Geostationary Research Articles

Automated Detection and Tracking of Equatorial Plasma Bubbles Utilizing Global‐Scale Observations of the Limb and Disk (GOLD) 135.6 nm Data

AbstractEquatorial plasma bubbles (EPBs) are regions of depleted plasma within the ionosphere that form during post‐sunset hours near the magnetic equator. EPBs tend to align with local geomagnetic field lines, extend hundreds of kilometers along geomagnetic longitudes, and thousands of kilometers along geomagnetic latitudes. EPBs can attenuate lower frequency radio waves, such as HF radar. Smaller features, usually 10s of meters, along the EPB's wall can interfere with centimeter scale wavelengths, such as GPS, via Bragg scattering. Statistical analysis of EPBs can further understanding of their occurrence and behavior. The current study utilizes Global‐Scale Observations of the Limb and Disk (GOLD) 135.6 nm nightglow data from 5 October 2018 to 30 September 2022. GOLD has a unique perspective from geostationary orbit, allowing consistent viewing of nightglow and structures over the Americas and Atlantic. An EPB detection method is developed and used to generate a database of occurrences. Occurrences are used to calculate EPB drift speeds and separations. All data is looked at as a whole, including any possible solar or geomagnetic disturbance periods. Seasonality in occurrence rate is evident. Occurrences peak during December solstice and minimize during June solstice for longitudes seen by GOLD. Within GOLD's view, higher occurrences are seen to the west during December solstice and to the east during June solstice. EPB drift speeds and separations show consistent distributions regardless of magnetic latitude region, geographic region, or season. This suggests EPBs behave consistently and regularly once formed, at least on spatial scales observed by GOLD.

Sustainable Cross-Regional Transmission Control for the Industrial Augmented Intelligence of Things

Sustainable connectivity between an Augmented Intelligence of Things (AIoT) system and terminal devices can provide a strong guarantee for an enterprise's continuity of production. To ensure sustainable data interaction in emergency situations such as damage to communication infrastructure facilities in a postdisaster period, a pyramidal air–space–ground network model is proposed in this paper, and its transmission performance is investigated. The space network, constructed of geostationary (GEO) satellites and medium and low Earth orbit (MEO and LEO) satellites, is heterogeneously fused with the terrestrial AIoT network to ensure around-the-clock network service for enterprise production. Then, bandwidth–delay product (BDP) analysis is performed for this heterogeneous network. The proposed scheme improves the data transmission efficiency of the network model by mitigating the impact of a long round-trip time (RTT) and improving the congestion window optimization in the case of data loss. The proposed scheme increases the transmission rate in the network model by a factor of 3.72 compared to the traditional transmission scheme in the case of a 5-hop terrestrial AIoT network and has a 3.94-fold advantage in download time.

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

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

Satellite-Aerial Communications With Multi-Aircraft Interference

Satellite-aerial communication (SAC) is envisioned as a fundamental component of the sixth-generation (6G) wireless networks. Motivated by its importance, we investigate a SAC system including a geostationary satellite (S), a target aircraft (TA), and a set of interfering aircraft (IA). Specifically, TA sends signals to S in the presence of IA interference. Considering the trajectory, hierarchy, and safety distance of the aircraft’s flight routes, we propose a novel three-dimensional stacked Poisson line hardcore point process. That is, we introduce safety distances to the stacked Poisson line Cox process in order to describe the locations of IA in the sky. We also propose two approximations, namely, the equi-dense model and the discretization model, to maintain the tractability of the analysis. To this end, the uplink coverage probability is studied by using the two proposed mathematical models. Moreover, we investigate the coverage probability of the aviation use case with predefined flight altitudes. Finally, numerical results and Monte Carlo simulations are presented to validate the accuracy of the proposed analysis.

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

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

Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method

It is, now, universally accepted that modern solar resource assessment should proceed with acquiring satellite-derived irradiance; the underlying problem is one of retrieval. The Advanced Geostationary Radiation Imager (AGRI), which is one of the main payloads of China’s latest-generation geostationary satellite series, namely, Fengyun-4 (FY-4), offers such an opportunity, albeit its full potential remains largely unexplored. On this account, a region-adapted semi-empirical model based on Heliosat-2 is herein developed to estimate global horizontal irradiance (GHI) at the native resolution of FY-4 satellite (0.5 km, 15 min), using AGRI reflectance measurement at 640 nm. The adaptation of Heliosat-2 parameters to China’s climatology and geography is based on a research-grade ground-based radiometry network with 38 stations evenly distributed across the country. Model validation is conducted against ground-based observations made at 34 unseen stations, over the course of one year (2018). As compared to the 5-km Himawari-8 radiation product, the developed product not only has a 10-fold refinement in resolution, which can clearly depict those small-scale variations in solar resources that are not observable from the Himawari-8 product, but also attains a substantially lower bias (1.4% versus 7.6% under clear skies and –0.3% versus 9.5% under cloudy skies). This ultra-high-resolution product is of vital importance to solar resource assessment on a scale consistent with the size of typical utility-scale photovoltaic plants. The product is also of great value for agricultural and ecological studies, which often depends upon high-quality solar radiation at a high spatio-temporal resolution.

Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

To fully exploit the spectral information of modern geostationary satellites, this work proposes a deep learning framework using convolutional neural networks (CNNs) and attention mechanism for 5-min ground-level global horizontal irradiance (GHI) and direct normal irradiance (DNI) estimations. The inputs are spectral satellite images with the target ground station in the center, and the labels are irradiance measurements normalized by their clear-sky estimations. The use of CNNs and attention mechanism aims to better extract the spatial information for estimating ground-level solar irradiance. To improve the modeling efficiency, only a subset of spectral bands is selected based on correlation analysis, which has comparable performance with the usage of all satellite bands. The results show that the proposed method produces GHI estimation with a normalized root mean squared error (nRMSE) of 20.57% and a normalized mean bias error (nMBE) of −2.04%, and the DNI estimation has an nRMSE of 23.63% and the nMBE is 0.36%. Compared with the national solar radiation database (NSRDB), GHI and DNI estimations of the proposed method has the nRMSE reduction of 5.15% and 13.77%, respectively. Meanwhile, the proposed models generally yield better GHI and DNI estimations under different intervals of clear-sky index than NSRDB. The combination of deep learning and remote sensing shows potential in better extracting the cloud information via multispectral satellite images, which can better support solar resource assessment, especially for cloudy conditions.

Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min.

Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States

The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr−1, while oil films require glint >10−5 sr−1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.

Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season

Biomass burning is the main source of air pollution in several regions worldwide nowadays. This predominance is expected to increase in the upcoming years as a result of the rising number of devastating wildfires due to climate change. Harmful pollutants contained in the smoke emitted by fires can alter downwind air quality both locally and remotely as a consequence of the recurrent transport of biomass burning plumes across thousands of kilometers. Here, we demonstrate how observations of carbon monoxide and aerosol optical depth retrieved from polar orbiting and geostationary meteorological satellites can be used to study the long-range transport and evolution of smoke plumes. This is illustrated through the megafire events that occurred during summer 2020 in the Western United States and the transport of the emitted smoke across the Atlantic Ocean to Europe. Analyses from the Copernicus Atmosphere Monitoring Service, which combine satellite observations with an atmospheric model, are used for comparison across the region of study and along simulated air parcel trajectories. Lidar observation from spaceborne and ground-based instruments are used to verify consistency of passive observations. Results show the potential of joint satellite-model analysis to understand the emission, transport, and processing of smoke across the world.

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

AbstractPredicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

Snow Cover Detection Using Multi-Temporal Remotely Sensed Images of Fengyun-4A in Qinghai-Tibetan Plateau

Differentiating between snow and clouds presents a formidable challenge in the context of mapping snow cover over the Qinghai–Tibetan Plateau (QTP). The frequent presence of cloudy conditions severely complicates the discrimination of snow cover from satellite imagery. To accurately monitor the spatiotemporal evolution of snow cover, it is imperative to address these challenges and enhance the segmentation schemes employed for snow cover assessment. In this study, we devised a pixel-wise classification algorithm based on Support Vector Machine (SVM) called the 3-D Orientation Gradient algorithm (3-D OG), which captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions based on geostationary satellite “Fengyun-4A” (FY-4A) multi-spectral and multi-temporal optical imagery. This algorithm assumes that the speed and direction of clouds and snow are different in the process of movement leading to their discrepancy of gradient characteristics in time and space. Therefore, in this algorithm, the gradient of the images in the spatiotemporal dimensions is calculated first, and then the movement angle and trend are obtained based on that. Finally, the feature space is composed of the multi-spectral image, gradient image, and movement feature maps, which are used as the input of the SVM. Our results demonstrate that the proposed algorithm can identify snow and clouds more accurately during snowfall by utilizing the FY-4A’s high temporal resolution image. Weather station data, which was collected during snowstorms in the QTP, were used for evaluating the accuracy of our algorithm. It is demonstrated that the overall accuracy of snow cover segmentation by using the 3-D OG algorithm is improved by at least 12% and 10% as compared to snow products of Fengyun-2 and MODIS, respectively. Overall, the proposed algorithm has overcome the axial swing errors existing in Geostationary satellites and is successfully applied to cloud and snow segmentation in QTP. Furthermore, our study underscores that the visible and near-infrared bands of Fengyun-4A can be used for near real-time snow cover monitoring with high performance using the 3-D OG algorithm.

