Geostationary Operational Environmental Satellite Research Articles

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.

Desaturated EUV Flare Ribbons in an X-class Flare

Abstract This paper studies the energy dissipation of nonthermal electrons in the chromospheric flare ribbons during the peak time of a Geostationary Operational Environmental Satellite X-class flare (SOL2011-09-06) using desaturated Solar Dynamics Observatory/Atmospheric Imaging Assembly extreme-ultraviolet (EUV) narrow-band images. The temperature distribution in emission measure, called the differential emission measure (DEM), derived from the EUV fluxes from the flare ribbons shows an increase in the emission measure up to a temperature around 9 × 106 K, followed by a steep decline at higher temperatures. In contrast, the flare loop reaches temperatures up to 27 × 106 K. This result is in agreement with previously reported single-temperature measurements using soft X-ray filter images, as well as DEM distributions reported for smaller flares obtained from EUV line observations. The main difference between small and large flares appears to be an increased emission measure in the flare ribbons, while the ribbon peak temperature is similar for all flares. This is different from the flare loop temperatures, where the hottest temperatures occur in the largest flares. However, the physically relevant quantity for energy dissipation, the energy content of the heated plasma as a function of temperature, does not need to peak at the same temperature as the DEM. The poorly constrained source thickness in radial extent of the flare ribbons has a significant impact on the shape of the differential thermal energy distribution. In particular, if the highest temperatures occur over a wide radial extent as “evaporating” plasma starts expending, the largest amount of energy could potentially be hidden above the peak temperature of the DEM.

Solar Irradiance Variability Due to Solar Flares Observed in Lyman-Alpha Emission

As the Lyman-alpha (Lyupalpha ) line of neutral hydrogen is the brightest emission line in the solar spectrum, detecting increases in irradiance due to solar flares at this wavelength can be challenging due to the very high background. Previous studies that have focused on the largest flares have shown that even these extreme cases generate enhancements in Lyupalpha of only a few percent above the background. In this study, a superposed-epoch analysis was performed on ≈8500 flares greater than B1 class to determine the contribution that they make to changes in the solar EUV irradiance. Using the peak of the 1 – 8 Å X-ray emission as a fiducial time, the corresponding time series of 3123 B- and 4972 C-class flares observed in Lyupalpha emission by the EUV Sensor on the Geostationary Operational Environmental Satellite 15 (GOES-15) were averaged to reduce background fluctuations and improve the flare signal. The summation of these weaker events showed that they produced a 0.1 – 0.3% enhancement to the solar Lyupalpha irradiance on average. For comparison, the same technique was applied to 453 M- and 31 X-class flares, which resulted in a 1 – 4% increase in Lyupalpha emission. Flares were also averaged with respect to their heliographic angle to investigate any potential center-to-limb variation. For each GOES class, the relative enhancement in Lyupalpha at the flare peak was found to diminish for flares that occurred closer to the solar limb due to the opacity of the line and/or foreshortening of the footpoints. One modest event included in the study, a C6.6 flare, exhibited an unusually high increase in Lyupalpha of 7% that may have been attributed to a failed filament eruption. Increases of this magnitude have hitherto only been associated with a small number of X-class flares.

Characterization and monitoring of GOES-16 ABI stray light and comparison with Himawari-8 AHI and GOES-17 ABI

The Advanced Baseline Imager (ABI) aboard Geostationary Operational Environmental Satellite (GOES)-16 and -17 satellites represent the next-generation geostationary multispectral imaging instrument. Since GOES-16 ABI imagery data became available, stray light was observed in ABI visible, near-infrared (VNIR), and 3.9 μm, i.e., CH07, channels. A stray-light characterization scheme was developed to quantitatively monitor stray-light variation in ABI imagery. The stray-light analysis is focused on ABI CH07, whose nighttime radiometric performance being impacted by stray light is of main concern. It is found that the stray light in the ABI imagery occurs over ∼3 months around spring and fall equinox each year. The maximum stray light of GOES-16 ABI CH07 is ∼0.65 K at 300 K scene in zone of normal performance (ZONP), i.e., region with relative solar angle >7.5 deg, which is within the radiometric requirement of 1 K. The analysis of Himawari-8 Advanced Himawari Imager (AHI) CH07 data indicates that its maximum stray light is ∼3.35 K at 300 K scene in ZONP, much higher than that of GOES-16. This confirms the effectiveness of reducing the major stray-light leaking path in ABI as a result of lessons learned from Himawari-8 AHI. The magnitude of GOES-17 ABI CH07 stray light is shown to be ∼0.45 K in ZONP, slightly lower than GOES-16. The analysis of AHI 3.9 and 6.2 μm channel stray-light radiance ratio is shown to match the ratio of solar irradiance spectrum, which suggests that the stray-light issue is due to direct leakage of solar radiation through the instrument. Further characterization and monitoring of GOES-16 ABI VNIR channel stray light also help understand the solar origin of ABI stray light. This paper also investigated cases of strong atmospheric refraction-induced stray-light contamination onto ABI detectors during solar eclipse seasons and presents scheme to reduce such potentially harmful contamination.

