Geostationary Operational Environmental Satellite Research Articles

Observations of Lightning NOx Production From Tropospheric Monitoring Instrument Case Studies Over the United States

AbstractNitrogen oxides produced by lightning (LNOx) play an important role in determining mid‐ and upper‐tropospheric concentrations of the hydroxyl radical (OH), methane (CH4), and ozone (O3). The moles of NOx produced per flash was examined using nitrogen dioxide (NO2) columns and cloud properties from the Tropospheric Monitoring Instrument (TROPOMI) and flash counts from the Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite‐16 (GOES‐16) and Earth Networks Total Lightning Network (ENTLN) for 29 convective systems over the United States that occurred during 2018 and 2019. For each of the case studies, the LNOx production efficiency (PE) was estimated using TROPOMI pixels over deep convection. First, the NOx columns associated with the TROPOMI NO2 columns were estimated using a specially derived air mass factor (AMF). The tropospheric column due to recent lightning was then determined by subtracting from the median NOx column a background representative of the NOx column due to sources other than recent lightning. Then, the PE was calculated by multiplying the LNOx column by the storm area and dividing by the number of flashes contributing to the column. For a three‐hour chemical lifetime, the mean PE was found to be 175 ± 100 mol per flash for optical flashes from GLM and 120 ± 65 mol per flash for radio‐wave‐detected flashes from ENTLN. The uncertainty associated with these values is mostly due to uncertainties in tropospheric background, AMF, and detection efficiency. GLM PE for individual systems was found to be positively correlated with optical energy.

On the semi-annual variation of relativistic electrons in the outer radiation belt

Abstract. The nature of the semi-annual variation in the relativistic electron fluxes in the Earth's outer radiation belt is investigated using Van Allen Probes (MagEIS and REPT) and Geostationary Operational Environmental Satellite Energetic Particle Sensor (GOES/EPS) data during solar cycle 24. We perform wavelet and cross-wavelet analysis in a broad energy and spatial range of electron fluxes and examine their phase relationship with the axial, equinoctial and Russell–McPherron mechanisms. It is found that the semi-annual variation in the relativistic electron fluxes exhibits pronounced power in the 0.3–4.2 MeV energy range at L shells higher than 3.5, and, moreover, it exhibits an in-phase relationship with the Russell–McPherron effect, indicating the former is primarily driven by the latter. Furthermore, the analysis of the past three solar cycles with GOES/EPS indicates that the semi-annual variation at geosynchronous orbit is evident during the descending phases and coincides with periods of a higher (lower) high-speed stream (HSS) (interplanetary coronal mass ejection, ICME) occurrence.

Monitoring precipitable water vapour in near real-time to correct near-infrared observations using satellite remote sensing

Context. In the search for small exoplanets orbiting cool stars whose spectral energy distributions peak in the near infrared, the strong absorption of radiation in this region due to water vapour in the atmosphere is a particularly adverse effect for ground-based observations of cool stars. Aims. To achieve the photometric precision required to detect exoplanets in the near infrared, it is necessary to mitigate the effect of variable precipitable water vapour (PWV) on radial-velocity and photometric measurements. The aim is to enable global PWV correction by monitoring the amount of precipitable water vapour at zenith and along the line of sight of any visible target. Methods. We developed an open-source Python package that uses imagery data obtained with the Geostationary Operational Environmental Satellites (GOES), which provide temperature and relative humidity at different pressure levels to compute the near real-time PWV above any ground-based observatory that is covered by GOES every 5 min or 10 min, depending on the location. Results. We computed PWV values on selected days above Cerro Paranal (Chile) and San Pedro Mártir (Mexico) to benchmark the procedure. We also simulated different pointing at test targets as observed from the sites to compute the PWV along the line of sight. To asses the accuracy of our method, we compared our results with the on-site radiometer measurements obtained from Cerro Paranal. Conclusions. Our results show that our publicly available code proves to be a good supporting tool for measuring the local PWV for any ground-based facility within the GOES coverage, which will help to reduce correlated noise contributions in near-infrared ground-based observations that do not benefit from on-site PWV measurements.

