Advanced Baseline Imager Research Articles

Forecast Applications of GLM Gridded Products: A Data Fusion Perspective

Abstract The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including flash extent density (FED), minimum flash area (MFA), and total optical energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April to 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210–230 K and isothermal reflectivity at −10°C of 20–30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts. Significance Statement The Geostationary Lightning Mapper (GLM) is a lightning sensor on the current generation of U.S. weather satellites. This research shows how data from the space-based lightning sensor can be combined with radar, satellite imagery, and ground-based lightning networks to improve how forecasters monitor thunderstorms and issue warnings for severe weather. The rate of GLM flashes detected and the area they cover correspond well with radar and satellite signatures, especially in cases of intense and severe thunderstorms. When the GLM observes the same thunderstorm from the GOES-East and GOES-West satellites, the optical energy (brightness) of the flashes may help forecasters interpret the types of flashes observed from each sensor.

Evaluation of cloud-type impact on performance of the GL model version 1.2 for estimation of solar irradiance

ABSTRACT This work evaluates the quality of the satellite-based GLobal radiation model version 1.2 (GL1.2) estimates for four cloud classes. The GL1.2 calculates the global solar irradiance at ground level using images from a single visible band channel (VIS) of the Geostationary Operational Environmental Satellite – GOES (GOES-East). The model´s performance was assessed by comparing hourly mean GL1.2 values with ground-based hourly measurements from the Brazilian National Institute of Meteorology (INMET, 354 automatic weather stations), for the entire year 2017. A satellite-based cloud classifier was adopted to discriminate the datasets according to prevailing cloud conditions (cumulus, stratus, cirrus and multilayer), but the clear sky behaviour was presented. The results were analysed for the five Brazilian regions. In the first analysis, we selected days with a predominance for five stations. It was found that the diurnal cycle was well reproduced. Then the regional investigation for the cloud types reveals that the best results are found for the Center West region over multilayer cloud (mean bias error, MBEannual = -2 ± 91 Wm−2 and root mean squared error, RMSE = 91 Wm−2), while the worst ones are in the North over cumulus fields (MBEannual = 101.5 ± 136.4 Wm−2). When considering all cloud types, the MBEannual is lower than 5 Wm−2 for the Northeast, Southeast and South regions, but it reaches 101 Wm−2 in the North. It is noteworthy that winter has the highest MBE in all classes analysed in the North, as well as cirrus situations in other regions. Although the inhomogeneities of cumulus and the semitransparent cirrus clouds tend to propagate errors to the model, the quality of GL1.2 data has a high degree of agreement with the observations. Improvements including an updated monthly minimum reflectance (R min) and water vapour column (H2Ovapour), and a better spatial resolution of the Advanced Baseline Imager (ABI) of GOES-16 (ABI/GOES-16) VIS imagery will allow refinement, especially for cumulus clouds.

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Abstract Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a machine learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly desirable synthetic PMW product with nearly continuous spatiotemporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian deep learning is employed to generate synthetic Global Precipitation Measurement (GPM) Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic residual networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatiotemporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e., uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.

The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States

Abstract This paper presents the Thunderstorm Nowcasting Tool (ThunderCast), a 24-h, year-round model for predicting the location of convection that is likely to initiate or remain a thunderstorm in the next 0–60 min in the continental United States, adapted from existing deep learning convection applications. ThunderCast utilizes a U-Net convolutional neural network for semantic segmentation trained on 320 km × 320 km data patches with four inputs and one target dataset. The inputs are satellite bands from the Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) in the visible, shortwave infrared, and longwave infrared spectra, and the target is Multi-Radar Multi-Sensor (MRMS) radar reflectivity at the −10°C isotherm in the atmosphere. On a pixel-by-pixel basis, ThunderCast has high accuracy, recall, and specificity but is subject to false-positive predictions resulting in low precision. However, the number of false positives decreases when buffering the target values with a 15 km × 15 km centered window, indicating ThunderCast’s predictions are useful within a buffered area. To demonstrate the initial prediction capabilities of ThunderCast, three case studies are presented: a mesoscale convective vortex, sea-breeze convection, and monsoonal convection in the southwestern United States. The case studies illustrate that the ThunderCast model effectively nowcasts the location of newly initiated and ongoing active convection, within the next 60 min, under a variety of geographical and meteorological conditions. Significance Statement In this research, a machine learning model is developed for short-term (0–60 min) forecasting of thunderstorms in the continental United States using geostationary satellite imagery as inputs for predicting active convection based on radar thresholds. Pending additional testing, the model may be able to provide decision-support services for thunderstorm forecasting. The case studies presented here indicate the model is able to nowcast convective initiation with 5–35 min of lead time in areas without radar coverage and anticipate future locations of storms without additional environmental context.

Assimilation of GOES-16 ABI All-Sky Radiance Observations in RRFS Using EnVar: Methodology, System Development, and Impacts for a Severe Convective Event

Abstract The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events.

Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

Do Optically Denser Trade‐Wind Cumuli Live Longer?

AbstractThis study investigates the lifetime and temporal evolution of physical properties of trade‐wind cumuli based on tracking individual clouds in observations with the Advanced Baseline Imager aboard the geostationary GOES‐16 satellite during the “ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte” (EUREC4A) campaign east of Barbados in winter 2020. A first application of our upgraded cloud‐tracking toolbox to measurements with high spatio‐temporal resolution (2 × 2 km2 and 1 min) provides probability density functions of lifetime and area of clouds that develop as a consequence of meso‐to‐synoptic scale motions. By separately considering clouds that exist during daytime and live in distinct lifetime intervals, we find that shallow marine cumuli live longer when they cover a larger surface area and show higher cloud optical thickness (COT). Besides the effect of COT, the scale of the atmospheric motions with which the clouds interact is also critical to their lifetime.

Comparison between deep learning architectures for the 1 km, 10/15-min estimation of downward shortwave radiation from AHI and ABI

The retrieval of downward shortwave radiation (DSR) with high spatiotemporal resolution and short latency is critical. It is the fundamental driving force of surface energy, carbon, and hydrological circulations, and a key energy source for photovoltaic electricity. However, existing methods face significant challenges owing to cloud heterogeneity and their reliance on other satellite-derived products, which hinder the retrieval of accurate and timely DSR with high spatiotemporal resolution. In addition to the spectral features used in traditional approaches, deep learning (DL) can incorporate the spatial and temporal features of satellite data. This study developed and compared three DL methods, namely the DenseNet, the bidirectional gated recurrent unit without surface albedo as inputs (BiGRUnor), and the convolutional neural network with gated recurrent unit without surface albedo as inputs (CNNGRUnor). These methods were used to estimate DSR at 1 km and 10/15 min resolutions directly from top-of-atmosphere reflectance over the Advanced Himawari Imager (AHI) onboard Himawari-8 and the Advanced Baseline Imager (ABI) onboard GOES-16 coverage, achieving high accuracies. The instantaneous root mean square error (RMSE) and relative RMSE for the three models were 68.4 (16.1%), 69.4 (16.3%), and 67.1 (15.7%) W/m2, respectively, which are lower than the baseline machine learning method, the multilayer perceptron model (MLP), with RMSE at 76.8 W/m2 (18.0%). Hourly accuracies for the three DL methods were 58.6 (14.1%), 57.8 (14.0%), and 57.3 (13.8%) W/m2, which are within the DSR RMSEs that we estimated for existing datasets of the Earth's Radiant Energy System (CERES) (88.8 W/m2, 21.4%) and GeoNEX (77.8 W/m2, 18.8%). The study illustrates that DL models that incorporate temporal information can eliminate the need for surface albedo as an input, which is crucial for timely monitoring and nowcasting of DSR. Incorporating spatial information can enhance retrieval accuracy in overcast conditions, and incorporating infrared bands can further improve the accuracy of DSR estimation.

Monitoring the Mauna Loa (Hawaii) eruption of November–December 2022 from space: Results from GOES-R, Sentinel-2 and Landsat-8/9 observations

Mauna Loa, one of the most actives volcanoes on Earth, is a shield volcano, located on the Island of Hawaii (USA). On 27 November 2022, after about 38 years of quiescence, a new eruptive activity took place at the Moku‘āweoweocaldera, continuing in the following days (i.e. until 10 December) from the fissure vents opening on the Northeast Rift Zone.In this work, we investigate the Mauna Loa November − December 2022 eruption from space, integrating the information from different satellite sensors. The analysis of short-wave infrared (SWIR) data, at 10 min temporal resolution, from the Advanced Baseline Imager (ABI), aboard the Geostationary Operational Environmental Satellites − R series (GOES-R), performed through the Normalised Hotspot Indices (NHI), indicates that the Mauna Loa eruption started on 27 November in between 23:10–23:20 LT (28 November at 09:10–09:20 UTC). The same analysis shows the increase of thermal activity and its progressive reduction from the early morning of 28 November, in agreement with the eruption migration from the summit caldera to the Northeast Rift Zone. By analysing the second phase of eruption through SWIR data from the Multispectral Instrument (MSI) and Operational Land Imager (OLI), respectively aboard Sentinel-2 and Landsat 8/9 satellites, we estimated a maximum lava flow length of 17 km. Moreover, we retrieved values of the volcanic radiative power (VRP) up to 65 GW, and a time-averaged discharge rate (TADR) of ∼1000 (±500) m3/s. These results show that SWIR observations, at different spatial and temporal resolution, may give an important contribution to the monitoring, mapping and characterisation of intense lava effusions.

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.

Multispectral Satellite Imagery Products for Fire Weather Applications

Abstract Two multispectral satellite imagery products are presented that were developed for use within the fire management community. These products, which take the form of false color red–green–blue composites, were designed to aid fire detection and characterization, and for assessment of the environment surrounding a fire. The first, named the Fire Temperature RGB, uses spectral channels near 1.6, 2.2, and 3.9 μm for fire detection and rapid assessment of the range of fire intensity through intuitive coloration. The second, named the Day Fire RGB, uses spectral channels near 0.64, 0.86, and 3.9 μm for rapid scene assessment. The 0.64 μm channel provides information on smoke, the 0.86 μm channel provides information on vegetation health and burn scars, and the 3.9 μm channel provides active fire detections. Examples of these red–green–blue composite images developed from observations collected by three operational satellite imagers (VIIRS on the polar-orbiting platform and the Advanced Baseline Imager and Advanced Himawari Imager on the geostationary platform) demonstrate that both red–green–blue composites are useful for fire detection and contain valuable information that is not present within operational fire detection algorithms. In particular, it is shown that Fire Temperature RGB and Day Fire RGB images from VIIRS have similar utility for fire detection as the operational VIIRS Active Fire products, with the added benefit that the imagery provides context for more than just the fires themselves. Significance Statement The current generation of operational polar-orbiting weather satellites that began with the launch of Suomi NPP offers new capabilities with regard to fire detection and monitoring. In particular, false color red–green–blue composite imagery is now being used by fire managers, incident meteorologists, and others in the fire management community to visualize a fire’s behavior and the context in which it occurs. This paper outlines two of these red–green–blue composites that have gained widespread use throughout the U.S. National Weather Service and the Alaska Fire Service. These red–green–blue composites have been applied to the current generation of geostationary and polar-orbiting satellites to great effect and have changed how incident management teams respond to wildland fires.

An Enhanced Storm Warning and Nowcasting Model in Pre-Convection Environments

A storm tracking and nowcasting model was developed for the contiguous US (CONUS) by combining observations from the advanced baseline imager (ABI) and numerical weather prediction (NWP) short-range forecast data, along with the precipitation rate from CMORPH (the Climate Prediction Center morphing technique). A random forest based model was adopted by using the maximum precipitation rate as the benchmark for convection intensity, with the location and time of storms optimized by using optical flow (OF) and continuous tracking. Comparative evaluations showed that the optimized models had higher accuracy for severe storms with areas equal to or larger than 5000 km2 over smaller samples, and loweraccuracy for cases smaller than 1000 km2, while models with sample-balancing applied showed higher possibilities of detection (PODs). A typical convective event from August 2019 was presented to illustrate the application of the nowcasting model on local severe storm (LSS) identification and warnings in the pre-convection stage; the model successfully provided warnings with a lead time of 1–2 h before heavy rainfall. Importance score analysis showed that the overall impact from ABI observations was much higher than that from NWP, with the brightness temperature difference between 6.2 and 10.3 microns ranking at the top in terms of feature importance.

A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model

Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multi-angle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60° can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG).

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.

Parallax Shift in GOES ABI Data

A parallax shift is a displacement in the apparent navigated position of a feature that arises because of its perspective from the viewing platform and is also a function of the feature height. For Geostationary Operational Environmental Satellite (GOES) imagery, this shift is especially apparent away from the satellite subpoint. Users should understand the degree of this shift when combining GOES Advanced Baseline Imager (ABI) imagery with other data, such as radar and lightning. However, it can be challenging, especially at spatial resolutions around the cloud/storm scale. This article explores parallax displacement for both uniform and computed cloud-top heights. Parallax shift will be shown using two case studies. The first case is from 7 September 2021, in which northern Illinois hailstorms are examined using ground-based Level II NEXRAD radar data, GOES-16 ABI imagery, and Geostationary Lightning Mapper data. The second case, on 9 April 2021, examines an eruption of the La Soufrière volcano on St. Vincent from the differing perspectives of GOES16 and -17. The discussion of these cases will show how parallax is an apparent displacement that will vary depending on what satellites are used for observation, where the phenomenon is with respect to the satellite, and the height of the phenomenon being analyzed. Newer satellite instruments with finer spatial resolutions and improved georeferencing will maximize data usability at more extreme angles and require users to account for the accompanying enhanced parallax shift. Even at lesser angles, parallax displacement is an important consideration for many meteorological and other applications.

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.

Time-Dependent Systematic Biases in Inferring Ice Cloud Properties from Geostationary Satellite Observations

Geostationary satellite-based remote sensing is a powerful tool to observe and understand the spatiotemporal variation of cloud optical-microphysical properties and their climatologies. Solar reflectances measured from the Advanced Baseline Imager (ABI) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 correspond to different spatial pixel resolutions, from 0.5 km in a visible band, up to 2 km in infrared bands. For multi-band retrievals of cloud properties, reflectances with finer spatial resolution need to be resampled (averaged or sub-sampled) to match the coarsest resolution. Averaging all small pixels within a larger pixel footprint is more accurate but computationally demanding when the data volume is large. Thus, NOAA operational cloud products incorporate sub-sampling (selecting one high-resolution pixel) to resample the reflectance data, which could cause potential retrieval biases. In this study, we examine various error sources of retrieval biases of cloud optical thickness (COT) and cloud effective radius (CER) caused by sub-sampling, including the solar zenith angle, viewing zenith angle, pixel resolutions, and cloud types. CER retrievals from ice clouds based on sub-sampling have larger biases and uncertainties than COT retrievals. The relative error compared to pixel averaging is positive for clouds that have small COT or CER, and negative for clouds that have large COT or CER. The relative error of COT decreases as the pixel resolution becomes coarser. The COT retrieval biases are attributed mainly to cirrus and cirrostratus clouds, while the largest biases of CER retrievals are associated with cirrus clouds.

Radiance consistency and deviation characteristics in the regions of overlap between three new-generation GEO meteorological satellites

ABSTRACT The combination of multi-source Geostationary Earth Orbit (GEO) satellites can realize simultaneous global observation with high frequency, which is significant to fine weather forecast and global climate change research. Here the radiance consistency and deviation characterization in the overlapping regions are analysed for three new generation GEO satellites, including the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite, the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-17 (GOES-17) and the Advanced Geostationary Radiation Imager (AGRI) onboard FengYun satellite-4A (FY-4A). The result shows that the radiance has higher consistency for visible and near-infrared (VNIR) channels with correlation coefficient r > 0.98 than infrared (IR) channels with r > 0.90 in the overlapping regions of different GEO satellites. For FY-4A & Himawari-8, the average percentage deviation (APD) of VNIR channels is 5.54%–9.03%. The brightness temperature (BT) bias of IR channels is 1.16 K-2.50 K. For GOES-17 & Himawari-8, the APD of VNIR channels is 4.48%–6.71%, and the BT bias of IR channels is 0.96 K −2.76 K. Solar-viewing geometries significantly influence the radiance deviation in the over regions. The future work is to design a radiance fusion method and achieve cloud detection in ample space with high frequency by combining multi-source GEO satellites.

Monitoring the 2021 Cumbre Vieja Volcanic Eruption Using Satellite Multisensor Data Fusion

AbstractMultisensor satellite data fusion merges measurements or products from imaging and sounding instruments with different spatial, spectral, and temporal resolution to obtain more comprehensive information about key atmospheric variables and processes. Here, data from low Earth and geostationary orbits, such as the Joint Polar Satellite Systems and Geostationary Operational Environmental Satellites platforms, respectively, are integrated using spatial‐temporal fusion to enhance the detection of trace gas emissions from volcanoes. Not only does this yield trace gas information with improved spatial detail but, more importantly, the fusion product is also made available at significantly increased temporal resolution to help monitor the variable dispersion of trace gas emissions. The emission and dispersion of volcanic sulfur dioxide and ash plumes from the Cumbre Vieja volcano (Canary Islands, Spain) eruptions in October 2021 are studied through the synergistic exploitation of measurements and products from the Visible Infrared Imaging Radiometer Suite, the Cross‐track Infrared Sounder, the TROPOspheric Monitoring Instrument, and the Advanced Baseline Imager. Fusion results show increased spatial and temporal detail and describe evolution and directionality of the volcanic ash plumes; the potential benefits range from improved air quality monitoring to better guidance from aircraft safety systems.