Brightness Temperature Difference Research Articles

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

Impact of astrophysical effects on the dark matter mass constraint with 21 cm intensity mapping

ABSTRACT We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with varying dark matter particle masses and generate mock 21 cm radio intensity maps to trace the dark matter distribution at z = 3 in the postreionization epoch. Our proposed method complements the traditional power-spectrum analysis. We compare the results of the CNN classification between the mock maps with different dark matter masses with those from the two-dimensional power spectrum of the differential brightness temperature map of 21 cm radiation. We find that the CNN outperforms the power spectrum. Moreover, we investigate the impact of baryonic physics on the dark matter model constraint, including star formation, self-shielding of H i gas, and ultraviolet background model. We find that these effects may introduce some contamination in the dark matter constraint, but they are insignificant compared to the system noise of the SKA instruments.

Improving Dust Aerosol Optical Depth (DAOD) Retrieval from the GEOKOMPSAT-2A (GK-2A) Satellite for Daytime and Nighttime Monitoring.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 h) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 μm). For the dust cloud, the brightness temperature differences (BTDs) of 10.5 and 12.3 μm were consistently negative, while the BTD of 8.7 and 10.5 μm varied based on the dust intensity. This study exploited these optical properties to develop a physical approach for DAOD lookup tables (LUTs) using IR channels to retrieve the DAOD. To this end, the characteristics of thermal radiation transfer were simulated using the forward model; dust aerosols were explained by BTD (10.5, 12.3 μm)-an intrinsic characteristic of dust aerosol. The DAOD and dust properties were gained from a brightness temperature (BT) of 10.5 μm and BTD of 10.5, 12.3 μm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-h DAOD. The CDF was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR, with daytime visible aerosol optical depth as the true value. The results show that the DAOD product can be successfully applied during the daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation results for IR DAOD were similar to the active satellite product (CALIPSO/CALIOP) results, which exhibited a tendency similar to that for IR DAOD at night.

Fire monitoring and detection using brightness-temperature difference and water vapor emission from the atmospheric infrared sounder

Radiance data from the Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua satellite provide an opportunity for fire detection. As wildfires play an increasingly great role in the environments of the United States West Coast, emergency teams face an ever-challenging task of mitigation and prediction. Furthermore, the increasing rate of wildfires in the American West Coast places an ever-increasing strain on ecosystems and global climate. Of particular interest is the ability to create effective near real-time (NRT) imaging and prediction. Advances in this field can play a crucial role in assisting wildfire detection and monitoring. Typical sources for satellite fire imaging study are the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), the Advanced Very High Resolution Radiometer (AVHRR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments, but we present AIRS as complementary to these instruments with a new method of hotspot analysis. In this study, we propose a new method of fire detection by using AIRS's cloud-cleared radiance to detect hotspots and find new applications for this method of retrieval. We present the fire detection algorithm and initial assessments showing AIRS's ability to detect fires. In addition, we notice that there is more water vapor from the surface to the upper troposphere during the fire event as a result of biomass burning and an increase in air temperature.

Cloud detection from Himawari-8 spectral images using K-means++ clustering with the convolutional module

ABSTRACT Accurate and reliable cloud detection is one of the crucial preprocessing steps in satellite remote sensing. This paper adopts the spectral data in different channels observed by Himawari-8 geostationary satellite and proposes a cloud detection algorithm based on K-means++ clustering with a sharpening convolutional module (C-Kmeans++). The analysis found that the sum of the reflectance of channels 3 and 4, the brightness temperature of channel 15 and the difference of brightness temperature of channels 7 and 14 can be used as clustering features of the K-means++ algorithm. Before the clustering operation, the RS image is sharpened using the Laplacian operator to enhance the precision in the detection of thin clouds. A proper k value (k = 7) is obtained by the sum of squared error and silhouette coefficient. To evaluate the C-Kmeans++, the comparison of detection results using K-means++, the multi-spectral threshold, the k-nearest neighbour and the Vgg16+U-Net was carried out. The visible light image and Cloud-Aerosol Lidar with Orthogonal Polarization product were used as reference labels. It is found that RR, ER, HR and KSS of C-Kmeans++ are best throughout the day.

