Geostationary Satellite Observations Research Articles

Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution

At present, traditional satellite datasets still grapple with inadequacies in terms of capturing solar radiation with fine spatiotemporal granularity. This study utilizes the high spatiotemporal resolution of CARE data, which is developed based on geostationary satellite observations, and employs multivariate analysis techniques to conduct an in-depth investigation into the multidimensional spatiotemporal variations of different types of solar radiation across various regions in China from 2016 to 2020. In addition, the potential of solar energy resources was also assessed using cluster analysis method. The results revealed an upward trend in different components of solar radiation across most of China, with shortwave radiation exhibiting a significantly negative correlation with PM2.5 concentrations (R = −0.91, p < 0.05). This finding suggests that the increase in SWR may be attributed to the effective implementation of China's Air Pollution Prevention and Control Action Plan. It suggests that China's endeavors to mitigate air pollution have not only resulted in improvements in national air quality but have also had an indirect positive effect on enhancing the potential for photovoltaic power generation. The assessment of solar energy resources potential indicated, with 99 % statistical confidence, that western China constitutes the core region for solar energy resources development, whereas northeastern and southeastern regions face certain constraints in solar energy resources utilization. Furthermore, we examined the specific influence of atmospheric circulation patterns on solar energy resources, uncovering that the El Niño phenomenon, by altering cloud distribution and variability, indirectly affects solar radiation intensity in specific regions. This study aids in understanding China's solar radiation variations, crucial for shaping effective energy policies towards carbon neutrality.

Observational Overview of the May 2024 G5-Level Geomagnetic Storm: From Solar Eruptions to Terrestrial Consequences

This study reports comprehensive observations for the G5-level geomagnetic storm that occurred from May 10 to 12, 2024, the most intense event since the 2003 Halloween storm. The storm was triggered by a series of coronal mass ejections (CMEs) originating from the merging of two active regions 13664/13668, which formed a large and complex photospheric magnetic configuration and produced X-class flares in early May 2024. Among the events, the most significant CME, driven by an X2.2 flare on May 9, caught up with and merged with a preceding slower CME associated with an X-class flare on May 8. These combined CMEs reached 1 AU simultaneously, resulting in an extreme geomagnetic storm. Geostationary satellite observations revealed changes in Earth’s magnetosphere due to solar wind impacts, increased fluxes of high-energy particles, and periodic magnetic field fluctuations accompanied by particle injections. Extreme geomagnetic storms resulting from the interaction of the solar wind with the Earth’s magnetosphere caused significant energy influx into Earth’s upper atmosphere over the polar regions, leading to thermospheric heating and changes in the global atmospheric composition and ionosphere. As part of this global disturbance, significant disruptions were also observed in the East Asian sector, including the Korean Peninsula. Ground-based observations show strong negative storm effects in the ionosphere, which are associated with thermospheric heating and resulting in decreases in the oxygen-to-nitrogen ratio (O/N2) in high-latitude regions. Global responses of storm-time prompt penetration electric fields were also observed from magnetometers over the East-Asian longitudinal sector. We also briefly report storm-time responses of aurora and cosmic rays using all-sky cameras and neutron monitors operated by the Korea Astronomy and Space Science Institute (KASI). The extensive observations of the G5-level storm offer crucial insights into Sun-Earth interactions during extreme space weather events and may help establish better preparation for future space weather challenges.

Insights Into Internal Solitary Waves East of Dongsha Atoll From Integrating Geostationary Satellite and Mooring Observations

