Geostationary Operational Environmental Satellite-R Series Research Articles

Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5–15 min temporal resolution and 0.5–1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDF-Adjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave.

Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Characterization of the East—West Spatial Uniformity for GOES-16/17 ABI Bands Using the Moon

The Advanced Baseline Imager (ABI) is the primary instrument onboard the NOAA Geostationary Operational Environmental Satellite-R Series (GOES-R) satellites, providing continuous weather imagery over the vast area in the Western Hemisphere. It is imperative to ensure consistent calibration accuracy within the instrument’s field of regard (FOR). This paper characterized the spatial uniformity in the east–west (EW) direction for the six ABI visible and near-infrared (VNIR) bands of the first two GOES-R satellites, GOES-16 (G16) and GOES-17 (G17), using a special collection of lunar chasing images during their post-launch testing and post-launch product testing (PLT/PLPT) periods. The EW response versus scan-angle (RVS) is examined with the normalized lunar irradiance ratios at varying scan angles combined from multiple lunar-chasing events. The impacts of straylight from the Earth were found in some of the B01–B03 lunar images. The straylight, including those scattered into the spacelook scenes near the polar regions and those leaked into space near the Moon, can cause RVS variation up to 1% for B01 and to a lesser magnitude for the other two bands. Straylight correction algorithms are applied for the accurate ABI lunar image irradiance calculation. After the corrections, the RVS variation is reduced to less than 0.3% for all the VNIR bands of both G16/17 in full-disk (FD) images. Results of this study also confirm that the Global Space-based Inter-Calibration System (GSICS) Implementation of the ROLO (GIRO) model has high relative accuracy for the ABI VNIR bands when the lunar images are collected within a relatively short time. The method described in this paper can be applied to validate the EW spatial uniformity for imagers on other geostationary satellites, including the recently launched GOES-18 and the future GOES-U satellites.

Validation of the improved GOES-16 aerosol optical depth product over North America

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) every 5 min. However, the current version of the official GOES-16 ABI AOD product is affected by diurnally varying biases. An improved ABI AOD product based on an empirical bias correction algorithm has become available recently. This study aims to comprehensively validate this improved ABI AOD product over land and ocean. Three years of Aerosol Robotic Network (AERONET) ground-based measurements at 95 stations over North America are used for the validation. Results suggest the product performs moderately well with an overall R2 of 0.46, an MBE of 0.04, a slope of 0.97, and ∼70.0% retrievals falling within the expected error (EE) envelope. A slight overestimation is found at a majority of ground sites: ≈87% of those sites have MBE values between 0 and 0.1. The analysis shows that the retrievals perform better over water and forests, but perform poorly over urban areas, grasslands and shrublands, and is worst over mountains. Besides, the present results indicate that the combination of the retrieval and bias correction algorithms yields a systematic underestimation of AOD for coarse particles over croplands, as well as large uncertainties for smaller-size particles over shrublands. At seasonal scale, the AOD retrievals perform better in summer and autumn than in spring and winter. Despite the empirical bias correction algorithm, a systematic overestimation of 0.07 at 1700 UTC and 0.08 during 1000–1300 UTC still exist. The sampling rate of the top-2 quality AOD retrievals are 14%, 12%, 10% and 11% for the four seasons, respectively, which constitutes a significant reduction of valid data in comparison with the sampling rate of all four quality retrievals. The limited availability and accuracy of the top-2 bias-corrected ABI AOD product raises concerns about its application in air quality and energy-related applications, in particular. This study also suggests a few potential improvements to the aerosol retrieval algorithm.

