Geostationary Operational Environmental Satellite Research Articles

Using the STIX background detector as a proxy for GOES

The Spectrometer/Telescope for Imaging X-Rays (STIX) on board Solar Orbiter was designed to observe solar flares in the X-ray range of 4-150 keV, providing spectral, temporal, and spatial information. Besides 30 imaging detectors, STIX has two additional detectors: the coarse flare locator (CFL) and the background (BKG) detector, which are used in the present study. Flares observed from Earth are classified using their peak X-ray flux observed by the Geostationary Operational Environmental Satellite (GOES) instruments. Given to the Solar Orbiter mission design, roughly half of all flares observed by STIX are located on the backside of the Sun. These flares lack a GOES-class classification. In this paper, we describe the calibration of the BKG detector aperture sizes. Using the calibrated measurements of the BKG detector, we explore the relationship between the peak flux for flares jointly observed by STIX and GOES. This allows us to estimate the GOES flare classes of backside flares using STIX measurements. We looked at the 500 largest flares observed by both STIX and GOES in the time range February 2021 to April 2023. The aperture size calibration was done by comparing 4-10 keV counts of the BKG detector with the CFL measurements. In a second step, we correlated the calibrated STIX BKG peak flux with the GOES peak flux for individual flares. We calibrated the BKG detector aperture sizes of STIX, which are now ready to be implemented into the ground-software (GSW) of STIX. Furthermore, we showed that for the larger flares (C class and above) a close power law fit exists between the STIX BKG and GOES peak flux, with a Pearson correlation coefficient of 0.97. This correlation provides a GOES proxy with a one sigma uncertainty of ≈ 11%. We were able to show that the BKG detector can reliably measure a broad range of GOES flare classes from roughly B5 up to at least X85 (assuming a radial distance of 1AU). This makes it an interesting detector-concept for future space weather missions. Thus far, the largest flare observed by STIX to date is an estimated X16.5 ± 1.8 backside flare of May 20, 2024.

All-Sky Assimilation of GOES-16 Water Vapor Channels in Consideration of Cloud-Dependent Interchannel Observation-Error Correlations

Abstract The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite-16 (GOES-16) includes three water vapor channels (8, 9, and 10) that are specifically designed to monitor tropospheric water vapor at high, mid-, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) Model, alongside the NCEP Gridpoint Statistical Interpolation analysis system (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) configuration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to under clear-sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems.

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

AbstractAccurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian, Zhang, Klein, & Schumacher, 2021, https://doi.org/10.3390/rs13122309, 2022, https://doi.org/10.1029/2021gl097070). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

131 and 304 Å Emission Variability Increases Hours Prior to Solar Flare Onset

Abstract Thermal changes in coronal loops are well studied, both in quiescent active regions and in flaring scenarios. However, relatively little attention has been paid to loop emission in the hours before the onset of a solar flare; here, we present the findings of a study of over 50 off-limb flares of Geostationary Operational Environmental Satellite class C5.0 and above. We investigated the integrated emission variability for Solar Dynamics Observatory Atmospheric Imaging Assembly channels 131, 171, 193, and 304 Å for 6 hr before each flare and compared these quantities to the same time range and channels above active regions without proximal flaring. We find significantly increased emission variability in the 2–3 hr before flare onset, particularly for the 131 and 304 channels. This finding suggests a potential new flare prediction methodology. The emission trends between the channels are not consistently well correlated, suggesting a somewhat chaotic thermal environment within the coronal portion of the loops that disturbs the commonly observed heating and cooling cycles of quiescent active region loops. We present our approach and the resulting statistics and discuss the implications for heating sources in these preflaring active regions.

Characterization of Reflectance Signatures Within Above Anvil Cirrus Plumes in GOES‐16 Infrared and Visible Imagery

