2.1.

IPATS Architecture

To fully assess the INR accuracy, IPATS produces five types of INR quality assessment metrics:

IPATS employs a modular algorithm architecture because (1) most of the processing steps of above metrics are common and (2) there are multiple algorithms available for each processing step. During IPATS development, members of the team who are image-processing subject matter experts brought various techniques to the table. Given the need to design, develop, test, and deploy IPATS in advance of GOES-R (now GOES-16) launch, the effort was focused on those techniques familiar to the IPATS team members, and a more comprehensive research effort to identify all alternatives was not undertaken. Sometimes, multiple algorithms to perform a given function, such as Pearson cross correlation (PCC) and normalized mutual information (NMI) algorithms in the image registration module, were both implemented into IPATS, with the selection of algorithm being left to the IPATS user.

Figure 1 shows a high-level diagram of IPATS. Two input images are preprocessed and then the EW and NS registration differences are calculated. In the last step, the IPATS measurements are screened to identify the high-quality measurements used to produce the assessment reports. All the metrics above, except for WIFR, use similar modules with different image types, configuration, and screening parameters. Metrics, except SSR, are generated separately for each of the image types: FD, CONUS, and MESO. WIFR is an additional step that operates on the NAV results.

Fig. 1

High-level algorithm diagram of IPATS. The image pair characteristics include cloud coverage, seasonal change, ground surface type, and other factors which impact the quality of image registration.

SSR is evaluated on two successive MESO images, which are specially tasked by mission operations to specific Earth locations. The top of the lower swath of the first MESO image overlaps the bottom of the upper swath of the second MESO image. The evaluation window with a size of $128\times 8$ GOES pixels lies within the overlap region of the two tasked MESO images. The misregistration errors measured within the evaluation windows provide the assessment of SSR.⁷

2.2.

Landsat Chips

The ABI NAV accuracy is assessed through comparing subsets of ABI images with subsets of Landsat 8 images, called chips here, which are considered to have a negligible NAV error. The geolocation accuracy of the Landsat images is within 15 m,⁸ which is 3% or less of the spatial resolutions of GOES-R ABIs.

The chips for assessing ABI NAV are mostly along the shorelines of North and South America (Fig. 2). The shorelines are emphasized because they tend to exhibit high contrast, low spatial frequency image features that are particularly suitable for image registration at the spatial scale of ABI images. The size of a Landsat chip is about $150\mathrm{km}\times 150\mathrm{km}$. The Landsat 8 images used to generate chips were acquired between years 2013 and 2017. Based on our experience with other instruments, the chips should be refreshed with latest Landsat images every 10 years or so. The Landsat chips are either cloud free or contain limited cloud coverage, usually $<5\%$. For a single chip location, multiple chips corresponding to multiple seasons were collected when Landsat 8 images of sufficient quality were available.

Fig. 2

The chip locations in the view of (a) GOES-16 and (b) GOES-17. There are 644 chip locations in the view of GOES-16 and 574 chip locations in the view of GOES-17.

The spectral response of different channels varies for the same target. This is a major error source when registering the images from different spectral channels. For example, the land/water boundary is the primary feature for registering ABI and Landsat subsets. The locations of the land/water boundary could be at different locations in different channels due to spectral response difference and ground feature characteristics, e.g., steep or mild slope on the shoreline. Therefore, it is necessary to use a multispectral chip library when assessing ABI NAV accuracy. Table 1 shows a comparison of the spectral channels of ABI and Landsat 8. For VNIR channels, ABI and Landsat 8 channels show good spectral overlap. For MWIR and LWIR channels, there is no close correspondence between ABI and Landsat 8 channels. We determined the corresponding channels based on the spectral response characteristics and also made necessary adjustment after carefully examining registration performance for ABI and Landsat subsets. Originally, we utilized channel 7 of Landsat 8 (short-wave infrared channel) to assess channel 7 of ABI (MWIR).⁸ After examining operational ABI data, we found that channel 10 of Landsat 8 (LWIR) is a better choice to assess ABI channel 7 considering nighttime emissions. Such adjustments occurred throughout the IPATS development and testing process from 2014 to 2018.

Table 1

ABI channels and the corresponding Landsat channels utilized for NAV measurements. The ABI water vapor-sensitive channels (4, 8, 9, and 10) are excluded from the NAV measurements because they cannot see the ground and so cannot be compared with static Landsat chips.

