Geostationary Operational Environmental Satellite Research Articles

Intertropical Convergence Zone as the Possible Source Mechanism for Southward Propagating Medium-Scale Traveling Ionospheric Disturbances over South American Low-Latitude and Equatorial Region

This paper presents the Intertropical Convergence Zone (ITCZ) as the possible source mechanism of the medium-scale traveling ionospheric disturbances (MSTIDs) propagating to the southeast direction over the South American region. Using the data collected by the GNSS dual-frequency receivers network from January 2014 to December 2019, detrended TEC maps were generated to identify and characterize 144 MSTIDs propagating southeastward over the South American low-latitude and equatorial region. We also used images from the Geostationary Operational Environmental Satellite (GOES) 13 and 16 in the infrared (IR) and water vapor (WV) channel, and reanalisys data from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) to study the daily features and seasonal migration of ITCZ. In the winter, when ITCZ migrates to the northern hemisphere around 10–15° N, 20 MSTIDs propagated southeastward. During summer, when the ITCZ lies within the continent, around 0–5° S 80 MSTIDs were observed to propagate southeastward; in the equinoxes (spring and fall), 44 MSTIDs were observed. Again, the MSTIDs propagating southeastward showed a clear seasonality of their local time dependence; in summer, the MSTIDs occurred frequently in the evening hours, whereas those in winter occurred during the daytime. We also found for the first time that the day-to-day observation of ITCZ position and MSTIDs propagation directions were consistent. With regard to these new findings, we report that the MSTIDs propagating southeastward over the South American region are possibly induced by the atmospheric gravity waves, which are proposed as being generated by the ITCZ in the troposphere. The mean distribution of the horizontal wavelength, period, and phase velocity are 698 ± 124 km, 38 ± 8 min, and 299 ± 89 m s−1, respectively. For the first time, we were able to use MSTID propagation directions as a proxy to study the source region.

A Framework for Satellite-Based 3D Cloud Data: An Overview of the VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era.

High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.

On some features of the solar proton event on 2021 October 28 – GLE73

ABSTRACT In addition to several recent articles devoted to the rare event of ground-level enhancement of the solar relativistic proton flux observed on 2021 October 28 – GLE73, we study the 10–100 MeV solar energetic particle (SEP) component of this event. Based on the Geostationary Operational Environmental Satellite data for 26 GLEs recorded since 1986, we have formed a scatter plot displaying the ratio of the peak fluxes of the $\gt $10 MeV ($J_{10}$) and $\gt $100 MeV ($J_{100}$) protons and their energy spectra. Two extreme characteristics of the prompt component of the SEP–GLE73 event were revealed: (1) very small $J_{10}$ and $J_{100}$ proton fluxes and (2) a very hard energetic spectrum in the 10–100 MeV range. There are only two events with these characteristics similar to SEP–GLE73, namely GLE40 (1989 July 25) and GLE46 (1989 November 15). A correspondence was demonstrated between the hard frequency spectrum of microwave radio bursts of initiating flares and the hard SEP energy spectrum of these two and other GLEs. These results suggest that the flare magnetic reconnection both in the impulsive and post-eruption phases plays an important role in the acceleration of the SEP–GLE protons.

The first OPEA model for solar X-ray flares

ABSTRACT In this study, the flares detected in the solar hard X-ray (λ0.5–4.0 Å) data accumulated by the Geostationary Operational Environmental Satellite (GOES) from 2001 March–May were analysed. For the first time in the literature, the One Phase Exponential Association (OPEA) model was derived for the Sun and, for the first time, over X-ray data. 1014 flares were detected in the analysis. Contrary to expectations, it was seen that the flares can be modelled with three different OPEA models, not one model. The Plateau value was found to be 5.053 ± 0.110 s with the half-time of 2316.12 s for the flares during 2001 March, while it was 5.676 ± 0.138 s with the half-time of 3967.59 s for 2001 April and 5.338 ± 0.080 s with the half-time of 2755.66 s for 2001 May. The results indicate that the models exhibit a fluctuation in their form time by time. This means that there is a phenomenon that changes the energy of flares obtained in three consecutive months. We also demonstrate that the linear parts of the flare cumulative frequency distributions obtained in these data interestingly show a separation.