Modelling the day–night temperature variations of ultra-hot Jupiters: confronting non-grey general circulation models and observations

ABSTRACT Ultra-hot Jupiters (UHJs) are natural laboratories to study extreme physics in planetary atmospheres and their rich observational data sets are yet to be confronted with models with varying complexities at a population level. In this work, we update the general circulation model of Tan & Komacek (2019) to include a non-grey radiative transfer scheme and apply it to simulate the realistic thermal structures, phase-dependent spectra, and wavelength-dependent phase curves of UHJs. We performed grids of models over a large range of equilibrium temperatures and rotation periods for varying assumptions, showing that the fractional day–night brightness temperature differences remain almost constant or slightly increase with increasing equilibrium temperature from the visible to mid-infrared wavelengths. This differs from previous work primarily due to the increasing planetary rotation rate with increasing equilibrium temperature for fixed host star type. Radiative effects of varying atmospheric compositions become more significant in dayside brightness temperature in longer wavelengths. Data-model comparisons of dayside brightness temperatures and phase-curve amplitudes as a function of equilibrium temperature are in broad agreement. Observations show a large scatter compared to models even with a range of different assumptions, indicating significantly varying intrinsic properties in the hot Jupiter population. Our cloud-free models generally struggle to match all observations for individual targets with a single set of parameter choices, indicating the need for extra processes for understanding the heat transport of UHJs.

Probabilistic Convective Initiation Nowcasting Using Himawari-8 AHI with Explainable Deep Learning Models

Abstract Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property caused by severe convective weather. A novel deep learning method based on the U-Net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with probability of detection (POD) at 93.3% ± 0.3% and false alarm ratio (FAR) at 18.3% ± 0.4% at a lead time of 30 min. Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between band 10 (7.3 μm) and band 13 (10.4 μm), which represents the cloud-top height relative to the lower troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Characteristics and Mechanisms of Differential Infrared Brightness Temperature Change of Rock Specimen Partly Loaded to Fracturing

Proposal and Geological Significance of Lunar Low Brightness Temperature Difference Anomaly Based on CE-2 MRM data

Proposal and Geological Significance of Lunar Low Brightness Temperature Difference Anomaly Based on CE-2 MRM data

Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.

A satellite chronology of plumes from the April 2021 eruption of La Soufrière, St Vincent

Abstract. Satellite instruments play a valuable role in detecting, monitoring and characterising emissions of ash and gas into the atmosphere during volcanic eruptions. This study uses two satellite instruments, the Infrared Atmospheric Sounding Interferometer (IASI) and the Advanced Baseline Imager (ABI), to examine the plumes of ash and sulfur dioxide (SO2) from the April 2021 eruption of La Soufrière, St Vincent. The frequent ABI data have been used to construct a 14 d chronology of a series of explosive events at La Soufrière, which is then complemented by measurements of SO2 from IASI, which is able to track the plume as it is transported around the globe. A minimum of 35 eruptive events were identified using true, false and brightness temperature difference maps produced with the ABI data. The high temporal resolution images were used to identify the approximate start and end times, as well as the duration and characteristics of each event. From this analysis, four distinct phases within the 14 d eruption have been defined, each consisting of multiple explosive events with similar characteristics: (1) an initial explosive event, (2) a sustained event lasting over 9 h, (3) a pulsatory phase with 25 explosive events in a 65.3 h period and (4) a waning sequence of explosive events. It is likely that the multiple explosive events during the April 2021 eruption contributed to the highly complex plume structure that can be seen in the IASI measurements of the SO2 column amounts and heights. The bulk of the SO2 from the first three phases of the eruption was transported eastwards, which based on the wind direction at the volcano implies that the SO2 was largely in the upper troposphere. Some of the SO2 was carried to the south and west of the volcano, suggesting a smaller emission of the gas into the stratosphere, there being a shift in wind direction around the height of the tropopause. The retrieved SO2 heights show that the plume had multiple layers but was largely concentrated between 13 and 19 km, with the majority of the SO2 being located in the upper troposphere and around the height of the tropopause, with some emission into the stratosphere. An average e-folding time of 6.07±4.74 d was computed based on the IASI SO2 results: similar to other tropical eruptions of this magnitude and height. The SO2 was trackable for several weeks after the eruption and is shown to have circulated the globe, with parts of it reaching as far as 45∘ S and 45∘ N. Using the IASI SO2 measurements, a time series of the total SO2 mass loading was produced, with this peaking on 13 April (descending orbits) at 0.31±0.09 Tg. Converting these mass values to a temporally varying SO2 flux demonstrated that the greatest emission occurred on 10 April with that measurement incorporating SO2 from the second phase of the eruption (sustained emission) and the beginning of the pulsatory phase. The SO2 flux is then shown to fall during the later stages of the eruption: suggesting a reduction in eruptive energy, something also reflected in ash height estimates obtained with the ABI instrument. A total SO2 emission of 0.63±0.5 Tg of SO2 has been derived, although due to limitations associated with the retrieval, particularly in the first few days after the eruption began, this, the retrieved column amounts and the total SO2 mass on each day should be considered minimum estimates. There are a number of similarities between the 1979 and 2021 eruptions at La Soufrière, with both eruptions consisting of a series of explosive events with varied heights and including some emission into the stratosphere. These similarities highlight the importance of in-depth investigations into eruptions and the valuable contribution of satellite data for this purpose; as these studies aid in learning about a volcano's behaviour, which may allow for better preparation for future eruptive activity.

Cloud-Type Classification for Southeast China Based on Geostationary Orbit EO Datasets and the LighGBM Model

The study of clouds and their characteristics provides important information for understanding climate change and its impacts as it provides information on weather conditions and forecasting. In this study, Earth observation (EO) data from the FY4A AGRI and Himawari-8 CLP products were used to classify and identify distinct cloud types in southeastern China. To reduce the impact of parallax between geostationary satellites, we proposed adopting a sliding detection method for quality control of cloud-type data. Additionally, the Bayesian optimization method was employed herein to tune the hyperparameters of the LightGBM model. Our study results demonstrated that Bayesian optimization significantly increased model performance, resulting in successful cloud-type classification and identification. The simultaneous use of visible and shortwave infrared channels, and brightness temperature difference channels, enhanced the model’s classification performance. Those channels accounted for 43.79% and 21.84% of the overall features, respectively. Certainly, the model in this study outperformed compared with the traditional thresholding method (TT), support vector machine (SVM), and random forest (RF). Results showed a model prediction accuracy of 97.54%, which was higher than that of TT (51.06%), SVM (96.47%), and RF (97.49%). Additionally, the Kappa coefficient of the model was 0.951, indicating the model’s classification results were consistent with the true values. Notably, this performance also surpassed TT (0.351), SVM (0.929), and RF (0.950).

A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.

Prediction of extreme rain in Kototabang using Himawari-8 satellite based on differences in cloud brightness temperature

The accurate prediction of extreme rain events is essential for the management and mitigation of hydrometeorological disasters. The Himawari-8 satellite provides cloud observations that can accurately forecast short-term extreme rain events using the Brightness Temperature (BT) data and Brightness Temperature Difference (BTD) method. The statistical evaluation of this method was conducted using optical rain gauge data taken from Kototabang, West Sumatra, Indonesia (100.32° E, 0.20° S) to determine the best threshold for detecting extreme rain. We used three bands, bands 11 (B11), 13 (B13) and 15 (B15) and tested three combinations of BTDs from these bands, namely BTD1 (B11-B13), BTD2 (B13-B15), and BTD3 (BTD1-BTD2). This study emphasizes the importance of parameter selection in extreme rain identification and forecasting using cloud BT and BTD methods. Effective parameter optimization is essential for adapting these approaches to different rainfall intensities, therefore selecting appropriate thresholds is necessary. The research particularly highlights the impact of temperature and rainfall intensity on BT accuracy, with BT excelling at 250 K for light rain but showing reduced accuracy as rainfall intensity increases. BTD1 demonstrates improved accuracy with higher rainfall intensity, especially at a 3 K threshold, allowing for predictions of extreme rain events with a 10–20 min lead time. However, the limitation of this threshold is shown by consistent critical success index (CSI). Therefore, BTD1 with threshold 0 K give better performances with good accuracy, CSI and false alarm ratio (FAR) for various rain intensities. BTD2 shows improved accuracy at lower thresholds with reduced rainfall intensity and at the 3 K threshold, offering potential for extreme rainfall anticipation, but with declining CSI as rainfall intensity rises. The 0 K threshold, despite high probability of detection (POD), yields increased FAR in moderate to severe rainfall scenarios. BTD3 generally exhibits increased accuracy and CSI with rising rainfall intensity, except at the −3 K threshold, with the 0 K threshold standing out as the optimal choice, providing a 10–20 min lead time for intense rain predictions. This study shows that the selection of the appropriate BT and BTD techniques, and parameter values should align with specific rainfall levels and forecasting goals.

Simulation of Thermal Infrared Brightness Temperatures from an Ocean Color and Temperature Scanner Onboard a New Generation Chinese Ocean Color Observation Satellite

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms.

Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600–1650 cm−1 Region

The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of <0.1 K. The comparison results for Jacobians showed that the Jacobians calculated by ARTS and LBLRTM were close for temperature (can be used for Numerical Weather Prediction application) and O3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community.

Formation of the hydrogen line 21-cm in Dark Ages and Cosmic Dawn: dependences on cosmology and first light

ABSTRACT We analyse the formation of the redshifted hyperfine structure line 21-cm of hydrogen atom in the Dark Ages, Cosmic Dawn, and Reionization epochs. The evolution of the global differential brightness temperature in this line was computed to study its dependence on the values of cosmological parameters and physical conditions in the intergalactic medium. Variations of the depth of the Dark Ages absorption line at z ∼ 80 with variations of the cosmological parameters Ωb, Ωcdm, ΩΛ, ΩK, and H0 are studied. The standard model with post-Planck parameters predicts a value of the differential brightness temperature in the centre of the absorption line ∼30–50 mK. The profile of this line can be quite another in the non-standard cosmological models, which include the annihilating or decaying dark matter, a primordial stochastic magnetic field, etc. It can be shallower or be an emission bump instead of an absorption trough. It is also shown that the position and depth of the Cosmic Dawn absorption line formed at 10 < z < 30, due to the Wouthuysen–Field effect, is mainly defined by the spectral energy distribution of the first sources of light. If reionization occurs at zri = 7 ± 1, then the differential brightness temperature in the centre of this line is ∼80 mK. During the reionization, the emission with an amplitude of ∼20 mK is possible. It is also shown that the temperature, density, and degree of ionization of the baryonic component are decisive in calculating the intensity of the 21-cm absorption/emission line from these epochs.

Thermophysical properties of surface deposits in Tsiolkovskiy crater and its geologic significance revealed by CE-2 MRM data

Tsiolkovskiy crater presents one of the few mare deposits on the lunar farside, which are significant to understanding the basaltic magmatism of the Moon. In this paper, for the first time, the thermophysical properties of surface deposits in the Tsiolkovskiy crater were assessed with the brightness temperature (TB), normalized TB (nTB), and TB difference (dTB) maps derived from the Chang'E-2 microwave radiometer data combined with the topography, composition, and rock abundance maps. The main results are presented as follows. (1) The nTB is highly influenced by surface topography, particularly at daytime, as observed in high-frequency maps. However, the influence is limited in the nTB maps at low-frequency channels and dTB maps. (2) We postulate that the substrate temperature in the SW portion of the crater floor might be considerably higher than previously understood. (3) Our observations reveal the presence of mare deposits occurring in two distinct episodes within the mare unit. Notably, the younger mare unit is situated in the NE portion of the crater. (4) The centimeter-scale rocks should be widely distributed in the NE portion beyond the Tsiolkovskiy crater. These findings hold significant implications for advancing our comprehension of the Moon's thermal and basaltic evolutionary processes.