2.3.

Image Preprocessing

2.4.

Image Registration

The core of the IPATS algorithms is the correlation module that ingests two preprocessed subsets at the same resolution, in which one subset is larger than the other in both the EW and NS directions. The smaller subset, called the float image, is then shifted in both directions to all possible locations coincident with a subarray of the larger subset, which is called the fixed image. For each such shifted location, a similarity metric is computed, either PCC or NMI. The results of these computations are captured into a 2-D array of similarity metrics, called the correlation array, one for each shifted location. The size of the correlation array is always odd, with the center location corresponding to zero shift in both the EW and NS directions. The size of the correlation array is determined by the maximum anticipated registration error, a user provided input to IPATS, and by the resolution scale, defined by the SPF, at which the correlation is performed. Some additional padding of the correlation array is required to ensure that the raw correlation peak, prior to application of the peak location refinement algorithms, lies in the interior of the array, and furthermore that the subarray centered on the peak required for both peak location interpolation algorithms lies within the correlation array (Sec. 2.4.3).

2.5.

Screening of IPATS Results

The accuracy of the IPATS measurements depends on the characteristics of the image pair. In addition to the differences between the ABI and Landsat sensor, other factors, such as cloud coverage, seasonality, and image acquisition time, lead to additional registration errors between Landsat chips and the corresponding GOES-R subsets. The measured INR errors due to these factors could be as large as several GOES-R pixels, and so it is necessary to remove such poor-quality measurements to obtain a better estimate of the true INR errors.

2.5.2.

Analytic measurement uncertainty screening

De Luccia et al.⁷ introduced a parameter, called “analytic measurement uncertainty (aMU),” that quantitatively describes the quality of image registration. The aMU value is high when two images have very different scene content, in which would typically result in unreliable image registration results. Dr. De Luccia then revised the original aMU equation and developed a second version of aMU, called aMU2, to provide an improved indicator of measurement quality:

Eq. (16)

$$\mathrm{aMU}{2}_x=\frac{1}{\mathrm{SPF}}\frac{1}{PkS{h}_x}\sqrt{1-z_{pk}^2}\frac{D}{NM}\frac{1}{2}(\frac{1}{c_1}+\frac{1}{c_2}),$$

where $\mathrm{aMU}{2}_x$ is the measurement uncertainty in the $x$ direction, $PkS{h}_x$ is the peak sharpness in the $X$ direction [Eq. (14)], $z_{pk}$ is refined PCC (refer to Sec. 2.4.3), $N$ and $M$ are the image dimensions, $D$ is the normalized contrast difference between two images at overlap region [Eq. (17)], $c_1$ and $c_2$ are the normalized contrast of two images [Eqs. (18) and (19)], and SPF is a subpixel factor (refer to Sec. 2.3.1).

The values $D$, $c_1$, and $c_2$ are calculated as

Eq. (17)

$$D=\sqrt{\sum _{x,y}{[\frac{f(x,y)}{\overline{f}}-\frac{t(x,y)}{\overline{t}}]}^2},$$

Eq. (18)

$$c_1=\frac{{\sigma }_f}{\overline{f}},$$

Eq. (19)

$$c_2=\frac{{\sigma }_t}{\overline{t}},$$

where $f(x,y)$ and $t(x,y)$ are the fixed and float subsets, $\overline{f}$ and $\overline{t}$ are the mean values of the two subsets, and ${\sigma }_f$ and ${\sigma }_t$ are the standard deviations (STDs) of the two subsets.

Note that the formulation of aMU2 (nor aMU) does not use the misregistration measurement as an input; in other words, aMU2 does not reject misregistration measurements based on their magnitudes. Peak sharpness is calculated in the EW and NS directions separately. Therefore, aMU2 is also evaluated independently in each direction.

Figure 3 shows the relationship between the scatter in unscreened IPATS measurements versus PCC coefficient, peak sharpness of PCC, and 1/aMU2 in the EW direction. The plot is from GOES-16 channel 2 data acquired on April 11, 2019. The variation of measurements decreases quickly with increasing 1/aMU2 values, which indicates aMU2 represents the measurement quality very well. On the other hand, there is still considerable amount of scattering IPATS measurements when PCC coefficient and peak sharpness are high. The plots show that aMU2 is more effective at discriminating between likely invalid measurements and likely valid measurements than screening with PCC coefficient or peak sharpness alone. Currently for NAV, the aMU2 threshold was visually examined and manually set to 0.357 (1/aMU2 = 2.8) for all ABI channels, which is marked as the vertical line in Fig. 3(a). The measurements with an aMU2 value in the EW direction that are larger than the threshold are removed. A similar screening process is also applied in the NS direction.