Water and Light‐Use Efficiency Are Enhanced Under Summer Coastal Fog in a California Agricultural System

AbstractIn coastal California, the peak growing season of economically important crops is concurrent with fog events, which buffer drought stress during the dry season. Coastal fog patterns are changing, so we quantified its effects on the energy, water, and carbon fluxes of a strawberry farm located in the fog‐belt of the Salinas Valley, California. We used Geostationary Operational Environmental Satellite (GOES) total albedo to detect and quantify large scale patterns of coastal fog. We used eddy covariance (EC) to quantify actual evapotranspiration and gross primary productivity (GPP) at the field scale (approximately 0.5–3 hectares) from June to September 2016. We measured canopy‐scale (approximately 0.6 m2) strawberry physiology on foggy and non‐foggy days within the measurement footprint of the EC tower. Downwelling longwave radiation (L↓), observed by a surface‐mounted pyrgeometer, was consistently higher on foggy compared to clear‐sky days (regardless of fog‐drip), indicating that emission of longwave radiation was derived almost entirely from the cloud base. L↓ and total GOES albedo were positively and strongly correlated (R2 = 0.68, P < 0.01). For both field‐ and canopy‐scales, water‐use and light‐use efficiency increased by as much as 50% and 70%, respectively, during foggy compared to non‐foggy conditions. The initial slope of the curvilinear relationship fit between GPP and photosynthetically active radiation was twice as steep during foggy (α = 0.0395) than non‐foggy (α = 0.0210) conditions, suggesting that the scattering of light during fog events enhances photosynthetic output of whole‐plants. Our results suggest that irrigation for these fields could be rescheduled during foggy periods without sacrificing plant productivity.

Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data

Incident photosynthetically active radiation (PAR) is a key parameter in plant physiological, biological and physical process-based terrestrial ecosystem models. Deriving highly accurate and spatially continuous PAR from remote sensing data can fill in the gaps of insufficient ground-based measurements. In this study, we provide an efficient approach for estimating PAR by employing look-up table (LUT) method using observations from both MODIS and GOES-16. The GOES-16 is the first satellite of the new-generation US Geostationary Operational Environmental Satellite R-series (GOES-R), which can capture dynamic changes in the atmosphere every 5 min. First, the diurnal variation of surface bidirectional reflectance is characterized assuming that the surface characteristics and solar zenith angles in tropical and middle latitudes at a given time of day do not change dramatically within a month. Next, the aerosol optical depth (AOD) and cloud optical depth (COD) are retrieved from the GOES-16 visible observations under both clear-sky and cloudy conditions. Finally, PAR is estimated via the retrieved AOD and COD using the LUT method and a MODIS-based surface albedo product. The estimated PAR was compared against ground-based measurements collected from seven SURFace RADiation budget network (SURFRAD) sites and 23 National Ecological Observatory Network (NEON) sites at instantaneous, hourly and daily scales. The instantaneous PAR validation results achieved R2 values of 0.97 and 0.82, and root mean square errors (RMSEs) of 18.1 and 51.1 W m‐2, for clear and cloudy skies, respectively. The RMSEs of the hourly averaged PAR were reduced to 16.8 W m−2 and 40.4 W m−2, respectively, for clear-sky and cloudy conditions. The daily mean PAR evaluation for all sky conditions shows an overall mean bias error (MBE) of 0.97 W m−2 and an RMSE of 10.5 W m−2. Compared with other global PAR datasets, the estimated PAR values in this study have lower RMSEs and higher spatial-temporal resolution. Uncertainties in the retrieved PAR caused by topography and solar-cloud-geometry effects (SCGE) can be reduced by spatial and temporal averaging. Based on an evaluation of retrievals at different spatial-temporal scales, we recommend that a 20 × 20 km2 domain-averaged PAR be utilized for instantaneous PAR validation, whereas hourly and daily averaged PAR values are nearly independent of spatial scale.

Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation

Third-generation geostationary meteorological satellites (GEOs), such as Himawari-8/9 Advanced Himawari Imager (AHI), Geostationary Operational Environmental Satellites (GOES)-R Series Advanced Baseline Imager (ABI), and Meteosat Third Generation (MTG) Flexible Combined Imager (FCI), provide advanced imagery and atmospheric measurements of the Earth’s weather, oceans, and terrestrial environments at high-frequency intervals. Third-generation GEOs also significantly improve capabilities by increasing the number of observation bands suitable for environmental change detection. This review focuses on the significantly enhanced contribution of third-generation GEOs for disaster monitoring and risk mitigation, focusing on atmospheric and terrestrial environment monitoring. In addition, to demonstrate the collaboration between GEOs and Low Earth orbit satellites (LEOs) as supporting information for fine-spatial-resolution observations required in the event of a disaster, the landfall of Typhoon No. 19 Hagibis in 2019, which caused tremendous damage to Japan, is used as a case study.

Observations of Lightning NOx Production From GOES‐R Post Launch Test Field Campaign Flights

AbstractA primary goal of the Geostationary Operational Environmental Satellite R‐series Post Launch Test (GOES‐R PLT) Field Campaign during spring 2017 was the performance evaluation of the Geostationary Lightning Mapper (GLM) aboard the GOES‐16 satellite. The NASA Goddard Geo‐CAPE Airborne Simulator (GCAS), an ultra‐violet visible spectrometer, piggybacked on the aircraft mission to allow continuous hyper‐spectral measurements at high spectral and spatial resolutions simultaneously with optical lightning detection by the Fly’s Eye GLM Simulator while overflying convective systems. NO2 columns retrieved from GCAS were used to estimate the moles of NOx produced per flash, referred to as lightning NOx production efficiency (LNOx PE) for convective systems over the United States and western Atlantic. The mean PE was determined to be 360 ± 180 mol per flash for optically detected GLM flashes and 230 ± 115 mol per flash for radio‐wave detected Earth Networks Total Lightning Network flashes. These values span the commonly cited range of 100–500 mol per flash for midlatitude flashes. LNOx PE was found to be positively correlated with GLM flash multiplicity and flash optical energy but negatively correlated with flash density. The positive correlations provide encouragement for PE parameterizations in terms of flash energy or multiplicity. Observations during the GOES‐R PLT field campaign provide a preview of the analysis that will be possible when continuous lightning detection is coupled with hourly NO2 columns from a geostationary instrument such as Tropospheric Emissions: Monitoring of Pollution.

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Abstract. An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite

ABSTRACT Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.

Surface Thermal Structure and Variability of Upwelling Shadows in the Gulf of Arauco, Chile

AbstractThe upwelling shadow in the Gulf of Arauco (GA) is studied using 15 years of daily satellite images of Sea Surface Temperature (SST) from Geostationary Operational Environmental Satellites (GOES), as well as heat flux and wind data from the ERA5 reanalysis product. An Upwelling Shadow Index (USI) is developed based on the SST differences between the GA and the region off Punta Lavapie (PL) farther offshore, characterized by active upwelling. USI values greater than 0.91°C and SST differences less than 1.1°C, correspond to an upwelling shadow event. These cases occurred 10.29% of the time and were more frequent in spring/summer during events of upwelling‐favorable winds with strong cyclonic wind stress curl, interspersed with wind relaxations. Multiple correlations between USI and wind stress curl and solar radiation showed an r2 = 40%–80% for some summer months. Most events persisted for only 1 day (53%), whereas 34% of upwelling shadow events lasted between 2 and 4 days and 9% of events were longer than 4 days. Water residence times as long as 15 days were observed in the GA during upwelling shadow events. During southerly wind relaxation (less than 2 days), cold surface waters flowing north from PL curved onshore and entered the GA from the north, weakening the thermal gradient between PL and GA and the upwelling shadow. Long periods of wind relaxation (at least 1 week) resulted in the dissipation of the thermal gradient due to the warming of offshore waters.

Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data

AbstractA total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

On the Origin of Rotation Derived from Super Rapid Scan Satellite Imagery at the Cloud-Tops of Severe Deep Convection

AbstractSevere thunderstorms routinely exhibit adjacent maxima and minima in cloud-top vertical vorticity (CTV) downstream of overshooting tops within flow fields retrieved using sequences of fine-temporal resolution (1-min) geostationary operational environmental satellite (GOES)-R series imagery. Little is known about the origin of this so-called “CTV couplet” signature, and whether the signature is the result of flow field derivational artifacts. Thus, the CTV signature’s relevance to research and operations is currently ambiguous. Within this study, we explore the origin of near-cloud-top rotation using an idealized supercell numerical model simulation. Employing an advanced dense optical flow algorithm, image stereoscopy, and numerical model background wind approximations, the artifacts common with cloud-top flow field derivation are removed from two supercell case studies sampled by GOES-R imagers. It is demonstrated that the CTV couplet originates from tilted and converged horizontal vorticity that is baroclinically generated in the upper levels (above 10 km) immediately downstream of the overshooting top. This baroclinic generation would not be possible without a strong and sustained updraft, implying an indirect relationship to rotationally-maintained supercells. Furthermore, it is demonstrated that CTV couplets derived with optical flow algorithms originate from actual rotation within the storm anvils in the case studies explored here, though supercells with opaque above anvil cirrus plumes and strong anvil-level negative vertical wind shear may produce rotation signals as an artifact without quality control. Artifact identification and quality control is discussed further here for future research and operations use.

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Abstract. Monitoring leaf phenology tracks the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam network, provide valuable information on phenological state at high temporal resolution, but they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p values ≤0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than those from MODIS NDVI for the estimates of the beginning and middle of spring. GOES will improve the monitoring of phenology at large spatial coverages and provides real-time indicators of phenological change even when the entire spring transition period occurs within the 16 d resolution of these MODIS products.

GOES-16 observations of rapidly-intensifying tropical cyclones: Hurricanes Harvey (2017), Maria (2017), and Michael (2018)

AbstractThis study utilizes brightness temperatures (Tb’s) observed by the infrared longwave window band (Ch 14; 11.2 μm) from the Geostationary Operational Environmental Satellite-16 (GOES-16) to examine the structure of Hurricanes Harvey, Maria, and Michael throughout their lifetimes. During the times leading up to their rapid intensifications (RI), two-dimensional inner-core structures are examined to analyze the strength and location of the developing convection. Moderate vertical wind shear in the environments of Harvey and Michael induced a pronounced convective asymmetry prior to RI, followed by a rapid axisymmetrization that occurred essentially in conjunction with RI. The evolutions of the tropical cyclones’ (TCs’) coldest Tb’s indicate that the inner-core convective activity began to increase in the 12 h prior to RI onset, primarily in 2–4-h substantial “bursts”, while substantial convection dominated essentially the entirety of the region within 100 km of the surface center within 12 h of the onset of intensification.Azimuthally averaged Tb evolutions illustrate the development of each TCs’ eye and eyewall, the variability of the radial extent of the central dense overcast associated with the diurnal cycle, as well as details of the evolving convective structures throughout intensification. Hövmoller diagrams of data at constant radii reveal areas of cold Tb’s propagating around the TCs on timescales of 2–3-h. The examination of these features in a deep-layer shear-relative sense reveals that they initiate primarily downshear of the TCs’ surface centers. As RI is reached, these areas of convection are able to propagate into the upshear quadrants, which helps facilitate the onset of more substantial intensification.

Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations

AbstractFew studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.