72‐Hour Time Series Forecasting of Hourly Relativistic Electron Fluxes at Geostationary Orbit by Deep Learning

AbstractIn this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr using a deep learning model based on multilayer perceptron. The input data of the model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from Geostationary Operational Environmental Satellite (GOES)‐15 and ‐16, and perform a mapping for matching these two data. Total period of the data is from 2011 January to 2021 March (GOES‐15 data for 2011–2017 and GOES‐16 data for 2018–2021). We divide the data into training set (January–August), validation set (September), and test set (October–December) to consider the solar cycle effect. Our main results are as follows. First, our model successfully predicts hourly electron fluxes for the next 72 hr. Second, root‐mean‐square error of our model is from 0.18 (for 1 hr prediction) to 0.68 (for 72 hr prediction), and prediction efficiency is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long‐term sequential space weather events.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

BACKGROUND: Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. AIM: We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. METHODS: Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)’s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. RESULTS: Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. CONCLUSIONS: Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode. KEYWORDS: PM2.5, GOES16, Wildland fire, Remote sensing, AOD, SMOTE, Weighted Random Forest

Spectrally simplified approach for leveraging legacy geostationary oceanic observations.

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, Kd(PAR). First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of Kd(PAR) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situKd(PAR) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination (R2) value (0.88) resulted in a root mean square difference of 0.319m-1, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability (R2=0.60) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, Kd(490), values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

Geostationary operational environmental satellite-R advanced baseline imager reflective solar band absolute validation using Sonoran desert scenes

The advanced baseline imagers (ABIs) on board the geostationary operational environmental satellite (GOES)-R series, GOES-16 and GOES-17 satellites, have accumulated long data series that have been updated throughout their missions to enhance their utility for imaging the Earth’s weather, oceans, and environment. In preparing for a reprocessing effort aimed at improving the consistency and image quality and reducing radiometric uncertainties necessary for some Earth science applications, we developed a vicarious validation technique for five of the six reflective solar bands. This technique uses Sonoran desert surface reflectance data derived from the moderate resolution imaging spectrometer bidirectional reflectance distribution function parameters product along with other atmospheric products to generate a predicted at-sensor radiance for both GOES-16 and GOES-17 for comparison with ABI observations and to enable ABI intercomparisons. This technique identified known instrument biases and demonstrated absolute validation over the course of a year, showing its capability to detect changes, monitor stability, and assess radiometric uncertainties for operational data applications and future reprocessed data sets.

Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications

Biomass burning influences atmospheric composition and regional air quality. The hourly biomass-burning emissions are usually required by air quality models, yet most available emission inventories provide daily or monthly estimates in 0.1° or coarser grids, limiting the prediction accuracy. The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellites – R Series (GOES-R) observes fires across the conterminous United States (CONUS) every 5 min at a spatial resolution of 2 km, which allows for characterizing fires and emissions on diurnal scale. In this study, we developed a new operational algorithm to generate regional hourly 3 km fire emission across the CONUS by fusing temporally resolved ABI fire radiative power (FRP) and fine spatial-resolution (375 m) FRP from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) satellites. To do this, ABI FRP was first calibrated against and fused with VIIRS FRP in 3 km grids. Then, FRP diurnal cycles at an interval of 5 min were reconstructed using the fused ABI-VIIRS FRP and the land cover-ecoregion-specific FRP diurnal climatologies. The reconstructed FRP diurnal cycles were applied to estimate hourly emissions of eight species (e.g., carbon monoxide (CO) and fine particulate matter with diameters <2.5 μm (PM2.5)). The accuracy was verified by comparing with CO observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, and with PM2.5 emissions from eight other inventories. The results of the ABI-VIIRS estimates during one year from April 2020 to March 2021 indicate that fires burned 221 Tg dry matter and emit 2.25 Tg PM2.5 emissions across the CONUS. The seasonal and diurnal patterns of emissions vary with land cover types. The largest and smallest seasonal variations are shown in forest and agriculture fire emissions, respectively. The diurnal emission patterns of different land cover types share similar shapes but differ largely in magnitude. Moreover, the diurnal pattern of forest fire emissions suggests that emissions are dominated during daytime in the eastern U.S. but strong during both daytime and nighttime in the western U.S. The evaluation shows that the fused ABI-VIIRS based CO agrees well with the TROPOMI CO, with a difference of 11%. However, the agreement between fused ABI-VIIRS emissions and other inventories varies for different fire events.