Empirical Model of 10 – 130 MeV Solar Energetic Particle Spectra at 1 AU Based on Coronal Mass Ejection Speed and Direction

We present a new empirical model to predict solar energetic particle (SEP) event-integrated and peak intensity spectra between 10 and 130 MeV at 1 AU, based on multi-point spacecraft measurements from the Solar TErrestrial RElations Observatory (STEREO), the Geostationary Operational Environmental Satellites (GOES) and the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) satellite experiment. The analyzed data sample includes 32 SEP events occurring between 2010 and 2014, with a statistically significant proton signal at energies in excess of a few tens of MeV, unambiguously recorded at three spacecraft locations. The spatial distributions of SEP intensities are reconstructed by assuming an energy-dependent 2D Gaussian functional form, and accounting for the correlation between the intensity and the speed of the parent coronal mass ejection (CME), and the magnetic field line connection angle. The CME measurements used are from the Space Weather Database Of Notifications, Knowledge, Information (DONKI). The model performance, including its extrapolations to lower/higher energies, is tested by comparing with the spectra of 20 SEP events not used to derive the model parameters. Despite the simplicity of the model, the observed and predicted event-integrated and peak intensities at Earth and at the STEREO spacecraft for these events show remarkable agreement, both in the spectral shapes and their absolute values.

Further Investigation Into Detection Efficiency and False Alarm Rate for the Geostationary Lightning Mappers Aboard GOES‐16 and GOES‐17

AbstractThe Geostationary Lightning Mapper (GLM) is a geostationary lightning detection and location instrument, developed for the R generation of Geostationary Operational Environmental Satellites (GOES‐R, S, T, and U). This paper details a new technique to assess detection efficiency (DE) and false alarm rate (FAR), which indicate how well the instrument is detecting lightning and rejecting nonlightning. In an attempt to compare GLM with the best possible ground truth data, we clustered several ground‐based lightning networks into a single “virtual” network and compare it to the GLM results. A major issue with determining the GLM DE and FAR values is that over much of the instrument field of view (FOV), there are no high DE systems. To assess the GLM DE and FAR over these regions, we modified our prior coincidence criteria by increasing the time window from ±1 s to as much as ±10 min to account for the lower DE of the ground truth systems. Using the expanded time window, we compare GLM flash data from August 1, 2019 through January 31, 2020 for both instruments against the virtual network lightning flash data. We find that increasing the time window, while maintaining the distance criteria of 50 km, greatly improve the DE and FAR values. With the full ±10 min time window, over the whole GLM FOV, the GLMs on GOES‐16 and GOES‐17 have a DE of over 90%. For the same time window, the FAR for GLM on GOES‐16 is just over 5%, while the FAR for the GLM on GOES‐17 is just under 20%.