Recurrent analysis of solar flare signals

In this paper, the signals of the sun's X-ray radiation during solar flares are studied using the method of recurrent diagrams. The time fluxes of X-ray radiation at the wavelengths λ = 0.1 ÷ 0.8 nm and λ = 0.05 ÷ 0.4 nm, obtained by the GOES-15 (Geostationary Operational Environmental Satellite) space satellite, were used as the objects of the study. It was found that in the period from 2015 to 2019, the strongest solar flares occurred in September 2017 in the group of spots №2673. The causes of the X9,3 class flare from 06.09.2017 that occurred in this group of spots are analyzed in detail. Time and recurrent diagrams are plotted for each solar flare event. Analysis of recurrent diagrams for X -, M -, and C-class solar flare events revealed that soft X-ray radiation provides more information about the processes occurring on the Sun than hard X-ray radiation. It is shown that the method of recurrent diagrams more clearly captures the features and detects changes (periodicity) in a non-stationary non-uniform time series that corresponds to time intervals of high-energy solar events. Key words: Sun, group of spots, flare, X-ray radiation, nonlinear analysis, recurrent diagram

Catalog and Statistical Examinations of Lyα Solar Flares from GOES/EUVS-E Measurements

Abstract The Lyman-alpha (Lyα) line of neutral hydrogen at 121.6 nm is by far the brightest emission line in the vacuum ultraviolet spectral range of the Sun. The emission at this line could be a major energy input to the upper layers of the Earth’s atmosphere, strongly impacting the geospace environment. The Geostationary Operational Environmental Satellite (GOES) series, starting with GOES-13, began to carry a multichannel Extreme UltraViolet Sensor (EUVS) with one channel (E-channel) targeting the Lyα line. In the present work, we produce a Lyα flare catalog from the GOES-15/EUVS-E data between 2010 April 8 and 2016 June 6 with an automatic flare detection algorithm. This algorithm is designed to search events at various scales and find their real start and end times. Based on the obtained flare list, statistics on the temporal behavior such as the duration, rise, and decay times, and the event asymmetries of Lyα flares is presented. On average (defined by the median of the distributions), the duration, rise and decay times of the flares were estimated to be 20.8 minutes, 5.6 minutes, and 14.2 minutes, respectively. We also discuss the frequency distributions of the peak flux and the fluence of Lyα flares, both of which reveal power-law behaviors with power-law indices of 2.71 ± 0.06 and 2.42 ± 0.06, respectively, implying that more flares are accumulated at small scales and these small-scale events play an important role in explaining the violent solar energy release.

Averaged Solar Torque Rotational Dynamics for Defunct Satellites

Spin state predictions for defunct satellites in geosynchronous Earth orbit (GEO) are valuable for active debris removal and servicing missions as well as material shedding studies and attitude-dependent solar radiation pressure (SRP) modeling. Previous studies have shown that solar radiation torques can explain the observed spin state evolution of some GEO objects via the Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect. These studies have focused primarily on uniform rotation. Nevertheless, many objects are in nonprincipal axis rotation (i.e., tumbling). Recent exploration of the tumbling regime for the family of retired Geostationary Operational Environmental Satellite (GOES) 8–12 satellites has shown intriguing YORP-driven behavior including spin-orbit coupling, tumbling cycles, and tumbling period resonances. To better explore and understand the tumbling regime, a semi-analytical tumbling-averaged rotational dynamics model has been developed. The derivation requires analytically averaging over the satellite’s torque-free rotation, defined by Jacobi elliptic functions. Averaging is facilitated by a second-order Fourier series approximation of the facet illumination function. The averaged model is found to capture and explain the general long-term behavior of the full dynamics while reducing computation time by roughly three orders of magnitude. This improved computation efficiency promises to enable rapid exploration of general long-term rotational dynamics for defunct satellites and rocket bodies.

Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship to Physical Parameters

Abstract In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts “Yes” or “No” for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.