Geostationary Research Articles

Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning

Airborne ground-level particulate matter of size <2.5 μm (PM2.5) is a potential health hazard, even with short-term exposure. To quantify the amount of ambient PM2.5, there have been many advances in the use of satellite observations in estimating PM2.5 concentrations over broader areas. Although hindcasts have successfully estimated PM2.5 concentrations at locations with no ground-based observations, nowcasts are crucial in preventing potential exposure to PM2.5 in real time. In this study, a near-real-time hourly PM2.5 prediction framework was designed using a geostationary earth orbit satellite, Geostationary Ocean Color Imager-II (GOCI-II) aerosol products, and random forest (RF) models to yield hourly PM2.5 predictions over East Asia 10 times daily at 2.5 km resolution. The framework includes an oversampling stage, which increases the number of infrequent PM2.5 occurrences. The algorithm was tested over a one-year period, during which predictions and observations were strongly correlated (R2 = 0.79) with a relative root mean square error of 41.99%. The results were comparable to or better than those of previous hindcast models. Uncertainties in prediction stemmed mainly from the number of valid predictions and the absolute volume of training samples. Due to the high temporal and spatial resolution, predicted PM2.5 concentrations successfully filled in regions with no ground-based stations. The products also identified concentration gradients within sub-districts, and their temporal changes during daytime. Prediction with a model with a training dataset from an hour ago still produced robust results. The products may be used in releasing health hazard alerts through real-time monitoring of air quality.

Optical Thin Films in Space Environment: Investigation of Proton Irradiation Damage.

The present work reports a systematic study of the potential degradation of metals and dielectric thin films in different space environments. The mono- and bilayers selected are made of materials commonly used for the realization of optical components, such as reflective mirrors or building blocks of interferential filters. More than 400 samples were fabricated and irradiated with protons at different energies on ground-based facilities. The fluences were selected as a result of simulations of the doses delivered within a long-term space mission considering different orbits (Sun close, Jovian, and Geostationary orbits). In order to stress the samples at different depths and layer interfaces, experiments were carried out with a range of proton energies within 1 and 10 MeV values. An estimate of a safe maximum fluence has been provided for each type of sample at each energy. The damage mechanism, when present, has been investigated with different optical and structural techniques.

Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico

AbstractThe detection and quantification of strong sea surface winds, reaching up to 25 m·s−1, whether associated with deep convection aloft or not, have been extensively discussed in previous studies. This method involves the combined observation of the same event from both low‐orbit altitude and geostationary (GEO) satellites. Strong surface winds observed by the Sentinel‐1 C‐band synthetic aperture radar (SAR) are robustly associated with deep convective clouds detected by GEO infrared sensors. The current paper aims to generalize the previous assessment of several convective wind events by collecting a larger dataset and comparing these data to in‐situ wind observations. To achieve this, we evaluated wind speeds retrieved from Sentinel‐1 SAR images against corresponding in‐situ wind measurements from all active buoys/stations in the Gulf of Mexico. Significant agreement between satellite‐based winds and in‐situ data was achieved, particularly for wind speeds exceeding 3 m·s−1, with even better agreement for wind speeds over 10 m·s−1. From this dataset, three specific convective cases were extracted to illustrate various stages of convective squall events: before, during, and after the occurrence of a squall peak. In each case, comparison with in‐situ measurements showed that SAR‐estimated wind speeds closely matched observed speeds, including the peak convective winds, which were estimated at 18.90 m·s−1 and measured at 20.69 m·s−1. Furthermore, combining these findings with GOES‐16 sequential images illustrates the temporal and spatial similarity between deep convection areas, estimated strong sea surface wind patterns, and measured wind speeds.

