Environmental Satellite Research Articles

Enhancing ozone nowcasting over East Asia using a data-to-data translation approach with observations from a geostationary environment monitoring spectrometer

The degradation of air quality caused by excessive anthropogenic ozone (O3) concentrations negatively affects ecosystems and the atmosphere. To monitor this issue, the Geostationary Environment Monitoring Spectrometer (GEMS) aboard the Geostationary Korea Multi-Purpose Satellite (GK)-2B provides ozone products over Asia-centered circular regions based on the ozone algorithm derived from representative polar-orbit atmospheric environmental satellites. In this study, we developed a few-hour GEMS O3 nowcasting model using data-to-data (D2D) translation with a conditional generative adversarial network. This model is based on hourly GEMS O3 time-series products and can be used to predict ozone concentrations. The D2D model underwent training and testing employing paired input and output datasets of GEMS O3, with data collected from March 22, 2020, to June 21, 2020, and from March 22, 2021, to June 18, 2021, respectively. The resulting D2D ozone nowcasting model was used to determine ozone concentrations in time zones where GEMS O3 products were unavailable. Test results of the D2D model demonstrated excellent statistical scores, including a bias of 2.162 Dobson Units (DU, where 1 DU corresponds to 2.687 × 1016 molecules/cm2), root-mean-square error (RMSE) of 5.606 DU, a correlation coefficient (CC) of 0.994 for 1-h prediction, a bias of 1.421 DU, RMSE of 5.903 DU, and CC of 0.992 for 2-h prediction, and a bias of 1.169 DU, RMSE of 6.797 DU, and CC of 0.988 for 3-h prediction. Despite the dataset pairing and number limitations, the D2D prediction model accurately forecasted GEMS O3 within 3 h in East Asia.

Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the first geostationary earth orbit (GEO) environmental instrument, onboard the Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B) launched on 19 February 2020, and is measuring reflected radiance from the earth's surface and atmosphere system in the range of 300–500 nm in the ultraviolet–visible (UV–Vis) region. The radiometric response of a satellite sensor that measures the UV–Vis wavelength region can depend on the polarization states of the incoming light. To reduce the sensitivity due to polarization, many current low earth orbit (LEO) satellites are equipped with a scrambler to depolarize the signals or a polarization measurement device (PMD) that simultaneously measures the polarization state of the atmosphere, then utilizes it for a polarization correction. However, a novel polarization correction algorithm is required since GEMS does not have a scrambler or a PMD. Therefore, this study aims to improve the radiometric accuracy of GEMS by developing a polarization correction algorithm optimized for GEMS that simultaneously considers the atmosphere's polarization state and the instrument's polarization sensitivity characteristics. The polarization factor and axis were derived by the preflight test on the ground as a function of wavelengths, showing a polarization sensitivity of more than 2 % at some specific wavelengths. The polarization states of the atmosphere are configured as a look-up table (LUT) using the Vector Linearized Discrete Ordinate Radiative-Transfer model (VLIDORT). Depending on the observation geometry and atmospheric conditions, the observed radiance spectrum can include a polarization error of 2 %. The performance of the proposed GEMS polarization algorithm was assessed using synthetic data, and the errors due to polarization were found to be larger in clear regions than in cloudy regions. After the polarization correction, polarization errors were reduced close to zero for almost all wavelengths, including the wavelength regions with high peaks and curvatures in the GEMS polarization factor, which sufficiently demonstrates the effectiveness of the proposed polarization correction algorithm. From the actual observation data after the launch of GEMS, the diurnal variation for the spatial distribution of polarization error was confirmed to be minimum at noon and maximum at sunrise/sunset. This can be used to improve the quality of GEMS measurements, the first geostationary environmental satellite, and then contribute to the retrieved accuracy of various Level-2 products, such as trace gases and aerosols in the atmosphere.

Forecasting solar flares with a transformer network

Space weather phenomena, including solar flares and coronal mass ejections, have significant influence on Earth. These events can cause satellite orbital decay due to heat-induced atmospheric expansion, disruption of GPS navigation and telecommunications systems, damage to satellites, and widespread power blackouts. The potential of flares and associated events to damage technology and disrupt human activities motivates prediction development. We use Transformer networks to predict whether an active region (AR) will release a flare of a specific class within the next 24 h. Two cases are considered: ≥C-class and ≥M-class. For each prediction case, separate models are developed. We train the Transformer to use time-series data to classify 24- or 48-h sequences of data. The sequences consist of 18 physical parameters that characterize an AR from the Space-weather HMI Active Region Patches data product. Flare event information is obtained from the Geostationary Operational Environmental Satellite flare catalog. Our model outperforms a prior study that similarly used only 24 h of data for the ≥C-class case and performs slightly worse for the ≥M-class case. When compared to studies that used a larger time window or additional data such as flare history, results are comparable. Using less data is conducive to platforms with limited storage, on which we plan to eventually deploy this algorithm.