Understanding the Imaging Capability of Tundra Orbits Compared to Other Orbits

For operational weather forecasting and nowcasting, a refresh rate (RR) of 10 min and a better-than-4-km footprint size for Infrared (IR) bands is desired. Such imaging capability is only available from geostationary orbit (GEO) satellites, such as ABI onboard geostationary operational environmental satellites (GOES)-16/-17, but mainly limited to the tropics and mid-latitudes (referred to as GEO-like imaging capability). For high latitudes such as the Alaskan region, the IR footprint size from ABI/GOES-17 is worse than 6 km, limiting the application over the region. Tundra satellites, with a nonzero inclination angle and a nonzero eccentricity, have longer dwell times near the apogee than the perigee and can be used to monitor the high latitudes and polar regions. This study investigates Tundra satellites’ imaging capability by assuming an ABI-like instrument with the same IR spatial resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$56~\mu $ </tex-math></inline-formula> rad onboard. For regional applications, a constellation of two Tundra satellites may provide GEO-like imaging capability for a large domain. This useful domain is further improved when combined with a GEO, i.e., two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tundra + GOES-17$ </tex-math></inline-formula> . For global applications, a constellation of three Tundra satellites may provide GEO-like imaging capability for high latitudes (improved capability over GEO) and polar regions (unprecedented capability) in both hemispheres. The additional capability from Tundra satellites in tropics and mid-latitudes makes the global space-based meteorological observing system more robust and resilient. While a constellation of three <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tundra + 3$ </tex-math></inline-formula> GEOs may provide global GEO-like imaging capability, having more than three GEOs may be desired by agencies/countries, allowing for improved spatial resolutions over their sub-point locations.

Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series.

Although cloud base height is a relevant variable for many applications, including aviation, it is not routinely monitored by current geostationary satellites. This is probably a consequence of the difficulty of providing reliable estimations of the cloud base height from visible and infrared radiances from current imagers. We hypothesize that existing algorithms suffer from the accumulation of errors from upstream retrievals necessary to estimate the cloud base height, and that this hampers higher predictability in the retrievals to be achieved. To test this hypothesis, we trained a statistical model based on the random forest algorithm to retrieve the cloud base height, using as predictors the radiances from Geostationary Operational Environmental Satellites (GOES-16) and variables from a numerical weather prediction model. The predictand data consisted of cloud base height observations recorded at meteorological aerodrome report (METAR) stations over an extended region covering the contiguous USA. Our results indicate the potential of the proposed methodology. In particular, the performance of the cloud base height retrievals appears to be superior to the state-of-the-science algorithms, which suffer from the accumulation of errors from upstream retrievals. We also find a direct relationship between the errors and the mean cloud base height predicted over the region, which allowed us to obtain estimations of both the cloud base height and its error.

Improvements in high-temporal resolution active fire detection and FRP retrieval over the Americas using GOES-16 ABI with the geostationary Fire Thermal Anomaly (FTA) algorithm

Geostationary imaging sensors offer unique high temporal resolution capabilities with which to characterise the fast-changing dynamics of landscape fires. The new R-Series of Geostationary Operational Environmental Satellite (GOES-R) are the most advanced geostationary weather satellites currently operating, and each carry the new Advanced Baseline Imager (ABI) which greatly improves on the spatial, temporal and radiometric characteristics of the previous GOES Imager. Here we develop and evaluate the first landscape fire radiative power (FRP) observation system for the first of the GOES-R series (GOES-16), comparing its outputs to those generated near simultaneously from the forerunner GOES-13 Imager and from the far higher spatial resolution, but far less frequently imaging, Moderate Spatial Resolution Imaging Spectroradiometer (MODIS). Across the Americas, when examining near-simultaneous data from August to September 2017, the geostationary Fire Thermal Anomaly (FTA) algorithm originally developed for use with Meteosat’s Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) enables GOES-16 ABI to detect substantially more (6%) active fire (AF) pixels than GOES-13 Imager, due mainly to the former sensors‘ smaller pixel area. ABI detects even greater numbers of AF pixels due to its higher temporal resolution, but here we are only comparing results from near-simultaneous imagery. The majority of fires having an FRP substantially less than ~30 ​MW remain undetected even by ABI, and since these are the most common fire type ABI still shows a high error of omission (68%) compared to MODIS centre of swath data (±30° scan angle). ABI AF pixel errors of commission are far lower than errors of omission however at ~12% when using MODIS as a benchmark. This appears far more favorable than results found when using the Fire Detection and Characterization (FDC) algorithm, an adaptation of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm long-applied to GOES, which when applied to ABI data was recently assessed to have an 88% commission error. When ABI and MODIS both successfully detect a fire at the same time there is strong agreement and low bias in terms of their retrieved FRP. Overall therefore we find that the FTA approach to generating AF data from GOES-16 ABI sensor appears well performing across the Americas, and is compatible with the data from this system joining that from Meteosat, Meteosat over India Ocean and Himawari-8 to become part of the global geostationary active fire observation system now reaching maturity. The AF detection and FRP data provided from GOES-16 operating as GOES-East, and also now GOES-17 operating from the GOES-West position, are now available in near real-time for use worldwide.

Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm

The latest Geostationary (GEO) Operational Environmental Satellite-16 (GOES-16) equipped with Advanced Baseline Imager (ABI) has comparable spectral and spatial resolution as low earth orbiting (LEO) sensors [i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS)], but with up-to-the-minute image acquisition capability. This enables greater opportunities to generate two essential climate variables-Leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation with more cloud-free observations and at much higher frequency. The improved GEO LAI/FPAR products will increase the capacity for monitoring highly dynamic ecosystems in a timely manner. However, the radiative transfer (RT)-based MODIS operational algorithm cannot be directly applied to GOES-16 ABI data due to different sensor characteristics. Fortunately, it has been shown theoretically and practically, that the RT-based algorithm can be transplanted to any other optical sensors by optimizing the sensor-specific parameters-the single scattering albedo (SSA) and relative stabilized precision (RSP). We built the RT-based ABI-specific lookup tables (LUTs) using a global optimizing algorithm (SCE-UA) that can quickly find the optimal solution. SCE-UA optimizes the SSAs and RSPs in the LUTs by minimizing the difference between ABI and MODIS retrievals and maximizing the main algorithm execution rate. Our efforts indicate that these strategies of parametric optimization is able to decrease the discrepancy between the ABI and MODIS LAI/FPAR products. Comprehensive evaluations were conducted to evaluate ABI retrievals. These indirect inter-comparisons suggest a spatiotemporal consistency between ABI and the benchmark MODIS products, while direct validation with field measurements increases confidence in their accuracy. The proposed approach is applicable to any other optical sensors for LAI/FPAR estimation, especially, GEO sensors (i.e., Himawari-8, Geo-KOMPSAT-2A, FengYun-4 etc.).

Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data

ABSTRACT A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Radiation Data Portal: Integration of Radiation Measurements at the Aviation Altitudes and Solar‐Terrestrial Environment Observations

AbstractThe impact of radiation dramatically increases at high altitudes in the Earth's atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires convenient access to multisource radiation environment data and enhancement of visualization and search capabilities. The Radiation Data Portal represents an interactive web‐based application for search and visualization of in‐flight radiation measurements. The portal enhances the exploration capabilities of various properties of the radiation environment and provides measurements of dose rates along with information on space weather‐related conditions. The Radiation Data Portal back‐end is a MySQL relational database that contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS) device and the soft X‐ray and proton flux measurements from the Geostationary Operational Environmental Satellite. The implemented application programming interface and Python routines allow a user to retrieve the database records without interaction with the web interface. As a use case of the Radiation Data Portal, we examine ARMAS measurements during an enhancement of the solar proton (SP) fluxes, known as solar proton events, and compare them to measurements during SP‐quiet periods.

Statistical Studies on Modified Neupert Effect

The qualitative description of the Neupert effect is that there is a causal relationship between the pulse component (hard X-ray, microwave burst) and the gradual component (soft X-ray emission) in a flare. The initial energy of the flare is released in the form of accelerating particles. The energetic particles produce HXR (hard X-ray) via nonthermal electron-ion bremsstrahlung as they lose their energies in the chromosphere. The SXR (soft X-ray) emission of the flare is the response of energetic particles injected into the chromosphere. According to the quantitative description of the classic Neupert effect, SXR should reach maximum instantly at the end of HXR emission (sign of nonthermal electron injection). However, previous observations have found that for quite a number of flares the SXR peak time (t2) is significantly later than the end time of HXR (t1) (τ=t2−t1,τ>0), deviating from the classic Neupert effect. In order to study the events deviating from the classic Neupert effect or not, we checked the RHESSI (Reuven Ramaty High Energy Solar Spectroscopic Imager) and GOES (Geostationary Operational Environmental Satellites) events list from 2002 to 2015, and found out flare samples that have simple lightcurves at 25–50 keV together with other criteria. A total of 276 flare samples were finally selected. We have analysed the τ distribution of these flares, as well as the relationship between the loop length (represented by the distance between two footpoint sources d) and τ. We found that: (1) 227 sample flares present τ>0, which means that about 82% of total samples deviate from the classical Neupert effect; (2) there is a roughly linear correlation between τ and d, that is, the longer the loop is, the later the maximum time of SXR with respect to the end of HXR; (3) there seems to have a critical distance, within which the classic Neupert effect works. These results confirm the necessity of modifying Neupert effect, and expound its physical significance.

