Meteorological Satellite Research Articles

A Statistical Study of Space Hurricanes in the Southern Hemisphere

AbstractThe space hurricane is a large‐scale three‐dimensional magnetic vortex structure that can cause massive local energy injections in the polar cap. By analyzing Defense Meteorological Satellite Program (DMSP) F16–F19 satellite observations from 2005 to 2016, we found that the Southern Hemisphere space hurricane mainly occurs in summer under negative By dominated northward interplanetary magnetic field (IMF) conditions. In particular, the space hurricanes are more likely to occur in the dayside polar cap at magnetic latitude greater than 80°. The characteristics for the Southern Hemisphere are basically consistent with the characteristics of space hurricanes in the Northern Hemisphere. The different dependences of By component in different hemispheres supports the high‐latitude lobe reconnection as the formation mechanism. Plasma data from DMSP satellites in both hemispheres show that the appearance of the space hurricane greatly enhances the convection in the polar cap and the electron density on its dawn side. Within the space hurricane, electron temperatures typically increase significantly, accompanied by strong upward field‐aligned currents and electron precipitation. These results give us a better understanding of the solar wind‐magnetosphere‐polar ionosphere coupling process under northward IMF conditions.

GOLD Observations of Equatorial Plasma Bubbles Reaching Mid‐Latitudes During the 23 April 2023 Geomagnetic Storm

AbstractA coronal mass ejection erupted from the Sun on 21 April 2023 and created a G4 geomagnetic storm on 23 April. NASA's global‐scale observations of the limb and disk (GOLD) imager observed bright equatorial ionization anomaly (EIA) crests at ∼25° Mlat, ∼11° poleward from their average locations, computed by averaging the EIA crests during the previous geomagnetic quiet days (18–22 April) between ∼15°W and 5°W Glon. Reversed C‐shape equatorial plasma bubbles (EPBs) were observed reaching ∼±36° Mlat (∼40°N and ∼30°S Glat) with apex altitudes ∼4,000 km and large westward tilts of ∼52°. Using GOLD's observations EPBs zonal motions are derived. It is observed that the EPBs zonal velocities are eastward near the equator and westward at mid‐latitudes. Model‐predicted prompt penetration electric fields indicate that they may have affected the postsunset pre‐reversal enhancement at equatorial latitudes. Zonal ion drifts from a defense meteorological satellite program satellite suggest that westward neutral winds and perturbed westward ion drifts over mid‐latitudes contributed to the observed latitudinal shear in zonal drifts.

Generation of monthly VIIRS nighttime lights time-series (1992–2013) images using deep learning (cGAN) technique

Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

Estimation of Ionospheric Field‐Aligned Currents Using SuperDARN Radar and DMSP Observations

AbstractStudies commonly assumed that variations in ionospheric conductance were insignificant and proposed that vorticities can be a reliable proxy or diagnostic for ionospheric field‐aligned currents (FACs). We propose a complete method for measuring FACs using data from the Super Dual Auroral Radar Network radar and the Defense Meteorological Satellite Program. In our method, the FACs are determined by three terms. The first term is referred to as magnetospheric‐origin FACs, while the second and third terms are known as ionospheric‐origin FACs. This method incorporates height‐integrated conductances based on observational data, thereby addressing the limitation of assuming uniform conductances. Different from previous works, we can calculate FACs at a low altitude of 250 km and obtain high‐resolution measurements within observable areas. Another advantage of this method lies in its ability to directly calculate and analyze the impact of ionospheric vorticity and conductance on FACs. We apply this method to obtain FACs in the Northern Hemisphere from 2010 to 2016 and analyze the distributions of height‐integrated conductances and total FACs. Our analysis reveals that the average FACs clearly exhibit the large‐scale R1 and R2 FAC systems. We conduct statistical analysis on magnetospheric‐origin FACs and ionospheric‐origin FACs. Our findings show that within the auroral oval, ionospheric‐origin FACs reach a comparable level to magnetospheric‐origin FACs. However, ionospheric‐origin FACs are significantly minor and almost negligible in other regions. This implies that height‐integrated conductance gradients and vorticities play equally significant roles within the auroral oval, whereas vorticities dominate in other regions.

