Geostationary Operational Environmental Satellite Research Articles

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network

Abstract Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database from GOES-12/13 to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.

The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States

Abstract This paper presents the Thunderstorm Nowcasting Tool (ThunderCast), a 24-h, year-round model for predicting the location of convection that is likely to initiate or remain a thunderstorm in the next 0–60 min in the continental United States, adapted from existing deep learning convection applications. ThunderCast utilizes a U-Net convolutional neural network for semantic segmentation trained on 320 km × 320 km data patches with four inputs and one target dataset. The inputs are satellite bands from the Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) in the visible, shortwave infrared, and longwave infrared spectra, and the target is Multi-Radar Multi-Sensor (MRMS) radar reflectivity at the −10°C isotherm in the atmosphere. On a pixel-by-pixel basis, ThunderCast has high accuracy, recall, and specificity but is subject to false-positive predictions resulting in low precision. However, the number of false positives decreases when buffering the target values with a 15 km × 15 km centered window, indicating ThunderCast’s predictions are useful within a buffered area. To demonstrate the initial prediction capabilities of ThunderCast, three case studies are presented: a mesoscale convective vortex, sea-breeze convection, and monsoonal convection in the southwestern United States. The case studies illustrate that the ThunderCast model effectively nowcasts the location of newly initiated and ongoing active convection, within the next 60 min, under a variety of geographical and meteorological conditions. Significance Statement In this research, a machine learning model is developed for short-term (0–60 min) forecasting of thunderstorms in the continental United States using geostationary satellite imagery as inputs for predicting active convection based on radar thresholds. Pending additional testing, the model may be able to provide decision-support services for thunderstorm forecasting. The case studies presented here indicate the model is able to nowcast convective initiation with 5–35 min of lead time in areas without radar coverage and anticipate future locations of storms without additional environmental context.

Relationship of peak fluxes of solar radio bursts and X-ray class of solar flares: Application to early great solar flares

Abstract Large solar flares occasionally trigger significant space-weather disturbances that affect the technological infrastructures of modern civilization, and therefore require further investigation. Although these solar flares have been monitored by satellite observations since the 1970s, large solar flares occur only infrequently and restrict systematic statistical research owing to data limitations. However, Toyokawa Observatory has operated solar radio observations at low frequencies (at 3.75 and 9.4 GHz) since 1951 and captured the early great flares as solar radio bursts. To estimate the magnitudes of flares that occurred before the start of solar X-ray (SXR) observations with the Geostationary Operational Environmental Satellite (GOES) satellites, we show the relationship between microwave fluxes at 3.75 and 9.4 GHz and X-ray fluxes of flares that occurred after 1988. In total, we explored 341 solar flares observed with the Nobeyama Radio Polarimeters and Toyokawa Observatory from 1988–2014 and compared them with the SXR observations recorded by the GOES satellites. The correlation coefficient was approximately 0.7. Therefore, the GOES X-ray class can be estimated from the peak flux at 3.75 and 9.4 GHz with a large variance and an error of factor of 3 (1σ). Thus, for the first time, we quantitatively estimated the light curves of two early solar flares observed in 1956 February by the Toyokawa solar radio observations using the relationship between SXR thermal radiation and microwave nonthermal radiation (Neupert, 1968, ApJ, 153, 59).

Validation of the Geostationary Lightning Mapper With a Lightning Mapping Array in Argentina: Implications for Current and Future Spaceborne Lightning Observations

AbstractA validation study of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite 16 (GOES‐16) was done using a ground‐based lightning mapping array (LMA) deployed as part of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign in Argentina. GLM detected lightning with 74.6% efficiency over 61 thunderstorm days in December 2018 through April 2019. However, GLM detection efficiency (DE) was negatively correlated (r = −0.49) with LMA flash rate. GLM DE also was negatively correlated with LMA flash altitude (r = −0.24), reflecting the influence of multiple competing trends. GLM DE was positively correlated (r = 0.27) with number of LMA sources in a flash, indicating improved DE for larger flashes. During periods with anomalously electrified storms, GLM DE was reduced to 50.9%. Statistics were found to be sensitive to analysis criteria, but most of the above trends remained consistent regardless of specific criteria. Because the methodology allowed a GLM flash to match more than one LMA flash, actual GLM flash rate was a factor of 2.9 lower than the LMA flash rate, and this ratio grew larger as LMA flash rate increased. A sensitivity study examined the impact of improved DE for smaller flashes; that is, an improved sensor (or algorithm) that was better able to detect and distinguish between separate small lightning flashes. The results showed improved correlation with LMA flash rates, as well as improved ability to identify lightning jumps associated with intensifying convection.