Reliability Analysis Based on Air Quality Characteristics in East Asia Using Primary Data from the Test Operation of Geostationary Environment Monitoring Spectrometer (GEMS)

Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O3 ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO2 ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO2 than that of OMI and TROPOMI. Based on the sources of O3 and NO2, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis.

Soft Error Simulation of Near-Threshold SRAM Design for Nanosatellite Applications

This paper presents the benefit of the near-threshold design of random-access memory (SRAM) design to reduce software errors during very low-power operations in nanosatellites. The near-threshold design is based on an optimization of the use of the Schmitt trigger structure for a 45 nm technology. The results of the soft error susceptibility of the optimized design are compared to a standard 6T SRAM cell. These two designs are modeled and validated by comparing the results with experimental measurements of both static noise margin (SNM) and single event upset (SEU). The optimized circuit reduces the multiple upsets occurrence from 95% down to 14%. Based on the use of simulation tools, the paper demonstrates that the near-threshold design of SRAM is an excellent candidate for the radiation point of view for agile nanosatellites. The results computed for the near-threshold SRAM device demonstrate an improvement of a factor of up to 25 of the soft error rate (SER) in a GEO orbit.

MLP-based user-applicable method for SBAS GIVD calibration: A comparative study based on CODE final GIM products

Satellite-Based Augmentation Systems (SBASs) are designed to enhance the performance of Global Navigation Satellite Systems (GNSSs) for civil aviation users. As SBAS Geostationary Earth Orbit (GEO) satellite signals are received in a wide area, far more non-aeronautical GNSS users utilize SBAS to improve navigation performance. Since ionospheric delay is the principal error source of GNSS and a leading correction aspect of SBAS, the GEO-broadcast ionospheric data quality of six SBASs over the past three years is assessed in this article. Results show that European Geostationary Navigation Overlay Service (EGNOS), System for Differential Correction and Monitoring (SDCM), and Wide-Area Augmentation System (WAAS) have the highest Grid Ionospheric Vertical Delay (GIVD) accuracy judged by the residual Standard Deviation (STD), then followed by BeiDou SBAS (BDSBAS) and Multi-functional Satellite Augmentation System (MSAS), while GPS-Aided GEO Augmented Navigation (GAGAN) is the worst. The 6 m Grid Ionospheric Vertical Error (GIVE) availability is over 90 percent of the time for most Ionospheric Grid Points (IGPs) of all tested SBASs. However, GAGAN and WAAS have a significant availability decline at 3 m GIVE. In addition, the integrity limits of MSAS and WAAS are fairly conservative. In contrast, the confidence bounds of BDSBAS and SDCM are excessively radical that fail to meet the integrity requirement. To improve the ionospheric data quality received from SBAS, a Multi-Layer Perceptron (MLP)-based user-applicable GIVD calibration method is proposed in the article. A well-trained MLP learned temporal and spatial correlations of the IGP delays and extracted transfer functions between broadcast and reference GIVDs to eliminate error. The SBAS processing errors in Differential Code Bias (DCB) compensation, GIVD estimation modeling, and GEO message quantization are rectified by the MLP trained by abundant historical data with Center for Orbit Determination in Europe (CODE) final Global Ionospheric Maps (GIMs) as reference. Test results on real-time data show that over 85 percent IGPs have STD decline after calibration. The STD declination rates of six SBASs except for SDCM are over 15 percent, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMS) calibration effects are dramatic for all systems. Finally, the average GIVEs are reduced by more than 50 percent for EGNOS, GAGAN, MSAS, and WAAS with a 99.9 percent integrity level maintained. Despite no promotion on availability for BDSBAS and SDCM, the proposed method compensates for the integrity issues of the systems.

Interference Analysis of NGSO Constellation to GEO Satellite Communication System Based on Spatio–Temporal Slices

The co-frequency interference (CFI) of the non-geostationary orbit (NGSO) constellation to the geostationary earth orbit (GEO) satellite internet of things (IoT) system is an nonnegligible problem. In this paper, based on the calculation method of the NGSO constellation satellite distribution state period, we propose a global characteristic method to analyze the CFI of the NGSO constellation to the GEO satellite IoT system by spatio-temporal slices. In order to analyze and evaluate the global characteristic and overall situation of interference, the temporal and spatial cut set of interference is established by fragmenting time and gridding space. The correctness of the proposed NGSO constellation satellite distribution state period is verified by taking the OneWeb constellation’s interference to the SINOSAT-5 satellite communication system as the simulation scenario. Furthermore, the global interference characteristic of the OneWeb constellation to the SINOSAT-5 satellite communication system is analyzed with the method mentioned above in the range of 80∘ 140∘E and 0∘ 50∘N. The results show that the analysis method can reflect the overall situation of the CFI of the NGSO constellation to the GEO satellite IoT system.

Anisotropic ionospheric scintillation in weak scattering regime

Transionospheric radio signals might undergo random modulations of their amplitude and phase caused by scattering on irregular structures in the ionosphere. These scintillation phenomena are highly anisotropic, depend on local geomagnetic field configuaration and on the relative position of the signal receiver and scattering irregularity. We derive analytical expressions of anisotropic amplitude and phase scintillation indices using the model of a thin random phase screen. These results extend the classical derivations of [C. Rino, Radio Sci. 14, 1135 (1979)] to a larger domain of applicability including very slant propagation links of the radio wave signals. The derived generalization is based on the assumption that the ionospheric scattering layer has a spherical symmetry as opposed to the original Rino’s assumption of a plan-parallel ionospheric layer. The derived scintillation indices have the simpler analytical form, determine the regions of enhanced scintillation by taking into account the finite curvature of the Earth and of the ionospheric shell, and are divergence-free for large zenith angles of propagation links. For the illustration we discuss the geometric enhancement of scintillation for communication links via a geostationary beacon satellite over the equator.

Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection

Solar energy supply is usually highly volatile, limiting its integration into the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increase the share of photovoltaic (PV) energy in the grid and enable cost-efficient balancing of power demand and supply. Solar PV production mainly depends on downwelling surface solar radiation (SSR). By estimating SSR from geostationary satellites, we can cover large areas with high spatial and temporal resolutions, allowing us to track cloud motion. State-of-the-art probabilistic forecasts of solar resources from satellite imagery account only for the advective motion of clouds. They do not consider the evolution of clouds over time, their growth, and dissipation, even though these are major sources of forecast uncertainty. To address the uncertainty of cloudiness evolution, we present SolarSTEPS, the first optical-flow probabilistic model able to simulate the temporal variability of cloudiness. We demonstrate that forecasting the autocorrelated scale-dependent evolution of cloudiness outperforms state-of-the-art probabilistic advection-based forecasts by 9% in continuous ranked probability score (CRPS). This corresponds to an extension of the forecast lead time by about 45 min at constant CRPS. Our work is motivated by the scale-dependent predictability of cloud growth and decay. We demonstrate that cloudiness is more variable in time at smaller spatial scales than at larger ones. Specifically, we show that the temporal autocorrelation of cloudiness is related to its spatial scale by an inverse power law. We also demonstrate that decomposing cloudiness into multiple spatial scales in the forecasts further improves the forecast skill, reducing the CRPS by 10% and the RMSE by 9%.

Wildfire monitoring technologies of transmission line corridors based on Fengyun-3E satellite imaging

Facing towards the Carbon Neutrality goal by 2060, renewable energy sources are boosted in China in the past decade. Either these renewables or power systems are sensitive and vulnerable to natural disasters, such as wildfire and typhoon. In this paper, a fire spot identification algorithm is proposed for wildfire disaster occurred in transmission line corridors based on Fengyun-3E (FY-3E). It mitigates the effect of large observation of solar zenith angle, as well as changes of satellite views, on the infrared channel detection. And cloud information under complex atmospheric conditions is analyzed to extract the cloud pixel fire spot, which decreases false ratio of fire-spot alerts. According to the analysis of hybrid pixel linear spectrums, the fire-spot detection sensitivity of FY-3E is four times as that of geostationary meteorological satellites. The effectiveness of the proposed algorithm was confirmed by an application in the Shanxi province power grid of State Grid in China. It shows that and the FY-3E has a superiority in wildfire monitoring for power system in terms of fire detection sensitivity, spatial range accuracy, and positioning accuracy. Compared to geostationary meteorological satellites, the fire locating accuracy of FY-3E is increased by more than one-fold. This research can assist the operation and maintenance departments of transmission lines to rapidly detect wildfire, thereby reducing the damage of wildfires on the power grid.

Cool Skin Effect as Seen from a New Generation Geostationary Satellite Himawari-8

The cool skin effect refers to the phenomenon where the surface skin temperature of the ocean is always slightly cooler than the temperature of the water directly underneath due to the ubiquitous cooling processes at the ocean surface, especially in the absence of solar radiation. The cool skin effect plays a critical role in the estimation of heat, momentum, and gas exchange between the air and the sea. However, the scarcity of observational data greatly hinders the accurate assessment of the cool skin effect. Here, the matchup data from the new generation geostationary satellite Himawari-8 and in situ sea surface temperature (SST) observations are used to evaluate the performance and dependence on the cool skin effect in the low/mid-latitude oceans. Results show that the intensity of the cool skin effect as revealed by Himawari-8 (−0.16 K) is found to be relatively weaker than previously published cool skin models based on in situ concurrent observations. A considerable amount of warm skin signals has been detected in the high-latitude oceans (e.g., Southern Ocean) under the circumstances of positive air–sea temperature difference and high wind, which may be the main cause of discrepancies with previous thoughts on the cool skin effect.