Low-Energy Transfers to Lunar Distant Retrograde Orbits from Geostationary Transfer Orbits

This study focuses on the low-energy transfers to lunar distant retrograde orbits (DROs) from geostationary transfer orbits (GTOs) in the bicircular-restricted sun–Earth–moon four-body problem. The low-energy transfer is essential for low-cost small satellites reaching out to the Moon, and the departure from GTO allows more rideshare opportunities. We first created several large-scale databases of trajectory segments, such as GTO to apogee in the weak-stability area, apogee to perilune, and DRO to perilune. Then, millions of GTO–DRO transfer trajectories with double powered lunar flybys (PLFs) and weak stability boundary (WSB) ballistic transfer were constructed through trajectory patching. The key flight information, such as the ΔV–time-of-flight Pareto fronts, launch windows, and the flight mode via WSB ballistic transfer, is obtained from feasible solutions. Results show that low-energy GTO–DRO transfers can be achieved by exploiting PLFs and WSB ballistic arcs, which suggests potential applications in the cislunar space.

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

AbstractThe cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds.

Deep reinforcement learning-based dynamic multi-beam power allocation for GEO-LEO co-existing satellites

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user’s received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability.

Методика балістичного планування місій низькоорбітального сервісного обслугову-вання з малою постійною тягою рушійних систем

The current stage of space exploration is characterized by an increased interest in the development, deployment, and operation of low-orbit satellite constellations (LOSC) for Earth and near-Earth space remote sensing for military and civilian purposes and for global and regional satellite communications. Reusable space launch vehicles have significantly reduced the orbital injection cost. As a result, satellite operators are developing and deploying large-scale LOSCs of various orbital structures with a large number of spacecraft. According to current estimates, more than 70% of all the operating satellites operate in low-Earth orbits (LEOs) at altitudes between 160 km and 2,000 km. Since LEO satellites are generally much cheaper than satellites in geostationary orbits, the possibility of their on-orbit servicing (OOS) has not been the focus of research. However, the use of LEO OOS has prospects for growth. Techniques for ballistic planning of LEO OOS missions have been and are being developed. The disadvantages of approximate techniques include the use of simplified flight dynamics models. Most of the existing exact techniques are based on the use of full mathematical models of flight dynamics and the shooting method to solve the boundary value problem of an orbit transfer. Using the shooting method requires a sufficiently accurate initial guess, which is difficult to determine. To obtain a second approximation, use is mainly made of optimization methods, which do not always find a global minimum. In this regard, there is a need to develop new techniques that would be free from the above disadvantages. The goal of the article is to develop a ballistic planning technique for low-orbit servicing missions with low constant thrust propulsion systems. The technique includes the identification of LEO areas promising for OOS, a mathematical model of the dynamics of perturbed OOS orbit transfers in modified equinoctial orbital elements, and a solution algorithm for the boundary value problem of determining the control parameters of perturbed OOS low-orbit transfers. The problem is solved using methods of statistical analysis, flight dynamics, shooting, genetic optimization, and mathematical simulation. The novelty lies in the identification of LEO areas promising for OOS and the development of a mathematical model of orbit transfer dynamics in modified equinoctial orbital elements and a solution algorithm for determining the control parameters of perturbed OOS low-orbit transfers. The results of the work may be used in the justification and planning of LEO OOS missions and the formulation of requirements for LEO OSS mission propulsion systems.

Short‐Term Sea Fog Area Forecast: A New Data Set and Deep Learning Approach

AbstractPrompt and precise forecast of sea fog regions ensures maritime navigational safety. This paper establishes a prompt and precise forecast of sea fog regions that ensure maritime navigational safety. This paper establishes a multivariable sea fog forecast (MV‐SFF) data set and proposes a deep learning‐based forecast method named rich‐element aggregated (REA) for short‐term forecasts. The MV‐SFF data set contains 122 sea fog events from 2010 to 2020. Each event in the MV‐SFF data set includes meteorological variables from reanalysis data and geostationary ocean color imager satellite images captured hourly on the day of the sea fog occurrence. This data set aims to comprehensively utilize meteorological elements and remote sensing observations to predict the spatial changes of sea fog areas in the next 7 hours. The proposed REA model can extract and integrate features from historical meteorological elements of different types, times, and spatial locations. In addition, REA utilizes a position‐aware edge detection mechanism to locate the exact position of sea fog. We perform a seven‐hour ahead forecast starting from 09:16 local time and show promising forecasting results, which demonstrate the fog area forecast ability of our model. Compared with existing advanced deep learning networks, the REA model is superior in performance and stability.