MODIS sensors can monitor spatiotemporal trends in fog and low cloud cover at 1 km spatial resolution along the U.S. Pacific Coast

Fog and low cloud cover (FLCC) provide critical moisture for ecosystems along the Pacific Coast during the summer months and it is currently unclear how climate change has affected FLCC occurrence. Additionally, FLCC impacts visibility and transportation safety for both road and air traffic. As such, a method for the monitoring of FLCC is necessary to inform land management decisions for fog-obligate species and to improve transportation safety, among other applications. Several gaps exist in current FLCC detection methodologies and while the Moderate Resolution Spectroradiometer (MODIS) sensor has previously been used to detect fog, its utility has not been validated. In this study, we create a 20 year (2000–2020) FLCC dataset using the Terra MODIS cloud flags and examine its effectiveness in detecting daily and monthly FLCC presence along the California and southern Oregon coast for the summer months (June–September). We validate the accuracy of this method using an existing FLCC dataset derived from Geostationary Operational Environmental Satellite (GOES) observations collected at 15 min intervals from 2000 to 2009. The two FLCC datasets have a strong linear relationship for FLCC frequency for each summer month, with an average r2 of 0.82 and p-value of <0.01. This strong relationship demonstrates the ability of Terra MODIS to reliably and accurately detect FLCC. Finally, we demonstrate a case study application of our FLCC dataset in a time series analysis over five coast redwood (Sequoia sempervirens) state parks in the Big Sur region of coastal California. This case study highlights the benefits provided by a 1 km resolution FLCC dataset for ecological applications and for monitoring spatiotemporal FLCC patterns across summer months over two decades. Our case study results showed that the number of foggy days fluctuates considerably year-to-year with no discernible positive or negative trend occurring between 2000 and 2020. Finally, we present a freely-accessible Google Earth Engine application to view and download the monthly FLCC data for all summer months for the years 2000–2020. The methods and dataset presented in this paper provide a means for efficient, daily FLCC monitoring at 1 km2 resolution, as well as the capacity for historical FLCC analyses.

YORP Effect on Long-Term Rotational Dynamics of Debris in GEO

The Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect describes the torque induced on space objects produced by solar radiation and thermal re-emission. Previous analyses have demonstrated its influence on long-term rotational dynamics of space debris objects in Geostationary Orbit (GEO), where YORP becomes predominant with respect to other external perturbations (e.g., atmospheric drag, gravity gradient, eddy current torque), leading to a wide variety of possible behaviors. The capability of forecasting time windows of slow uniform rotation, if any, would bring significant advantages in operations of Active Debris Removal and on-orbit servicing, especially in the detumbling phase. Also, a non-negligible impact of the End-of-Life configuration, in terms of movable surfaces orientation and center of mass location, could lead to guidelines for future satellites to be easier targets in the disposal phase. In this work, a previously derived semi-analytical tumbling-averaged YORP rotational dynamics model is leveraged. Exploiting an averaged model, computational time is strongly reduced while maintaining sufficient accuracy compared to propagation of Euler’s equations of motion. First, a satellite of the Geostationary Operational Environmental Satellite (GOES) family is analyzed and compared to previous studies to verify the correct implementation of the model. A wider analysis is performed on simple geometric models, such as a box-wing satellite, a 3U CubeSat, and a rocket body. The impact of object size, surface optical properties, and center of mass position on long-term rotational behavior is investigated, providing a general insight into these phenomena with a possible future application to existing objects in GEO.

GOES ABI Detection of Thin Cirrus over Land

Abstract This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of &lt;0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

Evaluation of the Proton Contamination to MeV Electrons by Solar Proton Events Based on CSES Observations

AbstractThe high energy particle package (HEPP) onboard the China Seismo‐Electromagnetic Satellite (CSES) consists of three subsystems: HEPP‐H, HEPP‐L, and HEPP‐X. HEPP‐H and HEPP‐L detect 0.1–55 MeV electrons and 2–230 MeV protons. HEPP‐X is used to monitor 0.9–35 keV solar X‐rays. The primary objective of this study is to present the measurement techniques and instrument parameters, show the main features of particle observations recorded by these instruments and evaluate the proton contamination to MeV electrons. Based on CSES observation, we report a typical solar X‐ray and solar proton event and compare with observations from Polar Orbiting Environmental Satellites (POES) and Geostationary Operational Environmental Satellites (GOES) satellite. Based on the observations of solar proton events, we put forward one method to evaluate the proton contamination level using comparatively pure proton samples on high L‐shells where the proton flux is near zero at quiet time. The percentage of protons that are mistakenly identified to be electrons by HEPP‐H is estimated to be approximately 0.022%. The proton contaminations only contribute no more than 10% of the total observed electron populations at L = 4 with energy range 8–9 MeV in the outer radiation belt region. Therefore, we conclude that the on‐orbit performance of HEPP instrument has satisfyingly met the design expectations by providing plenty of high‐quality high energy particle observation data and reference for scientific uses in the fields of space weather research and natural hazard monitoring.

Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

AbstractAlthough numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (&lt;100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US‐Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US‐ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r &gt; 0.97, RMSE ∼2°C; ET: r &gt; 0.99, and RMSE = 1–7 W m−2). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear‐sky conditions) at both US‐Seg and US‐ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

Integrated Geostationary Solar Energetic Particle Events Catalog: GSEP

We present a catalog of solar energetic particle (SEP) events covering solar cycles 22, 23 and 24. We correlate and integrate three existing catalogs based on Geostationary Operational Environmental Satellite integral proton flux data. We visually verified and labeled each event in the catalog to provide a homogenized data set. We have identified a total of 341 SEP events of which 245 cross the Space Weather Prediction Center threshold of a significant proton event. The metadata consists of physical parameters and observables concerning the possible source solar eruptions, namely flares and coronal mass ejections for each event. The sliced time-series data of each event, along with intensity profiles of proton fluxes in several energy bands, have been made publicly available. This data set enables researchers in machine learning and statistical analysis to understand the SEPs and the source eruption characteristics useful for space weather prediction.

Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Thermal infrared imagery from NOAA's Geostationary Operational Environmental Satellites (GOES) R-series presents an opportunity to observe mountain surface temperatures at high temporal resolution. These observations are needed to better understand the transient surface energy balance, influenced by near-surface air temperatures and water fluxes ranging from evapotranspiration to snowmelt. From geostationary orbit, the GOES Advanced Baseline Imager (ABI) instrument views the Earth from a fixed longitude above the equator. ABI images therefore have increasingly off-nadir view angles and increasingly larger pixel dimensions the further a point on the Earth's surface is from the sub-satellite point. In thermal infrared imagery from ABI, these 2+ km pixels blur together the different surface temperatures of heterogeneous mountain landscapes. We compared GOES-16 ABI band 14 (11.2 μm) brightness temperatures to 90 m spatial resolution ASTER band 14 (11.3 μm) and 1 km spatial resolution MODIS band 31 (11.03 μm) brightness temperatures and investigated how differences change over space and time for a study region in the central Sierra Nevada of California for the 2017–2020 snow seasons. We demonstrated the necessity of orthorectifying ABI imagery of mountain terrain to correct for the parallax effect in off-nadir imagery. This reduced the mean difference between ABI and ASTER ~11 μm brightness temperatures from 1.6 °C to 1.0 °C and increased the r-squared value between ABI and ASTER thermal infrared brightness temperatures from 0.43 to 0.93. The ABI brightness temperatures were found to more closely match those of forest canopy temperatures than snow surface (with RMS differences of 1.2 °C and 3.0 °C respectively), and those of sunlit slopes as opposed to slopes facing away from the sun (with RMS differences of 1.6 °C and 3.4 °C respectively), in coincident ASTER imagery. This work demonstrates the use of GOES ABI, and associated challenges, for observing surface temperatures of forested mountain environments with seasonal snow.