Enhancing urban green spaces: A resilience-based approach to plant dust retention in older residential neighborhoods

This research focuses on developing a comprehensive understanding of plant dust retention resilience in older residential neighborhoods, emphasizing protection, recovery, and sustainability. Plant dust retention resilience encompasses the ability of plant combinations to respond to emergencies, cooperate with emergency service systems, adapt effectively, recover swiftly from disasters, and enhance future sustainability. Key quantitative indicators include the scale of green space and the structure of plant arrangements, with a focus on types and factors affecting plant arrangement structure. Data processing involves the selection of relevant indicators, the Delphi Method for indicator screening, and the Analytic Hierarchy Process for indicator weight determination. Data sources include environmental reports, government websites, measurements, and satellite images. The research establishes a comprehensive evaluation indicator system for plant dust retention resilience and a resilience capacity model for plants in old residential areas. The results suggest that the best plant configuration model for dust retention resilience capacity includes a mix of coniferous and deciduous trees and shrubs with a significant grass component.

Predicting Solar Proton Events of Solar Cycles 22–24 Using GOES Proton and Soft-X-Ray Flux Features

Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.

Can We Intercalibrate Satellite Measurements by Means of Data Assimilation? An Attempt on LEO Satellites

AbstractLow Earth Orbit satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit that can observe all equatorial pitch‐angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a Data assimilation (DA) approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar‐orbiting Operational Environmental Satellites (POES) NOAA‐15,‐16,‐17,‐18,‐19, and MetOp‐02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites observations into 3‐D Versatile Electron Radiation Belt (VERB‐3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES data set and estimate the flux ratios at each time, location, and energy. From these ratios, we derive energy and L* dependent recalibration coefficients. To validate our results, we analyze on‐orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data‐assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of DA allows for improved statistics, as the possible comparisons are increased 10‐fold. Data‐assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large data sets using short periods of data.

Impact of HY‐2B SMR radiance assimilation on CMA global medium‐range weather forecasts

AbstractThe Chinese second ocean dynamic environment satellite Haiyang‐2B (HY‐2B) was successfully launched on October 25, 2018, carrying a scanning microwave radiometer (SMR) to provide information about the ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY‐2B SMR in the four‐dimensional variational (4DVar) data assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA‐GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY‐2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA‐GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one‐month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear‐sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improving the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1–5 in the lower troposphere of the tropical region.

Wet Tropospheric Correction for the HY-2C Altimeter over the Coastal Ocean Based on GNSS and ERA5 Data

For the impact of land, sea ice, rainfall and strong wind, the wet tropospheric correction (WTC) of HY-2C (the third marine dynamic environmental monitoring satellite of China) altimeter cannot be obtained accurately by the calibration microwave radiometer (CMR) over the coastal ocean. The conducted evaluation of the HY-2C CMR WTC over the coastal ocean by the Global Navigation Satellite System (GNSS) and ECMWF Reanalysis v5 (ERA5) data showed that the CMR WTC is abnormal or invalid. To solve this issue, a new method to determine the WTC of the HY-2C altimeter based on objective analysis (OA) by combining the GNSS WTC, ERA5 WTC and valid CMR WTC is developed. The OA WTC and HY-2C CMR WTC are compared to the Integrated Global Radiosonde Archive (IGRA) WTC to validate the OA method. The results show that the RMSE of the WTC differences significantly decreases and the correlation coefficient increases. The number of available WTC data increases over the open ocean. The variances of sea level anomaly corresponding to the OA WTC in most areas are significantly smaller than those of the HY-2C CMR WTC. In general, the availability and accuracy of HY-2C altimeter data are largely enhanced with the OA WTC.

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.