Estimate of daytime single-layer cloud base height from advanced baseline imager measurements

Cloud base height (CBH) is an important parameter to describe cloud state and is highly related to the vertical motions in the atmosphere. CBH information is critical for both aviation safety and synoptic analysis. In this study, daytime CBH is estimated directly from Geostationary Operational Environmental Satellite-R Series (GOES-16) Advanced Baseline Imager (ABI) level 1b data and the European Centre for Medium-Range Weather Forecasts' (ECMWF) fifth generation reanalysis (ERA5) data using the Gradient Boosted Regression Trees (GBRT) machine learning technique. The CBH estimate algorithm, which is named as GETCBH, covers the same areal extent as the full disk of the ABI/GOES-16 and only for single-layer clouds. The 2-years of CBH measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite is used as the label (which is the true value/class of the model output for regression/classification problem in machine learning terminology). A quality flag algorithm using another machine learning technique, the Gradient Boosted Decision Trees machine learning technique is developed to provide a confidence level for the CBH estimate. The evaluations show an overall root mean square error (RMSE) of 1.87 km and Pearson's correlation coefficient (Pearson's r) of 0.92 before any quality control. After excluding CBH estimates with low confidence (19.2% of all samples), the RMSE is reduced to 1.14 km, Pearson's r increases to 0.97, and 96% of the estimates are within 2 km of the CALIOP results. By analyzing model bias and feature importance, cloud phase information has the biggest impact on the CBH estimate, although all input features have positive impact on the estimate accuracy. Limited by the penetrability of CALIOP, GETCBH is valid for clouds with COD < 8.5. The CBH estimates have reduced accuracy (Pearson's r of 0.88) for optically thin clouds (clouds with cloud optical depth [COD] < 0.1) where little cloud information is contained in the ABI measurements, as well as for optically thick clouds (clouds with COD ≥ 3) where a larger proportion of opaque clouds is excluded. Furthermore, for the GBTCBH model using 9 months of CloudSat measurements as label, the CBH estimates are improved with an RMSE of 1.41 km and Pearson's r of 0.92. In a case study of Hurricane Dorian, CBHs for most of the single-layer clouds are successfully estimated with small errors and flagged with high confidence, for both high and low clouds. Deep convective clouds and multi-layer clouds, both of which are not included in the training, are reasonably flagged as low confidence with large CBH estimate errors. In this particular case, 65% of cloudy pixels have CBH estimate with high confidence in the scene. Daytime CBH with high spatial (2 km) and temporal (10 min) resolution can be derived from ABI measurements using this methodology.

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

AbstractGross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite‐based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar‐orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R series (GOES‐R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light‐use efficiency model, (b) a linear relationship between the product of near‐infrared reflectance of vegetation and photosynthetically active radiation (LIN‐NIRvP) and EC tower GPP, and (c) a light response curve (LRC‐NIRvP) between NIRvP and EC GPP. The LRC‐NIRvP achieved the lowest mean absolute error for winter (2 µmol CO2 m−2 s−1), spring (2.51 µmol CO2 m−2 s−1), summer (1.43 µmol CO2 m−2 s−1), and fall (1.35 µmol CO2 m−2 s−1). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC‐NIRvP and the light‐use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

Hourly mapping of surface air temperature by blending geostationary datasets from the two-satellite system of GOES-R series

Spatio-temporally continuous surface air temperature (SAT) is a valuable input for many research fields such as environmental studies and hydrological models. Substantial efforts have been focused on mapping daily SAT using land surface temperature (LST) derived from polar-orbiting satellites such as NASA’s Terra and Aqua. However, reconstruction of SAT at very high temporal scales such as sub-daily or hourly was carried out in very few studies, most of which are based on the Meteosat Second Generation (MSG) satellites for small-scale areas with limited stations. In this study, we developed estimation schemes based on random forest for mapping hourly SAT over a large-scale region (CONUS) by blending LST datasets from the two-satellite system of the Geostationary Operational Environmental Satellite-R (GOES-R) Series with the aim of reconstructing the hourly SAT maps with spatially uniform resolutions of about 2 km over CONUS. Estimation schemes with different variables were compared to evaluate the contributions of reanalysis variables and the hour of day (HOD) variable to model predicative performance, and the impacts of station density on model performance were also explored. We found that the model scheme with reanalysis variables and the scheme with HOD achieved higher predicative performance with the mean RMSE of 2.0 K and 2.5 K, respectively. Systematic predictive errors across the synoptic hours of a diurnal cycle are significantly reduced when considering HOD in the modeling of hourly SAT. The model scheme considering both reanalysis variables and HOD achieved the highest performance with a mean RMSE of 1.9 K. We observed that the influence of station density on model performance is related to types of cross-validation and model schemes, and that the uncertainty levels associated with the evaluation of predicative performance for the model schemes decrease with station density, which increases our confidence in the evaluation results for the schemes. Therefore, the mapping accuracy of hourly SAT over the large-scale area CONUS could be conservatively estimated to be 1.9–2.5 K using site-based cross-validation. We expect that this study will facilitate the further mapping of SAT at high-temporal scales over large-scale areas based on abundant and sustained observations from global systems of meteorological geostationary satellites.

Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.

Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data

Incident photosynthetically active radiation (PAR) is a key parameter in plant physiological, biological and physical process-based terrestrial ecosystem models. Deriving highly accurate and spatially continuous PAR from remote sensing data can fill in the gaps of insufficient ground-based measurements. In this study, we provide an efficient approach for estimating PAR by employing look-up table (LUT) method using observations from both MODIS and GOES-16. The GOES-16 is the first satellite of the new-generation US Geostationary Operational Environmental Satellite R-series (GOES-R), which can capture dynamic changes in the atmosphere every 5 min. First, the diurnal variation of surface bidirectional reflectance is characterized assuming that the surface characteristics and solar zenith angles in tropical and middle latitudes at a given time of day do not change dramatically within a month. Next, the aerosol optical depth (AOD) and cloud optical depth (COD) are retrieved from the GOES-16 visible observations under both clear-sky and cloudy conditions. Finally, PAR is estimated via the retrieved AOD and COD using the LUT method and a MODIS-based surface albedo product. The estimated PAR was compared against ground-based measurements collected from seven SURFace RADiation budget network (SURFRAD) sites and 23 National Ecological Observatory Network (NEON) sites at instantaneous, hourly and daily scales. The instantaneous PAR validation results achieved R2 values of 0.97 and 0.82, and root mean square errors (RMSEs) of 18.1 and 51.1 W m‐2, for clear and cloudy skies, respectively. The RMSEs of the hourly averaged PAR were reduced to 16.8 W m−2 and 40.4 W m−2, respectively, for clear-sky and cloudy conditions. The daily mean PAR evaluation for all sky conditions shows an overall mean bias error (MBE) of 0.97 W m−2 and an RMSE of 10.5 W m−2. Compared with other global PAR datasets, the estimated PAR values in this study have lower RMSEs and higher spatial-temporal resolution. Uncertainties in the retrieved PAR caused by topography and solar-cloud-geometry effects (SCGE) can be reduced by spatial and temporal averaging. Based on an evaluation of retrievals at different spatial-temporal scales, we recommend that a 20 × 20 km2 domain-averaged PAR be utilized for instantaneous PAR validation, whereas hourly and daily averaged PAR values are nearly independent of spatial scale.

Estimation of Land Surface Incident Shortwave Radiation From Geostationary Advanced Himawari Imager and Advanced Baseline Imager Observations Using an Optimization Method

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data [1] and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an R² of 0.93, bias of 0.52 W/m², and RMSE of 106.52 W/m² for instantaneous estimates; an R² of 0.95, bias of -0.12 W/m², and RMSE of 22.49 W/m² for daily mean ISR; and a bias of -0.18 W/m² and RMSE of 7.72 W/m² for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an R² value of 0.93, bias of 8.71 W/m², and RMSE of 102.30 W/m² for instantaneous estimates; an R² of 0.95, bias of -2.38 W/m², and RMSE of 27.17 W/m² for daily mean ISR; and a bias of 1.40 W/m² and RMSE of 14.75 W/m² for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

Geostationary Operational Environmental Satellite-R series advanced baseline imagery artifacts

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion. Yet, there are rare times when visual artifacts, or anomalies, occur. Our study highlights and explains a number of these artifacts, some of which are traditional imagery defects for imagers such as striping and stray light, and colorfully named artifacts such as “caterpillar tracks” and “shark fins.” In addition, multiple resources are presented for more information about image quality and near-real-time image monitoring.

