitized at the SIGRID points. These values were then considered to apply to the entire grid cell even though the grid cell on the charts may have contained other values. Finally, the area min/max approach is recommended. Although more demanding in processing time and computer storage, this approach does not have the data loss problems of the nearest neighbor regridding scheme. Further evaluation of these two techniques and testing on more AARI files is recommended.
The errors in selection of initial longitudes encountered during the regridding of AARI files requires additional investigation. We should ascertain whether this remains a problem in SIGRID-2 or in a comparison of AARI data to NIC data. If the 0.25° replicated grid is required to deal with this problem, or if another solution is proposed, it should be thoroughly examined.
7. Future Work Plans
This document has detailed the work that has been completed in defining a regridding method to take AARI data in the SIGRID format and display it in the EASE-Grid northern hemisphere projection. This section outlines the additional work remaining to select the optimum regridding scheme (Section 7.1) and to produce a data set of EASE-Grid images of the AARI data (Section 7.2 and Section 7.3). Furthermore, quality control procedures needed to ensure the user receives correct and reliable data are proposed (Section 7.4). Finally, issues regarding the revision of the SIGRID format (Section 7.5), the proposal to compare AARI data with NIC and/or SSM/I data (Section 7.6), and the regridding of AARI data for the southern hemisphere (Section 7.7) are presented.
7.1 Further Evaluation of Nearest Neighbor and Area Min/Max Resampling
The remaining work for deciding between the nearest neighbor and area min/max resampling consists of:
- implementing the area min/max regridding
- using both methods to regrid a test set of AARI data (for instance, one year of data from the east and west sectors)
- evaluating the results in terms of processing time, interpretation of images, and computer storage needs.
The bulk of time to accomplish this task is required for the first step. The method sketched out in Section 3.3e needs to be refined and coded. Evaluating the two methods in terms of the interpretation of the images will be the second most time consuming task. That is, describing what the grid-point-based nearest neighbor images represent as opposed to the two min/max images created under a grid cell paradigm.
7.2 Processing (optimization/re-coding)
In order to evaluate the different regridding schemes programs were written in IDL. While IDL has advantages for displaying and manipulating images, it can be slow for processing tasks. Therefore, it is suggested that routines for creating the EASE images from the AARI data be written in C. For generating EASE images using nearest neighbor, the C code required is trivial and the speed increase in processing the images will be big. This is especially true when considering the task of processing the entire data set. In addition, the C code could include a subroutine to subset the data as outlined in Section 7.3.
As discussed in Section 7.5, the routine that parses the AARI files is currently written in IDL and should be re-coded in PERL. Finally, as discussed in Section 7.4 some quality control procedures should be incorporated at various stages in the processing: in the PERL script that parses the data, in the C code to regrid the AARI files, and subsequently in an IDL routine to visually check the EASE-Grid images.
7.3 Subset of Full EASE-Grid
The EASE-Grid projection was established for the full hemisphere; however, we are interested in displaying sea ice information which covers the polar regions. In order to eliminate the need to store the full image when it is only partially filled with data, a subset of the EASE-Grid should be used (see Figure 16). This subset covers the area in the northern hemisphere for which sea ice is likely to appear. The subset has the corner points at 29.7127° latitude and 135° W, 135° E, 45° W, and 45° E longitude. These coordinates match the outside corners of EASE grid cells at the column,row coordinates of (180,180), (540,180), (180,540), and (540,540) respectively. The subset matches that defined for the "Pathfinder AVHRR Polar Products." An example of the subset of the AARI file "w900904.sigrid" is shown in Figure 17. The subsetted image has dimensions of 361 columns by 361 rows and requires 130,321 bytes for uncompressed storage. This is only 25% of the size of the full EASE-Grid uncompressed image.
7.4 Quality Control
The production of EASE-Grid images from the AARI data should incorporate some measure of quality control before the data are regridded, during the regridding process, and for the EASE-Grid image in order to ensure the user receives a correct and reliable data set. While the data are still in SIGRID format some summary information should be generated for internal records at NSIDC and to provide to the user. For instance, the amount of information in the data file, that is, the number of digitized points. Also needed is the distribution of that information. For example, how many points are unknown or land, and how many of the digitized points contain information on the total ice concentration, 1st, 2nd and 3rd thickest ices and their partial concentrations, stage of development and ice forms? For example, see Table 7).
During the regridding process some data regarding the transformation of data should be recorded. For instance, the amount of data loss and duplication for the nearest neighbor resampling or, for the area min/max approach, the number of pixels that were assigned to the min or max images when that value covered less than 10% of the EASE grid cell.
Finally, a visual inspection of the final EASE image should be made and, also, the generation of meta data. At some stage in the processing a simple check on data values should be made to check that all data values lie in the proper range for the data types. Also, a check should be made on sea ice values on land or land values in the ocean. Preliminary evaluation of the data shows that AARI sometimes digitized points as land and these points appear to be in the ocean after regridding. Likewise, some sea ice values appear on land.
In 1994, a revision of the SIGRID format for storing the digitized sea ice charts was finalized. This revision corrected shortcomings in the original version. The document (6) describing the new format, SIGRID-2, lists the shortcomings as:
- An arbitrary origin of grid lines results in the longitudes of grid points being non-coincident.
- The designation by code figures of the variables and their quantitative values leads to the need to preserve a constant number of symbols in the data group and to use frequently the figures 99 (undetermined/unknown), thus resulting in an increase of the volume of the stored and reported information.
