The GOES-R flight project has developed the Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) to perform independent INR evaluations of the optical instruments on the GOES-R series spacecraft. In this paper, we document the development of navigation (NAV) evaluation capabilities within IPATS for the Geostationary Lightning Mapper (GLM). We also discuss the post-processing quality filtering developed for GLM NAV, and present example results for several GLM datasets. Initial results suggest that GOES-16 GLM is compliant with navigation requirements

Isaacson, Peter J.

Krimchansky, Alexander

Rafal, Marc D.

Reth, Alan D.

NASA Technical Reports Server

Proceedings for the 2018 EUMETSAT Meteorological Satellite Conference, 17-21 
September 2018, Tallinn, Estonia 
  
ON-ORBIT VERIFICATION OF GLM NAVIGATION ON GOES-16 
 
 
 
Peter J. Isaacson1, Alan D. Reth2,3, Alexander Krimchansky2, Marc D. Rafal2,4 
1The Aerospace Corporation, El Segundo, CA, 2NASA Goddard Space Flight Center, Greenbelt, MD, 
3Cheseapeake Aerospace LLC, Grasonville, MD, 4ASRC Federal Technical Services, Beltsville, MD 
 
Abstract 
The GOES-R flight project has developed the Image Navigation and Registration (INR) Performance 
Assessment Tool Set (IPATS) to perform independent INR evaluations of the optical instruments on 
the GOES-R series spacecraft. In this paper, we document the development of navigation (NAV) 
evaluation capabilities within IPATS for the Geostationary Lightning Mapper (GLM). We also discuss 
the post-processing quality filtering developed for GLM NAV, and present example results for several 
GLM datasets. Initial results suggest that GOES-16 GLM is compliant with navigation requirements. 
INTRODUCTION 
The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the next generation of 
geostationary weather satellites for the United States [1]. The first of this series was launched on 
November 19, was renamed “GOES-16” upon reaching geostationary orbit, and was designated 
“GOES-East” upon reaching its operational position over the western Atlantic. The primary optical 
payload on GOES-16 is the Advanced Baseline Imager (ABI), a scanning multispectral imaging 
radiometer [2]. GOES-16 also includes a newly-developed instrument, the Geostationary Lightning 
Mapper (GLM). The first operational lightning mapper flown in geostationary orbit, GLM measures total 
lightning activity continuously over the Americas and adjacent ocean regions, enabling forecasters to 
focus on developing severe storms much earlier, before they produce damaging winds, hail, or 
tornadoes [3]. The GLM level 1B product is near real-time optical lightning events that have been 
calibrated, navigated, and time tagged. GLM also generates regular snapshot “background images” of 
its field of view. These background images are not a formal product and thus do not have formal 
navigation requirements. However, they are navigated with the same algorithm as the lightning events, 
and thus the navigation performance (NAV) of the background images can be considered a proxy for 
the NAV accuracy of the lightning events [4]. The GOES flight project performs independent 
assessments of the GLM Image Navigation and Registration (INR) performance by evaluating the 
background images with the INR Performance Assessment Tool Set (IPATS) [5]. The GLM 
background images have several unique features that lead to non-standard image processing and INR 
evaluation, as described further herein. 
IPATS OVERVIEW 
IPATS, developed by the GOES-R Flight Project to facilitate evaluation of INR performance of ABI and 
GLM, is comprised of two distinct tools, the Image Pair Selector and Evaluator (IPSE) and the Output 
Database Analysis Tool (ODAT). IPSE determines the misregistration in pixels between two input 
images, and can draw on a variety of image correlation algorithms and pre-processing optimizations. 
ODAT is used to process the IPSE output, perform post-processing optimizations, and generate 
reports and visualizations of IPSE results. More detail is provided in [5], and the registration evaluation 
process is described qualitatively in Figure 1. 
 
