Geostationary Operational Environmental Satellite Research Articles

Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign

Abstract. The high temporal resolution of thermal infrared imagery from the Geostationary Operational Environmental Satellites R-series (GOES-R) presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow- and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation), respectively. As part of the NASA SnowEx field campaign in February 2020, coincident brightness temperature observations from ground-based and airborne IR sensors were collected to compare with those from the geostationary satellites. Observations over the course of 2 cloud-free days spanned the entire study site. The brightness temperature observations from each dataset were compared to find their relative differences and how those differences may have varied over time and/or as a function of varying forest cover across the study area. GOES-16 and -17 brightness temperatures were found to match the diurnal cycle and temperature range within ∼ 1 h and ± 3 K of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun–satellite phase angle was near its daily minimum. The phase angle, the angle between the direction of incoming solar illumination and the direction from which the satellite is viewing, reached daily minimums in the morning for GOES-16 and afternoon for GOES-17. In morning observations, warm biases in GOES-16 brightness temperature were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R brightness temperatures or derived land surface temperatures (LSTs) over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.

Short-term Classification of Strong Solar Energetic Particle Events Using Multivariate Time-series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors, including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle data set covering solar cycles 22, 23, and 24. We develop a framework using time-series-based machine-learning (ML) models with the aim of developing robust short-term forecasts by classifying SEP events. For this purpose, we introduce an ensemble learning approach that merges the results from univariate time series of three proton channels (E ≥10, 50, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions and analyze their performance. We consider three models, namely, time series forest, supervised time series forest (STSF), and Bag-of-Symbolic Fourier Approximation Symbols. Our study also focuses on understanding and developing confidence in the predictive capabilities of our models. Therefore, we utilize multiple evaluation techniques and metrics. Based on that, we find STSF to perform well in all scenarios. The summary of metrics for the STSF model is as follows: the area under the ROC curve = 0.981, F 1-score = 0.960, true skill statistics = 0.919, Heidke skill score = 0.920, Gilbert skill score = 0.852, and Matthew’s correlation coefficient = 0.920. The Brier score loss of the STSF model is 0.077. This work lays the foundation for building near-real-time short-term SEP event predictions using robust ML methods.

The effects of changes in HITRAN and the water vapor continuum model on infrared radiative transfer calculations and remote sensing applications

This study examines the implications of recent updates to the High Resolution Transmission (HITRAN) database and the MT_CKD water vapor continuum model on infrared radiative transfer calculations and satellite remote sensing applications. Specifically, it assesses the impact of the latest versions of the HITRAN database (i.e. HITRAN2016 and HITRAN2020) and the MT_CKD WV continuum model (i.e. versions 3.2 and 4.1.1) on clear-sky infrared flux calculations and the simulation of satellite infrared channels, specifically those of the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI). The results indicate that updates to the MT_CKD model tend to reduce atmospheric opacity, while updates to the HITRAN database have the opposite effect. The most significant differences noted are attributed to updates in the MT_CKD, leading to an increase in upward flux of 0.13 Wm−2 for the TRO and a decrease in downward flux of 0.28 Wm−2 for the MLS profile. However, these impacts are not uniform across the spectrum, atmospheric height, or among different atmospheric profiles. Notably, changes in the WV continuum model have significant effects in the far-infrared region. The updates in HITRAN exhibit pronounced differences associated with changes in the spectroscopic parameters of H2O, CO2 and O3. The results of GOES’ ABI channels indicate that changes in HITRAN lead to an average brightness temperature decrease of 0.3 K for channel 12, which exceeds the channel’s sensitivity. This research underscores the importance of continuous updates and evaluations of spectroscopic databases and radiative transfer models to improve the accuracy of atmospheric remote sensing data.

Intersatellite Comparisons of GOES Magnetic Field Measurements

AbstractGOES‐16 and GOES‐17 are the first of NOAA's Geostationary Operational Environmental Satellite (GOES)‐R series of satellites. Each GOES‐R satellite has a magnetometer mounted on the end (outboard) and one part‐way down a long boom (inboard). This paper demonstrates the relative accuracy and stability of the measurements on a daily and long‐term basis. The GOES‐16 and GOES‐17 magnetic field observations from 2017 to 2020 have been compared to simultaneous magnetic field observations from each other and from the previous GOES‐NOP series satellites (GOES‐13, GOES‐14 and GOES‐15). These comparisons provide assessments of relative accuracy and stability. We use a field model to facilitate the inter‐satellite comparisons at different longitudes. GOES‐16 inboard and outboard magnetometers data suffer daily variations which cannot be explained by natural phenomena. Long‐term‐averaged GOES‐16 outboard (OB) data has daily variations of ±3 nT from average values with one‐sigma uncertainty of ±1.5 nT. Long‐term averaged GOES‐17OB magnetometer data have minimal daily variations. Daily average of the difference between the GOES‐16 outboard or GOES‐17 outboard measurements and the measurements made by another GOES satellite are computed. The long‐term averaged results show the GOES‐16OB and GOES‐17OB measurements have long‐term stability (±2 nT or less) and match measurements from magnetometers on other GOES within limits stated herein. The GOES‐17OB operational offset (zero field value) was refined using the GOES‐17 satellite rotated 180° about the Earth pointing axis (known as a yaw flip).