- The coding procedure at an additional subdivision of the grid areas is very complicated and at the same time does not actually decrease the volume of coded data.
The abridged format does not contain all the data in the original version. As a result, the syntax of the data files under SIGRID-2 differs from the original.
7.6 Considerations for Comparisons with NIC or Satellite Passive Microwave Data
In the future, the AARI sea ice data may be compared to data from the National Ice Center (NIC) or measurements from the SSM/I or SMMR sensors. In this section are a few of the considerations for performing such comparisons.
The first complication in comparing the data is that AARI, NIC and SSM/I or SMMR differ in the temporal sampling of the sea ice. AARI files have 10 day sampling, NIC files have 7 day sampling and SSM/I has two images per day (from the NSIDC EASE-Grid product) or one daily average image (from the NSIDC polar stereographic products). Furthermore, the AARI and NIC files could either be composites or averages. In a composite, all coverage in the time period is combined by selecting the "best" data. For an average image, if there is data from several days for the same point then that data is averaged rather than selecting the "best" value. Since the AARI and NIC differ in their sampling, a 7 day NIC file may only partially overlap the 10 days in the AARI files. So perhaps a longer time average of the two data sets should be compared, for example monthly NIC and monthly AARI averages. Also, in comparing an AARI or a NIC multi-day composite/average to SSM/I averaged images, we must consider that in the SIGRID data a pixel may represent a measurement from a single day, but the SSM/I is the result of averaging 7 or 14 measurements over the course of a week (10 or 20 for a 10 day period). In areas where the ice is variable (near the ice edge) or during times when the ice is variable (during melt or growth stages) such measurements could differ greatly.
A second consideration is that of spatial sampling. AARI and NIC data are compiled from many measurement techniques all of which have differing spatial resolutions. Thus, you might be comparing AARI sea ice concentration compiled at 30 km with NIC data gathered at 10 km resolution or SSM/I at 25 km. Again, this may cause differences in situations where the ice is highly variable. Furthermore, in comparing a SIGRID data set to the SSM/I the regridding method used to create the EASE images must be considered. If nearest neighbor is used than the EASE cells are not truly showing the data gathered in that grid cell, but rather the data digitized at a point near the center of the grid cell. Likewise, the area min/max does not show values that occur in the entire cell, but rather the min or max that occur in some portion of the grid cell. Finally, the SIGRID data is composed of discrete points on the surface of the Earth and the SSM/I represent an area. Comparing point data with area measurements is always difficult. Instead, regimes for example 2° by 2° areas of the Laptev and Kara Seas should be compared, as opposed to the entire AARI coverage. This will ensure that there is consistently AARI data to compare with other data since the AARI charts focus on these regions.
7.7 Southern Hemisphere
The AARI data set has files that cover the southern as well as the northern hemisphere. The regridding methods suggested for application to the SIGRID data in the northern hemisphere are also applicable to the southern hemisphere. However, although the techniques are independent of which hemisphere is regridded, the routines written to perform the regridding are not. Thus, all routines need to be adapted to the southern hemisphere. This does not include the pre-existing routine "easeconv.pro."
8. References
- Thompson, T., 1981. Proposed Format for Gridded Sea Ice Information (SIGRID), Report prepared for the World Climate Program, World Meteorological Organization, February 1981, Revised December 1981.
- 2. Brodzik, M.J., 1995. README file for NOAA/NASA Pathfinder Program SSM/I Level 3 Brightness Temperatures Equal-Area SSM/I Earth Grid (EASE-Grid) CD , National Snow and Ice Data Center, Boulder, Colorado.
- Brodzik, M.J., 1995. Summary of EASE-Grid Map Projection Parameters, National Snow and Ice Data Center, Boulder, Colorado.
- Brodzik, M.J., 1995. Summary of NOAA/NASA Polar Pathfinder Grid Relationships, National Snow and Ice Data Center, Boulder, Colorado.
- Knowles, K.W., 1995. Points, Pixels, Grids, and Cells: A Mapping and Gridding Primer, National Snow and Ice Data Center, Boulder, Colorado.
- Format to Provide Sea Ice Data to the World Climate Program, (SIGRID-2). Prepared by the Steering Group for the Global Digital Sea Ice Data Bank, Commission for Marine Meteorology, World Meteorological Organization. Received from AARI at NSIDC on 13 April 1994.


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Bracket Prep: South Region AnalysisPosted by KDoyle on March 12th, 2012
Throughout Monday, we will roll out our region-by-region analysis on the following schedule: East (9 AM), South (11 AM), Midwest (2 PM), West (4 PM). Here, Kevin Doyle breaks down the South Region from top to bottom. Also, be sure to follow our RTC South Region handle on Twitter for continuous updates the next two weeks (@RTCsouthregion).
You can also check out our RTC Podblast with Kevin breaking down the South Region here.
South Region
Favorite: #1 Kentucky (32-2, 16-0 SEC). Shouldn't really need much of an explanation here. The most talented team in the nation — unquestionably — the Wildcats will be the odds-on favorite to not just emerge from the South Region, but also to cut down the nets in New Orleans. Anthony Davis and Terrence Jones spearhead a terrifyingly good starting five.