A Diagnostic Study on the Formation of Double Eyewalls of Typhoon Lekima (2019) Based on POES Microwave and GOES Infrared Observations

AbstractMicrowave observations of brightness temperature (TB) from multiple polar‐orbiting operational environmental satellite imagers and infrared observations of geostationary operational environmental satellite Himawari‐8 imager are used to investigate characteristic features of secondary eyewall evolution of Typhoon Lekima (2019). It is found that microwave observations showed a longer duration time of concentric double eyewalls than Advanced Himawari Imager (AHI) infrared observations, due to a stronger cloud penetration capability of microwave than infrared observations. The AHI infrared observations with high temporal and horizontal resolutions captured several actively propagating inner spiral rainbands before the secondary eyewall formation (SEF) of Lekima (2019). They are characterized by a wavenumber‐1 asymmetry propagating radially outward and wavenumbers‐2 and ‐3 asymmetries propagating anticlockwise. The wavenumber‐2 asymmetry approximates an azimuthal phase speed of wavenumber‐2 vortex Rossby wave. Later, an outer spiral rainband experienced an obvious axisymmetrization, then merged with multiple inner spiral rainbands propagating radially outward from the primary eyewall. The stagnation radius of these inner spiral rainbands coincides with the location of the highly symmetric outer spiral rainband, which accelerated the SEF. It is suggested that the SEF of Typhoon Lekima (2019) results from a concerted activity of inner and outer spiral rainbands.

Intercomparison of Landsat OLI and JPSS VIIRS Using a Combination of RadCalNet Sites as a Common Reference

Independent radiometric data collected from multiple ground sites as part of vicarious calibration activities can be combined to harmonize the data products of Earth observation sensors with different temporal, spectral, and spatial resolutions. Recent coordinated international efforts for open fiducial reference measurements have provided the worldwide user community with new ways to explore the calibration and harmonization of data produced by the sensors. To be correct, the results from each ground system must be traceable to the same well-understood standard system, and ideally to the international system of units (SI). Additionally, the calibration test site should be homogeneous over an area larger than the spatial resolutions of each sensor, so that ground measurements are representative of the area seen by the sensors being calibrated. Here, we use a combination of independent and SI-traceable radiometric data provided from two sites of the Radiometric Calibration Network (RadCalNet) to compare the radiometric response of sensors with different spectral and spatial resolutions that operate on different orbits. These sensors are Operational Land Imagers (OLI) of the Landsat-8 and Landsat-9 missions, and Visible Infrared Imaging Radiometer Suites (VIIRS) of the Suomi-National Polar-Orbiting Operational Environmental Satellite System Preparatory Project (SNPP) and Joint Polar Satellite System-1 (JPSS-1) missions. The sensor radiometric responses are compared via temporal averaging of the ratios of top-of-atmosphere reflectance values for each sensor to those reported by RadCalNet. Our intercomparison results show that these on-orbit sensors are calibrated within their absolute radiometric uncertainties. The absolute radiometric uncertainties of single-sensor over single-site intercomparisons at 550 nm is between 5% and 6%. Having the opportunity to look at the intercomparison results of Landsat-9 OLI compared to each calibration site individually and then in combination allowed us to investigate potential systematic site-dependent biases. We did not observe significant site-dependent biases in the behavior of the four on-orbit sensors compared to the calibration sites. The absolute radiometric uncertainty of a single sensor over multiple-site intercomparisons at 550 nm is 5.4%. We further investigated site-dependent biases by looking at the double-ratio calibration coefficients of the on-orbit sensors, calculated with reference to those sites.

Kinematical coherence between satellite galaxies and host stellar discs for MaNGA and SAMI galaxies

ABSTRACT The effect of angular momentum on galaxy formation and evolution has been studied for several decades. Our recent two papers using IllustrisTNG-100 simulation have revealed the acquisition path of the angular momentum from large-scale environment (satellites within hundreds of kpc) through the circumgalactic medium (CGM) to the stellar discs, putting forward the co-rotation scenario across the three distance scales. In real observations, although the rotation signature for the CGM and environmental three-dimensional angular momentum are difficult to obtain, line-of-sight kinematics of group member galaxies and stellar disc kinematics of central galaxies are available utilizing existing group catalogue data and integral field unit (IFU) data. In this paper, we use (1) the group catalogue of SDSS DR7 and MaNGA IFU stellar kinematic maps and (2) the group catalogue of GAMA DR4 data and SAMI IFU stellar kinematic maps, to test if the prediction above can be seen in real data. We found the co-rotation pattern between stellar discs and satellites can be concluded with 99.7 per cent confidence level (∼3σ) when combining the two data sets. And the random tests show that the signal can be scarcely drawn from random distribution.

Evaluation of total ozone measurements from Geostationary Environmental Monitoring Spectrometer (GEMS)

Abstract. The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of −2.38 % and −2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4.

Daily satellite-based sunshine duration estimates over Brazil: validation and intercomparison

Abstract. The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency.

Electron Precipitation Observed by ELFIN Using Proton Precipitation as a Proxy for Electromagnetic Ion Cyclotron (EMIC) Waves

AbstractElectromagnetic ion cyclotron (EMIC) waves can drive radiation belt depletion and Low‐Earth Orbit satellites can detect the resulting electron and proton precipitation. The ELFIN (Electron Losses and Fields InvestigatioN) CubeSats provide an excellent opportunity to study the properties of EMIC‐driven electron precipitation with much higher energy and pitch‐angle resolution than previously allowed. We collect EMIC‐driven electron precipitation events from ELFIN observations and use POES (Polar Orbiting Environmental Satellites) to search for 10s–100s keV proton precipitation nearby as a proxy of EMIC wave activity. Electron precipitation mainly occurs on localized radial scales (∼0.3 L), over 15–24 MLT and 5–8 L shells, stronger at ∼MeV energies and weaker down to ∼100–200 keV. Additionally, the observed loss cone pitch‐angle distribution agrees with quasilinear predictions at ≳250 keV (more filled loss cone with increasing energy), while additional mechanisms are needed to explain the observed low‐energy precipitation.

A Statistical Analysis of the Auroral Streamer Current System

AbstractMagnetosphere‐ionosphere coupling is an important process that involves the transport of vast quantities of mass, energy, and momentum in the Earth's near‐space environment. This transport involves extended, global sheets of electrical current that flows along the magnetic field lines. Many studies have discussed the magnetosphere‐ionosphere coupling between the fast‐moving earthward flows and the north‐south oriented streamers in the ionosphere. However, only recently, the temporal evolution of the ionospheric current pattern, from the start to the end of the streamer, has been investigated through case studies, by combining the ionospheric current pattern with the magnetotail fast‐moving earthward flows. In this study, we show the statistical spatial and temporal evolution of the ionospheric current pattern associated with north‐south oriented streamers using simultaneous THEMIS (Time History of Events and Macroscale Interaction during Substorms) observations of fast‐moving earthward flows within the magnetotail, Geostationary Operational Environmental Satellite observations, auroral all‐sky images, and ionospheric equivalent current maps derived from ground magnetometer measurements. We show that the streamer related ionospheric field‐aligned currents (FACs) are statistically almost equivalent in magnitude for the 38 events that were analyzed. Results show that the streamer related ionospheric FACs are statistically approximately similar in magnitude for 38 events. The duration of these events ranges from 2 to 21 min with a median value of 9 min.

Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observations

Abstract Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions. Significance Statement Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels.

Global ocean observations and applications by China’s ocean satellite constellation

Satellite remote sensing data form the basis of ocean observation and applications. China has established a satellite network platform comprising ocean color satellite constellations, ocean dynamic environment satellite constellations, and ocean observation and monitoring satellite constellations. This platform provides consistent and reliable ocean observation data crucial for marine scientific research, economic development, and early warning and forecasting. This paper comprehensively describes the development process and plans for China’s ocean satellites from their inception. It offers detailed technical specifications of ocean satellites and outlines the current applications of ocean water color satellites (HY-1), ocean dynamics and environment satellites (HY-2), and ocean surveillance and monitoring satellites (GF-3) in ocean parameter inversion, target identification and detection, and early warning and forecasting. In the future, to enhance the level of industrialization in ocean remote sensing in China, it is imperative to leverage the diversity and timeliness of ocean remote sensing data. Additionally, emerging technologies such as cloud computing and artificial intelligence should be harnessed, and the application potential of various satellite data resources should be explored.