The Hazards Posed by Mesoscale Lightning Megaflashes

AbstractLightning megaflashes extending over &gt;100-km distances have been observed by the Geostationary Lightning Mappers (GLMs) on NOAA’s R-series Geostationary Operational Environmental Satellites (GOES). The hazards posed by megaflashes are unclear, however, because of limitations in the GLM data. We address these by reprocessing GOES-16 GLM measurements from 1 January 2018 to 15 January 2020 and integrating them with Earth Networks Global Lightning Network (ENGLN) observations. ENGLN verified 194 880 GLM megaflashes as natural lightning. Of these, 127 479 flashes occurred following the October 2018 GLM software update that standardized GLM timing. Reprocessed GLM/ENGLN lightning maps from these postupdate cases provide a comprehensive view of how individual megaflashes evolve. This megaflash dataset is used to generate statistics that describe their hazards. The average megaflash produces 5–7 cloud-to-ground (CG) strokes that are spread across 40%–50% of the flash extent. As flash extent increases beyond 100 km, megaflashes become concentrated in key hot-spot regions in North and South America while the number of CG and intracloud events per flash and the overall peak current increase. CGs in the larger megaflashes occur over 80% of the flash extent measured by GLM, and the majority contain regions where the megaflash is the only lightning activity in the preceding hour. These statistics demonstrate that there is no safe location below an electrified cloud that is producing megaflashes, and current lightning safety guidance is not always sufficient to mitigate megaflash hazards.

Classification and Evaluation of Stable and Unstable Cloud Forecasts

Abstract A physics-based cloud identification scheme, originally developed for a machine-learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-h forecasts. The routine identifies stable and unstable environments by assessing the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed that stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative, and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less-severe short-term cloud cover errors. This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud-base retrievals for their effectiveness at identifying regions of lower-tropospheric cloud cover. Retrieved cloud-base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate that the most accurate cloud-base estimates existed in regions with cloud tops at or below 8 km. Significance Statement Cloud forecasts are difficult to verify because the height, depth, and type of the clouds are just as important as the spatial location. Satellite imagery and retrievals are good for verifying location, but these measurements are sometimes uncertain because of obscuration from above. Despite these uncertainties, we can learn a lot about specific forecast errors by tracking general areas of clouds based on their physical forcing mechanisms. We chose to sort by atmospheric stability because buoyant and stable processes are physically very distinct. Studies of this nature exist, but they typically assess mean cloud frequencies without considering spatial and temporal displacements. Here, we address displacement error by assessing the direct overlap between the observed and predicted clouds.

Tracking and short-term forecasting of mesoscale convective cloud clusters over southeast Brazil using satellite infrared imagery

This paper presents the tracking and short-term forecasting of mesoscale convective cloud clusters (CCs) that occurred over southeast Brazil and the adjacent Atlantic Ocean during 2009–17. These events produce intense rainfall and severe storms that impact agriculture, defence, hydroelectricity and offshore oil production. To identify, track and forecast CCs, the Geostationary Operational Environmental Satellite infrared imagery and the Forecasting and Tracking the Evolution of Cloud Clusters method are used. The forecast performance is investigated by applying statistical analyses between the observed and forecasted CCs’ physical properties. A total of 7139 mesoscale convective CCs were identified, tracked and selected for the short-term forecasting at their maturation phases. The CC tracking showed a high frequency of CCs over the Atlantic Ocean and mainly over continental and coastal southeast Brazil during the wet season. This indicates an important role played by the cold fronts and convective diurnal forcing on the organisation of convective cloudiness over that region. The majority of the CCs reached their maturation phases within the first 2 h of life cycle, which occurred mostly between the late afternoon and evening. The CCs had short lifetimes and were predominantly in meso-β scales, followed by meso-α convective CCs. The CCs showed cloud-top temperatures typical of clouds with strong vertical development and potential to produce rainfall. The short-term forecasting of CCs at their maturation phases revealed different behaviours of the statistical indices with forecast range. For the 30–60-min timeframe, the forecasts performed relatively well. For longer forecast lead times (90–120 min), the forecasts overestimated the occurrences, intensities and growth of the CCs and forecasted the CCs to be further north and east of their actual observed locations. Overall, our results may contribute to improving the forecast quality of these intense weather events.

