Environmental Satellite Research Articles

Upper-tropospheric pollutants observed by MIPAS: geographic and seasonal variations

Abstract. We present a global climatology of upper-tropospheric hydrogen cyanide (HCN), carbon monoxide (CO), acetylene (C2H2), ethane (C2H6), peroxyacetyl nitrate (PAN), and formic acid (HCOOH) obtained from observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on board the Environmental Satellite (Envisat) between 2002 and 2012. At northern midlatitudes and high latitudes, the biomass burning tracer HCN, but also CO, PAN, and HCOOH, exhibit maxima during spring and/or summer and minima during winter. On the contrary, maximum northern extratropical C2H2 and C2H6 amounts were measured during winter and spring, and minimum values were measured during summer and fall. In the tropics and subtropics, enhanced amounts of all pollutants were observed during all seasons, especially widespread and up to southern midlatitudes during austral spring. Other characteristic features are eastward transport of anthropogenic C2H6 and of biogenic HCOOH from Central and North America in boreal summer, accumulation of pollutants in the Asian monsoon anticyclone (AMA), and enhanced C2H2 over southeastern Asia in boreal winter. Clear indication of biogenic release of HCOOH was also found above tropical South America and Africa. A global correlation analysis of the other pollutants with HCN corroborates common release by biomass burning as a source of the widespread southern hemispheric pollution during austral spring. Furthermore, high correlation with HCN points to biomass burning as a major source of tropical and subtropical C2H2 and PAN during most of the year. In the northern extratropics, there are generally low correlations with HCN during spring and early summer, indicating the influence of anthropogenic and biogenic sources. However, in August, there are stronger correlations above Siberia and boreal North America, which points to common release by boreal fires. This is confirmed by the respective enhancement ratios (ERs). The ERs measured above northeastern Africa fit well to the emission ratios of the dominant local fire type (savanna burning) for C2H2, while those for CO, C2H6, and HCOOH rather indicate tropical forest fires or additional anthropogenic or biogenic sources. The southern hemispheric ΔC2H6/ΔHCN ERs obtained during August to October are in good agreement with the emission ratio for savanna fires. The same applies for ΔC2H2/ΔHCN in August and for ΔHCN/ΔCO and ΔHCOOH/ΔHCN in October.

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.

Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data

Abstract. Melt ponds on Arctic sea ice affect the radiative balance of the region as they introduce darkening of the sea ice during the Arctic summer. The temporal extent and spatial extent of the ponding, as well as its amplitude, reflect the state of Arctic sea ice and are important for our understanding of Arctic sea ice change. Remote sensing retrievals of melt pond fraction (MPF) provide information on both the present state of the melt pond development and its change throughout the years, which is valuable information in the context of climate change and Arctic amplification. In this work, we transfer the earlier published Melt Pond Detector (MPD) remote sensing retrieval to the Ocean and Land Colour Instrument (OLCI) data on board the Sentinel-3 satellite and so complement the existing Medium Resolution Imaging Spectrometer (MERIS) MPF dataset (2002–2011) from Environmental Satellite (ENVISAT) with recent data (2017–present). To evaluate the bias of the MPF product, comparisons to Sentinel-2 MultiSpectral Instrument (MSI) high-resolution satellite imagery are presented, in addition to earlier published validation studies. Both MERIS and OLCI MPD tend to overestimate the small MPFs (ranging from 0 to 0.2), which can be attributed to the presence of water-saturated snow and sea ice before onset of ponding. Good agreement for the middle-range MPF (0.2–0.8) is observed, and the areas of exceptionally high MPF = 100 % are recognized as well. The earlier published MERIS MPFs (2002–2011) were reprocessed using an improved cloud clearing routine and together with recent Sentinel-3 data provide an internally consistent dataset, which allows the MPF development in the past 20 years to be analyzed. Although the total summer hemispheric MPF trend is moderate, at +0.75 % per decade, the regional weekly MPF trends display a pronounced dynamic and range from −10 % to as high as +20 % per decade, depending on the region. We conclude the following effects: The global Arctic melt onset shifted towards spring by at least 2 weeks, with the melt onset happening in late May in recent years as compared to early June to mid-June in the beginning of the dataset. There has been a change in the pond onset regime in recent years, with the East Siberian and Laptev Sea dominating the melt onset and not the Beaufort Gyre region as before. The central Arctic, north Greenland and the Canadian Arctic Archipelago (CAA) have shown signs of increasing first-year ice (FYI) fraction in recent years. The daily gridded MPF averages are available on the web page of the Institute of Environmental Physics, University of Bremen, as a historic dataset for the ENVISAT data and as ongoing operational processing for the Sentinel-3 data.