Should They Falter: #2 Duke (27-6, 13-3 ACC). Austin Rivers does not play like a typical freshman and while Duke has its flaws on defense (perimeter defense, especially), the Blue Devils are more apt to make a run to the Final Four due to their balance on offense. Rivers and Seth Curry are prolific shooters/scorers in the backcourt, while the Plumlee brothers make for a formidable frontcourt. Much of Duke's success hinges on junior Ryan Kelly's health (sprained ankle). Kelly, while not a lockdown defender by any means, is 6'11″ and really helps in defending the three-point line for Duke. Even without a healthy Kelly, Duke still has an easier road to the Sweet Sixteen than other contenders in the South Region.
Grossly Overseeded: #11 Colorado (23-11, 11-7 Pac-12). Clearly, the committee thought higher of the Pac-12 than many others did. First, there was much debate whether this power six conference — far from "powerful" this season — would even receive an at-large bid, but they did in California. Secondly, Colorado was not on anybody's radar prior to the Pac-12 Tournament as it stood at 19-11 with seven losses in conference play. Yet, winning the conference tournament propelled Colorado to a very respectable seed at #11. Many prognosticators had the Buffaloes at a #13 seed going into Selection Sunday.
Grossly Underseeded: #14 South Dakota State (27-7, 15-3 Summit). It is too big of a stretch to say that South Dakota State is "grossly" underseeded, but I do believe they were worthy of a #13 seed. When comparing the Jackrabbits to the #13 seed in this region, their resume is every bit as good, if not better, than New Mexico State: SDSU has a better overall record, higher RPI, more wins against the Top 100 RPI, and a more challenging non-conference schedule. Not to mention South Dakota State's thrashing of Washington 92-73, even though the Huskies are not a Tournament team, is very impressive.
Sweet Sixteen Sleeper (#12 seed or lower): #12 Virginia Commonwealth (28-6, 15-3 CAA). Last year it was VCU's near-flawless outside shooting that carried them all the way to the Final Four, but the 2011-12 version of the Rams hang their hat more on the defensive end. Shaka Smart's squad is extremely active on defense, especially around the perimeter. If VCU is to make a run to the second weekend, they will have to go through Wichita State and then most likely Indiana, two of the top outside shooting teams in the Tournament. It will be no easy task slowing down these offenses, but Smart has built this team around creating turnovers, pressuring the ball, full-court pressure, and defending the three-point shot.
Final Four Sleeper (#4 seed or lower): #5 Wichita State (27-5, 16-2 MVC). Wichita is one of the most balanced yet prolific offensive teams in the Tournament who possesses the ability to win a shootout-type game with any opponent. Joe Ragland leads this explosive offense as he shoots 50% from beyond the arc, while Garrett Stutz provides a nice balance with his smooth inside play; Stutz has the ability to step out and drill the 15-footer, too. The road to the Final Four, in all likelihood, will run through Kentucky, but they actually match up with the Wildcats fairly well. First things first, though, defeating Virginia Commonwealth is far from a sure thing.
Carmelo Anthony Award: Austin Rivers, Duke (15.4 PPG, 3.4 RPG, 2.1 APG). We've all seen his three-point shot from the wing at the buzzer against North Carolina probably more than any other shot in college basketball this year, and that shot proved to the nation that Rivers is fearless. Sure, he may have a little cockiness/arrogance in him which gives him a propensity to take such a shot, but there is little doubt that Austin Rivers is a confident player who can will his team to victory. Like Carmelo was back in 2003, Rivers is a freshman that has developed into the clear leader of his team. It will be important early on for Rivers to hit his shots — especially from downtown — as he shot a low percentage in Duke's last four games (20-52 FG, 3-20 3FG).
Stephen Curry Award: Nate Wolters, South Dakota State (21.3 PPG, 5.2 RPG, 6.0 APG, 2.8:1 A/TO). If you haven't heard of Nate Wolters prior to the NCAA Tournament, don't worry because you will soon enough — the junior guard fills up a box score like nobody else. Wolters has scored in double figures in every game this year and is the main reason that South Dakota State has a shot at upsetting enigmatic Baylor. Closely following Wolters in this region is Lehigh's C.J. McCollum (21.9 PPG, 6.5 RPG, 3.5 APG), a player who is a legitimate NBA prospect.
Home Cooking: (#1 Kentucky/#2 Duke, 382 miles). From Rupp Arena to the Georgia Dome: 382 miles. From Cameron Indoor to the Georgia Dome: 382 miles. Amazing, isn't it? Are these two teams destined to meet in the South Region final? It sure seems that way. Prior to Atlanta, Kentucky and Duke will play in the friendly confines of their home states as the Wildcats are in Louisville and the Blue Devils in Greensboro. 382 miles isn't too bad of a drive for Big Blue Nation, although, let's be honest, these games could be played in Alaska and Big Blue Nation would still be there in full force decked out in blue.
Can't Miss First Round Game: #5 Wichita State vs. #12 Virginia Commonwealth (Thursday, March 15 at 7:15 PM ET). These are two of the most recognizable and dominant names in mid-major basketball over the last five years, so that should tip you off to start. Moreover, the contrasting styles of play make this an interesting game from purely an X's and O's perspective, as a high-powered offense in Wichita State will test the mettle of VCU's relentless defense. These teams met in last year's BracketBusters with the Rams besting Wichita State 68-67, so there certainly is some familiarity between the two.