Convective Initiation Nowcasting in South China Using Physics‐Augmented Random Forest Models and Geostationary Satellites

AbstractConvective initiation (CI) nowcasting in subtropical regions often faces challenges, such as complex physical processes and imbalanced samples of CI events, resulting in a high false alarm ratio (FAR). In this paper, we propose a Storm Warning System with Physics‐Augmentation (SWASP) based on the random forest algorithm and cloud physical conditions, using Himawari‐8 Advanced Himawari Imager data from April to September 2019 in South China. The cloud physical conditions (e.g., cloud‐top cooling rates) were investigated to establish regional thresholds for convection occurrence. Ancillary information, including elevation, satellite zenith angle, and latitude, was also incorporated into the SWASP model. Compared to conventional methods, the SWASP model exhibits an improved probability of detection by 0.11 and 0.08 and a decreased FAR by 0.38 and 0.44 for daytime and nighttime forecasts. Moreover, the SWASP model enables the detection of local convective storm systems about 30 min to 1 hr ahead of radar detection in typical convective storm cases. This study contributes to further advancements of the SWASP model by incorporating physical conditions and emphasizes the potential application of geostationary satellites in convective early warnings.

The Distribution of Pc5 Ultralow-frequency Waves at Geostationary Orbit

Using GOES magnetometer data spanning over 30 yr, we study the distribution of Pc5 ultralow-frequency waves at geosynchronous orbit. The CLEAN algorithm was applied to detect narrowband Pc5 waves. This method was first used in astronomical radio interferometry, but has since been applied to many other areas. The algorithm allows for identification of multiple individual peaks within the same power spectral density. We found close to 30,000 events in each magnetic field component in field-aligned coordinates. With some exceptions, results were in agreement with previous studies. Wave occurrence along magnetic local time (MLT) was higher in the 18–23 MLT sector. However, the largest accumulated wave amplitudes were observed on the dayside, peaking close to local noon. As expected, wave amplitudes were larger during more active solar wind conditions. For the radial and parallel components, amplitudes were maximum at the interplanetary magnetic field clock angle of −30° to −45°. The resultant database of Pc5 ultralow-frequency wave events can be used to constrain radiation belt models, while the CLEAN method provides a unique technique to automatically detect narrowband plasma waves in the magnetosphere and beyond.

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

Top-Down Phenomenon Observed in the Outer Rainbands of Typhoon Maysak (2020): Insights from the Comparative Analysis of Cloud and Precipitation Microphysics with a Typical Precipitation Event in Northeast China

Abstract Cloud and precipitation microphysics in tropical cyclones (TCs) moving toward Northeast China may exhibit significant distinctions compared with the typical precipitation of this region. In this study, observations from ground-based particle size velocity (Parsivel) disdrometers in Liaoning Province, China, and cloud property data from the Himawari geostationary satellite were utilized to analyze the raindrop size distribution (DSD) characteristics and cloud vertical evolution associated with the outer rainbands of Typhoon Maysak (2020). A comparative analysis was conducted with a typical precipitation event in Northeast China induced by a cold vortex (cold-core low). Our findings reveal distinctive DSD characteristics related to the TC, where medium-sized raindrops dominate, with a smaller diameter but higher concentration in the TC case compared to the typical cold-vortex-induced precipitation case in Northeast China. Convective precipitation falls between maritime-like and continental-like patterns, leaning more toward continental convection. This varies significantly with TCs in Southeast China but is similar to that observed in coastal-front-like rainbands, suggesting extratropical influence. A detailed analysis of the vertical profile of cloud droplets shows a unique “top-down” phenomenon during the extratropical transition process of the TC, where the development of lower-level clouds follows that of upper-level clouds, inconsistent with previous studies and the case for comparison. Further investigation indicates the significant role of the intrusion of dry and cold air from upper levels and the presence of high humidity in low levels in driving this phenomenon. Our results will provide novel insights into cloud and precipitation microphysics associated with TCs in midlatitude regions.