Wavelet and Cross-Correlation Analysis of Relativistic Electron Flux with Sunspot Number, Solar Flux, and Solar Wind Parameters

The Geostationary Operational Environmental Satellites (GOES) have been monitoring the Earth's radiation environment and is providing the electron flux data (of energy &gt;0.8 MeV, &gt;2 MeV, and &gt;4 MeV) by means of a connected sensor subsystem. Relativistic electron flux is one of the components of the radiation belt which not only affects the electrical system in satellites but also has an impact on Earth’s upper atmospheric climatic variation. We have carried out a study to determine the relation of sunspot number (R), solar flux (F10.7), and solar wind parameters i.e., solar wind velocity (Vsw), plasma density Nsw), the southern component of the interplanetary magnetic field (IMF-Bz), Plasma temperature (Tsw) with relativistic electron flux of energy &gt;0.8 MeV, &gt;2 MeV, and &gt;4 MeV in outer radiation belt using the data of 24 years (1996-2020) covering solar cycle 23 and 24. Time series analysis, Cross-correlation and wavelet analysis techniques have been used in this study. The time series plot displayed that the radiation is occupied mostly by electron flux of energy less than 4 Mev and solar cycle 23 (1996-2008) was strong to produce more intensity of relativistic electron flux of all energy in comparison to cycle 24 (2008-2019). Results from cross-correlation analysis illustrated that Bz has no significant impact on the enhancement of relativistic electron flux of any energy range in the radiation belt. Whereas other studied parameters have a positive correlation with relativistic electron flux, but with significantly different coefficient values for different energy. We found that electron flux &gt;0.8 MeV and &gt;2 MeV has a strong positive association with sunspot number, solar flux, solar wind velocity, plasma density and temperature whereas weak correlation with electron flux of energy &gt;4 MeV. This result leads us to conclude that solar activity and solar parameters have greater influence in producing relativistic electron flux of energy ~ 0.8-4 MeV, than of flux &gt; 4 MeV. The study made to observe the distribution of relativistic electrons in radiation belt with time through continuous wavelet analysis showed that electron flux of energy &gt;0.8 has a higher periodicity in comparison to the flux of other energy ranger.

Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites

The surface urban heat island (SUHI) is one of the most significant human-induced alterations to the Earth's surface climate and can aggravate health risks for city dwellers during heat waves. Although the SUHI effect has received growing attention, its diurnal cycles (i.e., the variations over the full 24 h within the diel cycle) are poorly understood because polar-orbiting satellites (e.g., Landsat Series, Sentinel, Terra, Aqua) only provide one or two observations over each repeat cycle (e.g., 16 days) with constant overpass time for the same area. Geostationary satellites provide high-frequency land surface temperature (LST) observations throughout the day and the night, and thereby offer unprecedented opportunities for exploring the diurnal cycles of SUHI. Here we examined how the SUHI intensity varied over the course of the diurnal cycle in the Boston Metropolitan Area using LST observations from the NOAA's latest generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R LST was strongly correlated with MODIS LST (R2 = 0.98, p < 0.0001) across urban core, suburban, and rural areas. We calculated the SUHI intensity at an hourly time step for both the urban core and suburban areas using GOES-R LST data. The maximum SUHI intensity for the urban core occurred near noon, and was +3.0 °C (12:00), +5.4 °C (12:00), +4.9 °C (11:00), and +3.7 °C (12:00) in winter, spring, summer, and autumn, respectively. The maximum intensity for the suburban area was about 3.0 °C lower in spring and summer and 2.0 °C lower in autumn and winter than that of the urban-core area. The minimum SUHI intensity occurred at nighttime, and ranged from −1.0 °C to +1.0 °C. The difference in the nighttime SUHI intensity between urban core and suburban area was insignificant for all seasons except the summer. The SUHI intensity showed similar diurnal variations across the seasons. Throughout the year, the maximum SUHI intensity (+2.7–+5.8 °C) at the urban core occurred at 11:00–14:00 (local time), while the minimum SUHI intensity (−0.6–+0.9 °C) was commonly observed at 00:00–07:00 and 17:00–23:00. We also found different relationships between SUHI intensity and potential drivers within a diurnal cycle, characterized by the strongest correlation with impervious surface area and population size during the middle of the day, and with tree canopy cover at night. Our research highlights the great potential of the new-generation geostationary satellites in revealing the detailed diurnal variations of SUHI. Our findings have implications for informing urban planning and public health risk management.