Don't Miss This One Either: #8 Iowa State vs. #9 Connecticut (Thursday, March 15 at 9:45 PM ET). If there was a city to be in on Thursday evening, Louisville is definitely the place to catch a pair of games. The Iowa State vs. Connecticut game is intriguing on two levels: 1) Connecticut was a Top 5 team in the preseason, while Iowa State was not even ranked or predicted to make the NCAA Tournament. Clearly, one team has underachieved while the other has exceeded expectations. 2) The victor can definitely pose a great challenge to Kentucky. Connecticut can match all of Kentucky's star power as they have at least three future NBAers in their starting lineup (Shabazz Napier, Jeremy Lamb, Andre Drummond). Iowa State's Royce White is one of the better kept secrets in the South Region and can go toe-to-toe with Anthony Davis. Plus, the Cyclones have demonstrated that they can hang with supremely talented and athletic teams as they have defeated Kansas and Baylor this year.
Lock of the Year: Baylor misses out on the second weekend. A bold prediction, to be sure, as the "lock of the year," but it really would surprise me to see Baylor advance to the Sweet Sixteen. The Bears ended the regular season with a very pedestrian 8-6 record in their final 14 regular season games, and greatly struggle in hindering an opponent's offense thanks to its porous zone defense. Their length and athletic ability make up for this, but all too often Baylor struggles slowing down a proficient offensive club. In these six losses, Baylor surrendered an average of 76 points. South Dakota State, the Bears' first opponent, shoots nearly 40% from beyond the arc and is one of the most efficient offensive teams in the tournament. Meanwhile, UNLV — a potential opponent in the third round — is equally impressive as they have a slew of offensive playmakers, most notably Mike Moser, Anthony Marshall, and Chace Stanback. I think Baylor's Achilles heel comes back to bite the Bears in one of these two first weekend rounds.
Juiciest Potential Matchup – purists: #3 Baylor vs. #6 UNLV in the third round would be a dandy. It would be a glorious collision of two of the better offenses in the tournament duking it out. UNLV made its splash onto the national scene earlier this year when they hung 90 points on North Carolina, while Baylor is coming off an impressive win against Kansas in the Big 12 semifinals. Ironically, it is the team that comes up with crucial defensive stands that will probably decide the outcome.
Juiciest Potential Matchup – media: #1 Kentucky vs. #9 Connecticut in the third round. When Connecticut's name popped up during the selection show directly underneath Kentucky's, the media began to salivate instantaneously. Last year's National Champion versus this year's projected champ. Anthony Davis versus Andre Drummond. Future lottery picks versus future lottery picks. There are countless storylines that can be formulated for this one. According to the preseason rankings, these are two Top 5 teams with serious star power. Kentucky lived up to its expectations, while Connecticut hit a rough patch and there was doubt that they may even make the NCAA Tournament. Can the Huskies put 40 minutes together and seriously challenge Kentucky? That, among many other questions, are what will be asked heading into this potential third round game.
We Got Screwed: #5 Wichita State (27-5, 16-2 MVC). I suppose that the Shockers have nobody to blame but themselves. If they did not lose to Illinois State in the Missouri Valley semifinals and had gone on to win the conference tournament, they would be a #4 seed and ostensibly have an easier draw. Now, they have the unenviable task of taking down a very good Virginia Commonwealth team and then, most likely, an Indiana squad who has proved they can beat the very best (Kentucky, Ohio State, Michigan State). If they are fortunate enough to make it through the first weekend unscathed, Kentucky looms ahead. Quite the road the Shockers have ahead of them.
Strongest Pod: Louisville, #1 Kentucky, #16 Mississippi Valley State/Western Kentucky, #8 Iowa State, #9 Connecticut. I will go on record saying that Western Kentucky will give Kentucky a competitive game for a half, assuming they get past Mississippi Valley State in Dayton, of course. Ever since Ray Harper took over the coaching duties at WKU, the Hilltoppers have been a different team. They knocked off a very good Middle Tennessee team in the final game of the regular season prior to winning four straight in the Sun Belt tournament. It also may help that this game is played in Louisville. But, this pod is not strong because of Western Kentucky, obviously. The #1 overall seed Kentucky certainly is the main reason that this could be the strongest pod in the South Region, but Iowa State and Connecticut are two of the better #8/#9 seeds in this year's draw. Iowa State, in particular, is getting partially overlooked because so much of the attention is on Connecticut and Kentucky, but the Cyclones will be a tough out. Fred Hoiberg has the Cyclones arriving onto the scene a year earlier than many expected, and that is largely because of great perimeter play. Iowa State's shoots 38% from three, and defends the arc at a 29.8% clip.
Impressive Track Records: There is a whole lot of history in the South Region. And by history, I mean National Championships: Kentucky (7), Indiana (5), Duke (4), Connecticut (3), and UNLV (1). Will one of these five teams add to their trophy cases?
So-Called Experts: Kentucky is the favorite, hands down. Duke may have been a popular pick two weeks ago, but there are simply too many question marks that surround the Blue Devils. Moreover, a team does not go the bulk of the season ranked as a near-unanimous #1 in the country and suddenly isn't the favorite to emerge from its region (as you'll see from the Vegas Odds listed below).
Vegas Odds to Win Region:

subset: commoncrawl


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Need to get into the Christmas mood and only have seven and a half minutes? I've got the perfect solution. Here is the first movement of J. S. Bach's Christmas Oratorio performed by Sir John Eliot Gardiner conducting the Monteverdi Choir and the English Baroque Soloists. After listening to this performance, no one could say, "Bah humbug!" Of special interest is the authentic period instruments, including the valveless trumpets, wooden flutes and tiny timpani.