AbstractAbove anvil cirrus plumes (AACPs) form atop deep convective storm anvils. Storms containing AACPs are closely associated with surface severe weather. AACPs often reside in the lower stratosphere, and their microphysics are thought to be characterized by smaller ice particles with increased reflectance in shortwave infrared (SWIR) satellite imagery. This study aimed to determine which SWIR channels exhibit the strongest unique signatures over AACP lifetimes, assess how NEXRAD GridRad radar‐derived fields associated with severe weather relate to AACP presence and evolution, and take steps toward a red‐green‐blue (RGB) recipe to identify AACPs. AACPs and non‐plume anvil regions were labeled and analyzed using 5‐min resolution Geostationary Operational Environmental Satellites (GOES)‐16 Advanced Baseline Imager observations for five storms between 2019 and 2021. Considered were 0.64, 1.37, 1.6, 2.24, 3.9 μm reflectances, and 10.3 μm brightness temperatures (BTs). Results show: (a) higher median reflectance of accumulated AACPs in the 0.64, 1.37, and 2.24 μm channels compared to non‐plumes, (b) the 60−79° solar zenith angle bin contained the most data, corresponded with an afternoon peak in storm intensity, and contained a large plume to non‐plume pixel ratio, (c) increased lower stratosphere water vapor associated with AACPs impacted the 1.37 μm reflectance, leading to smaller, more reflective cloud ice particles than non‐plume regions, (d) GridRad radar 10‐ and 40‐dBZ echo heights and column maximum reflectivity (CMR) show AACPs coinciding with a secondary maximum in CMR, and (e) an RGB combining 1.37 μm, 0.64 μm + 3.9 μm reflectance, and 10.3 μm BT visually revealed AACPs.

Retrieval of Boundary Layer Precipitable Water from GOES ABI Using Machine Learning Techniques

Abstract Low-level moisture is an important ingredient for forecasting severe storms, especially over the Great Plains where severe storms often develop along the dryline. Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes makes it difficult to track a rapidly developing dryline. Geostationary satellites provide high spatial and temporal observation, but the channels of current geostationary satellites are mostly sensitive to water vapor at mid- to upper levels. However, the split window difference (SWD) between the “clean” window channel (10.3 μm) and the “dirty” window channel (12.3 μm) is commonly used for estimating low-level water vapor. However, this estimation is complicated by surface temperature contributions, dependence on the lapse rate, and nonlinear relationships between SWD and moisture. This study applies machine learning techniques to infer boundary layer precipitable water (BLPW) from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural networks, especially for the atmosphere above the surface, High-Resolution Rapid Refresh (HRRR) model outputs are used as the truth for training. Statistical results show that using ABI channel 13, 14, and 15 brightness temperatures, skin temperature, solar zenith angle, and GOES total precipitable water product as additional inputs shows the lowest mean-square error (MSE). Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline. Significance Statement Knowledge of low-level moisture is important for monitoring drylines and moisture convergence preceding convective initiation. Traditional observations lack the spatial resolution, spatial coverage, or temporal update frequency needed by forecasters. The latest generation of Geostationary Operational Environmental Satellite (GOES), the GOES-R series, provides high spatial resolution and rapid temporal updates. However, the estimation of low-level moisture using the Advanced Baseline Imager (ABI) is complicated by dependencies on other variables and nonlinear relationships. Machine learning is shown to provide good estimates of low-level moisture from ABI observations. This application of machine learning to GOES-R can improve the ability to monitor low-level moisture and forecast convective initiation.

Global prediction of sub-relativistic and relativistic electron fluxes in the geosynchronous orbit using artificial neural networks