Evaluation of cloud-type impact on performance of the GL model version 1.2 for estimation of solar irradiance

ABSTRACT This work evaluates the quality of the satellite-based GLobal radiation model version 1.2 (GL1.2) estimates for four cloud classes. The GL1.2 calculates the global solar irradiance at ground level using images from a single visible band channel (VIS) of the Geostationary Operational Environmental Satellite – GOES (GOES-East). The model´s performance was assessed by comparing hourly mean GL1.2 values with ground-based hourly measurements from the Brazilian National Institute of Meteorology (INMET, 354 automatic weather stations), for the entire year 2017. A satellite-based cloud classifier was adopted to discriminate the datasets according to prevailing cloud conditions (cumulus, stratus, cirrus and multilayer), but the clear sky behaviour was presented. The results were analysed for the five Brazilian regions. In the first analysis, we selected days with a predominance for five stations. It was found that the diurnal cycle was well reproduced. Then the regional investigation for the cloud types reveals that the best results are found for the Center West region over multilayer cloud (mean bias error, MBEannual = -2 ± 91 Wm−2 and root mean squared error, RMSE = 91 Wm−2), while the worst ones are in the North over cumulus fields (MBEannual = 101.5 ± 136.4 Wm−2). When considering all cloud types, the MBEannual is lower than 5 Wm−2 for the Northeast, Southeast and South regions, but it reaches 101 Wm−2 in the North. It is noteworthy that winter has the highest MBE in all classes analysed in the North, as well as cirrus situations in other regions. Although the inhomogeneities of cumulus and the semitransparent cirrus clouds tend to propagate errors to the model, the quality of GL1.2 data has a high degree of agreement with the observations. Improvements including an updated monthly minimum reflectance (R min) and water vapour column (H2Ovapour), and a better spatial resolution of the Advanced Baseline Imager (ABI) of GOES-16 (ABI/GOES-16) VIS imagery will allow refinement, especially for cumulus clouds.

Impact of chlorine ion chemistry on ozone loss in the middle atmosphere during very large solar proton events

Abstract. Solar coronal mass ejections can accelerate charged particles, mostly protons, to high energies, causing solar proton events (SPEs). Such energetic particles can precipitate upon the Earth's atmosphere, mostly in polar regions because of geomagnetic shielding. Here, SPE-induced chlorine activation due to ion chemistry can occur, and the activated chlorine depletes ozone in the polar middle atmosphere. We use the state-of-the-art 1D stacked-box Exoplanetary Terrestrial Ion Chemistry (ExoTIC) model of atmospheric ion and neutral composition to investigate such events in the Northern Hemisphere (NH). The Halloween SPE that occurred in late October 2003 is used as a test field for our study. This event has been extensively studied before using different 3D models and satellite observations. Our main purpose is to use such a large event that has been recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on the Environmental Satellite (ENVISAT) to evaluate the performance of the ion chemistry model. Sensitivity tests were carried out for different model settings with a focus on the chlorine species of HOCl and ClONO2 as well as O3 and reactive nitrogen, NOy. The model simulations were performed in the Northern Hemisphere at a high latitude of 67.5∘ N, inside the polar cap. Comparison of the simulated effects against MIPAS observations for the Halloween SPE revealed rather good temporal agreement, also in terms of altitude range for HOCl, O3 and NOy. For ClONO2, good agreement was found in terms of altitude range. The model showed ClONO2 enhancements after the peak of the event. The best model setting was the one with full ion chemistry where O(1D) was set to photo-chemical equilibrium. HOCl and ozone changes are very well reproduced by the model, especially for nighttime. HOCl was found to be the main active chlorine species under nighttime conditions, resulting in an increase of more than 0.2 ppbv. Further, ClONO2 enhancements of 0.2–0.3 ppbv have been observed during both daytime and nighttime. Model settings that compared best with MIPAS observations were applied to an extreme solar event that occurred in AD 775, presumably once in a 1000-year event. With the model applied to this scenario, an assessment can be made about what is to be expected at worst for the effects of a SPE on the middle atmosphere, concentrating on the effects of ion chemistry compared to crude parameterizations. Here, a systematic analysis comparing the impact of the Halloween SPE and the extreme event on the Earth's middle atmosphere is presented. As seen from the model simulations, both events were able to perturb the polar stratosphere and mesosphere with a high production of NOy and HOx. Longer-lasting and stronger stratospheric ozone loss was seen for the extreme event. A qualitative difference between the two events and a long-lasting impact on HOCl and HCl for the extreme event were found. Chlorine ion chemistry contributed to stratospheric ozone losses of 2.4 % for daytime and 10 % for nighttime during the Halloween SPE, as seen with time-dependent ionization rates applied to the model. Furthermore, while comparing the Halloween SPE and the extreme scenario, with ionization rate profiles applied just for the event day, the inclusion of chlorine ion chemistry added ozone losses of 10 % and 20 % respectively.