Merry Christmas!
Jack Fishman
Email: firstname.lastname@example.com
Web: www.sasymphony.org
Twitter @SASym
Facebook: San Antonio Symphony
For more blogs by Jack Fishman visit: http://www.sasymphony.org/blog


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Seelye Celebrates Two New (Unlabeled) Liberal Journalism Projects
In a Tuesday online posting, Katharine Seelye enthused about "Campaign Coverage That Is Raw and Fresh" from two new journalism sites - staffed almost exclusively by liberals.
"We're taking a look today at two new Web ventures that could help change how politics is covered. One, OffTheBus.net, is all of three months old, which these days makes it practically establishment. The other, Scoop08.com, is so new it hasn't even started yet. It's a national daily online newspaper by and for college and high school students and is preparing to go live on Sunday. That makes it the newest entry in the field and therefore the one with most of its ideals still intact.
"They both want to tackle imaginative, under-reported stories with their armies of citizen correspondents. They have been aided by low start-up costs: $150,000 for Off The Bus, and less than $20,000 for Scoop's first months of operation. Both rely on free labor.
Here's the first clue of the political slant of the new ventures:
"Arianna Huffington, who publishes OffTheBus along with Jay Rosen at New York University (www.newassignment.net), said OTB tries to get away from the pack mentality that reinforces itself with conventional wisdom. OTB goes by the slogan 'Campaign coverage by the people who aren't in the club.'"
Seelye doesn't delve into the clear politicalbias: Huffington is a well-known Hollywood activist and founder of the left-wing Huffington Post, while professor and media critic Jay Rosen blogs a more sedate style of liberalism. Of the other three on the masthead, Zack Exley and Amanda Michel worked for the Kerry-Edwards campaign in 2004, while Neil Nagraj worked for the South Carolina Democratic party that year.
Seelye briefly described how one assignment got carried away into liberal advocacy:
"One followed Barack Obama organizers in a dozen states on a single day of national canvassing, a story that most media organizations ignored. Perhaps two dozen OTB correspondents fanned out, all filling out a uniform sheet (how many doors knocked on, etc.) and offering their own observations.
"The results were given to Mayhill Fowler, one of OTB's emerging star correspondents. She examined the reports and wrote a story from them, much the way the old Time magazine or this newspaper would use stringers in the field to send back files to be compiled by a writer. What's different at OTB is the transparency - the reader can see the full raw reports on which the final edited product is based."
"Some of them contained unabashed enthusiasm for Mr. Obama and suggested that some of OTB's correspondents had joined right in with the canvassing, a no-no in the MSM. I asked Ms. Huffington if that was a problem. She said that she didn't expect the correspondents to be completely neutral but she does expect them to be clear when they have a bias so that the reader can take that into account."
Besides the project from liberal activist Huffington, Seelye previewed another liberally stacked project, Scoop08.com, though Seelye managed not to call attention to the fact.
"The young men over at Scoop08 are still full of idealism about their project, which has already attracted 300 student journalists across the country. The two founders and editors are Alexander Heffner, 17, a senior at Phillips Academy in Andover, Mass., and Andrew Mangino, 20, a junior at Yale and the politics reporter for the Yale Daily News.
"The two met as interns on Senator Hillary Rodham Clinton's re-election campaign last year but say Scoop08 will be nonpartisan. It has an advisory board with some big names in journalism - Frank Rich of The New York Times, Jonathan Alter of Newsweek, Judy Woodruff of PBS - and in politics, including Senator Joseph Lieberman, former Senator Gary Hart, former Senator Al Simpson and Doug Sosnik, a former political director in the Clinton White House."
As documented by the Media Research Center and Times Watch, Rich, Alter, Woodruff are all liberal journalists, while Lieberman and Hart and Sosnik are Democrats, leaving only a moderate Republican, former RepublicanSen. Al Simpson of Wyoming on the other side.
Seelye showed an enthusiasm for these two liberally slanted online journalism projects utterly absent from the Times' dismissive, suspicious treatment of conservative blogs:
"The idealism of Scoop08 is evident in its promotional material, which promises groundbreaking reports on exclusive topics from a fresh perspective....Both sites, by the way, are still recruiting; with more people, the wisdom of the crowd, by definition, can only expand."


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950k/sec on another machine .
600k/sec from AMD K6 400 running XP with a realtek card
950k/sec from a P2 400 running XP with a 3com card.
Both with loads of ram, and maxtor 40gb's as the data drive, os on a seperate one
Time to replace those crappy realteks


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Welcome to the Department of Chemistry and Biochemistry!
The UO Department of Chemistry and Biochemistry offers undergraduate major and minor degrees in chemistry and biochemistry, and graduate degrees at the masters and PhD level.
Our undergraduate program provides training for students planning careers in the chemical and biological sciences and also for those in biology, health related disciplines, earth sciences, secondary education, business, journalism and law. Undergraduate research and other educational activities outside the traditional classroom are essential components of these majors.
Our graduate program recognizes the importance of diversity and breadth in graduate education and continues to respond to the shifts and changes in career opportunities available to our graduates. Research at the University of Oregon is designed to keep student researchers at the forefront of chemical science.
A unique strength of our program is its interdisciplinary approach to research and teaching. Chemical scientists may be interested in the Institute of Molecular Biology, the Institute of Theoretical Science, the Materials Science Institute, the Oregon Center for Optical, Molecular and Quantum Science, and the programs in cell biology and in molecular synthesis, structure, and dynamics.