Association of land surface temperature anomalies from GOES/ABI, MSG/SEVIRI, and Himawari-8/AHI with land earthquakes between 2010 and 2021

Every year, earthquakes cause numerous casualties and billions of dollars in economic damages, and so far, it is impossible to predict them. Many researchers have studied the land surface temperature (LST) anomalies as promising potential earthquake precursors. In this work, more than 30,000 land earthquakes with a Magnitude (Mw) larger than 4 have been studied from 2010 to 2021. Global LST nighttime (0–6 am) data from Meteosat Second Generation (MSG), Himawari-8, and the Geostationary Operational Environmental Satellite (GOES) satellites have been used to evaluate the LST anomalies in the zones affected by earthquakes with spatial resolutions of 1, 2, and 4 km, respectively. The time series moving standard deviation (STD) and Interquartile (IQT) have been used to estimate the LST anomalies. The Confusion Matrix (CM), the receiver operating curve (ROC), and some other figures of merit are computed to assess the goodness of positive LST anomalies and detection thresholds as potential proxies of earthquake occurrence. A positive LST anomaly is typically observed a few days [1–7 d] before the earthquake’s occurrence, followed by a negative LST anomaly afterwards [1–3 d].

Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager

Abstract. Originally developed for the moderate resolution imaging spectroradiometer (MODIS) in polar, sun-synchronous low earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a surface reflectance parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD) retrievals from ABIE. The primary reason appears to be that the geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regard to solar angles coupled with fixed-view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parameterization of both red–SWIR and blue–red spectral relationships to include additional information. The revised red–SWIR SRP includes the solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue–red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of the DT AOD compared to the Aerosol Robotic Network (AERONET) AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03. The agreement between the DT and AERONET AOD is established through a regression slope of 1.06 and a y intercept of 0.01 with a correlation coefficient of 0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 % + 15 %) is notably increased from 54 % to 78 %.

A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images

Land surface temperature (LST) is a critical parameter that drives the response of a variety of ecosystems to environmental and climatic changes. The geostationary satellite brings unique opportunities to monitor the LST at a hemispheric scale with temporal resolutions of up to 5 min. However, the ultra-coarse spatial resolutions ranging from 2 km to 5 km limit its application at local spatial scales. Downscaling the geostationary satellite LST image with the high-resolution low-Earth-orbit satellite images is a cost-effective way to circumvent this dilemma. Yet, the big gap between the observation scales of these satellite data poses a challenge for accurate downscaling. To address this problem, we proposed a stepwise temperature unmixing (TUM) model called ‘UnmixGO’, which downscales hourly LST images of Geostationary Operational Environmental Satellites (GOES-R) from 2 km to 100 m resolution. The spatially adaptive endmembers and the constrained solution space of the TUM model keep the errors in downscaled LSTs from being over-amplified in the stepwise data treatment. We validated the algorithm in six experimental areas and at five flux tower sites across the contiguous United States, revealing that UnmixGO outperformed conventional methods in accuracy by 0.49 K and 1.11 K on average for downscaling the simulated and real GOES-R LST images, respectively. Furthermore, the technical framework employed by UnmixGO is compatible with multi-source satellite images, enhancing the added value of our study in a future where various remote sensing data is increasingly accessible.

Distribution of Seed Electron Phase Space Density Minima in Earth's Radiation Belts

AbstractWe conducted a statistical analysis of local phase space density (PSD) minima across a wide energy range (∼20 keVs to ∼10 MeV), using observations from the Van Allen Probes and the Geostationary Operational Environmental Satellite. We identified deepening minima in PSD profiles of multi‐MeV (∼5% occurrence) and of “seed” electrons (up to 15% occurrence, corresponding to ∼70–100 s keV) and compared their distribution with a 3D diffusion model using the Versatile Electron Radiation Belts (VERB) code. The comparison of the observed and modeled distributions suggests that the PSD minima of seed electrons are likely associated with hiss waves and the corresponding L‐shell dependent electron lifetimes. However, the observed distribution was not fully reproduced by the model, potentially indicating other fast loss mechanisms of seed electrons.