Ion precipitation in the cusp for northward IMF and its relationship with magnetosheath flow

If the interplanetary magnetic field (IMF) has a northward component, a portion of the solar wind ions flowing in the high-latitude magnetosheath drastically alters the flow direction owing to lobe reconnection and follows the Earth's magnetic field lines toward the low-altitude cusp. Partly because of this folded path, the relationship between the speed of ion flow in the magnetosheath and the energy of the precipitating ions in the low-altitude cusp remains unclarified. Herein, we examined particle data from 11 years of observations by Defense Meteorological Satellite Program satellites to obtain 1990 cases through an automated event identification of the cusp. This corresponds to the largest number of cusp ion events reported to date for a stable northward IMF. The results revealed a positive correlation between the energy of the precipitating ions and the estimated flow speed in the magnetosheath, which answers the question that has remained unexplained for decades: why is the brightness of the cusp proton aurora more strongly correlated with the solar wind dynamic pressure than with the solar wind number density? We suggest additional energizations of the exhausted ions from lobe reconnection in the outflow region because of the turbulent electric field formed by the opposite flow of two ion populations—one in the tailward-directed magnetosheath flow and the other in the earthward-directed outflow jet.Graphical

Global Distribution of EMIC Waves and Its Association to Subauroral Proton Precipitation During the 27 May 2017 Storm: Modeling and Multipoint Observations

AbstractRecent simulation studies using the RAM‐SCB model showed that proton precipitation contributes significantly to the total energy flux deposited into the subauroral ionosphere thereby affecting the magnetosphere‐ionosphere coupling. In this study, we use the BATS‐R‐US + RAM‐SCB model to understand the evolution of ElectroMagnetic Ion Cyclotron (EMIC) waves in the inner magnetosphere, their correspondence to the proton precipitation into the subauroral ionosphere, and to assess the performance of the model in reproducing the EMIC wave‐particle interactions. During the 27 May 2017 storm, Arase and RBSP‐A satellites observed typical signatures of EMIC waves in the inner magnetosphere. Within this interval, Defense Meteorological Satellite Program (DMSP) and National Oceanic and Atmospheric Administration (NOAA)/MetOp satellites observed significant proton precipitation in the dusk‐midnight sector. Simulation results show that H‐ and He‐band EMIC waves are excited within regions of strong temperature anisotropy near the plasmapause. The simulated growth rates of EMIC waves show a similar trend to that of the EMIC wave power observed by the Arase and RBSP‐A satellites, suggesting that the model can reproduce the EMIC wave activity qualitatively. The simulated H‐band waves in the dusk sector are stronger than He‐band waves possibly due to the presence of excess protons in the boundary conditions obtained from the BATS‐R‐US code. The precipitating proton fluxes reproduced by the simulation with EMIC waves are found to agree reasonably well with the DMSP and NOAA/MetOp satellite observations. It is suggested that EMIC wave scattering of ring current ions can account for proton precipitation observed by the DMSP and MetOp satellites during the 27 May 2017 storm.

A Multi-Satellite Space Environment Risk Prediction and Real-Time Warning System for Satellite Safety Management