Preflare X-Ray Pulsations with Sources Outside the Main Flare Active Region

Earlier, we showed that according to the nature of the location of sources of preflare X-ray pulsationsrelative to the main solar flare, events are divided into at least two types: in type I events, the sources ofpulsations and the main flare are in the same active region (AR) and in type II events they are in differentregions. This paper presents an analysis of a type II event in which, according to data from the Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) space observatory, X-ray sources of preflare quasi-periodicpulsations (with a period P = 1.5 ± 0.1 min), which began at ~1802 UT, were located in AR 11884 in the WesternHemisphere, and the sources of the main flare M1.0 SOL2013-11-05T18:08 were located in AR 11890 inthe Eastern Hemisphere. The pulsations were also observed with the Gamma-Ray Burst Monitor (GBM)aboard the Fermi space observatory and the X-Ray Sensor (XRS) aboard the Geostationary OperationalEnvironmental Satellite (GOES), excluding the possibility of their artificial origin. According to the data ofthe Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) in the extremeultraviolet range, it was found that the sources of pulsations were located at the base of coronal jets that flowedout at velocities of ~100–1500 km/s. The distance between AR 11884 and AR 11890 was ~1.4 RS. It wouldtake ~17–250 min for the jet plasma to reach AR 11890, which is much longer than the time interval betweenthe onset of pulsations (jets) and the flare (~6 min). No loops connecting AR 11884 and AR 11890 wereobserved in the corona. Moreover, no connection of these regions by magnetic field lines extrapolated fromthe photosphere to the corona in the potential approximation was found. These arguments indicate that thejets (and associated pulsations) could not be the trigger for the flare. Thus, a vivid example of an event is presentedin which there was no physical connection between preflare X-ray pulsations (and jets) and the flarethat followed them. This event demonstrates the importance of spatially resolved observations in the study ofpulsations on the Sun and stars.

Parametrization of Energetic Ion and Electron Fluxes in the Near‐Earth Magnetotail

AbstractThe near‐Earth plasma sheet region is the main source of energetic (tens to hundreds keV) ion and electron populations transported by convection and injections into the inner magnetosphere. Energetic ions from the plasma sheet contribute to the ring current, whereas energetic electrons contribute to the radiation belt seed population for further acceleration to relativistic energies. Near‐Earth plasma sheet energetic fluxes have been traditionally used to set boundary conditions for radiation belt and ring current models. This study provides an empirical parametrization for ∼75 keV flux intensity as a function of the geomagnetic activity index auroral electrojet and the equatorial magnetic field Bz. Such parametrization includes the dynamic magnetic field configuration in the near‐Earth plasma sheet and may be merged with empirical magnetic field models. We also provide models extending this parametrization to the [20, 300] keV of electron energy range and [75, 300] keV of ion energy range. The parametrization is developed based on THEMIS and Geostationary Operational Environmental Satellite measurements, and verified by comparison with MMS measurements in the near‐Earth plasma sheet. This parametrization incorporates meso‐scale transient flux variations associated with Bz perturbations into ring current and radiation belt simulations.

Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery

Abstract Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories. Significance Statement Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.

Operational prediction of solar flares using a transformer-based framework

Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a gamma-class flare within the next 24 to 72 h. We consider three gamma classes, namely the geM5.0 class, the geM class and the geC class, and build three transformers separately, each corresponding to a gamma class. Each transformer is used to make predictions of its corresponding gamma-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.