Assessment of U.S. Urban Surface Temperature using GOES-16 and 17 Data: Urban Heat Island and Temperature Inequality

Abstract This study utilizes hourly Land Surface Temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of Surface Urban Heat Island Intensity (SUHII) across 120 of the largest U.S. cities and its surroundings. Distinct patterns emerge in the classification of seasonal daytime and nighttime SUHII. Specifically, vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and non-urban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the Social Vulnerability Index (SVI) are found in high UI regions. This results in a discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Systematically tracking the hourly progression of large wildfires using GOES satellite observations

Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).

Uncovering the heterogeneity of a solar flare mechanism with mixture models

The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly being adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, similar patterns should be shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events.

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

The terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

Localized Magnetopause Erosion at Geosynchronous Orbit by Reconnection

AbstractThis study presents observations of magnetopause reconnection and erosion at geosynchronous orbit, utilizing in situ satellite measurements and remote sensing ground‐based instruments. During the main phase of a geomagnetic storm, Geostationary Operational Environmental Satellites (GOES) 15 was on the dawnside of the dayside magnetopause (10.6 MLT) and observed significant magnetopause erosion, while GOES 13, observing duskside (14.6 MLT), remained within the magnetosphere. Combined observations from the THEMIS satellites and Super Dual Auroral Radar Network radars verified that magnetopause erosion was primarily caused by reconnection. While various factors may contribute to asymmetric erosion, the observations suggest that the weak reconnection rate on the duskside can play a role in the formation of asymmetric magnetopause shape. This discrepancy in reconnection rate is associated with the presence of cold dense plasma on the duskside of the magnetosphere, which limits the reconnection rate by mass loading, resulting in more efficient magnetopause erosion on the dawnside.

A Compact THz Photometer for Solar Flare Burst Studies from Space

Recent solar flare observations reveal the presence of THz components having a few orders of magnitude higher amplitude than the Radio Frequency (RF) and microwave radiations in the solar flare spectrum. This feature makes it remarkably beneficial for the detection of solar flares of low magnitudes such as Geostationary Operational Environmental Satellite (GOES) Class C, and M flares. Hence, space agencies including National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) are gearing up with THz instruments for solar flare observations. In line with this, Laboratory for Electro-Optics Systems (LEOS) has completed the development of a photometer that operates in continuum THz band, from 1.5 to 15 THz. The entire electro-opto-mechanical design was carried out to realize a compact, light-weight and power-efficient photometer to meet the size, mass and power constraints posed by inherent resource limitations of experimental satellite platforms such as Nanosatellite, Microsatellite and Polar Satellite Launch Vehicle’s 4th Stage Orbital Platform (PS4-OP). A Cassegrain optical configuration was designed to collect the sun radiation with an aperture effective area of 0.0027 m2 with ceramic low pass filters and resonant mesh filter for selective perception of THz radiation, rejecting the huge background of visible and Near Infrared (NIR) with a spectral selectivity of 107:1. Sensitivity of the Photometer was estimated to be 41.5 K/mV in 1.5 to 15 THz band by calibration using a blackbody radiation source. The radiometric resolution of the payload is 125 Solar Flux Unit (SFU) (1 SFU=1×10-22 Wm-2Hz-1). The payload was successfully developed and space-qualified for its deployment in Nanosatellite or PS4-OP platform.

Resilience of an Urban Coastal Ecosystem in the Caribbean: A Remote Sensing Approach in Western Puerto Rico

Utilization of remote sensing-derived meteorological data is a valuable alternative for tropical insular territories such as Puerto Rico (PR). The study of ecosystem resilience in insular territories is an underdeveloped area of investigation. Little research has focused on studying how an ecosystem in PR responds to and recovers from unique meteorological events (e.g., hurricanes). This work aims to investigate how an ecosystem in Western Puerto Rico responds to extreme climate events and fluctuations, with a specific focus on evaluating its innate resilience. The Antillean islands in the Caribbean and Atlantic are vulnerable to intense weather phenomena, such as hurricanes. Due to the distinct tropical conditions inherent to this region, and the ongoing urban development of coastal areas, their ecosystems are constantly affected. Key indicators, including gross primary production (GPP), normalized difference vegetation index (NDVI), actual evapotranspiration (ET), and land surface temperature (LST), are examined to comprehend the interplay between these factors within the context of the Culebrinas River Watershed (CRW) ecosystem over the past decade during the peak of hurricane season. Data processing and analyses were performed on datasets provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 OLI TRIS, supplemented by information sourced from Puerto Rico Water and Energy Balance (PRWEB)—a dataset derived from Geostationary Operational Environmental Satellite (GOES) data. The findings revealed a complex interrelationship among atmospheric events and anthropogenic activities within the CRW, a region prone to recurrent atmospheric disruptions. NDVI and ET values from 2015 to 2019 showed the ecosystem’s capacity to recover after a prolonged drought period (2015) and Hurricanes Irma and Maria (2017). In 2015, the NDVI average was 0.79; after Hurricanes Irma and Maria in 2017, the NDVI dropped to 0.6, while in 2019, it had already increased to 0.8. Similarly, average ET values went from 3.2339 kg/m2/day in 2017 to 2.6513 kg/m2/day in 2018. Meanwhile, by 2019, the average ET was estimated to be 3.8105 kg/m2/day. Data geoprocessing of LST, NDVI, GPP, and ET, coupled with correlation analyses, revealed positive correlations among ET, NDVI, and GPP. Our results showed that areas with little anthropogenic impact displayed a more rapid and resilient restoration of the ecosystem. The spatial distribution of vegetation and impervious surfaces further highlights that areas closer to mountains have shown higher resilience while urban coastal areas have faced greater challenges in recovering from atmospheric events, thus showing the importance of preserving native vegetation, particularly mangroves, for long-term ecosystem stability. This study contributes to a deeper understanding of the dynamic interactions within urban coastal ecosystems in insular territories, emphasizing their resilience in the context of both natural atmospheric events and human activity. The insights gained from this research offer valuable guidance for managing and safeguarding ecosystems in similar regions characterized by their susceptibility to extreme weather phenomena.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm

Abstract. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere. The advanced baseline imagers (ABIs) on the GOES-16, GOES-17, and GOES-18 platforms are the first GOES-series imagers that meet the precision requirements for high-quality, aerosol-related research. We present MAGARA, a Multi-Angle Geostationary Aerosol Retrieval Algorithm, that leverages multi-angle ABI imagery to exploit the differences in autocorrelation timescales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level (up to 1 km) aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 min), fine- and coarse-mode aerosol particle properties at a daily cadence, and surface properties by combining the multi-angle radiances with robust surface characterization inherent to temporally tiled algorithms. We present three case studies, and because GOES-17 was not making observations for one case, we present this as a unique demonstration of the multi-angle algorithm using only a single ABI sensor. We also compare MAGARA retrievals of fine-mode (FM) aerosol optical depth (AOD), coarse-mode (CM) AOD, and single-scattering albedo (SSA) statistically, with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period, and against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD. For MAGARA vs. coincident AERONET over-land 500 nm fine-mode fraction and AOD>0.3, MAE=0.031, RMSE=0.100, and r=0.902, indicating good sensitivity to fine-mode fraction over land, especially for smoky regions. For bias-corrected MAGARA vs. coincident AERONET spectral single-scattering albedo with MAGARA AOD>0.5 (n=116), MAE=0.010, RMSE=0.015, and the correlation is 0.87. MAGARA performs best in regions where surface reflectance varies over long timescales with minimal clouds. This represents a large portion of the western half of the United States, much of north-central Africa and the Middle East, some of central Asia, and much of Australia. For these regions, aerosol type and aerosol loading on timescales as short as 10 min could allow for novel research into aerosol–cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.

Forecasting solar flares with a transformer network

Space weather phenomena, including solar flares and coronal mass ejections, have significant influence on Earth. These events can cause satellite orbital decay due to heat-induced atmospheric expansion, disruption of GPS navigation and telecommunications systems, damage to satellites, and widespread power blackouts. The potential of flares and associated events to damage technology and disrupt human activities motivates prediction development. We use Transformer networks to predict whether an active region (AR) will release a flare of a specific class within the next 24 h. Two cases are considered: ≥C-class and ≥M-class. For each prediction case, separate models are developed. We train the Transformer to use time-series data to classify 24- or 48-h sequences of data. The sequences consist of 18 physical parameters that characterize an AR from the Space-weather HMI Active Region Patches data product. Flare event information is obtained from the Geostationary Operational Environmental Satellite flare catalog. Our model outperforms a prior study that similarly used only 24 h of data for the ≥C-class case and performs slightly worse for the ≥M-class case. When compared to studies that used a larger time window or additional data such as flare history, results are comparable. Using less data is conducive to platforms with limited storage, on which we plan to eventually deploy this algorithm.

Predicting Solar Proton Events of Solar Cycles 22–24 Using GOES Proton and Soft-X-Ray Flux Features

Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.

Can We Intercalibrate Satellite Measurements by Means of Data Assimilation? An Attempt on LEO Satellites

AbstractLow Earth Orbit satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit that can observe all equatorial pitch‐angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a Data assimilation (DA) approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar‐orbiting Operational Environmental Satellites (POES) NOAA‐15,‐16,‐17,‐18,‐19, and MetOp‐02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites observations into 3‐D Versatile Electron Radiation Belt (VERB‐3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES data set and estimate the flux ratios at each time, location, and energy. From these ratios, we derive energy and L* dependent recalibration coefficients. To validate our results, we analyze on‐orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data‐assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of DA allows for improved statistics, as the possible comparisons are increased 10‐fold. Data‐assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large data sets using short periods of data.