AbstractUnderstanding of internal solitary wave (ISW) behavior has been limited due to sparse observations. We used high‐resolution Himawari‐8 satellite imagery and mooring observations to reveal the two‐dimensional (x–y) propagation process of ISWs in the South China Sea as they westward propagate onto the Dongsha plateau and encounter Dongsha Atoll. The 2D depiction of wave speed distribution, derived from detected wave crest positions every 10 min, shows the wave speeds range from 3 m s−1 to 1 m s−1 and have a tight correspondence to the local water depth. The correlation coefficient between the wave speeds and the Dubreil–Jacotin–Long (DJL) solutions is around 0.7, with a root mean squared value of 0.26 m s−1, and the representative available potential energy for this region is considered to be 130 MJ m−1. However, diffusions of wave speed in the ISW's lateral direction, particularly around abrupt topography, contribute to occurrences of outliers. Pairs of incident and reflected waves are well recognized east of Dongsha Atoll. The incident wave packet is known to be classified into a‐type and b‐type waves. The reflected waves associated with the b‐wave, identifiable as mode‐1 depression ISWs, are traced back to their generation site at depths of 100–200 m. In contrast, the reflected waves of the a‐wave remain elusive in shallower waters (&lt;300 m), likely due to interference from their longer incident counterparts. The reflected wave, however, is slower and decelerates toward deeper water, deviating from the DJL prediction. These comprehensive observations can help refine models for improved accuracy.

Potential Precursory Signals of Localized Torrential Rainfall From Geostationary Satellite and Radar Observations: A Case Study of the 2022 Seoul Flood

Potential Precursory Signals of Localized Torrential Rainfall From Geostationary Satellite and Radar Observations: A Case Study of the 2022 Seoul Flood

Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia

Abstract. Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO2 reflects a dynamic balance between emission and chemical loss.

Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.

Solar irradiance variability around Asia Pacific: Spatial and temporal perspective for active use of solar energy

This study discusses solar irradiance variability in the spatial and temporal domains for the active use of solar energy. The spatial domain was calculated using the difference between one location and another in a focused small tile area. The principal component analysis (PCA) was used for this analysis in the spatial and temporal domain. The Umbrella Effect Index (UI) was calculated to determine the probability of an umbrella effect event within a specific time range. This study discusses solar irradiance variability using solar irradiance data analyzed every 10 min from high-temporal observations of geostationary satellites in the Asia-Pacific region. This study shows that solar heterogeneity around the Asia-Pacific ranges from 0–135 W/m2 with a high tendency in the area around the equator and highlands. In addition, this study also shows that the Asia Pacific region has umbrella effect events of approximately 50–2224 h/year with an index ranging approximately 0–0.34. This study presents the annual analysis and seasonal trends of solar irradiance variability over the entire study area. Determining seasonal trends is important because electricity demand fluctuates significantly as seasons progress. An integrated analysis of heterogeneity and UI revealed trends in spatiotemporal variations in solar irradiance. This approach provides useful information for optimizing and managing the locations for installing solar power plants. Moreover, an evaluation of existing solar power plants is presented. Evaluations based on spatiotemporal data reveal impossible characteristics using traditional approaches that use long-term simple averages or typical solar irradiance data. This research shows the distributed photovoltaic (PV) system in wide area can increase the stability of solar energy supply to the grid.

UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations

AbstractThe frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa

Abstract. The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 W m−2. Despite this, due to the opposite sign of these fluxes, the net average of the anvil cloud radiative effect (CRE) over the tropics is observed to be neutral. Research into the response of the anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of the anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact the anvil CRE. To study the connection between the deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). Retrieved cloud properties and derived broadband radiative fluxes are provided by the Community Cloud retrieval for CLimate (CC4CL) algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of the SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs, while larger, organised systems tend to have a CRE closer to 0. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is close to neutral (−0.94 ± 0.91 W m−2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks.

Radiative Effect of Two Contrail Cirrus Outbreaks Over Western Europe Estimated Using Geostationary Satellite Observations and Radiative Transfer Calculations

AbstractEstimation of the perturbation to the Earth's energy budget by contrail outbreaks is required for estimating the climate impact of aviation and verifying the climate benefits of proposed contrail avoidance strategies such as aircraft rerouting. Here we identified two successive large‐scale contrail outbreaks developing in clear‐sky conditions in geostationary and polar‐orbiting satellite infrared images of Western Europe lasting from 22–23 June 2020. Their hourly cloud radiative effect, obtained using geostationary satellite cloud retrievals and radiative transfer calculations, is negative or weakly positive during daytime and positive during nighttime. The cumulative energy forcing of the two outbreaks is 7 PJ and −8.5 PJ, with uncertainties of 3 PJ, stemming each from approximately 15–20 flights over periods of 19 and 7 hr, respectively. This study suggests that an automated quantification of contrail outbreak radiative effect is possible, at least for contrails forming in clear sky conditions.

On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific

Abstract. Marine atmospheric boundary layer (MABL) clouds cover vast areas over the ocean and have important radiative effects on the Earth's climate system. These radiative effects are known to be sensitive to the local organization, or structure, of the mesoscale cellular convection (MCC). A convolutional neural network model is used to identify the two idealized classes of MCC clouds, namely open and closed, over the Southern Ocean (SO) and Northwest Pacific (NP) from high-frequency geostationary Himawari-8 satellite observations. The results of the climatology show that MCC clouds are evenly distributed over the mid-latitude storm tracks for both hemispheres, with peaks poleward of the 40∘ latitude. Open-MCC clouds are more prevalent than closed MCC in both regions. An examination of the presumed meteorological forcing associated with open- and closed-MCC clouds is conducted to illustrate the influence of large-scale meteorological conditions. We establish the importance of the Kuroshio western boundary current in the spatial coverage of open and closed MCC across the NP, presumably through the supply of strong heat and moisture fluxes during marine cold-air outbreaks events. In regions where static stability is higher, we observe a more frequent occurrence of closed MCCs. This behavior contrasts markedly with that of open MCCs, whose formation and persistence are significantly influenced by the difference in temperature between the air and the sea surface. The occurrence frequency of closed MCC over the SO exhibits a significant diurnal cycle, while the diurnal cycle of closed MCC over the NP is less noticeable.

Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure.

We demonstrate geostationary satellite monitoring of large transient methane point sources with the US Geostationary Operational Environmental Satellites (GOES). GOES provides continuous 5- to 10-min coverage of the Americas at 1 to 2 km nadir pixel resolution in two shortwave infrared spectral bands from which large methane plumes can be retrieved. We track the full evolution of an extreme methane release from the El Encino-La Laguna natural gas pipeline in Durango, Mexico on 12 May 2019. The release lasted 3 h at a variable rate of 260 to 550 metric tons of methane per hour and totaled 1,130 to 1,380 metric tons. We report several other detections of transient point sources from oil/gas infrastructure, from which we infer a detection limit of 10 to 100 t h-1. Our results show that extreme releases of methane can last less than an hour, as from deliberate venting, and would thus be difficult to identify and quantify with low-Earth orbit satellites.

ProxyVis—A Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations

Abstract Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.

Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

Abstract Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts.

Exploring geometrical stereoscopic aerosol top height retrieval from geostationary satellite imagery in East Asia

Abstract. Despite the importance of aerosol height information for events such as volcanic eruptions and long-range aerosol transport, spatial coverage of its retrieval is often limited because of a lack of appropriate instruments and algorithms. Geostationary satellite observations in particular provide constant monitoring for such events. This study assessed the application of different viewing geometries for a pair of geostationary imagers to retrieve aerosol top height (ATH) information. The stereoscopic algorithm converts a lofted aerosol layer parallax, calculated using image-matching of two visible images, to ATH. The sensitivity study provides a reliable result using a pair of Advanced Himawari Imager (AHI) and Advanced Geostationary Radiation Imager (AGRI) images at 40∘ longitudinal separation. The pair resolved aerosol layers above 1 km altitude over East Asia. In contrast, aerosol layers must be above 3 km for a pair of AHI and Advanced Meteorological Imager (AMI) images at 12.5∘ longitudinal separation to resolve their parallax. Case studies indicate that the stereoscopic ATH retrieval results are consistent with aerosol heights determined using extinction profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Comparisons between the stereoscopic ATH and the CALIOP 90 % extinction height, defined by extinction coefficient at 532 nm data, indicated that 88.9 % of ATH estimates from the AHI and AGRI are within 2 km of CALIOP 90 % extinction heights, with a root-mean-squared difference (RMSD) of 1.66 km. Meanwhile, 24.4 % of ATH information from the AHI and AMI was within 2 km of the CALIOP 90 % extinction height, with an RMSD of 4.98 km. The ability of the stereoscopic algorithm to monitor hourly aerosol height variations is demonstrated by comparison with a Korea Aerosol Lidar Observation Network dataset.