Extremely large flares/multiple large flares expected from sunspot groups with large area

The possible occurrence of major space weather events, such as large solar flares within one hundred years, is studied anticipating their effects on our social facilities. However, the continuous soft X-ray (SXR) observation of flares by Geostationary Operational Environmental Satellites (GOES) started in 1975, and the period of data collection is less than 50 years. On the other hand, ground-based sunspot observations have a long history. Their duration of data collection exceeds 100 years. The possibility of the occurrence of extremely large flares is estimated using the daily sunspot area data of individual sunspot groups between 1879 and 2016 using the catalogue complied by the Debrecen Heliophysical Observatory in Hungary and the catalogue updated by Mandal, Krivova, Solanki, Shinha, and Banerjee in 2020. It had become clear that large sunspot groups with the potential to produce Carrington-class flares (areas of more than 3000 MSH) have appeared on a total of 119−139 days between 1879 and 2016, and a sunspot group with the potential to produce an X100-class flare appeared between March and April 1947. According to the past major space weather events, the large sunspot groups caused a series of multiple large flares instead of just one large flare. We tried to estimate the probabilities of occurrence of a SXR flare ge X100 for 30-, 50-, and 100-year periods to be 0.70−0.76, 0.87−0.91, and 0.98−0.99, respectively, using the complementary cumulative distribution function (CCDF) of sunspot areas for the 138-year data.Graphical

Determining Tropical Cyclone Center and Rainband Size in Geostationary Satellite Imagery

Brightness temperature (TB) observations at an infrared channel (10.3 μm) of the Advanced Baseline Imager (ABI) on board the U. S. 16th Geostationary Operational Environmental Satellite (GOES-16) are used for determining tropical cyclone (TC) center positions and rainband sizes. Firstly, an azimuthal spectral analysis method is employed to obtain an azimuthally symmetric center of a TC. Then, inner and outer rainbands radii, denoted as RIR and ROR, respectively, are estimated based on radial gradients of TB observations at different azimuthal angles. The radius RIR describes the size of the TC inner-core region, and the radius ROR reflects the maximum radial extent of TC rainbands. Compared with the best track centers, the root mean square differences of ABI-determined centers for tropical storms and hurricanes, which totals 108 samples, are 45.35 and 29.06 km, respectively. The larger the average wavenumber-0 amplitude, the smaller the difference between the ABI-determined center and the best track center. The TB-determined RIR is close but not identical to the radius of the outermost closed isobar and usually coincides with the radius where the strongest wavenumber 1 asymmetry is located. The annulus defined by the two circles with radii of ROR and RIR is the asymmetric area of rainbands described by azimuthal wavenumbers 1–3. In general, amplitudes of wavenumber 0 component centered on the ABI-determined center are greater than or equal to those from the best track. For a case of a 60 km distance between the ABI-determined and the best track TC center, the innermost azimuthal waves of wavenumbers 1–3 are nicely distributed along or within the radial distance RIR that is determined based on the ABI-determined TC center. If RIR is determined based on the best track, the azimuthal waves of wavenumbers 1–3 are found at several radial distances that are smaller than RIR. The TC center positions, and rainband size radii are important for many applications, including specification of a bogus vortex for hurricane initialization and verification of propagation mechanism of vortex Rossby waves.

Statistical Study of EMIC Wave Propagation Using Space‐Ground Conjugate Observations

AbstractIn the present study, we explore the observational characteristics of Electromagnetic Ion Cyclotron (EMIC) wave propagation from the source region to the ground. We use magnetometers aboard Geostationary Operational Environment Satellite (GOES) 13, the geosynchronous orbit satellite at 75°W, and at Sanikiluaq ground station (SNK, 79.14°W and 56.32°N in geographic coordinates, and L ∼ 6.0 in a dipole magnetic field) which is located in northern Canada. Using these magnetically conjugate observatories, simultaneous EMIC wave observations are carried out. We found a total of 295 coincident and 248 non‐coincident EMIC wave events between GOES 13 and the SNK station. Our statistical analysis reveals that the coincident events are predominantly observed on the dayside. The wave normal angles are slightly higher for the non‐coincident events than for coincident events. However, the coincidence of the waves is mostly governed by the intensity and duration of the wave. This is confirmed by the geomagnetic environment which shows higher auroral electrojet (AE) and Kp indices for the coincident events. We also found that some events show high‐frequency (f &gt; 0.4 Hz) wave filtering. The statistics of the high‐frequency filtered and non‐filtered wave events show that there are clear magnetic local time (MLT) and F10.7 index differences between the two groups, as well as in ionospheric electron density measurements. In addition, we also found differences in the wave properties which possibly indicate that the propagation in the magnetosphere also plays an important role in the wave filtering.