High-energy particles in geosynchronous orbit (GEO) present significant hazards to astronauts and artificial satellites, particularly during extreme geomagnetic activity conditions. In the present study, based on observations onboard the GOES-15 (Geostationary Operational Environmental Satellites) spanning from 2011 to 2019 as well as the historical values of solar wind and geomagnetic activity indices, an artificial neural network model was established to predict the temporal evolution of the GEO sub-relativistic and relativistic (>0.8 MeV and >2 MeV) electron fluxes one day in advance. By adding the last-orbital observations of electron flux in each of all 24 different magnetic local times (MLTs) and its two MLT-adjacent values into inputs, the current model can provide accurate predictions with an MLT resolution of one hour for the first time. Moreover, it achieves the best performance in comparison with previous methods, with overall root mean square errors of 0.276 and 0.311, prediction efficiencies of 0.863 and 0.844, and Pearson correlation coefficients of 0.930 and 0.921 for >0.8 MeV and >2 MeV electrons, respectively. More than 99% of the samples exhibit an observation-prediction difference of less than one order of magnitude, while over 90% demonstrate a difference of less than 0.5 order. Further analysis revealed that it can precisely track the global variations of the electron flux during both quiet times and active conditions. The present model would be an important supplement for examining the temporospatial variations of inner magnetospheric particles and helping to establish a warning mechanism for space weather disaster events.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Precise and Accurate Short-term Forecasting of Solar Energetic Particle Events with 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 using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification framework on a multivariate time-series data set covering solar cycles 22, 23, and 24. We implement ensemble modeling that merges the results from three proton channels (E ≥ 10 MeV, 50 MeV, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions. Our task is binary classification, such that the aim of the model is to distinguish strong SEP events from nonevents. Here, strong SEP events are those crossing the Space Weather Prediction Center’s “S1” threshold of solar radiation storm and proton fluxes below that threshold are weak SEP events. In addition, we consider periods of nonoccurrence of SEPs following a flare with magnitudes ≥C6.0 to maintain a natural imbalance of sample distribution. In our data set, there are 244 strong SEP events comprising the positive class. There are 189 weak events and 2460 “SEP-quiet” periods for the negative class. We experiment with summary statistic, one-nearest neighbor, and supervised time-series forest (STSF) classifiers and compare their performance to validate our methods for prediction windows from 5 minutes up to 60 minutes. We find the STSF model to perform better under all circumstances. For an optimal classification threshold of ≈0.3 and a 60 minutes prediction window, we obtain a true skill statistic TSS = 0.850, Heidke skill score HSS = 0.878, and Gilbert skill score GSS = 0.783.

Merging TEMPEST microwave and GOES-16 geostationary IR soundings for improved water vapor profiles

Abstract. The Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) demonstrated the capability of CubeSat satellites to provide high-quality, stable microwave signals for estimating water vapor, clouds, and precipitation from space. Unlike the operational NOAA and MetOp series satellites, which combine microwave and hyperspectral infrared sensors on the same platforms to optimize retrievals, CubeSat radiometers such as TEMPEST do not carry additional sensors. In such cases, the high-temporal- and spatial-resolution and multi-channel measurements from the Advanced Baseline Imager (ABI) on the next-generation series of Geostationary Operational Environmental Satellites (GOES-R) are ideal for assisting these smaller, stand-alone radiometers. Based on sensitivity tests, the water vapor retrievals from TEMPEST are improved by adding water-vapor-sounding, window, and CO2 channels at 6.2, 6.9, 7.3, 8.4, 10.3, 11.2, 12.3, and 13.3 µm from ABI, which help to increase the vertical resolution of soundings and reduce retrieval errors. Adding three ABI water-vapor-sounding channels, under clear-sky conditions, retrieval biases and root mean square errors improve by approximately 10 %, while under cloudy skies, biases remain unchanged, but root mean square errors still decrease by 5 %; meanwhile, retrieval biases and root mean square errors are substantially reduced by adding more information from eight ABI bands in both clear and cloudy skies. Humidity soundings are also validated using coastal radiosonde data from the Integrated Global Radiosonde Archive (IGRA) from 2019 to 2020. When ABI indicates clear skies, water vapor retrievals improve somewhat by decreasing the overall bias in the microwave-only estimate by roughly 10 %, although layer root mean square errors remain roughly unchanged at 1 g kg−1 when three or eight ABI channels are added. When ABI indicates cloudy conditions, there is little change in the results. The small number of matched radiosondes may limit the observed improvement.

First Detections of Ionospheric Plasma Density Irregularities from GOES Geostationary GPS Observations during Geomagnetic Storms

In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth’s ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density.

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager on board Solar and Heliospheric Observatory) Active Region Patches (SMARPs) and Space-Weather HMI (Helioseismic and Magnetic Imager on board Solar Dynamics Observatory) Active Region Patches (SHARPs), which are two currently available data products containing magnetic field characteristics of solar active regions (ARs). The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space-weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP and SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI–HMI data set currently spans the period between 1996 April 4 and 2022 December 13, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space-weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites. Time-lagged cross correlation indicates that a relationship may exist, where some magnetic field properties of ARs lead the flare index in time. Applying the rolling-window technique makes it possible to see how this leader–follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.