Fig. 3

The unscreened IPATS measurements versus (a) PCC coefficient, (b) peak sharpness of PCC, and (c) 1/aMU2 in the EW direction from channel 2 data of 144 FD images acquired from 18:00 UTC April 11 to 17:59 UTC April 12, 2019. The variation of the measurements drops significantly with increasing 1/aMU2 value. There are still considerable variations of the measurements when the PCC coefficient and peak sharpness increase. 13,310 out of 58,745 IPATS unscreened measurements passed the aMU2 screening in both $X$ and $Y$ directions.

2.6.

Assessment of Measurement Error

INR error is composed of two components: INR intrinsic error and measurement error:

To estimate ${ϵ}_{\mathrm{ME}}$ and determine the optimal configuration parameters to minimize ${ϵ}_{\mathrm{ME}}$, we ran IPATS with 136 Landsat chips to process test images with known, intentionally induced INR errors. The induced errors are up to one GOES-R pixel in all four directions (East, West, South, and North). The root mean square error (RMSE), ${ϵ}_{\mathrm{ME}}$, of the 136 measured errors against induced errors in EW and NS directions are considered the measurement error ${ϵ}_{\mathrm{ME}}$.

Stair-step error is one important measurement error introduced by the image registration algorithm.¹⁸ The name “stair-step error” comes from the fact measured INR error plotted against the true INR error resembles a stair-step shape (see Fig. 1.1-1 in Ref. 19). The difference between measured and true error is therefore oscillatory, and the frequency and the amplitude of the oscillation depend on the spatial resolution at which the image correlation is performed. As mentioned, the spatial resolution for correlation is determined by the SPF parameter in the IPATS configuration. The theoretical stair-step error is 0 when the intrinsic error is zero.^18,19 However, it is observed that the ${ϵ}_{\mathrm{ME}}$ of the 136 measured error is close to but not exactly 0 in this test when the intrinsic error is zero in both directions (Fig. 4). This is due to other error sources, e.g., imperfect resampling algorithm, the possible true minor mismatch between Landsat chips and ABI subsets and insufficient spatial/spectral information in the coarse resolution image. The ${ϵ}_{\mathrm{ME}}$ for the zero-shift case increases with increasing SPF. This indicates that measurement error increases when interpolating to finer and finer resolution.

Fig. 4

The relationship between the RMSE of measured errors and SPF. There is no induced error in the test.

The frequency of the stair-step error, equal to the size of the target resolution of the image registration, and the amplitude of RMSE depend on the SPF value.^18,19 The stair-step error emerges when the intrinsic error is not zero. Table 2 shows the maximum RMSE for different SPF values. In contrast to the situation of zero intrinsic error, the maximum RMSE decreases with increasing SPF when the intrinsic error exists. This means the stair-step error is a more significant measurement error source than the error introduced by other sources. At the GOES resolution (SPF = 1), the maximum RMSE is about 0.19 pixels. There is a significant drop, from 0.19 to 0.06 pixels, with a change of SPF from 1 to 2. It then drops slowly from 0.06 to 0.02 pixels when the SPF increases from 2 to 12. The measurement quality improved slightly but the computation time increases significantly because the computation time of the image registration algorithm has O (${\mathrm{SPF}}^2$) time complexity. After examining the tradeoff between the accuracy and the computation cost, the SPF was set to 2 for NAV, CCR, and FFR in the IPATS baseline configuration. The measurement error is only 1% of the pixel size when the intrinsic error is close to 0 (Fig. 4). This is sufficient for assessing the NAV accuracy. The intrinsic error of GOES-16 and GOES-17 ABIs is close to 0 since they turned into the operational status. The measurement error could drop from 1% of the pixel size to 0.5% if the SPF value switches from 2 to 1. However, the intrinsic error often increased unexpectedly due to various reasons, which we will discuss later in this paper. In such a case, the measurement error increases significantly as the stair-step error emerges. IPATS is designed to not only monitor the normal operation status but also detect and assess the abnormal INR errors. Therefore, the optimal SPF value is set to 2 for NAV, CCR, and FFR through the mission. The selection of the optimal algorithms and parameters of SSR, not shown here, is independent to the selection of other metrics because of the small evaluation window over special tasked MESO image pairs. For SSR, the optimal SPF value is set to 3.

Table 2

Maximum RMSE in pixels in both EW and NS directions with induced error up to 1 pixel on each direction.

2.7.

IPATS Baseline Configuration for ABI

IPATS provides multiple algorithms for each processing step. In monitoring ABI INR performance, a set of IPATS algorithms and user-configurable parameters were selected as the baseline configuration for optimal monitoring results. The selection of the at-launch algorithms and parameters was based on simulated ABI data and surrogate Advanced Himawari Imager (AHI) data, and these algorithms and parameters were later refined with on-orbit GOES-16 data during the postlaunch test (PLT) phase. Table 3 shows the algorithm selections and parameter values for preprocessing, image registration, and screening IPATS results. For the metrics generated in the operational mode, including NAV, CCR, and FFR, the algorithm selections of preprocessing and image registration are the same. The primary difference is in screening results. The VZA screening is not applied in CCR and FFR because two ABI images are compared in these two metrics, and they do not suffer the small subset dimension issue when the VZA is large (Sec. 2.5.1). The STAND screening is not applied in CCR and FFR also, but we plan to include it for these two metrics in the future. The aMU2 threshold value for NAV is uniformly set as 0.357 for all channels. However, the aMU2 thresholds for CCR and FFR vary by channel pairs or channels.

Table 3

IPATS baseline configuration for monitoring GOES-R ABI performance. WIFR is not included because it is calculated from NAV results offline.

For SSR, there is no Sobel edge enhancement applied in preprocessing, and only SZA and aMU2 are applied in screening SSR results because they are sufficient to remove poor quality measurements and meet the accuracy requirement.

3.1.

Long-Term NAV Record

Figures 5 and 6 show the 24-h NAV statistics for GOES-16 and GOES-17 ABIs, respectively, from the start of PLT to November 5, 2019. Channels 2 and 7 are used to represent visible and infrared (IR) channels, respectively. There are close to 3 years of GOES-16 data and about 1.5 years of GOES-17 data. Long-term ABI NAV results show the effects of the on-orbit calibration process of ABI navigation system. In general, GOES-16 NAV error in the NS direction is less than in the EW direction and is more stable over time. GOES-17 NAV errors are comparable in both directions. The gaps in the early stage of PLT are due to the very large NAV errors, which are out of the plot range.

Fig. 5

The time series plots of the 24-h mean and three STD of the GOES-16 ABI channels 2 and 7 NAV errors from January 27, 2017, to November 5, 2019. The vertical dash lines mark when the ABI products reached provisional and operational status.

Fig. 6

The time series plots of the 24-h mean and three STD of the GOES-17 ABI channels 2 and 7 NAV errors from April 13, 2018, to November 5, 2019. The vertical dash lines mark when the ABI products reached provisional and operational status.

The NAV error of GOES-16 in the EW direction of all channels and NS direction for channels 7 and 13 are significant from January 27 to April 27, 2017. The time period with significant GOES-17 ABI NAV errors is from May 1, to July 11, 2018. Figure 5 shows the NAV long-term trend of GOES-16. The major INR updates in April and November 2017 and April 2018 are apparent in the plots. Two updates in 2017 improved the mean NAV errors to around $1\mu \mathrm{rad}$ in both directions across all channels. The update in June 2018 removed gradual increase in EW NAV error, which increased about $1\mu \mathrm{rad}$ in all channels from June 2017 to November 2017 and from November 2017 to April 2018. This is due to a zonal tide term which was missing in the spacecraft’s Earth orientation calculations. The missing term drifts slowly over time and led to the EW error gradually increasing as observed. Figure 6 is the NAV long-term trend of GOES-17. The NAV errors dropped to around $1\mu \mathrm{rad}$ in VNIR channels and around $2\mu \mathrm{rad}$ in IR channels after the INR updates in July 2018. The EW errors increased about $0.5\mu \mathrm{rad}$ in VNIR channels and $2\mu \mathrm{rad}$ in MWIR/LWIR since the yaw flip on September 9, 2019. This indicates there is room to fine tune the INR parameters to improve the NAV accuracy to pre-yaw-flip level.

It took 3 months for GOES-17 to bring the NAV accuracy to about 1 to $2\mu \mathrm{rad}$ and 11 months for GOES-16 to obtain the similar improvement. The faster improvement of GOES-17 is because of the lessons learned from GOES-16. Currently, the NAV errors of GOES-17 are about $1\mu \mathrm{rad}$ larger than GOES-16.

3.2.

Long-Term CCR Record

The CCR performance of both ABIs is presented in Figs. 7 and 8. The CCR of channels 1 and 2 and channels 7 and 13 represents the CCR of visible and IR channel pairs. For GOES-16 ABI, CCR of visible channels achieves excellent accuracy, both mean and $\sigma$ are within $1\mu \mathrm{rad}$, in a month from the beginning of PLT. CCR of IR channels improved significantly from start of PLT to provisional status and to operational status. The mean error dropped from more than $10\mu \mathrm{rad}$ to around $1\mu \mathrm{rad}$ and to less than $0.5\mu \mathrm{rad}$. The STD of error also dropped from more than $5\mu \mathrm{rad}$ to less than $0.5\mu \mathrm{rad}$.

Fig. 7

The time series plots of the 24-h mean and three STD of the GOES-16 ABI CCR errors from January 27, 2017, to November 5, 2019. The vertical dash lines mark when the ABI product reached provisional and operational status.

Fig. 8

The time series plots of the 24-h mean and three STD of the GOES-17 ABI CCR errors from April 13, 2018, to November 5, 2019. The vertical dash lines mark when the ABI products reached provisional and operational status.

The CCR performance of GOES-17 ABI improved to $<0.5\mu \mathrm{rad}$ in about 5 months after the start of PLT, which is much faster than GOES-16 ABI. The INR algorithm/parameters were continually updated after reaching the operational status. The impact of the updates on CCR is showed clearly in Fig. 8. The latest CCR change is after the yaw flip on September 9, 2019, and the specific cause is being investigated, as is the prior jump near the end of July 2019. The variation of CCR between channels 1 and 2 increased in the NS direction.

For both ABIs, the CCR performance in NS direction is consistently better than EW direction since the start of PLT.

3.3.

Long-Term FFR Record

The mean of 24-h FFR assessment of both ABIs are close to zero since the start of PLT. The variation of FFR, represented by the STD of 24-h FFR measurements, dropped through PLT. The STD of both ABIs improved to about $1\mu \mathrm{rad}$ in the VNIR channels and $2.5\mu \mathrm{rad}$ in the IR channels after reaching operational status. GOES-17 has slightly better FFR performance in VNIR channels than GOES-16 (Figs. 9 and 10).

Fig. 9

The time series plots of the 24-h mean and three STD of the GOES-16 ABI FFR errors from January 27, 2017, to November 5, 2019. The vertical dash lines mark when the ABI products reached provisional and operational status.

Fig. 10

The time series plots of the 24-h mean and three STD of the GOES-17 ABI FFR errors from April 13, 2018, to November 5, 2019. The vertical dash lines mark when the ABI products reached provisional and operational status.

3.4.

INR Performance Summary

Tables 4 and 5 show the 24-h INR performance of both GOES-R ABIs from 18:00 UTC October 27 to 17:59 UTC October 28, 2019. This day was selected randomly. The INR performance of both ABIs is stable since they were relocated to the operational location (Figs. 5 and 6). These 24-h statistics are representative of the current ABI INR status. The water vapor-sensitive channels (channels 4, 8, 9, and 10) are not included in these metrics because there is little ground surface information captured by these channels. The ground surface information, especially the land and water boundaries, are the key features for IPATS image registration algorithms in the context of GOES-R INR.

Table 4

GOES-R ABIs’ 24-h INR performance from 18:00 UTC October 27, 2019, to 17:59 UTC October 28, 2019. The second column is the requirements of each category in the mission requirement. The worst performance, calculated as mean plus 3σ, among the channels is listed. The water vapor-sensitive channels (channels 4, 8, 9, and 10) are not included in these metrics. The units are μrad.

Table 5

GOES-R ABIs’ 24-h CCR performance from 18:00 UTC October 27, 2019, to 17:59 UTC October 28, 2019. The second column is the requirements of each category in the mission requirements. The worst performance, calculated as mean plus 3σ, among the channel pairs is listed. Both direct CCR and bridged CCR are presented. The units are μrad. The boldface highlights the measured CCR is out of mission requirements.

The requirements of each INR metrics are measured with 99.73% ($3\sigma$) absolute error (Tables 4 and 5).²⁰ The worst performance channel (of NAV and FFR) and channel pair (of CCR) are listed in Tables 4 and 5. The performance is calculated as the absolute value of the mean plus 3 STD. All metrics, except direct CCR between VNIR and MWIR/LWIR, are well within the mission requirements.

For this 24-h time period, GOES-16 has a slightly better NAV performance, especially in the EW direction (15.0 versus $18.1\mu \mathrm{rad}$). GOES-17 results are better for FFR and CCR of VNIR channels, but about $1.5\mu \mathrm{rad}$ higher than GOES-16 for CCR of the MWIR/LWIR channels in both directions. The CCR of VNIR and MWIR/LWIR being larger than the requirements does not necessarily indicate true large CCR misregistration but may due to the high measurement error between VNIR and MWIR/LWIR channels, because of the significant spectral response difference. An alternative method to reduce CCR measurement error is to apply one or more bridging channels to estimate CCR indirectly using the transitive property. Equation (21) shows how the mean indirect, or bridged, CCR between channels A and D are calculated from the means of direct CCR of channels A, B, C, and D:

Table 5 shows the assessments of CCR performance using this bridging approach. The estimated CCR performance with the bridging method improves from the direct CCR measurements. The most significant improvements is for the CCR of the VNIR to MWIR and LWIR channels, where the values drop from over $30\mu \mathrm{rad}$ to about $10\mu \mathrm{rad}$ and about $15\mu \mathrm{rad}$ to about $10\mu \mathrm{rad}$ in the EW and NS directions, respectively. The bridged CCR of VNIR to MWIR and LWIR channels of GOES-17 ABI is slightly out of mission requirements in the NS direction. This is due to the poor performance of channel 16 of GOES-17 ABI.²¹ When channel 16 of GOES-17 is excluded, the bridged CCR of VNIR-MWIR/LWIR is 11.0 and $8.1\mu \mathrm{rad}$ in EW and NS, respectively. They are all now within the mission requirements.

3.5.

Anomaly Detection and In-Depth Analysis

Besides assessing the overall INR performance, IPATS also played an important role in anomaly detection and in-depth analysis. The following two sections show examples of each.

3.5.1.

NAV during eclipse

During the eclipse season, fast thermal deformations in the sensor around penumbral times lead to abnormal, large image navigation error for a short time period.^22,23 Figures 11 and 12 show the channel 13 scene temporal NAV plot of both ABIs from 18:00 UTC September 25 to 17:59 UTC September 27, 2019. This period is in the fall eclipse season of 2019. For GOES-16, the images around 4:00 UTC every day have a large EW error variation (from $-2$ to $10\mu \mathrm{rad}$) followed by another EW error variation around 5:20 UTC (up to $23\mu \mathrm{rad}$). The EW error slides to the opposite direction, about $-7\mu \mathrm{rad}$, right after the second variation, and then returned to normal NAV error gradually in approximate 1 h. The eclipse impact on the NS error is negligible.

Fig. 11

The time series plots of the mean and $3\sigma$ of the GOES-16 ABI FD channel 13 images from 18:00 UTC, September 9 to 17:59 UTC, and September 11, 2019. The rectangular boxes marked the 3-h period centered at when eclipse effect occurs.

Fig. 12

The time series plots of the mean and $3\sigma$ of the GOES-17 ABI FD channel 13 images from 18:00 UTC, September 9 to 17:59 UTC, and September 11, 2019. The rectangular boxes marked the 3-h period centered at when eclipse effect occurs.

For GOES-17, the impact of the eclipse is similar to GOES-16 but with larger amplitude. At first, the EW error changed from about $-1$ to $12\mu \mathrm{rad}$ around 8:20 UTC and then swung in the opposite direction, about $-10\mu \mathrm{rad}$. The error gradually shifted from $-10$ to $-5\mu \mathrm{rad}$ from 8:40 to 9:30 UTC, followed by another variation to $-20\mu \mathrm{rad}$. Then, the EW error gradually returned to normal level, about $-1\mu \mathrm{rad}$, over $\sim 1\mathrm{h}$. Similar to GOES-16, the eclipse impact in NS direction is not significant. In general, the eclipse effect is more significant and lasts longer on GOES-17 than GOES-16.