text: Catholicism believes that the Church is the on-going presence of Christ in the world. As an academic community, animated by the Spirit of Christ, Gannon University can look to the Church as a model of community, its fourth distinguishing characteristic. Thus, Gannon brings people together in a bond that searches for truth, meaning, and beauty, since true learning and human living are not to be strictly solitary and fragmented experiences. This bonding does not occur automatically, but is worked on each day in community interaction fostered by a spirit of collegiality, a shared vision, and a common mission. In the words of St. Paul, community means that we treat one another with "heartfelt compassion, kindness, humility, gentleness and patience, bearing with one another and forgiving one another" (Colossians 3: 12-13). Gannon is committed to promoting the pastoral care of all members of the University community, which includes the students, faculty, staff and administrators of all denominations. "Treat one another with 'heartfelt compassion, kindness, humility, gentleness and patience, bearing with one another and forgiving one another' (Colossians 3: 12-13)." As a Catholic academic community, Gannon can never be self-contained nor self-focused. Gannon is part of the larger community of the Catholic Church and maintains a sincere dialogue with the local Church and the diocesan Bishop in order to promote Catholic identity, develop mutual trust and faithfully contribute to the life of the Church. Such dialogue renews the Christian inspiration of the University community and provides the opportunity for a deeper communion with both the universal and local Church. While part of the Church community, Gannon also enjoys active membership in the academic community. Indeed, Gannon sees itself as belonging and contributing to the worldwide human community. "The freedom I have at Gannon is being able to show my faith and not be afraid. For example, when I pray before I eat in the cafeteria, people do not look at me funny." –Gannon Student


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Mayor's Office
Mayor Chris Beutler welcomes visitors to Lincoln for Husker football games and says Nebraska's Capital City is ready to accommodate fans and game-day traffic. Drivers will see traffic improvements near Memorial Stadium – two new multi-lane roundabouts at at 10th Street and Salt Creek Roadway.
"We are always looking for ways to improve safety and traffic flow, and those are the two main benefits roundabouts offer over traditional intersections with signals," Mayor Beutler said. "I encourage visitors to visit the City website to learn more about them and how easy they are to navigate." At lincoln.ne.gov (keyword: roundabout), drivers will find written driving instructions, a simulation of traffic flow at the two new roundabouts and a video produced by the Nebraska Department of Roads.
The Mayor encourages fans to plan ahead and use any of the following City programs to help beat game-day traffic and parking challenges:
The Mayor also encourages fans to enjoy the Capital City by arriving early and lingering downtown after games.
Those traveling downtown need to be aware that parking violations and towing will be more expensive this year. Meters are enforced Saturdays from 8 a.m. to 6 p.m., and a new fine system will be in effect:
The City also has implemented a $50 towing fine for vehicles towed by order of the Police or a Parking Control Officer. The fine is charged in addition to the $49.53 required to get a vehicle out of the impoundment lot. The towing fine does not apply to cars towed from a private lot.
On football game days, Interstate 80 exits at I-180/Downtown and 27th Street can be very congested, so visitors are encouraged to use other routes into Lincoln:
Contractors will not work on highway or Interstate projects on game days, so I-80 will have at least two lanes open in each direction, and all lanes will be open on highways entering Lincoln.
Other traffic reminders:
Other general reminders:
In addition to its regular routes, StarTran will provide its Big Red Express service on Husker game days starting two hours before kickoff from five locations:
Buses will drop-off and depart from the east side of the stadium. The cost is $4 each way for adults and $1 each way for children age 12 and under, and passengers will need exact change. Electronic signs will help direct fans to the Big Red Express locations. Big Red Express season tickets, good for round-trip travel for all home games, are available for $40 at StarTran, 710 "J" Street; the SouthPointe Pavilions office; Scheels at SouthPointe Pavilions; or at the lots on game day. For more information, call 402-476-1234 or visit startran.lincoln.ne.gov.
The City recommends reserving reduced-price, pre-paid, event parking through the City Web site, lincoln.ne.gov, and through parkandgo.org. Football parking locations and fees are:
Grills are not allowed in City garages. RV parking is available at Sun Valley and Charleston for $10, and at the North Bottoms lot, 14th and New Hampshire, for $20. Grills are allowed at these locations.
A number of private lots are available. Rates vary, and some offer season passes. Vehicles blocking drives, parked too close to the intersection, parked on public right of way or interfering with vehicle and pedestrian traffic will be towed.
UNL parking lots will be available for use six hours prior to kick off. Game-day parking information and maps are available at http://parking.unl.edu/ (keyword: football). Parking also is available at the following University areas for $15:
Wheelchair accessible parking is available at UNL Lot 5, Stadium Drive and Holdrege, for $15 per vehicle. Handicapped parking is available at:


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Enterprises tune into travel-tracking Web app
- 29 October, 2007 08:11
The Web 2.0-social networking phenomenon's seemingly irresistible force field has ensnared yet another niche: travel.
Dopplr, a hosted service, allows users to track the travel schedule and whereabouts of themselves and their friends through a visual interface reminiscent of Facebook, rather than traditional means such as e-mail.
Although the application is still in private-beta phase, its backers have wasted no time in courting workers in large enterprises -- those with the most intense travel schedules. While for most users, Dopplr is invite-only -- a buzz-building gimmick harkening back to the early days of Gmail -- the company more recently created the Dopplr 100. Employees from the 100 companies on the list can sign up for the service. Among the 100 are Sun, Microsoft and IBM.