MEMPSEP‐III. A Machine Learning‐Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach

AbstractWe introduce a new multivariate data set that utilizes multiple spacecraft collecting in‐situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C‐class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly‐developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP‐I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP‐II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Novel Comparison of Pyrocumulonimbus Updrafts to Volcanic Eruptions and Supercell Thunderstorms Using Optical Flow Techniques

AbstractConvective dynamics in a supercell thunderstorm, a volcanic eruption, and two pyrocumulonimbus (pyroCb) events are compared by computing cloud‐top divergence (CTD) with an optical flow technique called Deepflow. Visible 0.64‐μm imagery sequences from Geostationary Operational Environmental Satellites (GOES)‐R series Advanced Baseline Imager (ABI) are used as input into the optical flow algorithm. CTD is computed after post‐processing of the retrieved motions. Analysis is performed on specific image times, as well as the full time series of each case. Multiple CTD‐based parameters, such as the maximum and the two‐dimensional area exceeding a specified CTD threshold, are examined along with the optical flow‐retrieved wind speed. CTD is shown to accurately and quantitatively represent the behavior and magnitude of different deep convective phenomena, including distinguishing between convective pulses within each individual event. CTD captures updraft intensification as well as differences in convective activity between two pyroCb events and individual updraft pulses occurring within a single pyroCb event. Finally, the characteristics of high‐altitude smoke plumes injected by two separate pyroCb pulses are linked to CTD using ultraviolet aerosol index and satellite imagery. Optical flow‐derived parameters can therefore be applied to individual pyroCbs in real‐time, with potential to characterize pyroCb smoke source inputs for downstream smoke modeling applications and to facilitate future tools supporting air quality modeling and firefighting efforts.

Do Solar Energetic Particle (SEP) Events Influence the Formation of the V0 Layer in the Venusian Ionosphere?

AbstractThis study investigates the potential impact of Solar Energetic Particles (SEPs) on the V0 layer of the Venus ionosphere. Electron density profiles obtained from radio occultation experiments conducted by the Venus Express (VEX) and Akatsuki missions were utilized for this purpose. Background data from the Analyzer of Space Plasma and EneRgetic Atoms (ASPERA‐4) aboard VEX were used to detect SEP events. Additionally, observations from the Space Environment Monitor (SEM) suite onboard the Geostationary Operational Environmental Satellite (GOES) during alignments of Venus, Earth, and the Sun were also considered. Our findings indicate that while SEPs may contribute to the formation of the V0 layer, they are not the main driving force in the Venusian ionosphere.

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.

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 & 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.

Machine learning techniques for estimation of Pc5 geomagnetic pulsations observed at geostationary orbits during solar cycle 23

Pc5 geomagnetic pulsations can accelerate electrons in the radiation belts, which can pose adverse threats to both astronauts and satellites in space. The estimation of Pc5 waves in space is crucial to radiation belt dynamics studies and will help mitigate these challenges. Here, we explore the advantages of the Feed-forward Neural Network (FFNN) and Random Forest (RF) algorithm for effective estimation of Pc5 geomagnetic pulsations observed in space at geostationary orbit during solar cycle 23. The dataset used in this study is the vector magnetic field measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) and the solar wind parameters: Bz and Vx component of the solar wind in the Geocentric Solar Ecliptic (GSE) coordinate system, proton density, flow pressure, and plasma beta obtained from the OMNI Web database during part of solar cycle 23. Pc5 geomagnetic pulsations were extracted from the toroidal component of the magnetic field time series using a bandpass Butterworth filter. The continuous wavelet transform (CWT) was utilized to study the characteristics of the extracted wave in the time-frequency domain for its validation. The validated Pc5 events were used as the target in the model's development, with the solar wind parameters as the inputs. In addition to the solar wind parameters, we included an attribute of the magnetic field time series as an input variable in the model. The dataset is carefully divided to ensure effective training and testing of the models. Finally, we trained both models using the same inputs and targets and explored their estimation abilities. The model was tested during the maximum, descending, and minimum phases of solar cycle 23. Both the FFNN and RF models have a similar estimation, with average cross-correlation score (R) values of 0.74 and 0.73 and corresponding average root mean squared error (RMSEs) of 0.16 nT and 0.67 nT, respectively. The model was deployed to investigate the response of Pc5 waves during three storm days in each testing year. The machine learning (ML) model outputs showed good coherence with the observed Pc5 waves. To validate the models, we studied the correlation between the estimated Pc5 events with the Kp index, and a good correlation was seen to exist between both events. This validates the good performance of the developed models. This work will aid in the study of radiation belt dynamics and the construction of electron depletion regions in the radiation belt.

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology

Abstract. The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells.