Satellite Zenith Angle Research Articles

Hybrid sea surface temperature inversion model for the South China sea based on IMLP and DBN

ABSTRACT Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively.

Retrieval of total precipitable water from INSAT-3D Imager observations using deep neural network

Total precipitable water (TPW) is a key parameter for monitoring the development of the various weather phenomenon, viz., deep convective systems, thunderstorms, etc. Moreover, various nowcasting applications such as heavy rainfall alerts, cloudbursts, thunderstorms, etc. require high spatio-temporal TPW information, which can be achieved with the satellite-based observations from geostationary platform. Therefore, the present study deals with the estimation of TPW from observations in the water vapour (WV) absorption (6.5–7.0 μm) and thermal infrared (TIR) channels (TIR1 (10.3–11.2 μm); TIR2 (11.5–12.5 μm)) of Imager onboard the third generation Indian National Satellite (INSAT-3D) deployed in a geostationary orbit, using the machine-learning technique. The deep neural network (DNN) technique is utilized here to estimate TPW from INSAT-3D Imager observations over the Indian subcontinent region. Herein, the observations from three channels (WV, TIR1 & TIR2) of INSAT-3D Imager for three years from 2018 to 2020 are collocated with TPW from European Center for Medium range Weather Forecast (ECMWF) reanalysis (ERA5) available at 0.25° uniform spatial resolution to prepare a matchup dataset. The input vector consists of Julian day, geolocation (latitude/longitude), brightness temperatures of WV, TIR1 and TIR2 channels of INSAT-3D Imager, surface emissivity of all three channels and the satellite zenith angles. The ERA5 TPW is the output (predictand) of the DNN. For the training of the DNN, 70 % of the total collocated matchups are selected randomly, and the rest 30 % are used for the testing. The DNN is developed for land and ocean surfaces separately, due to a larger variation in the emissivity over land. The assessment of the developed DNN over both surfaces show the similar statistics. The quality evaluation of the training and testing dataset shows the negligible biases and root-mean-squared error (RMSE) of ∼3.5 mm over the land and ∼4.5 mm over the ocean, respectively, in the predicted TPW when compared with ERA5 TPW. However, the bias (RMSE) in the predicted TPW is observed -0.3 (4.4) mm and 0.4 (4.8) mm, respectively over the land and ocean surfaces with reference to the ERA5 TPW for the independent testing of the developed DNN, which was performed on the observations of INSAT-3D for 2021. Additionally, the comparison with radiosondes’ TPW demonstrates the 6.7 mm RMSE with bias of -0.7 mm for 2021.

CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.

Convective Initiation Nowcasting in South China Using Physics‐Augmented Random Forest Models and Geostationary Satellites

AbstractConvective initiation (CI) nowcasting in subtropical regions often faces challenges, such as complex physical processes and imbalanced samples of CI events, resulting in a high false alarm ratio (FAR). In this paper, we propose a Storm Warning System with Physics‐Augmentation (SWASP) based on the random forest algorithm and cloud physical conditions, using Himawari‐8 Advanced Himawari Imager data from April to September 2019 in South China. The cloud physical conditions (e.g., cloud‐top cooling rates) were investigated to establish regional thresholds for convection occurrence. Ancillary information, including elevation, satellite zenith angle, and latitude, was also incorporated into the SWASP model. Compared to conventional methods, the SWASP model exhibits an improved probability of detection by 0.11 and 0.08 and a decreased FAR by 0.38 and 0.44 for daytime and nighttime forecasts. Moreover, the SWASP model enables the detection of local convective storm systems about 30 min to 1 hr ahead of radar detection in typical convective storm cases. This study contributes to further advancements of the SWASP model by incorporating physical conditions and emphasizes the potential application of geostationary satellites in convective early warnings.

Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Correcting for Undercounting of Lightning Flashes by Space‐Based Optical Sensors

AbstractAccurate measurements of global lightning are essential for understanding present and future atmospheric electricity, composition, and climate. The latest space‐based lightning detector, the Geostationary Lightning Mapper (GLM), was the first to be placed in geostationary orbit, with a continuous view of most of the American continents. Prior to the GLM, the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite collected lightning measurements from which numerous lightning climatologies have been developed, including those used in global models. However, this study finds that both the GLM and a second, similar LIS placed on the International Space Station (ISS) in 2017 detect lightning at similar rates and are undercounting lightning compared to ground‐based Lightning Mapping Arrays (LMAs). The GLM undercounts lightning by an average factor of 7.0, reaching a maximum over 120 as a function of satellite zenith angle, radar reflectivity at a height where the temperature is −10°C, flash height, and thunderstorm polarity. The LIS is estimated to undercount lightning by an average factor of 5.6, reaching a maximum of 75.0 as a function of radar reflectivity at the −10°C level, flash height, and thunderstorm polarity. Preliminary predictive equations for the GLM and LIS lightning undercount factor, or scaling factor (SF), use ice‐water content, equilibrium level, flash height, and satellite zenith angle, all of which can be derived in models. These equations are developed to encourage updating lightning parameterizations within global models and will likely increase modeled lightning's effects on atmospheric electrical circuits, composition, chemistry, and climate change.

Cross-Radiometric Calibration and NDVI Application Comparison of FY-4A/AGRI Based on Aqua-MODIS

To enhance the accuracy and stability of FY-4A/AGRI detection data, the MODIS, with highly accurate onboard calibration, is selected as the reference sensor for cross-radiation calibration calculations. The following are the data selection conditions: full considered time, observation geometries, field angles, cloud cover, etc. FY-4A/AGRI and Aqua-MODIS image data are selected as matching sample region locations, where the time difference between the observations for the same ground object is less than 15 min, the satellite zenith angle is less than 30°, and the field angle difference is less than 0.01. The 245 collected reflectance spectral curves are convolved with the spectral response functions of the two sensors, and the spectral band adjustment factors of the corresponding bands are calculated for spectral correction purposes. The cross-calibration coefficients for the red and near-infrared bands are calculated by linearly fitting the simulated top of the atmosphere reflectance values and digital number values from the AGRI sensor in a homogeneous area. In this paper, 16 cross-calibration calculations are performed on FY-4A/AGRI image data from August 2018 to September 2020, and the results are compared with the original calibration coefficients to test the feasibility of the proposed method. Additionally, 31 cross-calibration calculations are performed on image data from October 2020 to December 2022 to study the resulting AGRI sensor quality and performance changes. The NDVI of the FY-4A/AGRI image data was calculated before and after the cross-radiometric calibration using the maximum synthesis method. Additionally, the NDVI of the MODIS image data was compared and analyzed from three aspects: time, space, and the change trend. The results show that the spectral band adjustment factor calculated using the reflectance spectral curves of the ground objects in this paper can effectively correct for the spectral differences between the two sensors. Sixteen cross-calibration coefficients are less than 5.2% different from the original calibration coefficients, which fully proves the feasibility of the method used in this paper. All of the cross-calibration results show that the AGRI sensors have a certain degree of attenuation in the red and near-infrared bands, and the annual attenuation rates are approximately 1.37% and 2.55%, respectively. Cross-radiometric calibration has further improved the quality of the NDVI in FY-4A/AGRI imagery, enhancing the precision of its data application.

Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions

As the majority of active fire detection algorithms have been developed for worldwide applications using only satellite data without considering observing conditions and environmental factors, their performance varies regionally. This study investigates the viability of an adaptable active fire detection model that is applicable to diverse environmental and observing conditions by fusing numerical model data and satellite images. The model was developed for various land cover and climate types using commonly utilized brightness temperature-related variables (key variables) and supporting variables (sub-variables), including solar zenith angle, satellite zenith angle (SAZ), relative humidity (RH), and skin temperature. A dual-module (DM) convolutional neural network (CNN) structure was adopted to consider the different properties of key variables and sub-variables, and a control without sub-variables was used to assess the impact of observing and environmental variables. The proposed model was further evaluated using existing polar-orbiting and geostationary satellite-based active fire products. The recall and precision of the control model were 0.80 and 0.98, respectively, and the standard deviation of recall for the five focus sites was 0.140. However, the DM CNN model was notable for its higher recall and robustness compared to the control model (recall of 0.84, precision of 0.97, and standard deviation of recall of 0.126). High RH and SAZ, and the day-night transition period contributed to the poor performance of the control model which was mitigated by the DM CNN model. In particular, the use of RH improved the recall of the model, and SAZ contributed to the reduction of performance variation. Our model also outperformed the two geostationary satellite-based active fire products in terms of detection capacity, resulting in a spatial distribution of active fires similar to that of polar-orbiting satellite-based active fire products.

Comparison of FY-4A/AGRI SST with Himawari-8/AHI and In Situ SST

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted.

Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.

Study on the Slant-Path Effect in the Simulation of Clear-Sky Thermal Radiance for the GK2A AMI

Abstract Taking the slanted satellite viewing geometry into account is important in the simulation of satellite radiances, which vary with the atmospheric conditions along the line of sight. As a first step to take the slanted satellite viewing geometry into account in the numerical weather prediction system operated in the Korean Meteorological Administration, the slant-path modeling is applied for the simulation of clear-sky thermal radiances of a geostationary satellite imager, the Advanced Meteorological Imager (AMI) on board the Geo-KOMPSAT-2A (GK2A). The observations minus simulations (O − B) and the Jacobians before and after the slant-path calculation are compared. Since most infrared channels of AMI are not sensitive to the atmosphere above the tropopause, the size of slant-path effect for AMI is overall smaller than the effect shown in the microwave sounders. Still, the slant-path modeling is found to have a noticeable effect on the three water vapor absorption channels of AMI peaking between 300 and 600 hPa, particularly at large satellite zenith angles and on the regions with high water vapor variabilities in the model field along the line of sight. On average, the slant-path interpolation reduces the standard deviation of O − B of the water vapor channels by around 2.0% on land and 1.4% over the ocean for zenith angles 40°–60°, and it also influences not only the shape and magnitude but also the height of the peak of the Jacobians. In addition, the retrieval experiment based on the optimal estimation also demonstrates the impact of this new approach in the retrieved moisture field, though the improvement is not as significant as shown in the simulation experiment.

Space-Air-Ground FSO Networks for High-Throughput Satellite Communications

The next generation of satellite communication systems aims to achieve terabit-per-second throughput. Because of the limited spectrum available, traditional radio frequency (RF) communication links cannot provide such high throughput. Free-space optical (FSO) transmission is a possible alternative that has recently gained increased attention in the satellite community. However, FSO communications are vulnerable to the severe effects of atmospheric turbulence, such as beam-wandering-induced pointing errors and beam scintillation. To successfully remedy such effects, we propose a space-air-ground (SAG) FSO network with a strategically deployed high-altitude platform acting as a relay. We show that such a design can substantially mitigate the effects of atmospheric turbulence, especially when the satellite zenith angle is relatively high. Then we present a novel SAG satellite communication network that integrates the suggested SAG-FSO transmission and conventional hybrid single-hop FSO/RF transmission to improve overall system reliability and performance even further. The numerical results clearly show the potential of the proposed, highly innovative SAG-FSO network architecture.

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this article, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function matching method is adopted to reduce the errors. Statistical results show that the mean bias and root-mean-square error (RMSE) of FY-4A/AGRI SST are −0.37 °C and 0.98 °C, the median and robust standard deviation (RSD) are −0.30 °C and 0.90 °C. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding −1.0 °C in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to −0.02 °C and 0.72 °C, and the median and RSD are reduced to 0.00 °C and 0.60 °C. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 °C in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.

A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTHCAL), the results show that: the algorithm retrieved the cloud-top height (CTHXGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTHJMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTHJMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.