Coastal eutrophication transforms shallow micro-benthic reef communities.

Coral reefs are impacted worldwide by coastal eutrophication, which is often translated by a decrease in coral cover and an increase in potentially harmful invertebrates and algal blooms. Additionally to corals and other macro-benthos, micro-benthic communities are affected tremendously, however few studies reported the specific effect of eutrophication on those communities. This study addresses how micro-benthic communities are impacted by turbidity and associated eutrophication. To answer this question, we compared three groups that have been previously suggested as bioindicators of reef environmental conditions: foraminifera, diatoms and bacteria, from 12 islands (and 600 samples) in the Spermonde Archipelago (Indonesia) along a near- to offshore turbidity gradient. Insights from environmental DNA metabarcoding and satellite images highlighted differences between the reef slope (deep) and the reef flat (shallow) communities, a distinction that has been omitted in most previous studies. The reef flat communities were 1.5- to 2-fold more affected by turbidity parameters compared to the reef slope, which we argue is related to eutrophication. Based on previous work, we expected that prokaryotes would be the most impacted group by water quality. However, we found that large benthic foraminifera and diatom communities were highly impacted by turbidity, whereas the reef flat prokaryotic communities were primarily shaped by the substrate type. Additionally, a total of 112 exact sequence variants (ESVs) were identified as indicators of different turbidity levels, of which 87 foraminifera and diatom ESVs were associated with high-moderate turbid waters. Our study revealed fundamental knowledge and provides key information of the specific effect of turbidity and associated eutrophication and habitat variables (substrate type and reef area) on foraminifera, diatoms and prokaryotic communities. We argue that local and regional eutrophication participates in shaping reef flat micro-benthic communities, which may therefore play an important role as early warning signals for local degradation in coral reefs.

Prelaunch Reflective Solar Band Radiometric Performance of JPSS-3 and -4 VIIRS

The Joint Polar Satellite System 3 (JPSS-3) and -4 (JPSS-4) Visible Infrared Imaging Radiometer Suite (VIIRS) instruments are the last in the series (S-NPP VIIRS launched in October 2011, JPSS-1 VIIRS launched in November 2017, and JPSS-2 VIIRS launched in November 2022) of highly advanced polar-orbiting environmental satellites. Both instruments underwent a comprehensive sensor-level thermal vacuum (TVAC) testing at the Raytheon Technologies El Segundo facility to characterize the spatial, spectral, and radiometric aspects of the VIIRS sensor performance. This paper focuses on the radiometric performance of the 14 reflective solar bands (RSBs) that cover the wavelength range from 0.41 to 2.3 µm. Key instrument calibration parameters such as instrument gain, signal-to-noise ratio (SNR), dynamic range, and radiometric calibration uncertainty were derived from the TVAC measurements for both the primary and redundant electronics at three instrument temperature plateaus: cold, nominal, and hot. This paper shows that all the JPSS-3 and -4 VIIRS RSB detectors have been well characterized, with key performance metrics comparable to the previous VIIRS instruments on-orbit. The radiometric calibration uncertainty of the RSBs is within the 2% requirement, except in the case of band M1 of JPSS-4. Comparison of the radiometric performance to sensor requirements, as well as a summary of key instrument testing and performance issues, is also presented.

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.