Other startups, such as Tripit, are also trying to marry social networking and travel itineraries. Tripit takes user-submitted information such as airline and hotel confirmation e-mails and uses the data within to generate a tailored itinerary, replete with maps, local weather info and the like. It also allows users to create lists of friends.
But Dopplr may stand out in terms of early buzz -- the company claims nearly 100,000 users -- and for quickly orienting itself around business users.
Dopplr's chief operating officer, Taneli Tikka, said the company's enterprise ambitions arose from research into their existing user profiles. "We analyzed the user base and found that clearly there were industries ... that were using it more than others," he said.
James Governor, an analyst in the U.K. with Redmonk, is a vocal cheerleader for Dopplr. (Governor also shares an office with Dopplr founder Matt Biddulph, but says their business relationship ends there.) Governor recently issued a challenge to software companies listed on the Dopplr 100 through his blog, urging employees to sign up.
He says Dopplr ultimately provides "manufactured serendipity," for social and business-related scenarios.
For example, if you had to travel to a distant city on business, you might well spend the night in your hotel room alone. But Dopplr could alert you to the presence of a friend or colleague who by happenstance is also in town, Governor said.
Governor also notes the enduring presence of Web 2.0 conferences, despite the ever-growing richness of the communication-centric technologies that give rise to such events. "Increasingly we work and meet in online interactions. .... That's not to say it's not rich, but it's no substitution for face-to-face contact."
"Some people don't like other people, and Dopplr probably isn't for them," he joked.
Dopplr could also help enterprises save time and money, Governor speculated. A company might send three teams of workers with slightly overlapping responsibilities abroad on separate trips over time, for different reasons. "With Dopplr you might be able say wait, let's send one guy on all three calls."
The question outstanding, though, is how Dopplr will evolve its service into something fully enterprise-ready.
One crucial topic for CIOs is security. Business users have given Dopplr feedback regarding this issue, according to Tikka. "That's definitely something we need to carefully look at. We think companies should keep their confidential information with them and keep it confidential," he said. "We would only be limited to kind of trivial stuff that doesn't really help if someone was to eavesdrop."
In the meantime, Tikka said, Dopplr plans to monetize its service through a couple of avenues. Later this year, the company hopes to offer subscription-based premium accounts, which would include assorted value-added services to the basic platform. The company also intends to deliver finely tuned commercial advertising based on user profile information. Also in the works are plans to extend the Dopplr 100.


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UNECE-FIBA Road Safety Campaign 2010
The official launching of the UNECE-FIBA Road Safety Campaign took place on August 26 at the Palais des Nations in Geneva. The launch coincides with the Basketball World Championship 2010, which will be held in Turkey from 28 August to 12 September.
|Press Release 26 August 2010 : UNECE and FIBA launch global road safety campaign during the 2010 FIBA World Championship in Turkey
|Press Release 8 September 2010 : UNECE/FIBA road safety campaign gains momentum as 2010 FIBA World Championship enters final rounds
|Mr. Ján KUBIŠ speech PDF
|Statement by His Excellency Mr. Oğuz Demiralp PDF
|Statement by Mr. Andrey Vasilyev, Deputy Executive Secretary UNECE PDF
|FIBA 2010 Declaration PDF
|Photos and media
|Launching the campaign, Press Conference, Geneva 26 August 2010
|José Calderón and Keren Tunçeri


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If there really were a shadow conspiracy running the world, if there really were a secret cabal of men who were pulling the strings around the globe to produce a desired effect, being a part of that group without really knowing you were might end up being fairly boring. There would probably be lots of paperwork. You'd always get little glimpses of the evil being done in your name. You'd likely have no job security, not to mention life security. But you'd always feel like a part of something, like it or not. You'd always have a reason to keep waking up in the morning until you didn't.
AMC's new series and attempt to go three-for-three insofar as quality drama series go (if we just pretend that no one remembers Remember WENN), Rubicon, is pitched directly at that question. It's about the members of a shadowy think tank who really have no idea how deep the rabbit hole goes and don't terribly want to know. I'm not immediately convinced the pilot works as an episode of television, but as a sampler, as an excuse to get me to watch another hour, I'm hooked. It's not going to work for everyone - particularly for the people who didn't get the memo that this wasn't actually a nearly two-hour-long episode of Breaking Bad and are now upset about Rubicon being tacked on there - but it is going to work for some people very well. I suspect I'll be one of those people.
The best TV series take what could be called cinematic worlds and slowly expand them, pushing to the edges of those worlds and seeing what's out there. Deadwood does this with Westerns. The Sopranos did it with gangster movies. Mad Men is a take on the office politics movies of the '50s and '60s, while Breaking Bad hangs out in the American arthouse scene. Rubicon is essentially a take on '70s conspiracy thrillers, it looks like, though I'm willing to admit the first episode doesn't make this abundantly clear (or, really, make anything abundantly clear).
The biggest issue one has to face when coming up with a pilot is whether or not you're going to tell a self-contained story that hints at a larger world beyond the story's borders or whether you're going to just offer up an introductory look at the world. To return to our great television metaphors, The Sopranos is a very self-contained episode of TV in its pilot, where nearly every minor conflict that's set up is paid off, and we're expected to trust that the character conflicts will be what carry us through in the weeks to come. By contrast, The Wire is a sample of the world we're going to get to know. Many of the show's most major characters don't even appear, and the main action of the pilot - McNulty convincing a judge to let him set up a task force - is handled in a single scene. Most pilots take the former model, as a matter of course. It's simply easier to convince an audience to come back when you've shown you can tell one enjoyable story. The latter model relies on trust to a huge degree.
