Geostationary Operational Environmental Satellite-R Research Articles

Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data

ABSTRACT Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America.

One‐Minute Resolution GOES‐R Observations of Lamb and Gravity Waves Triggered by the Hunga Tonga‐Hunga Ha'apai Eruptions on 15 January 2022

AbstractWe use high temporal‐resolution mesoscale imagery from the Geostationary Operational Environmental Satellite‐R (GOES‐R) series to track the Lamb and gravity waves generated by the 15 January 2022 Hunga Tonga‐Hunga Ha'apai eruption. The 1‐min cadence of these limited area (∼1,000×1,000 km2) brightness temperatures ensures an order of magnitude better temporal sampling than full‐disk imagery available at 10‐min or 15‐min cadence. The wave patterns are visualized in brightness temperature image differences, which represent the time derivative of the full waveform with the level of temporal aliasing being determined by the imaging cadence. Consequently, the mesoscale data highlight short‐period variations, while the full‐disk data capture the long‐period wave packet envelope. The full temperature anomaly waveform, however, can be reconstructed reasonably well from the mesoscale waveform derivatives. The reconstructed temperature anomaly waveform essentially traces the surface pressure anomaly waveform. The 1‐min imagery reveals waves with ∼40–80 km wavelengths, which trail the primary Lamb pulse emitted at ∼04:29 UTC. Their estimated propagation speed is ∼315 ± 15 m s−1, resulting in typical periods of 2.1–4.2 min. Weaker Lamb waves were also generated by the last major eruption at ∼08:40–08:45 UTC, which were, however, only identified in the near field but not in the far field. We also noted wind effects such as mean flow advection in the propagation of concentric gravity wave rings and observed gravity waves traveling near their theoretical maximum speed.

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.

Optimized image registration algorithm at subpixel level for Geostationary Operational Environmental Satellite-R Advanced Baseline Imager navigation and registration performance assessment

The image navigation and registration (INR) performance assessment for the Geostationary Operational Environmental Satellite-R (GOES-R) Advanced Baseline Imager (ABI) is an image registration process at the subpixel level, which involves the registration of images from GOES-R ABI with corresponding images from Landsat at the same scene. We present an optimized image registration algorithm for GOES-R ABI INR performance assessment (GAIPA): a gradient descent algorithm with a Sobel edge-enhancement procedure and a normalized lookup table (NLUT) image transformation (GDSN), which is an area-based image registration approach with Pearson correlation as the similarity metric. Sobel edge enhancements increase the sensitivity of Pearson correlations that lead to much improved registration accuracy. NLUT transformations minimize radiance differences between GOES-R ABI images acquired at different times and Landsat reference images on image registration outcomes, which lead to improvements in correlation values and quality of image registration outputs. Evaluations of registration accuracy and quality metrics in GDSN show considerable improvements in computational efficiency, registration accuracy, and quality over the approach implemented on the image INR performance assessment tool set. Pearson correlations can be effective and robust similarity metrics in image registration at the subpixel level with implementing Sobel edge enhancement and NLUT transformation. The statistical analysis of the image registration outputs for GAIPA shows the new algorithm provides considerable improvements in reducing the uncertainties in the registration outputs.

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.

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.

Impact of Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data on Severe Convection Forecasts in a Warn-on-Forecast System

AbstractThe Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM-derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the ensemble Kalman filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events: one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case it degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.

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.

GeoColor: A Blending Technique for Satellite Imagery

AbstractValue-added imagery is a useful means of communicating multispectral environmental satellite radiometer data to the human analyst. The most effective techniques strike a balance between science and art. The science side requires engineering physical algorithms capable of distilling the complex scene into a reduced set of key parameters. The artistic side involves design and construction of visually intuitive displays that maximize information content within the product image. The utility of such imagery to human analysts depends on the extent to which parameters or features of interest are conveyed unambiguously. Here, we detail and demonstrate a dynamic blended imagery technique, based on spatially variant transparency factors whose values are tied to algorithmically isolated parameters. The technique enables seamless display of multivariate information, and is applicable to any imaging system based on red–green–blue composites. We illustrate this technique in the context of GeoColor—an application of the Geostationary Operational Environmental Satellite R (GOES-R) series Advanced Baseline Imager (ABI) supporting operational forecasting and used widely in public communication of weather information.

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.

Arcjet Thruster Influence on Local Magnetic Field Measurements from a Geostationary Satellite

The 16th Geostationary Operational Environmental Satellite (GOES-16) is the first satellite launched from National Oceanic and Atmospheric Administration’s (NOAA’s) next-generation Geostationary Operational Environmental Satellite-R series. Observations from GOES-16 are used by the NOAA to provide terrestrial and space weather forecasts, warnings, and alerts. The magnetometer (referred to as MAG) on GOES-16, which monitors the geomagnetic field, consists of two triaxial fluxgate magnetometers mounted on an 8.5 m boom. The spacecraft uses hydrazine arcjet thrusters for station keeping; when the arcjets are fired, a large magnetic field disturbance contaminates the MAG data. Here, the characteristics of the arcjet contamination are described and possible physical mechanisms for the magnetic field disturbance are discussed. The arcjets operate for approximately every four days, and the contamination significantly impacts the utility of the MAG data for space weather operations. The arcjets create step changes in the geomagnetic field observations of up to about 20 nT ( of the nominal field strength). Two separate physical mechanisms are suggested: a large-scale diamagnetic effect caused by the dense plasma in the thruster plume, and a local current source caused by plasma pressure gradients near the thruster.