Assistant Technical Representative: John E. Janowiak

Contributors: Robert J. Joyce

Period of Performance: November 11 - 16, 2001

1. WORK PERFORMED DURING THE PAST WEEK

A. Created software enabling development of daily, global, most frequently used geostationary satellite ID, 5 degree latitude/longitude resolution templates using CPC's half hourly merged IR images. Along with the most frequent satellite ID is the number of images that the satellite was the dominant satellite in the region for that day. Although for any image time, two satellites may contribute information within a particular 5 degree region in overlapping coverage, the satellite with the greater coverage within the region is determined as dominant. Resulting templates for 6, 7, and 8 August 2001 yielded over 45 images per day for the METEOSAT-5 and 7 domains as well as northern hemispheric GOES-8 and GOES-10 northern hemispheric sector images, 35-43 in the southern hemispheres. The division of the most frequent satellite used within these regions is and should be the midpoint between sub-satellite positions. However since GMS-5 scans only hourly images on a regular basis, and less frequently in the southern hemisphere, MET-5 and GOES-10 (conic sector region only) are the most used satellites in their southern hemispheric overlap coverage with GMS-5 which averages only 14-16 images in the area.

B. Inspection of satellite ID in sequential half hourly merged IR images the within southern hemispheric region of the GMS domain reveals the irregular temporal sampling of the GMS-5 scanner. Typical image times of the GMS-5 southern hemispheric sector would be : 2:30, 4:30, 5:00, 5:30, 8:00, 10:30, 11:00, 11:30, 14:00, 17:00, 17:30, 18:00, 20:00, 22:30, 23:00, and 23:30 GMT. Unfortunately GMS-5 is the most dominant satellite in the southern hemispheric region between MET-5 and GOES-10 coverage, with these infrequent, irregular scans. This deficiency leads toward studying spatial and temporal interpolation of half hourly advection vectors derived from successive half hourly images (and hourly GMS images).

C. Set up software to average 1 to 6 half hourly advection vector arrays to then correlate with a future vector array. Among other problems with this approach, the result is one number per correlation, thus regional properties are not shown. However this work is not lost because the software needed to compare the spatial and temporal interpolation properties of the advection vector fields (#2 below) will be modified from this attempt.

2. WORK PLANNED FOR NEXT WEEK

A. The overall goal is to get the best half hourly global advection field, at all locations.

1. The assumption that this must begin with preforming spatial lag correlations at lag of only a half hour between successive images (or hourly only in GMS domain) must be proved. Show the decay in correlation by increasing the temporal lag between successive images. The resulting spatial lag correlation at each location should be averaged for each temporal lag increment. Also, smoothed maps of temporally lagged correlation are produced by averaging several maps of correlation at the same lag for a view of regional dependency.

2. The assumption that advection vector correlation changes quicker spatially than temporally must be proven. This can be shown by:

A. Start with 13 successive half hourly advection vector maps and focus over an

area derived from spatially and temporally complete IR images say over the GOES 8 domain. Take a copy of the 7th vector field and remove a large interior section.

B. Replace the missing field first by performing a bilinear interpolation within the missing array. Then correlate all interpolated values to the original vectors at each interpolation distance interval to gauge the degradation of spatial interpolation with distance.

C. Replace the missing field by a linear temporal interpolation from the 6th and 8th vector fields (one half hour past and future). Repeat the procedure by interpolating from vector fields at increased half hourly increments, (i.e. 5th and 9th, 4th and 10th). Correlate only the replaced portion of the vector field for each temporal interpolation increment with the original vector array to gauge the degradation of correlation with incrementing temporal interpolation distance.

D. Compare the effects of spatially interpolating advection vectors to temporal interpolation at the various intervals shown above (B&C), to be potentially used in tandem varying upon spatial and temporal dependencies.

3. Compare collocated vector fields derived from separate satellites. In order to do this parallax and zenith angle corrected full resolution IR from both the even and odd geostationary satellite configuration must be written to disk. This is necessary in order to perform spatial lag correlation separately for both geostationary satellites in their overlap regions for the same period of time. Comparisons of these vector fields derived from separate satellites will determine if an additional satellite will improve the vector field, in an overlap region, if the dominant satellite displays large temporal gaps in imagery. These results should be compared to spatial and temporal interpolation properties of single satellite derived vector fields (2B&C) to determine usefulness.

4. Repeat procedure tallying all IR images per day at 5.0 degree latitude/longitude region for the dominant satellite (#1 Work Performed Past Week) by including all satellites observing the region.