One of the biggest problems with all of the Lost and 24 copycats that popped up around the middle of last decade is that all of them thought their overarching stories were so interesting that the audience would stick around based on hints of bigger things to come, forgetting that the pilots for Lost and 24 suggested much, much larger worlds, sure, but also set up fairly concrete storylines and goals for their characters to accomplish. Jack has to figure out just what's going on with his family, even as he's called in on an assassination attempt. The Lost gang has to figure out a way to get a message out to the world for rescue, never mind the Smoke Monster in the trees. By and large, the copycats that worked were the ones that figured out a way around this problem of splitting a larger story into more manageable chunks. The ones that failed were the ones that insisted it would all make so much sense once we got to the end, and wouldn't we just pull up a chair for a little while and settle in. These shows relied too heavily on the faith of an audience that had been burned too many times.
By the standards of creating a compelling episode of television, a singular unit that stands apart, Rubicon's pilot is kind of a failure. It gives us little to no idea of what kind of stories we'll be seeing from week to week, and it hinges almost everything on a character relationship we've only gotten a few minutes to know (though that relationship is well-developed in those few minutes). There are intriguing ideas throughout - despite the heavyhandedness of the reveal, I liked the idea that there'd be a conspiracy messenger service in the crosswords - but they never add up to a complete story, at least not one that we immediately understand as an episode of TV or even the first chapter of a larger story. Hell, even the oblique Wire pilot gave us some sense of where the show might be headed from here on out. All we get in Rubicon is the idea that there are some guys who are doing nefarious things, and eventually, our hero might find out about them and decide to do something about it. We're stuck in the first 15 minutes of the paranoid thriller, where the hero doesn't yet know everyone's out to get him and is just marveling at the weirdness of there being some sort of message in the crossword puzzle.
And yet …
I really liked a lot of the pilot. I liked how stylishly filmed it was. I liked the simple economy the show used to lay out that Will Travers (James Badge Dale) is the smartest guy around. I liked the little windows we got into the lives of the other people who work with him at his job, which appears to be an Insane Theory Factory. I loved that shot of the train suddenly veering into the path of another train, the subdued menace Will's boss gave off, the banality of stepping into that hall up on the top floor and feeling something sinister leaking around the edges. I loved the way the show shot things from odd camera angles to highlight the geometric shapes surrounding Will or the ways overhead shots would suggest the patterns swirling all around us we're not even cognizant of. I liked the way the conspiracy we're probably going to uncover seems to have ruined lives, like the friend of David's that Will goes to visit in a low-rent house. And I liked the way the opening scene suggested there's something much, much larger going on, without being explicit about it. It's all good stuff, and it's all going to bring me back.
But what's really going to keep bringing me back is the fact that this is AMC, and they've earned just a bit of trust from me at this point. These are guys who've taken two premises that seemed deeply constraining and spun three seasons worth of incredible material out of them. If you had told me five years ago that my two favorite series on TV would be about ad men in the '60s and a chemistry teacher who becomes a meth dealer, I would have scoffed. Instead, Mad Men and Breaking Bad leave me jonesing for more, the way the best TV does. Rubicon doesn't have as immediately assured a pilot as either of those series did, but it has a weird confidence of its own, and I can see the kind of show I might become obsessed with around its edges, dense and menacing and filled with a knowledge that the world is not as it should be.
Stray observations:
- A friend compared this to the first episode of several top-flight BBC dramas in roughly the same genre, and I think that's right. This is less a traditional pilot and more a first chapter of something larger.
- I trust the fact that the show has Miranda Richardson means that we're going to get more for that character to do, yes?
- AMC has had pretty good luck with scoring in the past, but this show defines overscored. I don't mind the music, but it's used oppressively.
- Also, hello! We'll be talking about Rubicon throughout the first season here, unless it just turns out to be incredibly stupid and pointless. But we'll look at at least the first handful of episodes, and I'll be your guide, as it were.
- And, man, that "coming up on Rubicon" promo made me think we could be in for something that has a slow build and then becomes all kinds of awesome.


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Courtesy of Riley Kontek and official MMAWeekly.com content partner Bleacher Report.
Have you ever heard of the Culinary Union? It has nothing to do with the Culinary Academy, a place for cooking that Jared Allen likes to joke about when introducing himself on Sunday Night Football.
The Culinary Union I am talking about is an adversary the UFC has gone up with many times before. It is the group that is currently ensuring that the UFC stays out of New York.
However, this time around, they are leading the charge with other women's groups to try and get Quinton Jackson taken off of the UFC on Fox 6 card in Chicago.
This may not come as a shock, as Jackson is quite controversial. However, you may be thinking, "What did Jackson do this time?"
No, he didn't hump any more female reporters. And no, he didn't motorboat Karen Bryant and tell her that "Jamaican me horny," again.
Don't worry, "Rampage" simply made a video about how to rape a woman in a parking garage last year, no big deal.
Sarcasm aside, this video has drawn the ire of women's groups that are being assisted by the Culinary Union. They have demanded via letter and protest that Jackson be pulled from competition this weekend, a demand that will not be met.