The importance of global synchronous observation for estimating oceanic chlorophyll-a

Over the last three decades, ocean color satellites have recorded continuous change in phytoplankton concentration, and in doing so, the satellites have helped marine remote sensing to become an emerging discipline. However, ocean color satellite has provided the state of global phytoplankton only around midday whereas phytoplankton have significant diurnal variation characteristics over an entire day-night cycle, which would lead to large estimation uncertainty in downstream products. A large global field observations show that the diurnal variation curves can be constructed as the sum of an exponential and a polynomial function. After correction with our diurnal variation curves, the intermission difference between MODISA and MODIST phytoplankton product could be decreased from 7.57% to 6%, which is critical to monitor the long-term changing trend with multi-mission data. When the satellite-recorded chlorophyll-a concentration is simply equal to a daily average value, it results in overestimating uncertainties of 16% and 54% for the northern and southern hemispheric oceans, respectively. Moreover, as a typical example of downstream product of the chlorophyll-a, the coverage of oligotrophic waters can oscillate 76% from day to night simulated from satellite images with our diurnal variation curves, further confirming the importance of global synchronous observation. The geostationary satellite might be an efficient regional tool for diurnal observation, but it must account for its limitations under the weak light conditions of early morning and late afternoon. In the real world, the effects of diurnal variations in ocean properties reach far beyond chlorophyll-a in the global oceans. We anticipate these quantitatively findings to be a starting point for quantitatively re-examining the proper application of ocean color products in scientific communication.

Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing

The utilization of satellite remote sensing images for retrieving aerosol optical parameters has been extensively discussed over the past few decades. While employing machine learning models is indeed a viable approach, a significant portion of these studies still rely on redundant data. Moreover, the discussion regarding aerosol absorption, a crucial factor for determining aerosol radiative impact and distinguishing aerosol components, is limited in current machine learning studies. In this study, we propose a random forest model to retrieve high-precision aerosol properties and their absorption over land from Himawari-8 geostationary satellite images. Remarkably, this model attains a high degree of accuracy in estimating aerosol optical depth (AOD), absorption aerosol optical depth (AAOD), and single scattering albedo (SSA) of heavy air mass using only seven primary predictors (observational radiances or their mathematical combinations, geometries, and wavelength). For AOD, the new random forest model demonstrates excellent performance on an hourly scale (R2 ≥ 0.89, MAE < 0.07, RMSE <0.13), >80% of the samples fall within the expected error (EE) range. Concerning AAOD, the validation indicates that at least 65% of AAODs have a bias of ≤50%, with an R2 exceeding 0.78, MAE ≤ 0.008 and RMSE ≤0.016. SSA also demonstrates a high accuracy (R2 ≥ 0.57, MAE < 0.03, RMSE <0.05), with >70% of the results have an error ≤ 0.03. Through more comprehensive independent spatiotemporal cross validation, it can be determined that the model also offers reliable spatial and temporal predictions. The proposed RF model is capable of learning aerosol properties under most atmosphere scenarios, providing a reasonable conversion from predictors to AOD and AAOD/SSA under high aerosol loadings. The spatial patterns of these parameters suggest that the retrievals show considerable potential in capturing high aerosol loading in East Asia and biomass burning in Southeast Asia. The method introduced in this study offers a new approach to obtaining aerosol properties from geostationary satellite remote sensing, featuring a flexible process, simple inputs, high accuracy, and enhanced robustness. Additionally, it furnishes supplementary insights into aerosol absorption, presenting new possibilities in determining aerosol radiative impact and distinguishing aerosol components.

Coupled Spacecraft Charging Due to Continuous Electron Beam Emission and Impact

Spacecraft charge naturally in orbit due to the plasma environment and the electromagnetic radiation from the sun. By emitting an electron beam, a servicer spacecraft can control its electric potential and also the potential of a neighboring target if the electron beam is aimed at the target spacecraft. In addition, the impacting electron beam excites secondary electrons and x-rays, providing a way to touchlessly sense the potential of the target. Because of the electron beam, the charging dynamics of the two spacecraft are coupled. This paper studies the effects of the beam on the electric potentials using a numerical charging model. It is found that multiple equilibria may exist due to the electron beam. Jumps between equilibrium configurations are possible when the electron beam energy is quickly reduced or when current fluctuations are present. Being aware of multiple equilibrium configurations is important for feedback-based charge control but also enables a new open-loop charge control around one of the equilibria. The effect of the electron beam on the spacecraft potentials is studied for geostationary Earth orbit and cislunar space. It is found that the current applied by the beam to the target may influence remote electric potential sensing methods.