https://ntrs.nasa.gov/search.jsp?R=20180006771 2019-08-31T18:51:52+00:00Z
 
Figure 1: Cross correlation approach to INR evaluation using image registration. The evaluation image is shifted within 
the reference image, and the misregistration is determined from the offset between the similarity metric maximum and 
the unshifted location. Modified from [5]. 
For relative assessments, images are compared to other images of the same type (e.g., ABI image to 
ABI image). Relative assessments of ABI imagery include frame-to-frame registration (FFR), in which 
adjacent temporal collects are compared, and band-to-band registration (BBR), in which different 
bands of a single ABI scan are compared. For absolute assessments (ABI NAV), images are 
compared to truth images (Landsat-based image “chips”) [5].  
GLM NAV with IPATS 
For GLM navigation assessment, IPATS computes the misregistration between navigated background 
images and the temporally-closest well-calibrated and navigated ABI level-1B full disk imagery from 
band 3 (0.86 µm, 28 µrad/pixel), which has been found to offer the best performance as a reference 
band. IPSE includes several GLM-specific pre-processing optimization options, including a user-
definable threshold for temporal offset between ABI and GLM images, based on the start time of the 
ABI image acquisition and the acquisition time of the GLM image. If no ABI image is found that 
satisfies this temporal offset threshold, evaluation of the GLM background image is skipped. Note that 
temporal mismatches between ABI and GLM images is a significant potential error source in GLM 
NAV analyses, especially as the temporal offset is spatially variable within the image. This variable 
offset is due to fundamental differences between the scanning ABI and staring GLM sensors; ABI 
scans require several minutes to acquire the full disk (exact timeline dependent on ABI mode), 
whereas GLM background images are snapshots (captured near-instantaneously) [4, 6]. 
GLM Background Image Resampling 
Wide field-of-view staring sensors at geostationary altitudes would typically suffer from significant 
extreme parallax effects that substantially degrade spatial resolution at large off-nadir angles. GLM 
has a novel design to mitigate these effects using a non-traditional irregular focal plane pitch (pixel 
size) [3]. While typical geostationary imagers, including ABI, collect data that is regularly sampled in 
angular (fixed grid) space [6], the GLM background images (after downsampling by the GOES ground 
system) have non-uniform angular spacing because of the unusual focal plane design [4]. This 
irregular pixel size violates a key assumption of modern digital image processing techniques, that 
pixels are arranged on a regular grid. To account for this, IPSE includes a bespoke irregular image 
resampler, which resamples the irregularly-spaced GLM images to a regular “ABI-like” angular grid 
and enables the GLM background images to be treated as if they were sampled to a regular grid. The 
current baseline configuration resamples both ABI and GLM images to an intermediate resolution of 
56 µrad/pixel. The irregular grid resampling algorithm is illustrated conceptually in Figure 2. 
 Figure 2: Conceptual illustration of GLM irregular angle resampler algorithm. The solid blue lines indicate the angular 
coordinates of the GLM pixels (lines are plotted every 10 pixels), and the regular “ABI-like” grid is shown in dotted 
black lines. The irregular nature of the GLM grid is demonstrated by the spatially-variable misalignment between the 
solid and dotted lines. A small subset of a full GLM background image is illustrated, with grid resolution reduced for 
clarity. A local search algorithm is employed to assign GLM pixel values to resampled “ABI-like” pixels. 
GLM NAV evaluation locations 
IPATS performs correlation analyses at a series of geographic locations defined a priori. For ABI NAV, these 
locations are defined by the locations of the Landsat-based truth image chips. For all other evaluation modes, the 
locations, referred to as “windows” are defined by the IPSE input location database file. The master location 
database file includes windows placed at the geographic location of the Landsat-based truth images used for ABI 
NAV evaluations, as well as a series of windows placed in a regular grid across the full disk. These windows can 
be enabled or disabled for specific evaluation types. For example, in the case of FFR, windows over oceans are 
disabled because the temporal offset between ABI frames (at least 5 minutes) allows for significant cloud motion 
between images and clouds are the dominant signal over oceans. However, such windows are more useful for BBR 
evaluations since the bands within an image are captured simultaneously (indeed, as some bands do not “see to 
ground”, clouds are often the only feature on which cross-band registration can be based in some cases). For GLM 
NAV, windows are excluded from analysis if their center is over water, as the dominant error in such locations 
tends to be driven by cloud motion, similar to FFR evaluations. Windows are also excluded if they are close to the 
edge of either the earth limb or of the GLM field of regard. The set of evaluation locations used for GLM NAV 
analyses at the checkout orbit of 89.5 W is illustrated in Figure 3. 
 Figure 3: Illustration of GLM evaluation locations for the 89.5 W longitude checkout orbit. Points illustrate the centers 
of the evaluation location “windows”. Locations are defined based on the locations of the Landsat chip truth images 
used for ABI NAV evaluations plus a set of regular grid locations. Windows near the edge of the limb of GLM field of 
view are excluded and not shown in this figure. Background image source: NASA. 
QUALITY FILTERING OF IPATS RESULTS 
IPATS evaluates INR performance for an image by statistically evaluating a set of localized 
correlations (IPSE results for individual evaluation windows). That said, many of the local correlations 
suffer from reduced accuracy for a variety of reasons (clouds/cloud motion, illumination conditions, 
scene content differences from truth image, errors in the correlation process, etc.). Judicious filtering 
of results to exclude such correlations dramatically improves the INR assessment. Appropriate quality 
filtering is particularly important for GLM NAV because of the temporal offset between a GLM 
background image and the temporally-closest ABI image (cloud motion, even variably within an 
individual image, is a major concern). Significant effort has been expended to optimize this post-
processing filtering for GLM NAV. 
The primary parameters used to perform quality filtering for GLM NAV are analytic measurement 
uncertainty (AMU), solar zenith angle (reject low sun angles), extreme outlier rejection using the 
median absolute deviation (MAD), and the “clear sky ratio” (CSR; fraction of clear to cloudy pixels 
based on the ABI clear sky mask product). The progressive application of these filters is illustrated in 
Figure 4. AMU is a mathematical construct that parameterizes the level of false misregistration 
derived, for images that are perfectly navigated and registered, resulting from “noise” sources such as 
variation in illumination conditions, scene content differences, and error in the correlation process. 
AMU incorporates image contrast, image size (number of pixels) and the typical magnitude of image 
perturbations not associated with image translations. For more detail, please see [5]. 
 Figure 4: Progressive application of quality filtering thresholds to the filter training dataset. Plots show Y (NS) error vs. 
X (EW) error in microradians (µrad). Upper left: SZA<75°; Upper right: AMU<2.52 µrad; Lower left: CSR>250 (25%); 
Lower right: 9*MAD extreme outlier rejection. The bimodal N/S behavior is an artifact of a GLM focal plane anomaly and 
is addressed via hemispheric stratification of results. 
GLM NAV is a relative assessment (there is no absolute truth reference), so the INR performance is 
assessed primarily through statistical assessments like dispersion (standard deviation) and stability. 
Filtering thresholds are evaluated by trading reduced dispersion against maintaining sufficient sample 
size for reliable statistics. Baseline quality filtering thresholds were selected from analysis of multiple 
full days of GLM background images collected in the fall of 2017. The filtering configuration illustrated 
in Figure 4 is considered the baseline configuration for processing other GLM background image 
datasets. The thresholds are revisited on an as-needed basis to ensure that they are tuned 
appropriately for analysis of newer background images. 
IPATS GLM NAV EXAMPLE RESULTS 
Results for two datasets collected in fall of 2017 are presented below in Table 1, in the form of mean 
values compiled across the full multi-day span of each dataset, and stratified by hemisphere in each 
case. The 28 Sep 2017 set is the quality filter “training set”, and the post-filtering results are illustrated 
in Figure 4 (lower right). The 31 Oct 2017 set was evaluated using the filtering thresholds derived from 
the 28 Sep 2017 (training) set. Systematic NAV error between N and S hemispheres is a known 
artifact that is not currently addressed by the GLM NAV algorithm. 
 