Consistent retrieval of multiple parameters from GOES-R top of atmosphere reflectance data

ABSTRACT Current remote sensing products are mainly generated from polar-orbiting satellite data using parameter-specific algorithms. These products lack physical consistency and cannot accurately characterize intra-day variations of parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR) and surface albedo. In this study, a multi-parameter consistent retrieval method is proposed to simultaneously retrieve aerosol optical depth (AOD), leaf area index (LAI), photosynthetically active radiation (PAR), FAPAR, surface albedo, and incident shortwave radiation (ISR) from top of atmosphere (TOA) reflectance data acquired by the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite-R series (GOES-R). The retrieved parameter values were evaluated through comparisons with corresponding Moderate Resolution Imaging Spectroradiometer (MODIS), the second version of the Geoland2 (GEOV2), and GOES-R products and ground measurements over five surface radiation budget network (SURFRAD) sites with different vegetation types. The results demonstrate that the retrieved AOD, PAR, ISR, and surface albedo values have consistent intra-day variations with the ground measurements, and the retrieved parameter values achieve good performance against the ground measurements for all the five SURFRAD sites. The root mean square errors of the retrieved AOD, shortwave albedo, ISR, and PAR values against the ground measurements are 0.071, 0.032, 50.943 W m–2, and 27.975 W m–2, respectively.

Preliminary detection efficiency and false alarm rate assessment of the Geostationary Lightning Mapper on the GOES-16 satellite

The Geostationary Lightning Mapper (GLM) is a new geostationary lightning detection and location instrument, developed for the current generation of Geostationary Operational Environmental Satellite-R Series (GOES-R, S, T, U) satellites. Detection efficiency (DE) and false alarm rate (FAR) are evaluations of what an instrument detects and what it misses, compared with other well-characterized measurements. These are critical parameters to be assessed for the GLM. We now have enough data to produce preliminary DE and FAR assessments of the prelaunch predicted DE and FAR performance specifications for GLM. However, one of the challenges of doing this assessment is that all available ground truth data sets have much smaller coverage, and in some places, lower DE than GLM. In an attempt to overcome these limitations, we have created a “virtual” lightning network by clustering flashes from several different ground-based networks, using a technique similar to the clustering of GLM pixels. All ground truth sources that are within 330 ms and 16.5 km of any other ground truth source are added to the current cluster. Treating this as a single, “virtual” network, we calculated the relative DE and FAR of GLM. We found that the GLM easily meets the DE spec of better than 70% averaged across the whole field-of-view, over 24 h. It meets the FAR spec in most of CONUS, and we suspect it will improve with updated processing software.

GOES-R series image navigation and registration performance assessment tool set

An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.

GOES-16 Advanced Baseline Imager visible near-infrared channel low-light signal-to-noise ratio

Quantifying Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) Channel (Ch) 2 low-light signal-to-noise ratio (SNR) is a core ABI postlaunch product test to validate prelaunch performance expectations of this parameter. For GOES-16, an SNR analysis has been performed to accomplish this goal using ABI Mode-3 MESO visible near-infrared (VNIR) channel L1b radiance product images taken every 30 s between 17:00 UTC and 17:15 UTC on May 23, 2017. Scene pixel data used in the analysis are first screened to ensure they meet the low-light criteria and to minimize scene temporal variability associated with meteorological evolution and geolocation uncertainty in highly inhomogeneous scenes. After such screening, image-to-image radiance time difference statistics are compiled and analyzed to estimate instrument SNR. Based on GOES-R ABI vendor prelaunch testing, minimum and mean expected ABI Ch 2 postlaunch SNR performance at 5% albedo was estimated to be 44.2 and 64.5, respectively. Meanwhile, the SNR computed in this analysis is found to be about 57 ± 8. Thus, low-light SNR for ABI Ch 2 is shown to meet minimum user expectations. The analysis for the other five GOES-16 ABI VNIR channels is also shown to contrast the SNR for these channels.