Evaluation of Satellite-Derived Atmospheric Temperature and Humidity Profiles and Their Application as Precursors to Severe Convective Precipitation

This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation.

METHODS TO QUANTIFY THE IMPACT OF COVID-19 RESTRICTIONS ON CHANGES IN AIR POLLUTION: A REVIEW OF STATISTICAL METHODS

The COVID-19 pandemic led to strict measures, such as lockdowns, to control the virus. After the first lockdown, improved air quality was noted due to reduced non-essential activities. However, uncertainty has surrounded the sustainability of this pollution reduction post-lockdown, influenced by increased private transport, reduced public transit, and smart-work adoption. Additionally, given the complexity of the phenomenon, there is no consensus on suitable statistical methods for analyzing the relationship between lockdowns and pollution reduction. This review of statistical methods aims to provide a comprehensive overview of statistical models used to assess the relationship between COVID-19 restrictions and changes in air pollution, highlighting advantages and pitfalls. MEDLINE, Web of Science, Cochrane Library, and Scopus databases were searched for observational studies reporting the association between lockdown and change in air pollution. Excluded articles were those unrelated to COVID-19 pollution impact, those on COVID-19 waste, and reviews lacking specific data or qualitative/quantitative information. We presented a selection of 49 studies, comprising 16 with advanced statistical analyses and 33 with descriptive analyses and graphs trend. Air pollution data came from environmental agency databases and satellite instruments, examining single pollutants or composite indices. Studies used various methods to analyze lockdown’s impact on air pollution. Most employed standard statistical methods like means comparison, while some used advanced econometric models such as difference-in-differences model. Despite the observed general reduction in particulate matter, an increase in the air quality index, there has been a downward trend in primary pollution and a concurrent upward trend in secondary pollution.

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.

Short-Range Prediction of Squall-Line-Induced Heavy Rainfall: An Added Impact of GPS RO to FY-4A AGRI Data Assimilation

Abstract The GPS radio occultation technique has prompted a new era as GPS receivers are placed onboard traditional polar-orbiting operational environmental satellites and newly emerging commercial small satellite constellations. With high vertical resolutions of a few hundred meters, GPS radio occultation-observed refractivity profiles effectively complement the geostationary satellite observations of infrared brightness temperatures at high horizontal resolutions (∼2 km) for data assimilation in numerical weather prediction. In this study, numerical experiments are performed to assimilate radio occultation (RO) refractivity observations from FY-3D/FY-3E, MetOp-B/MetOp-C, COSMIC-2, SPIRE, and GeoOptics and brightness temperature observations at channels 9 (6.25 μm) and 10 (7.1 μm) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A. The benefits of adding the refractivity to the brightness temperature observations for improving quantitative precipitation forecasts (QPFs) of two squall-line cases over eastern China are shown using the Weather Research and Forecasting Model and the Gridpoint Statistical Interpolation analysis system. In the first case, the squall line was caused by a superposition of cold and dry air advection behind an upper-level trough over low-level warm and humid air by a low-level jet. A combined assimilation of the GPS RO and FY-4A AGRI observations produced a skillful forecast of the squall-line distribution, characterized by a warm zone 100–300-km long ahead of a cold front near the ground; this led to more consistent improvements in the forecast of a narrow band of squall-line thunderstorms than those from two separate assimilations. The threat scores of 24-h QPFs associated with both squall-line cases are significantly improved, especially for heavier rainfall events.