 
28 Sep 2017 
 N Hem 
31 Oct 2017 
N Hem 
 
28 Sep 2017 
 S Hem 
 
31 Oct 2017 
S Hem 
σx 11.2 10.0 11.3 12.1 
σy 9.5 9.5 15.4 14.3 
Mean X -18.1 -14.0 -22.4 -27.2 
Mean Y 12.7 11.4 -49.8 -54.1 
|X̅| + 3σx 51.8 44.2 56.4 63.5 
|Y̅| + 3σy 41.2 39.8 96.0 96.9 
n 15420 10322 5764 2062 
# images 186 166 175 141 
Table 1: GLM NAV results for two datasets collected in fall 2017, stratified by hemisphere. Positive error indicates the 
image is shifted to the east (EW) or north (NS) with respect to the reference/truth image. 
The 28 Sep 2017 set results summarized in Table 1 are illustrated graphically in Figure 5, which 
illustrates per-image temporal trends in estimated navigation error and sample size. 
 
 
Figure 5: GLM NAV results for 3 days of GLM background images from September 2017. Quality filtering has been 
applied as described above. Results are shown as a function of GLM background image acquisition time, on a per-
image basis. EW error is shown in the top (blue) plot, NS error in the middle (red) plot, and number of correlations 
contributing to the error estimate for each image in the bottom (black) plot. Error bars on the EW and NS plots are ±3σ. 
DISCUSSION 
While the number of individual correlations (n) is smaller in the 31 Oct 2017 results as compared to 
the 28 Sep 2017 results, in both cases the metric of mean X or Y error plus 3σ are mostly on the order 
of 40-60 rad. The exception is the Y (north-south) error in the southern hemisphere, where this error 
“metric” approaches 100 rad. Recall that IPATS is the GOES-R Flight Project’s tool for performing 
independent evaluation of NAV performance. Note that while IPATS results are suggestive of the 
formal GLM NAV performance, the IPATS assessment is not a formal validation of GLM NAV 
accuracy against the L1B event navigation requirement. In both datasets presented above (Table 1, 
Figure 5), results suggest compliance with the GLM NAV requirement of 112 µrad navigation accuracy 
for the lightning events [7]. The 31 Oct 2017 set has a smaller number of correlations remaining after 
filtering. This is likely due to increased cloud cover over a few key areas of the disk during those 
collections, although sample size issues are the subject of ongoing research. Sample size roughly 
follows the expected trend with time of day, with maxima near local noon (Figure 5, bottom). The 
somewhat irregular nature of this correlation is likely due to the discrete spatial sampling of the disk 
(Figure 3) and variable temporal offset between the ABI and GLM images. 
SUMMARY/CONCLUSIONS 
We have demonstrated functional, quantitative assessment of the navigation accuracy of GLM 
background images using IPATS. In the process, we have defined a set of post-processing quality 
filtering thresholds that are effective at clarifying INR performance. Independent (though informal) 
GLM NAV evaluation with IPATS has been demonstrated. The filtered results for the GLM background 
image datasets evaluated herein suggest that GLM is complaint with its level 1B navigation 
requirements. 
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On-Orbit Verification of GLM Navigation on GOES-16

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