Odd hydrogen response thresholds for indication of solar proton and electron impact in the mesosphere and stratosphere

Abstract. Understanding the atmospheric forcing from energetic particle precipitation (EPP) is important for climate simulations on decadal time scales. However, presently there are large uncertainties in energy flux measurements of electron precipitation. One approach to narrowing these uncertainties is by analyses of EPP direct atmospheric impacts and their relation to measured EPP fluxes. Here we use observations from the microwave limb sounder (MLS) and Whole Atmosphere Community Climate Model (WACCM) simulations, together with EPP fluxes from the Geostationary Operational Environmental Satellite (GOES) and Polar-orbiting Operational Environmental Satellite (POES) to determine the OH and HO2 response thresholds to solar proton events (SPEs) and radiation belt electron (RBE) precipitation. Because of their better signal-to-noise ratio and extended altitude range, we utilize MLS HO2 data from an improved offline processing instead of the standard operational product. We consider a range of altitudes in the middle atmosphere and all magnetic latitudes from pole to pole. We find that the nighttime flux limits for day-to-day EPP impact detection using OH and HO2 are 50–130 protonscm-2s-1sr-1 (E&gt;10 MeV) and 1.0–2.5×104 electronscm-2s-1sr-1 (E = 100–300 keV). Based on the WACCM simulations, nighttime OH and HO2 are good EPP indicators in the polar regions and provide best coverage in altitude and latitude. Due to larger background concentrations, daytime detection requires larger EPP fluxes and is possible in the mesosphere only. SPE detection is easier than RBE detection because a wider range of polar latitudes is affected, i.e., the SPE impact is rather uniform poleward of 60∘, while the RBE impact is focused at 60∘. Altitude-wise, the SPE and RBE detection are possible at ≈ 35–80 and ≈ 65–75 km, respectively. We also find that the MLS OH observations indicate a clear nighttime response to SPE and RBE in the mesosphere, similar to the simulations. However, the MLS OH data are too noisy for response detection in the stratosphere below 50 km, and the HO2 measurements are overall too noisy for confident EPP detection on a day-to-day basis.

A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

Fog is a hazardous weather event that can endanger navigation, aviation, and transportation. While human has several limitations in detecting and forecasting offshore fog, satellite remote sensing offers cost-effective images. In this study, a probability-based daytime sea fog detection algorithm, applied to geostationary operational environmental satellite (GOES) 16 satellite data over the Grand Banks offshore Eastern Canada, is presented and compared with the National Oceanographic and Atmospheric Administration (NOAA)'s Low Instrument Flight Rules (LIFR) probability map. Initially, clear-sky and ice cloud classes were delineated in the GOES-16 image and then the remaining pixels were assigned a fog probability by conducting small droplet proxy, spatial homogeneity, and temperature difference tests. Moreover, a green band was linearly interpolated using the first three bands of GOES-16 images to generate pseudotrue color composites. The resulting maps were evaluated both during an extended sea fog event and using several statistical measures. The average detection probability for the observed advection fog events was 66% for the proposed method, while that for NOAA's LIFR map was 38%. Furthermore, by thresholding the generated maps at the probability of 60%, the false alarm rate, probability of detection, hit rate, and Hanssen–Kuiper skill score were 0.09, 0.77, 0.83, and 0.68, respectively. The proposed method is operationally being used in this region to detect and monitor sea fog, facilitating safe navigation and aviation. This is the first study that uses GOES-16 for daytime fog detection and discusses a satellite-based solution for fog modeling in Grand Banks, NL.