Comparisons of Energetic Electron Observations Between FIREBIRD‐II CubeSats and POES/MetOp Satellites From 2018 to 2020

AbstractPrecipitation into the atmosphere is one of the main processes by which high energy electrons trapped in Earth's inner magnetosphere are lost from the system. Precipitating electrons can affect the chemical composition of the atmosphere and provide insight into the complex dynamics of the Van Allen radiation belts. This study compares energetic electron precipitation measurements at low‐Earth‐orbit by the Focused Investigations of Relativistic Electron Burst Intensity, Range, and Dynamics (FIREBIRD‐II) CubeSats with NOAA Polar‐orbiting Operational Environmental Satellite (POES) and ESA Meteorological Operational satellite (MetOp) satellites, which are equipped with the Medium‐Energy Proton Electron Detector (MEPED). The analysis considers 51 high quality conjunction events at >300 keV during times of low to moderate geomagnetic activity. The spacecraft capture similar electron flux variability, and FIREBIRD‐II observations fall between POES/MetOp and telescopes, likely a result of FIREBIRD‐II sampling both precipitating and mirrored electrons due to uncertainties in pointing direction. Results demonstrate the value of high‐resolution differential energy observations of electron precipitation by low‐cost CubeSats such as FIREBIRD‐II, especially during periods of low flux.

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.

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

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.

Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)

Abstract. In satellite remote sensing applications, enhancing the precision of level 2 (L2) algorithms relies heavily on the accurate estimation of the surface reflectance across the ultraviolet (UV) to visible (VIS) spectrum. However, the mutual dependence between the L2 algorithms and the surface reflectance retrieval poses challenges, necessitating an alternative approach. To address this issue, many satellite algorithms generate Lambertian-equivalent reflectivity (LER) products as a priori surface reflectance data; however, this often results in an underestimation of these data. This study is the first to assess the applicability of background surface reflectance (BSR), derived using a semi-empirical bidirectional reflectance distribution function (BRDF) model, in an operational environmental satellite algorithm. This study pioneered the application of the BRDF model to hyperspectral satellite data at 440 nm, aiming to provide more realistic preliminary surface reflectance data. In this study, the Geostationary Environment Monitoring Spectrometer (GEMS) data were used, and a comparative analysis of the GEMS BSR and GEMS LER retrieved in this study revealed an improvement in the relative root mean squared error (rRMSE) accuracy of 3 %. Additionally, a time series analysis across diverse land types indicated a greater stability exhibited by the BSR than by the LER. For further validation, the BSR was compared with other LER databases using ground-truth data, yielding superior simulation performance. These findings present a promising avenue for enhancing the accuracy of surface reflectance retrieval from hyperspectral satellite data, thereby advancing the practical applications of satellite remote sensing algorithms.

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.

Genomic evolution and rearrangement of CTX-Φ prophage elements in Vibrio cholerae during the 2018–2024 cholera outbreaks in eastern Democratic Republic of the Congo

ABSTRACT Between 2018 and 2024, we conducted systematic whole-genome sequencing and phylogenomic analysis on 263 V. cholerae O1 isolates from cholera patients across four provinces in the Democratic Republic of Congo (North-Kivu, South-Kivu, Tanganyika, and Kasai Oriental). These isolates were classified into the AFR10d and AFR10e sublineages of AFR10 lineage, originating from the third wave of the seventh El Tor cholera pandemic (7PET). Compared to the strains analysed between 2014 and 2017, both sublineages had few genetic changes in the core genome but recent isolates (2022-2024) had significant CTX prophage rearrangement. AFR10e spread across all four provinces, while AFR10d appeared to be extinct by the end of 2020. Since 2022, most V. cholerae O1 isolates exhibited significant CTX prophage rearrangements, including a tandem repeat of an environmental satellite phage RS1 downstream the ctxB toxin gene of the CTX-Φ-3 prophage on the large chromosome, as well as two or more arrayed copies of an environmental pre-CTX-Φ prophage precursor on the small chromosome. We used Illumina data for mapping and coverage estimation to identify isolates with unique CTX-Φ genomic features. Gene localization was then determined on MinION-derived assemblies, revealing an organization similar to that of non-O1 V. cholerae isolates found in Asia (O139 VC1374, and environmental O4 VCE232), but never described in V. cholerae O1 El Tor from the third wave. In conclusion, while the core genome of AFR10d and AFR10e showed minimal changes, significant alterations in the CTX-Φ and pre-CTX-Φ prophage content and organization were identified in AFR10e from 2022 onwards.