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause. This paper presents an image-based approach for the short-term nighttime forecasting of FLS during the next 5 h over Morocco, based on geostationary satellite observations (Meteosat SEVIRI). To achieve this, a dataset of hourly night microphysics RGB product was generated from native files covering the nighttime cold season (October to April) of the 5-year period (2016–2020). Two optical flow techniques (sparse and dense) and three deep learning techniques (CNN, Unet and ConvLSTM) were used, and the performance of the developed models was assessed using mean squared error (MSE) and structural similarity index measure (SSIM) metrics. Hourly observations from Meteorological Aviation Routine Weather Reports (METAR) over Morocco were used to qualitatively compare the FLS existence in METAR, where it is also shown by the RGB product. Results analysis show that deep learning techniques outperform the traditional optical flow method with SSIM and MSE of about 0.6 and 0.3, respectively. Deep learning techniques show promising results during the first three hours. However, their performance is highly dependent on the number of filters and the computing resources, while sparse optical flow is found to be very sensitive to mask definition on the target phenomenon.

Data-to-data translation-based nowcasting of specific sea fog using geostationary weather satellite observation

Dense sea fog events are responsible for many traffic accidents and fatalities. Research has demonstrated the difficulty in distinguishing sea fog from clouds. This study presents a sea fog nowcasting method that uses brightness temperature (BT) at a mid-wave infrared (MWIR) 3.7 μm bands and the BT difference between MWIR and long-wave infrared 10.8 μm bands of communication, ocean, and meteorological satellite data through a data-to-data (D2D) translation based on a conditional generative adversarial networks technique. Sixty fog events that occurred in the Yellow Sea from 2017 to 2020 were used to develop the model. The D2D prediction model for predicting sea fog from 15-min to 2-h ahead with 15 min intervals was trained by stacking individual prediction time datasets in an array of 512 × 512 × 4 pixels for training and 512 × 512 × 8 pixels for testing. Otsu's thresholding method was used to determine sea fog pixels. The sea fog prediction model showed excellent statistical scores, such as the probability of detection = 0.923, false alarm ratio = 0.032, critical success index = 0.895, Heidke skill score = 0.909, and post agreement = 0.968 for 1 h-ahead of a sea fog forecast. Consequently, our study can make a scientific and operational contribution to fog forecasting as useful fog-related data, in addition to other numerical prediction data and ground observation data.

Meteorological Satellite Observations Reveal Diurnal Exceedance of Water Quality Guideline Thresholds in the Coastal Great Barrier Reef

The Great Barrier Reef (GBR) is a marine protected area subject to natural and anthropogenic disturbances. Water quality is critical for the health and protecting resilience of GBR coral ecosystems against the synergistic and cumulative pressures of tropical cyclones, marine heat waves, and outbreaks of crown-of-thorns starfish. The concentration of Total Suspended Solids (TSS) is a key water quality parameter measured at multiple spatio-temporal scales from in situ probes to satellite observations. High TSS concentrations can adversely impact coral and seagrasses on the inshore GBR. We present diurnal TSS derived from Himawari-8 Geostationary satellite observations at 10 min frequency and demonstrate its applicability for improved monitoring of GBR water quality. Diurnal TSS obtained from Himawari-8 observations were compared to TSS computed from in situ bio-optical measurements at the Lucinda Jetty Coastal Observatory (LJCO). The coastal waters at LJCO experience diurnal variability of TSS (~7 mg L−1), where magnitude peaks followed the slack tides, and the largest diurnal changes were associated with freshwater discharge residuals from the wet season. Exceedance maps revealed that TSS is above guideline thresholds in the open coastal and mid-shelf waters for ~60% of the valid monthly observations, including during dry season months.