Space-Air-Ground FSO Networks for High-Throughput Satellite Communications

The next generation of satellite communication systems aims to achieve terabit-per-second throughput. Because of the limited spectrum available, traditional radio frequency (RF) communication links cannot provide such high throughput. Free-space optical (FSO) transmission is a possible alternative that has recently gained increased attention in the satellite community. However, FSO communications are vulnerable to the severe effects of atmospheric turbulence, such as beam-wandering-induced pointing errors and beam scintillation. To successfully remedy such effects, we propose a space-air-ground (SAG) FSO network with a strategically deployed high-altitude platform acting as a relay. We show that such a design can substantially mitigate the effects of atmospheric turbulence, especially when the satellite zenith angle is relatively high. Then we present a novel SAG satellite communication network that integrates the suggested SAG-FSO transmission and conventional hybrid single-hop FSO/RF transmission to improve overall system reliability and performance even further. The numerical results clearly show the potential of the proposed, highly innovative SAG-FSO network architecture.

Ergodic Capacity Analysis of Satellite Communication Systems With SAG-FSO/SH-FSO/RF Transmission

Future non-terrestrial networks aim to achieve a throughput of Terabits/s. Therefore, free-space optical (FSO) communications have been adopted as a candidate solution due to their ability to achieve an extremely high data rate. Nonetheless, FSO communications are sensitive to the adverse effects of beam scintillation, beam-wander-induced pointing errors, free-space loss, and weather conditions. Space-air-ground (SAG) FSO transmission and hybrid single-hop (SH) FSO/radio frequency (RF) transmission are promising solutions to improve the performance of FSO links and can be integrated into a satellite communication (Satcom) system. In this work, we carry out a thorough capacity analysis of the resulting integrated SAG-FSO/SH-FSO/RF Satcom system, where Gamma-Gamma and Rician distributions are used to characterize FSO and RF links, respectively. The exact analytical expressions are derived and validated by Monte-Carlo simulations. We also obtain asymptotic expressions for the ergodic capacity in the high signal-to-noise ratio region. The numerical results highlight the significant potential of the integrated SAG-FSO/SH-FSO/RF Satcom system over existing solutions. We also show that the integrated Satcom system with intensity modulation and direct detection can achieve a capacity gain over that with a heterodyne detection technique over all satellite zenith angles.

Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager

Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN.

Skin Sea Surface Temperatures From the GOES-16 ABI Validated With Those of the Shipborne M-AERI

Derived from satellite measurements, the skin sea surface temperature (SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> ) is a critical variable in research into the climate and other aspects of the ocean–atmosphere system As the new generation of visible and infrared imaging radiometers, the Advanced Baseline Imager (ABI) onboard the current U.S. geostationary satellites series provides marked improvements to remote sensing of the ocean surface from geostationary orbit, including a wider range of spectral bands and more rapid sampling. This research compares SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> derived from the ABI onboard the Geostationary Operational Environmental Satellite (GOES)-16 satellite from January 2018 to October 2019 with the independent data acquired from Marine-Atmospheric Emitted Radiance Interferometers (M-AERIs) deployed on ships. The result shows an averaged SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> mean bias of 0.086 K and a robust standard deviation of 0.220 K. The properties of the bias are determined based on various aspects such as geographical distribution, day/night, aerosol effect, and satellite zenith angle. This study also shows that the ABI sensor-specific error statistics (SSES) are effective to reduce the errors by slant-path water vapor and satellite zenith angle. The main result of this research is that the SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> products derived from the ABI compare well with independent, International System of Units (SI)-traceable radiometric skin temperatures taken from ships, and this should give confidence in the use of these remotely-sensed SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> fields for many scientific endeavors. Some remaining challenges have been identified for algorithm developers to address, and we hope these will be pursued in due course.

Orbit Simulator for Satellite and Near-Space Platforms Supporting Observing System Simulation Experiments

Abstract Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called nature run. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also global navigation satellite system/radio occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and ground instantaneous field of view (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons). Significance Statement An orbit simulator for satellite and near-space platforms for supporting observing system simulation experiments (OSSE) is developed in this study. It can compute spatial, temporal, and view geometry parameters of the new observations based on the specific hosting platform and onboard instrument characteristics. These parameters are important for the OSSE to extract atmospheric profiles and surface properties from nature run data and simulate new observations. In addition to simulating existing polar-orbiting and geostationary orbits, it is applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also radio occultation instruments.

Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.