In response to the need for a space security situation assessment during orbit, the multi-satellite space environmental risk prediction and early warning system is based on the detection results of the space weather payload of the Fengyun 4A and 4B satellites, as well as the prediction results of the National Space Weather Center, for the first time. By comprehensively utilizing some open-source data, it is the first time that we have achieved a 24 h advanced prediction of the space environment high-energy proton, low-energy particle, and high-energy electron risks for the safety of the Fengyun-series high-orbit satellites, and a real-time warning of satellite single-event upset, surface charging, and deep charging risks. The automation system has preliminarily achieved an intelligent space risk assessment for the safety of multiple stationary meteorological satellites, effectively improving the application efficiency of the space environmental data and the products of Fengyun-series satellites. The business status is stable in operation, and the resulting error between the predicted results of various risk indices and the measured data was less than one level. The warning accuracy was better than 90%. This article uses the system for the first time, to use Fengyun satellite data to, accurately and in a timely manner, predict and warn us about the low-energy particles and surface charging high-risk levels of the Fengyun 4A and 4B satellites during the typical space weather event on 21 April 2023, in response to the impact of complex spatial environmental factors on the safety of Fengyun-series high-orbit satellites. The construction and operation of a multi-satellite space environmental risk prediction and early warning system can provide a reference for the safety work of subsequent satellite ground system operations.

Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.

Improving GOCI ocean color data under high solar-zenith angle over open oceans using neural networks

ABSTRACT With hourly measurements available during daytime between local times of 09:00–16:00, ocean color data derived from the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological (COMS) satellite have been useful for research and surveillance of diurnal processes in the western Pacific Ocean region. However, in early morning and late afternoon measurements, there are significant errors in GOCI-derived ocean color products as the solar-zenith angle (θ 0 ) goes beyond 70°, especially in autumn and winter seasons. In this study, we employ a neural network (NN) model to make corrections on the GOCI-measured normalize water-leaving radiance spectra, nL w (λ), with high θ 0 (>70°) in open oceans. Results show that NN-corrected nL w (λ) are consistent with the previous-hour nL w (λ) and make the diurnal variations in the region much more stable and reasonable. Specifically, the GOCI-measured nL w (λ) with θ 0 ≤65° in earlier hours of the day (including some nL w (λ) diurnal variation) are considered accurate and used as the ground truth to train NN models for nL w (λ) correction with high θ 0 . Further analysis of the relationship of ratios (between the NN-corrected and original nL w (λ)) with θ 0 shows that the nL w (λ) ratios increase as θ 0 increase, which indicates that there are more significant corrections with larger θ 0 (>70°). The performance evaluation of the NN models is based on the comparison of NN-corrected nL w (λ) with the original previous hour nL w (λ) data. The ratios of NN-corrected nL w (λ) to the original previous hour nL w (λ) are 0.968–1.045 for the short blue/blue and green bands, and the performance of nL w (λ) correction at 14:00 and 15:00 is slightly better than that at 16:00, due to significantly large θ 0 at late afternoon hours. The NN-corrected nL w (λ) data are also used to derive chlorophyll-a (Chl-a) concentration, showing significantly improved Chl-a in GOCI’s late afternoon measurements with θ 0 >70°.

A global observing‐system simulation experiment for the EPS–Sterna microwave constellation

AbstractA constellation of microwave sounders named the EUMETSAT Polar System–Sterna (EPS–Sterna) is under study at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), with the aim of complementing the backbone orbits of the global observing system in low Earth orbit. The satellites of this constellation would be similar to the Arctic Weather Satellite (AWS), which is being developed by the European Space Agency (ESA). The microwave sounder on board AWS is equipped with temperature sounding channels around the 50‐GHz oxygen absorption band, water‐vapour sounding channels around the 183‐ and 325‐GHz absorption bands, and also window channels at 89 and 165 GHz. An observing‐system simulation experiment (OSSE) has been conducted at the Centre National de Recherches Météorologiques (CNRM) to evaluate the impact of this constellation on numerical weather prediction (NWP) at the global scale with the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model. Two periods ranging from August–October 2021 and December 2021–February 2022 have been chosen to compute the nature run and to run 4D‐Var data assimilation experiments. As validation of the OSSE framework, the impact of a Metop‐B denial experiment in the OSSE is compared with the impact of a Metop‐B denial with real observations. This comparison shows that the Metop‐B denial impacts are very similar in the OSSE and with real observations, with the OSSE slightly overestimating the impact. Then, the impacts of various scenarios for the EPS–Sterna constellation are assessed by computing forecast errors, fractions skill scores, and moist global energy norms, and comparing these with the results of a baseline experiment without the EPS–Sterna constellation. Significant and positive improvements of the forecasts are found up to 96 h, for every variable tested, with an impact increasing with the number of satellites.