GOES-16 Advanced Baseline Imager instrument performance monitor

Time series generated from NOAA operational satellite sensor temperature, noise, and calibration parameter statistics is a critical science analysis tool to trend instrument performance and detect and resolve on-orbit anomalies. Establishing this capability entails ingesting instrument engineering, housekeeping, and calibration data; performing statistics on them; and then storing and providing the resultant data and/or plots. For instruments with relatively small amounts of input and output data, this is a relatively easy task. For the NOAA Geostationary Operational Environmental Satellite R-Series Advanced Baseline Imager (ABI)—with three times more spectral information, four times the spatial resolution, and more than five times faster temporal coverage than previous GOES—instrument performance monitoring can be extremely complex because of the relatively large data volumes and number of parameters. Also of difficulty is that software and computing architecture needed to build such a system is usually proprietary and not openly documented. In order to fill this gap, we focus on the concept of operations and the results associated with the ABI instrument performance monitor. This monitoring system has proven to be extremely valuable in tracking instrument stability and detecting and performing initial diagnosis of ABI anomalies.

A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records

Satellite-based active fire data provide indispensable information for monitoring global fire activity and understanding its impacts on climate and air quality. Yet the limited spatiotemporal sampling capacities of current satellites result in considerable uncertainties in fire observation and emissions estimation. The mitigation of these uncertainties mainly relies on new remote-sensing technology. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R (GOES-R) Series observes fires across North and South Americas at an unprecedentedly spatiotemporal resolution of nominal 2 km every 5–15 min. This study evaluated the GOES-16 (the first GOES-R satellite) ABI active fire product using active fire data derived from the 30-m Landsat-8 and the 375-m and 750-m Visible Infrared Imaging Radiometer Suite (VIIRS), and ground-based burning data across the southeastern Conterminous United States (CONUS) during the 2018 peak fire season. Specifically, we characterized the overall fire detection performance of the ABI active fire detections, estimated omission and commission errors, and evaluated ABI fire radiative power (FRP). The results showed that the ABI fire detection probability and its omission and commission errors were highly related to fire size and temporal period. ABI detection probability was higher than 95% for the fire pixel that contained over 114 Landsat-8 (30 m) fire detections or 11 VIIRS (375 m) detections. During a period of ±8 h, ABI detected 19% and 29% more fires observed by Landsat-8 and 375-m VIIRS, respectively. Correspondingly, the omission error could reduce by up to 33%. Further, ABI was able to detect 6–22% and 31–42% more ground-recorded fires than VIIRS in Georgia and Florida States, respectively, but ABI still missed many very small fires because ABI was hard to detect fires smaller than ~34.5 MW. Additionally, compared with 750-m VIIRS FRP, ABI FRP was ~30–50% larger in individual fire events but was overall similar at a regional scale.

A Demonstration of Modern Geostationary and Polar-Orbiting Satellite Products for the Identification and Tracking of Elevated Mixed Layers

The elevated mixed layer (EML) can be an important aspect for severe thunderstorm forecasting. Because its thermodynamic characteristics vary as it moves eastward, tracking the EML is a crucial part of the forecasting process, something that previously has been quite challenging owing to the limited spatial and temporal resolution of observed soundings and numerical weather prediction (NWP) output. New satellite capabilities allow for improved monitoring/tracking of the EML. These include the 7.34-µm band on the Geostationary Operational Environmental Satellite-R series, as well as microwave instruments on polar-orbiting satellites used in the advected layer precipitable water product. Herein it is demonstrated—using several case studies—how using a combination of these products, in tandem with sounding data and NWP output, allows the forecaster to efficiently monitor the EML at greater spatial and temporal resolutions.

Normalized Projection Models for Geostationary Remote Sensing Satellite: A Comprehensive Comparative Analysis (January 2019)

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R.