Making a Superbolt: Reconciling Observations of the Optically Brightest Lightning on Earth From Different Satellites

AbstractWe previously documented geographic distributions of the optically brightest lightning on Earth—known as “superbolts”—using two space‐based instruments: the photodiode detector (PDD) on the Fast On‐orbit Recording of Transient Events (FORTE) satellite and the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites. In this study, we further examine the superbolts identified by the PDD and GLM to reconcile the differences between their geographic distributions. We find that both the physical extent of the parent flash and the development speed of its leaders are important for making a superbolt. The oceanic PDD superbolts tend to occur early in flashes that rapidly expand laterally into long horizontal “megaflashes.” The top GLM superbolts occur over land at later times in particularly large megaflashes. These land‐based flashes grow more slowly until they extend over multiple hundreds of kilometers. The FORTE PDD missed these delayed superbolts due to limitations in its triggering. Coincident Tropical Rainfall Measuring Mission measurements show that the warm season megaflash superbolts detected by Lightning Imaging Sensor/GLM and wintertime oceanic superbolts observed by the PDD occur in otherwise similar thunderstorm environments. Both are marked by: low storm heights (<10 km), widespread precipitation near the surface, small infrared brightness temperature gradients, and low flash rates. We suggest that the vertically compact, stratiform nature of these clouds provides favorable conditions for superbolt production.

Preliminary Use of Convection-allowing Models in Fire Weather

Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.

WWLLN Energetic Lightning Events Are Different From Optical Superbolts

AbstractThe most powerful optical emissions from lightning have been described as “superbolts” since the 1970s. Holzworth et al. (2019, https://doi.org/10.1029/2019jd030975) recently applied the superbolt label to the most energetic Radio Frequency emissions recorded by the World Wide Lightning Location Network (WWLLN). We compare the WWLLN energies to optical measurements by the photodiode detector on the Fast On‐orbit Recording of Transient Events satellite and the Geostationary Lightning Mappers on NOAA's Geostationary Operational Environmental Satellites to assess whether these energetic WWLLN events coincide with optical superbolts. We find no overlap between optical and WWLLN superbolts. Moreover, extreme WWLLN events occur in a contrasting meteorological context to optical superbolts. Despite similarities in their overall global patterns of occurrence, WWLLN superbolts correspond to a different phenomenon.

Machine Learning–Based Cloud Forecast Corrections for Fusions of Numerical Weather Prediction Model and Satellite Data

Abstract Given the diversity of cloud-forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning–based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numerical weather prediction model error trends as well as improving the accuracy and sensitivity of the forecasts. The framework implements a UNet convolutional neural network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite-16 (GOES-16) as well as clouds predicted by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper-tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3–12 h). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application and a comparative assessment of results for upper-tropospheric clouds. Significance Statement Clouds are difficult to forecast because they require, in addition to spatial location, accurate height, depth, and cloud type. Satellite imagery is useful for verifying geographical location but is limited by 2D technology. Multiple cloud types can coexist at various heights within the same pixel. In this situation, cloud/no cloud verification does not convey much information about why the forecast went wrong. Sorting clouds by physical attributes such as cloud-top height, atmospheric stability, and cloud thickness contributes to a better understanding since very different physical mechanisms produce various types of clouds. Using a fusion of numerical model outputs and GOES-16 observations, we derive variables related to atmospheric conditions that form and move the clouds for our machine learning–based cloud forecast. The resulting verification over the U.S. mid-Atlantic region revealed our machine learning–based cloud forecast corrects systematic errors associated with high atmospheric clouds and provides accurate and consistent cloud forecasts from 3 to 12 h lead times.

The First AGILE Solar Flare Catalog

We report the Astro-rivelatore Gamma a Immagini LEggero (AGILE) observations of solar flares, detected by the onboard anticoincidence system in the 80–200 keV energy range, from 2007 May 1 to 2022 August 31. In more than 15 yr, AGILE detected 5003 X-ray, minute-lasting transients, compatible with a solar origin. A cross-correlation of these transients with the Geostationary Operational Environmental Satellites (GOES) official solar flare database allowed us to associate an intensity class (i.e., B, C, M, or X) to 3572 of them, for which we investigated the main temporal and intensity parameters. The AGILE data clearly revealed the solar activity covering the last stages of the 23rd cycle, the whole 24th cycle, and the beginning of the current 25th cycle. In order to compare our results with other space missions operating in the high-energy range, we also analyzed the public lists of solar flares reported by RHESSI and Fermi Gamma-ray Burst Monitor. This catalog reports 1424 events not contained in the GOES official data set, which, after statistical comparisons, are compatible with low-intensity, short-duration solar flares. Besides providing a further data set of solar flares detected in the hard X-ray range, this study allowed to point out two main features: a longer persistence of the decay phase in the high-energy regime, with respect to the soft X-rays, and a tendency of the flare maximum to be reached earlier in the soft X-rays with respect to the hard X-rays. Both these aspects support a two-phase acceleration mechanism of electrons in the solar atmosphere.