Recognition of Severe Convective Cloud Based on the Cloud Image Prediction Sequence from FY-4A

Severe convective weather is hugely destructive, causing significant loss of life and social and economic infrastructure. Based on the U-Net network with the attention mechanism, the recurrent convolution, and the residual module, a new model is proposed named ARRU-Net (Attention Recurrent Residual U-Net) for the recognition of severe convective clouds using the cloud image prediction sequence from FY-4A data. The characteristic parameters used to recognize severe convective clouds in this study were brightness temperature values TBB9, brightness temperature difference values TBB9−TBB12 and TBB12−TBB13, and texture features based on spectral characteristics. This method first input five satellite cloud images with a time interval of 30 min into the ARRU-Net model and predicted five satellite cloud images for the next 2.5 h. Then, severe convective clouds were segmented based on the predicted image sequence. The root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and correlation coefficient (R2) of the predicted results were 5.48 K, 35.52 dB, and 0.92, respectively. The results of the experiments showed that the average recognition accuracy and recall of the ARRU-Net model in the next five moments on the test set were 97.62% and 83.34%, respectively.

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Fengyun-3E (FY-3E)/Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) is an extension Fengyun-3D (FY-3D)/HIRAS-I. It is crucial to fully explore and analyze the detection capabilities of these two instruments for atmospheric gas composition. Based on the observed spectral data from the infrared hyperspectral detection instruments FY-3D/HIRAS-I and FY-3E/HIRAS-II, simulated radiance data and Jacobian matrices are obtained using the Rapid Radiative Transfer Model RTTOV (Radiative Transfer for TOVS (TIROS Operational Vertical Sounder)). By perturbing temperature (T), surface temperature (Tsurf), water vapor (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrous oxide (N2O), the brightness temperature differences before and after the perturbations are calculated to analyze the sensitivity of temperature and various atmospheric gas components. The Improved Optimal Sensitivity Profile (OSP) algorithm is used to select the channels for atmospheric gas retrieval. The observation error covariance and background error covariance matrices are calculated, and then the information capacity is calculated, specifically the degrees of freedom for signal(DFS) and the entropy reduction (ER). Based on this, a comparative analysis is conducted on the information capacity of atmospheric water vapor and ozone components contained in the hyperspectral detection data from HIRAS-I and HIRAS-II instruments, respectively, to explore the retrieval capabilities of the two instruments for atmospheric gas components. We selected clear-sky data from the African oceanic region and the Chinese Yangtze River Delta terrestrial region for quantitative analysis of the information capacity of HIRAS-I and HIRAS-II. The results show that FY-3D/HIRAS-I and FY-3E/HIRAS-II exhibit different sensitivities to atmospheric gas components. In different experimental regions, temperature and water vapor show the most dramatic sensitivity changes, followed by ozone, methane, and nitrous oxide, while carbon monoxide and carbon dioxide exhibit the lowest variability. Regarding channel selection, HIRAS-II identifies more gas channels compared to HIRAS-I. The experiments concluded that HIRAS-II has a significantly higher information capacity than HIRAS-I, and the information capacity of atmospheric gas components varies across different experimental regions. Water vapor and ozone exhibit the highest information capacity, followed by nitrous oxide and methane, while carbon monoxide and carbon dioxide demonstrate the lowest capacity. The H2O ER (DFS) contained in FY-3E/HIRAS-II is 1.51 (0.35) higher than that in FY-3D/HIRAS-I, the O3 ER (DFS) in FY-3E/HIRAS-II is 1.51 (0.36) higher than that in FY-3D/HIRAS-I, while the N2O ER (DFS) in FY-3E/HIRAS-II is 0.17 (0.19) higher and the CH4 ER (DFS) is 0.07 (0.04) higher than that in FY-3D/HIRAS-I.