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.

Mesoscale Auroral Curls in Antarctica

AbstractThe morphology and motion of auroras have been widely studied due to their indications on magnetospheric processes. Here, we report a new kind of “auroral curls,” which have wavelengths in the mesoscale (∼100 km) and propagate azimuthally. Utilizing data from the Chinese Antarctic Zhongshan Station (the all‐sky imager and the high‐frequency radar), the Active Magnetosphere and Planetary Electrodynamics Response Experiment and the Defense Meteorological Satellite Program, we analyze an event occurred on 23 April 2019. We find these curls are fine structures in the poleward boundary of multiple arcs. Corresponding field‐aligned currents manifest as a series of longitudinally arranged pairs, while ionospheric flow velocities nearby oscillate with periods in the Pc 5 band. Observational evidence suggests these curls are connected with ultra‐low frequency (ULF) waves, which opens the possibility of using auroras to globally image ULF waves.

A novel sequence-to-sequence based deep learning model for satellite cloud image time series prediction

Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns, detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud image forecasting is crucial, and while deep learning models have shown promise in predicting spatio-temporal data, traditional methods face challenges with extracting long-term spatio-temporal features and high computation costs. To address these issues, we propose the Re-parameterized Sequence-to-Sequence Satellite Cloud Imagery Prediction Network (Rep-SSCIPN). Rep-SSCIPN utilizes Rep-convolution layers to reduce inference-time cost and memory consumption, enhancing efficiency by converting re-parameterized blocks into a single convolution layer during inference. The sequence normalization attention mechanism in Rep-SSCIPN highlights crucial feature sequences and establishes their inter-dependencies. We validate our novel method using a real-world satellite cloud image dataset from the meteorological satellite “Himawari.” Experimental results showcase significant improvements in prediction accuracy and reconstruction quality compared to ConvLSTM, PredRNN, FCLSTM, LMC, SimVP and SCSTque models. The efficiency gains make Rep-SSCIPN a promising advancement for satellite cloud image prediction.ARTICLE INFO.

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Validation of the IRI-2020 topside ionosphere options through in-situ electron density observations by low-Earth-orbit satellites

The topside ionosphere extends from the F2-layer peak, where the electron density reaches its absolute maximum in the ionosphere, to the overlying plasmasphere and magnetosphere. In the topside ionosphere, the electron density decreases with height with a vertical variation rate strongly dependent on height itself. The last version of the International Reference Ionosphere (IRI) model, i.e., IRI-2020, describes this complex behavior through four topside options based on different sub-models (i.e., options) developed from the 1970s to the present. All these options have in common the F2-layer peak as an anchor point, while they differ in their topside electron density profile and/or plasma effective scale height formulations. In this work, we perform a validation of the accuracy of the four IRI-2020 topside options based on the comparison against in-situ electron density observations by Gravity Recovery and Climate Experiment (GRACE), Ionospheric Connection Explorer (ICON), and Defense Meteorological Satellite Program (DMSP) F15 low-Earth-orbit satellites. Datasets used in this study encompass observations recorded from 1999 to 2022, covering different diurnal, seasonal, and solar activity conditions, on a global basis and for the height range 400–850 km above the ground. The nearly two solar cycles dataset facilitated the evaluation of IRI-2020 topside options ability to reproduce the spatial and time variations of the topside ionosphere for different solar activity conditions. The weaknesses and strengths of each IRI-2020 topside option are highlighted and discussed, and suggestions on how to improve the modeling of the challenging topside ionosphere region within the IRI model are provided for future reference.