NOAA Space Weather Prediction Center Radiation Advisories for the International Civil Aviation Organization

AbstractThe National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) issues several solar radiation storm products: the long standing proton Warnings and Alerts that are based on particle intensity levels observed by the Geostationary Operational Environmental Satellites; and the more recent International Civil Aviation Organization (ICAO) radiation advisories which specify effective dose rates at aviation flight levels. SWPC ICAO advisories are supported by the U.S. Federal Aviation Administration (FAA) CARI‐7A model. In this paper we use CARI‐7A modeling results for the Ground Level Enhancement 69 (GLE69) solar radiation storm which occurred on the 20th of January 2005 to demonstrate the ICAO advisory format. For the onset and peak of GLE69, we find that a severe (SEV) radiation advisory would have been issued for altitudes above 32,000 ft, for polar and mid latitude regions of the northern and southern hemisphere. At lower altitudes, down to 25,000 ft, the moderate (MOD) radiation threshold would have been exceeded. In total, 10 ICAO radiation advisories would have been issued over 6.5 hr. From the retrospective modeling of GLE69, and feedback from users, we identify ways in which the ICAO advisories should be improved.

Predicting Region-Dependent Biases in a GOES-16 Machine Learning Precipitation Retrieval

Abstract Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 (GOES-16) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region’s validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale.

Effect of solar proton events on test mass for gravitational wave detection in the 24th solar cycle

Free-falling cubic Test Masses (TMs) are a key component of the interferometer used for low-frequency gravitational wave (GW) detection in space. However, exposure to energetic particles in the environment can lead to electrostatic charging of the TM, resulting in additional electrostatic and Lorentz forces that can impact GW detection sensitivity. To evaluate this effect, the high-energy proton data set of the Geostationary Operational Environmental Satellite (GOES) program was used to analyze TM charging due to Solar Proton Events (SPEs) in the 24th solar cycle. Using the Geant4 Monte Carlo toolkit, the TM charging process is simulated in a space environment for SPEs falling into three ranges of proton flux: (1) greater than 10 pfu and less than 100 pfu, (2) greater than 100 pfu and less than 1000 pfu, and (3) greater than 1000 pfu. It is found that SPEs charging can reach the threshold within 535 s to 18.6 h, considering a reasonable discharge threshold of LISA and Taiji. We demonstrate that while there is a somewhat linear correlation between the net charging rate of the TM and the integrated flux of ge 10 MeV SPEs, there are many cases in which the integrated flux is significantly different from the charging rate. Therefore, we investigate the difference between the integral flux and the charging rate of SPEs using the charging efficiency assessment method. Our results indicate that the energy spectrum structure of SPEs is the most important factor influencing the charging rate. Lastly, we evaluate the charging probability of SPEs in the 24th solar cycle and find that the frequency and charging risk of SPEs are highest in the 3rd, 4th, 5th, 6th, and 7th years, which can serve as a reference for future GW detection spacecraft.

Monitoring the Mauna Loa (Hawaii) eruption of November–December 2022 from space: Results from GOES-R, Sentinel-2 and Landsat-8/9 observations

Mauna Loa, one of the most actives volcanoes on Earth, is a shield volcano, located on the Island of Hawaii (USA). On 27 November 2022, after about 38 years of quiescence, a new eruptive activity took place at the Moku‘āweoweocaldera, continuing in the following days (i.e. until 10 December) from the fissure vents opening on the Northeast Rift Zone.In this work, we investigate the Mauna Loa November − December 2022 eruption from space, integrating the information from different satellite sensors. The analysis of short-wave infrared (SWIR) data, at 10 min temporal resolution, from the Advanced Baseline Imager (ABI), aboard the Geostationary Operational Environmental Satellites − R series (GOES-R), performed through the Normalised Hotspot Indices (NHI), indicates that the Mauna Loa eruption started on 27 November in between 23:10–23:20 LT (28 November at 09:10–09:20 UTC). The same analysis shows the increase of thermal activity and its progressive reduction from the early morning of 28 November, in agreement with the eruption migration from the summit caldera to the Northeast Rift Zone. By analysing the second phase of eruption through SWIR data from the Multispectral Instrument (MSI) and Operational Land Imager (OLI), respectively aboard Sentinel-2 and Landsat 8/9 satellites, we estimated a maximum lava flow length of 17 km. Moreover, we retrieved values of the volcanic radiative power (VRP) up to 65 GW, and a time-averaged discharge rate (TADR) of ∼1000 (±500) m3/s. These results show that SWIR observations, at different spatial and temporal resolution, may give an important contribution to the monitoring, mapping and characterisation of intense lava effusions.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Solar active region magnetic parameters and their relationship with the properties of halo coronal mass ejections