The observed NAV abnormality during the eclipse is not only due to the thermal contraction on the hardware but also due to how the software, in particular the Kalman filter, handling the situation. The eclipse transients have been reduced since the beginning of the PLT with INR algorithm and tuning improvements (not discussed here). There should be room to further minimize the eclipse transient, at least for GOES-17.

3.5.2.

INR measurements for in-depth analysis

In addition to directly assessing INR accuracy, the measured INR errors are also useful to evaluate and understand more in-depth information, such as focal plane misalignment, scan encoder misalignment, etc. Figures 13 and 14 show one sample in-depth analysis. These plots show the linear least-squares fit between the measured NAV errors and the measurement locations in each scene based on 30 days of LWIR NAV results in April 2019. In the situation of perfect INR, the errors and the measurement location are independent. The lines plotted in Figs. 13(a), 13(b), 14(a), and 14(b) would ideally be horizontal lines with zero error, and the lines plotted in Figs. 13(c), 13(d), 14(c), and 14(d) would ideally be vertical lines with zero error. The slopes and the offsets of the lines in Figs. 13 and 14 indicate various issues could relate to satellite attitude, INR parameters, or scene variation as a function of viewing geometry.

Fig. 13

The linear least square fit between NAV errors and the image location of the measurement. The statistics are based on the LWIR data of GOES-16 in April 1 to 30, 2019. Besides the individual measured LWIR channels (channel 12 to 16), the average of all measured LWIR channels is also plotted and marked as “LWIR” in the plots.

Fig. 14

The linear least square fit between NAV errors and the image location of the measurement. The statistics are based on the LWIR data of GOES-17 in April 1 to 30, 2019. Besides the individual measured channel, the average of all measured LWIR channels is also plotted and marked as “LWIR” in the plots.

For GOES-16, the mild slope of lines in Figs. 13(a) and 13(d) indicate a scale error in EW and NS directions, respectively. On average, the measured EW errors at the east and west boundaries of an LWIR scene have a difference of $2.5\mu \mathrm{rad}$. Similarly, the measured NS errors at the north and south boundaries of an LWIR scene have a difference of $1.5\mu \mathrm{rad}$. The systematic offsets are also observed in both directions as the lines in Figs. 13(a) and 13(d) are all located to one side of the zero-error line. This is consistent with the conclusions in the 24-h NAV assessment reports. The dependency of NS errors in EW image location is negligible [Fig. 13(b)]. However, the EW errors are about $-1.2$ and $1.7\mu \mathrm{rad}$ at the north and south boundaries of an LWIR scene, respectively [Fig. 13(c)]. The different slopes between NS error versus EW image location and EW error versus NS image location indicate a possible orthogonality issue.

The scale error in GOES-17 ABI in both EW and NS directions is more significant than the scale error in GOES-16 ABI. On average, the measured EW errors at the east and west boundaries of an LWIR scene have a difference of $3.8\mu \mathrm{rad}$ [Fig. 14(a)]. The measured NS errors at the north and south boundaries of an LWIR scene have a difference of $10.3\mu \mathrm{rad}$ [Fig. 14(d)]. The systematic offsets are also observed in both directions [Fig. 14(a) and 14(d)]. The dependency of NS errors in EW image location is weak [Fig. 14(b)]. The NS error difference is only $<0.5\mu \mathrm{rad}$ at the east and west boundaries of an LWIR scene. However, the EW error difference is about $12.1\mu \mathrm{rad}$ at the north and south boundaries of an LWIR scene [Fig. 14(c)]. There are two possible reasons for this effect: the first is the potential orthogonality issue, and the second is the impact of EW scale error [Fig. 14(a)] due to the unbalanced spatial distribution of GOES-17 Landsat chips [Fig. 2(b)]. A thorough analysis is needed to confirm which reason, or the combination of two reasons, led to this effect. Channel 16 of GOES-17 is not included due to poor performance.²⁰

We have repeated similar analysis for GOES-17 data acquired in June and September 2019 (not shown) and the same effect was observed with minor variations. Further study is needed to determine the causes of these phenomena.