Inter‐Hemispheric Energy Input Into the Ionosphere‐Thermosphere System During Magnetic Storms: What We Can and Cannot Learn From DMSP Observations

AbstractWe present a long‐term superposed epoch analysis and statistical study with 427 magnetic storms caused by coronal mass ejections and the subsequent magnetospheric energy input into the ionosphere‐thermosphere system. Electron and ion precipitating energy flux data along with plasma drift velocity and magnetic field data (used for Earth‐bound Poynting flux computations) are used in this study. By carefully exploring conjugated inter‐hemispheric energy inputs as a function of storm intensity and seasons, we find that near two‐decades of Defense Meteorological Satellite Program observations indicate that the northern hemisphere (NH) receives slightly more energy than the southern hemisphere (SH) (Poynting flux and integrated electron/ion precipitating energy flux), with the NH receiving more energy as the storm becomes more intense. However, for the Poynting flux case, we find that generally the SH receives more energy when the east‐west component of the interplanetary magnetic field (IMF) becomes highly positive (By ≥ +5 nT), whereas this pattern reverses for cases when IMF By is highly negative (By ≤ −5 nT). The inter‐hemispheric interpretation for electron and ion precipitation energy inputs is ambiguous due to a severe lack of conjugated observations around the midnight sector for both hemispheres.

Poynting Flux Input to the Auroral Ionosphere: The Impact of Subauroral Polarization Streams and Dawnside Auroral Polarization Streams

AbstractThe Poynting vector (Poynting flux) from Earth's magnetosphere downward toward its ionosphere carries the energy that powers the Joule heating in the ionosphere and thermosphere. The Joule heating controls fundamental ionospheric properties affecting the entire magnetosphere‐ionosphere‐thermosphere system, so it is necessary to understand when and where the Poynting flux is significant. Taking advantage of new data sets generated from DMSP (Defense Meteorological Satellite Program) observations, we investigate the Poynting flux distribution within and around the auroral zone, where most magnetosphere‐ionosphere (M‐I) dynamics and thus Joule heating occurs. We find that the Poynting flux, which is generally larger under more active conditions, is concentrated in the sunlit cusp and near the interface between Region 1 and 2 currents. The former concentration suggests voltage generators drive the cusp dynamics. The latter concentration shows asymmetries with respect to the interface between the Region 1 and 2 currents. We show that these reflect the controlling impact of subauroral polarization streams and dawnside auroral polarization streams on the Poynting flux.

Lower Band Chorus Wave Scattering Causing the Extensive Morningside Diffuse Auroral Precipitation During Active Geomagnetic Conditions: A Detailed Case Study

AbstractThe diffuse aurora is a natural phenomenon observed over the Earth's polar region. Compared with the nightside diffuse aurora, the brightness of the dayside diffuse aurora (0600–1800 magnetic local time (MLT)) is relatively weak, thus requiring more stringent observation conditions. Therefore, the current understanding of what causes the dayside diffuse aurora is still quite limited. Here, we present an intense morningside diffuse aurora (0600–1000 MLT) event observed on 1 January 2016 during the recovery phase of the substorm, using conjugate observations of wave and particle spectrum from the Radiation Belt Storm Probes and auroral emission from the Special Sensor Ultraviolet Spectrographic Imagers on the Air Force Defense Meteorological Satellite Program (DMSP/SSUSI). We perform calculations of diffusion coefficients and simulations of the electron fluxes for this event. Our results show that the chorus waves are the primary contributors to the formation of the morningside diffuse aurora, with precipitated electron energies ranging from a few keV to tens of keV. The lower band chorus shows significant pitch angle scattering efficiency for electrons with energies from 5 to 20 keV. The upper band chorus waves induce acceleration effects on 1–20 keV electrons. We suggest that the upper band chorus waves accelerate low‐energy electrons to higher energies, enabling them to engage in the scattering process of the lower band chorus waves. Our study makes a contribution to recent research on the formation mechanisms of diffuse aurora and deepens our understanding of wave‐particle interactions leading to dayside electron precipitation.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.