The coronal mass ejections (CMEs) from the sun consist of the plasma and strong magnetic fields (∼4–5 G at 1.3 R⊙) and generally, the fast CMEs are often associated with energetic particles of energy few hundreds of MeV. When the fast CMEs are ejected towards the Earth, they can disrupt the flow of solar wind in the heliosphere and impact the Earth's magnetosphere causing known space weather phenomena such as, geomagnetic storms, auroras, communication blackouts, satellite drag and failures. In this work, we have investigated a set of 60 halo coronal mass ejections (CMEs) associated with X-ray flares (≥C1.0) observed in 49 active regions during the period 2010–2016 in solar cycle 24. The X-ray flares and white light CMEs were observed by Geostationary Operational Environmental Satellite (GOES) and the Large Angle Spectrometric Coronagraph (LASCO) coronagraph on the Solar and Heliospheric Observatory (SOHO) satellite, respectively. The active region magnetic properties are obtained from Space - weather HMI Active Region Patch (SHARP) parameters for this set of events. For each active region (AR), the mean value is calculated from a 1-day time series for each of the 16 photospheric magnetic parameters extracted from the vector magnetogram products at a 12-min cadence. The dependency of flare flux and CME kinematics properties on the active region magnetic properties is further confirmed through correlation studies. As a result, we found a moderate to strong correlation value for flare flux and halo CME kinematic properties (like linear speed, space speed and kinetic energy) among the seven (USFLUX, TOTUSJZ, TOTUSJH, ABSNJZH, SAVNCPP, TOTPOT and MEANJZH) SHARP parameters. Most of the events (≥73%) were produced by βγδ, βγ and βδ Hale - type sunspot groups and Ekc/Dkc/Fkc McIntosh sunspot classes. It reveals that the most complex active regions are feasible sources of strong flare-associated halo CME productivity. The front side halo CME kinematic properties depend more on the vertical current helicity and polarity magnetic parameters than the other magnetic parameters. In addition, the flare intensity and speed of CME, calculated using an empirical model with the active region magnetic parameters, are found nearly equal to the observed values. This result will be useful to identify the important magnetic parameters for modelling the occurrence of flare-associated halo CMEs and to understand the physical relationship between them.

Magnetopause Standoff Position Changes and Geosynchronous Orbit Crossings: Models and Observations

AbstractThis research examines the ability of current physics‐based models to predict the magnetopause location. We use the Run‐On‐Request capabilities at the Community Coordinated Modeling Center at NASA GSFC with 4 magnetohydrodynamic (MHD) models. The magnetopause position prediction and response time to the solar wind changes is then compared to extreme solar wind conditions where magnetopause crossing of geosynchronous orbit have been observed by the Geostationary Operational Environmental Satellite (GOES) satellites. Rigorous analysis/comparison of observations and models is critical in determining magnetosphere dynamics for model validation. This paper is a preliminary effort defining the metrics necessary to understand the current magnetosphere model capabilities and challenges. Results show that there are discrepancies between the MHD models' standoff positions of the dayside magnetopause for the same solar wind conditions on events that included (a) an increase in solar wind dynamic pressure and a step function in the Interplanetary Magnetic Field Bz component; (b) nominal solar wind conditions (values of approximated 400 km/s for solar wind speed and 5 nT for the magnetic field magnitude) with a northward IMF; and (c) compression caused by several coronal mass ejections impacting the near Earth environment. Overall, the models predicted different magnetopause subsolar locations sometimes in the order of 3 RE. Contingency tables were calculated to show model performance in comparison with the data observed with the GOES 13/15 geosynchronous orbit for extreme events and skill scores were calculated.