Temporal Characteristics Based Outlier Detection and Prediction Methods for PPP-B2b Orbit and Clock Corrections

The BeiDou Global Navigation Satellite System (BDS-3) provides real-time precise point positioning (PPP) service via B2b signals, offering real-time decimeter-level positioning for users in China and surrounding areas. However, common interruptions and outliers in PPP-B2b services arise due to factors such as the Geostationary Orbit (GEO) satellite “south wall effect”, Issue of Data (IOD) matching errors, and PPP-B2b signal broadcast priorities, posing challenges to continuous high-precision positioning. This study meticulously examines the completeness, continuity, and jumps in PPP-B2b orbit and clock correction using extensive observational data. Based on this analysis, a two-step method for detecting outliers in PPP-B2b orbit and clock corrections is devised, leveraging epoch differences and median absolute deviation. Subsequently, distinct prediction methods are developed for BDS-3 and GPS orbit and clock corrections. Results from simulated and real-time dynamic positioning experiments indicate that predicted corrections can maintain the same accuracy as normal correction values for up to 10 min and sustain decimeter-level positioning accuracy within 30 min. The adoption of predicted correction values significantly enhances the duration of sustaining real-time PPP during signal interruptions.

Investigation of factors affecting the reflectance spectra of GEO Satellites

Geostationary orbit (GEO) satellites have extensive application in fields like communications and navigation due to their stationary properties. Using additional methods to identify and characterise GEO satellites is crucial for enhancing cataloguing capabilities and verifying their operation status. Reflectance spectroscopy is a promising technique for characterising satellites, but a deeper understanding of the primary factors influencing satellite reflectance spectra is required. So we carried out slitless spectroscopic observations on six GEO satellites using the 80 cm high-precision telescope of the Purple Mountain Observatory located in Yaoan, Yunnan. Reflectance spectra and by-product light curves were then obtained after necessary spectral extraction and correction procedures. Our experiment and observation lasted over ten months, and this paper utilizes a subset of data with high Signal-to-Noise Ratio(SNR). Based on the reflectance spectral data obtained, we discovered that: The dominant factor impacting the reflectance spectra is the Multi-layer insulation(MLI) on the surface of three-axis stabilized GEO satellites. Reflectance spectral trends of GEO satellites do not change significantly over the course of a night with changes in the sun-satellite-observatory phase angle. The normalised reflectance spectral trends remain unchanged despite variations in the solar declination. The aforementioned findings establish a strong basis for utilising reflectance spectra to recognise and classify non-cooperative GEO satellites.

U-HRNet+: a real-time precipitation estimation method based on the fusion of temporal and spatial domain features

ABSTRACT The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product.

Evolution of Energetic Proton Parallel Pressure Anisotropy at Geosynchronous Altitudes: Potential Role in Triggering Substorm Expansion Phase Onset

AbstractThe sequence of events associated with the triggering of energy release during substorm expansion phase onset is still not well‐understood. Oberhagemann and Mann (2020b, https://doi.org/10.1029/2019gl085271) proposed a new substorm onset mechanism, where the transition toward parallel proton pressure anisotropy during tail stretching in the late growth phase could trigger a pressure anisotropic ballooning instability. Here we examine the evolution of energetic proton parallel pressure anisotropy at geosynchronous altitudes, seeking evidence in support of the proposed substorm onset mechanism. We use the Geostationary Operational Environment Satellite (GOES) proton flux and magnetometer data combined with substorm onset indicators derived from ground‐based magnetometers. Superposed epoch analysis of substorm onset times for 2014 using the isolated substorm list (Ohtani &amp; Gjerloev, 2020, https://doi.org/10.1029/2020ja027902) clearly shows signatures of energetic proton parallel pressure anisotropy immediately before substorm onset, potentially supportive of the Oberhagemann and Mann theory.

Quantifying the Impact of Aerosols on Geostationary Satellite Infrared Radiance Simulations: A Study with Himawari-8 AHI

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI’s six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations’ sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes.