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LCDs are today's workhorse for automotive displays. In recent years, some premium OEMs started to ship cars with OLED displays. The advantages of OLEDs compared to LCDs are short response time at low temperature (relevant for e‐mirrors) and perfect black at night as well as highly curved and transparent versions. Replacing edge‐lit backlight of LCDs by FALD (full array local dimming) results in highest luminance at acceptable halo. Furthermore, LCDs have a large supply chain, which results in competitive cost. Well‐known challenges of OLEDs deal with high luminance and temperature sensitivity, which can result in reduced lifetime (LT) incl. Burn‐In. Challenges and solutions for LCDs and OLEDs such as halo measurements and viewing angle performance will be discussed more in detail. Latest automotive demonstrators show the potential of micro‐LEDs and electrically pumped Quantum Dots (QD) displays. Both have the potential to outperform LCDs and OLEDs. [ABSTRACT FROM AUTHOR]

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric M A P , while the regression performance of machine learning was assessed with Root Mean Square Error ( R M S E ) and Mean Absolute Error ( M A E ). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results ( P r e c i s i o n smoke = 89.12 % ). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) is a meteorological disaster that occurs over tropical or subtropical oceans. Automatic TC identification from a satellite image is essential to the subsequent automatic intelligent determination of TC positioning and intensity. Few recent studies have examined the automatic detection of TCs from satellite images. In this paper, wavelet (W) transformation, efficient (E) channel attention mechanism and YOLOX (Y) are combined to construct a deep learning model for TC detection, referred to as WEY-TCNet. WEY-TCNet automatically detects TCs from infrared satellite images. To improve the detection accuracy, a discrete wavelet transform is first used to decompose the infrared satellite image, after which the horizontal high-frequency components, including the TC rainband structure and inner core area, are extracted and fused with the original image. This step enhances the TC structure and brightness temperature gradient characterization. The fused image is then used as the input to WEY-TCNet. The ECANet channel attention mechanism was added to the original YOLOX-S network structure to focus on the key features of TC, such as structure and brightness temperature while suppressing invalid features and noise. This study constructed a satellite image dataset based on the infrared images of the Chinese Fengyun2D geostationary satellite. The dataset contains infrared satellite images of TCs in various life cycle stages, from generation to extinction. Our experiments showed that the WEY-TCNet model proposed in this paper achieves 91.49% detection mAP on the TC dataset, compared with the 86.69% detection mAP of the original YOLOX-S model, significantly improving it. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

A CatBoost-based intelligent tropical cyclone (TC) intensity-detecting model was built to quantify the intensity of TCs over the Western North Pacific (WNP) with the cloud-top brightness temperature (CTBT) data of Fengyun-2F (FY-2F) and Fengyun-2G (FY-2G) and the best-track data of the China Meteorological Administration (CMA-BST) in recent years (2015–2018). The CatBoost-based model was featured with the greedy strategy of combination, the ordering principle in optimizing the possible gradient bias and prediction shift problems, and the oblivious tree in fast scoring. Compared with the previous studies based on the pure convolutional neural network (CNN) models, the CatBoost-based model exhibited better skills in detecting the TC intensity with the root mean square error (RMSE) of 3.74 m s−1. In addition to the three mentioned model features, there are also two reasons for the model's design. On one hand, the CatBoost-based model used the method of introducing prior physical factors (e.g., the structure and shape of the cloud, deep convections, and background fields) into its training process. On the other hand, the CatBoost-based model expanded the dataset size from 2342 to 13,471 samples through hourly interpolations of the original dataset. Furthermore, this paper investigated the errors of this model in detecting the different categories of TC intensity. The results showed that the deep learning-based TC intensity-detecting model proposed in this paper has systematic biases, namely, the overestimation (underestimation) of intensities in TCs which were weaker (stronger) than at the typhoon level, and the errors of the model in detecting weaker (stronger) TCs were smaller (larger). This implies that more factors than the CTBT should be included to further reduce the errors in detecting strong TCs. [ABSTRACT FROM AUTHOR]

This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario. [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Haiyang-1D (HY-1D) is the second operational satellite in China's Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved. [ABSTRACT FROM AUTHOR]

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people's property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

In a previous paper, we demonstrated a single-rung method for measuring cosmological distances in active galactic nuclei (AGNs) that can be used from low redshift (z < 0.1) to high redshift (z > 3). This method relies on the assumption that the variability seen in AGNs is constrained by the speed of light during a flare event and can therefore be used to estimate the size of an emitting region. A limitation of this method is that previously, the Doppler factor was required to be known. In this paper, we derive an extension of the 'standard speed-gun' method for measuring cosmological distances that depends on the maximum intrinsic brightness temperature that a source can reach, rather than the Doppler factor. If the precise value of the intrinsic brightness temperature does not evolve with redshift, and flares are statistically independent, we can in principle improve the errors on measurements of the matter content of the Universe (in a flat Lambda cold dark matter model) statistically. We then explored how well a future observing programme would constrain cosmological parameters. We found that recovering the input cosmology depends critically on the uncertainty of the intrinsic brightness temperature and the number of flares observed. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of existing models for describing surfaces of various types is performed, and the possibilities of their application at the level of mathematical modeling are analyzed. Moreover, due to the large number of models and the complexity of selecting the appropriate model, e.g., when conducting a practical experiment, an algorithm for choosing a specific model depending on the initial data is proposed. According to the algorithm, a software prototype that implements this algorithm (written in Python) is proposed. [ABSTRACT FROM AUTHOR]

Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D's (FY-3D) microwave radiometer imager's (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general. [ABSTRACT FROM AUTHOR]

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel. [ABSTRACT FROM AUTHOR]

The L band frequency shows high sensitivity to sea surface salinity. More stable brightness temperature (TB) measurements are required for L-band radiometers to reduce salinity retrieval errors than for high-frequency radiometers. Due to the complexity of L-band synthetic aperture radiometers, a carefully selected cold-sky target should be viewed using an L-band synthetic aperture radiometer for the purpose of absolute TB calibration since the celestial sky is relatively well characterized and stable in the L band. A novel, effective cold-sky calibration strategy is presented in this paper. The strategy of cold-sky calibration of the synthetic aperture radiometer is applied when and where the antenna main lobe points to the 'flat' celestial sky, and the impact of each type of foreign source, such as the sun or moon, on visibility values should be minimized in the meantime. Additionally, antenna thermal stability is also considered, which can cause antenna deformation, and the antenna patterns are affected. A high-precision and high-fidelity simulator is built for the cold-sky calibration optimized strategy. The orbital beta angle is introduced to characterize the variation in space environment temperatures. A planet that is considered spherical in shape requires significantly less computation than an ellipsoid one in the simulator. The trade-off study results for the planet shape assumption in the cold-sky calibration simulator are presented. Finally, the calibration uncertainty and performance are assessed. [ABSTRACT FROM AUTHOR]

For an aperture synthesis radiometer (ASR), the visibility and the modified brightness temperature (BT) are related to the Fourier transform when the distance between the system and the source is in the far-field region. BT reconstruction can be achieved using G-matrix imaging. However, for ASRs with large array sizes, the far-field condition is not satisfied when performing performance tests in an anechoic chamber due to size limitations. Using far-field imaging methods in near-field conditions can introduce errors in the images and fail to correctly reconstruct the BT. Most of the existing methods deal with visibilities, converting near-field visibilities to far-field visibilities, which are suitable for point sources but not good for extended source correction. In this paper, two near-field imaging methods are proposed based on the near-field distance. These methods enable BT reconstruction in near-field conditions by generating improved resolving matrices: the near-field G-matrix and the F-matrix. These methods do not change the visibility measurements and can effectively image both the point source and the extended source in the near field. Simulations of point sources and extended sources in near-field conditions demonstrate the effectiveness of both methods, with F-matrix imaging outperforming near-field G-matrix imaging. The feasibility of both near-field imaging methods is further validated by carrying out experiments on a 10-element Y-array system. [ABSTRACT FROM AUTHOR]

Context. Thanks to the Mid-InfraRed Instrument (MIRI) on the James Webb Space Telescope (JWST), our ability to observe the star formation process in the infrared has greatly improved. Due to its unprecedented spatial and spectral resolution and sensitivity in the mid-infrared, JWST/MIRI can see through highly extincted protostellar envelopes and probe the warm inner regions. An abundant molecule in these warm inner regions is SO2, which is a common tracer of both outflow and accretion shocks as well as hot core chemistry. Aims. This paper presents the first mid-infrared detection of gaseous SO2 emission in an embedded low-mass protostellar system rich in complex molecules and aims to determine the physical origin of the SO2 emission. Methods. JWST/MIRI observations taken with the Medium Resolution Spectrometer (MRS) of the low-mass protostellar binary NGC 1333 IRAS 2A in the JWST Observations of Young protoStars (JOYS+) program are presented. The observations reveal emission from the SO2v3 asymmetric stretching mode at 7.35 µm. Using simple slab models and assuming local thermodynamic equilibrium (LTE), we derived the rotational temperature and total number of SO2 molecules. We then compared the results to those derived from high-angular-resolution SO2 data on the same scales (~50–100 au) obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). Results. The SO2 emission from the v3 band is predominantly located on ~50–100 au scales around the mid-infrared continuum peak of the main component of the binary, IRAS 2A1. A rotational temperature of 92 ± 8 K is derived from the v3 lines. This is in good agreement with the rotational temperature derived from pure rotational lines in the vibrational ground state (i.e., v = 0) with ALMA (104 ± 5 K), which are extended over similar scales. However, the emission of the v3 lines in the MIRI-MRS spectrum is not in LTE given that the total number of molecules predicted by a LTE model is found to be a factor of 2 × 104 higher than what is derived for the v = 0 state from the ALMA data. This difference can be explained by a vibrational temperature that is ~100 K higher than the derived rotational temperature of the v = 0 state: Tvib ~ 200 K versus Trot = 104 ± 5 K. The brightness temperature derived from the continuum around the v3 band (~7.35 µm) of SO2 is ~180 K, which confirms that the v3 = 1 level is not collisionally populated but rather infrared-pumped by scattered radiation. This is also consistent with the non-detection of the v2 bending mode at 18–20 µm. The similar rotational temperature derived from the MIRI-MRS and ALMA data implies that they are in fact tracing the same molecular gas. The inferred abundance of SO2 , determined using the LTE fit to the lines of the vibrational ground state in the ALMA data, is 1.0 ± 0.3 × 10−8 with respect to H2, which is on the lower side compared to interstellar and cometary ices (10−8−10−7). Conclusions. Given the rotational temperature, the extent of the emission (~100 au in radius), and the narrow line widths in the ALMA data (~3.5 km s−1), the SO2 in IRAS 2A likely originates from ice sublimation in the central hot core around the protostar rather than from an accretion shock at the disk–envelope boundary. Furthermore, this paper shows the importance of radiative pumping and of combining JWST observations with those from millimeter interferometers such as ALMA to probe the physics on disk scales and to infer molecular abundances. [ABSTRACT FROM AUTHOR]

In the modern science and practice of remote sensing of any surface, including various types of land cover, radio systems are widely used. Due to low energy consumption and high stealth, the most promising direction of technical tools for remote sensing is the development of passive remote sensing systems. To estimate the specific parameters of the surface, information on the relation between the signals of the thermal radiation and these parameters is needed. Therefore, it is relevant to study and analyze the existing types of such relations that connect the brightness temperature and the parameters of the Earth's surfaces; this is the subject matter of the study in this paper. The goal of this study is to analyze existing relation models and study the conditions of their application in terms of operating frequencies, geometric characteristics, type of surface coverage (with vegetation, without vegetation, etc.), as well as the method of obtaining mathematical relations (electrodynamic models or empirical models). Due to the large number of different types of earth surface coverings and different types of surface models, in order to simplify remote sensing tasks for passive systems, as well as to improve the results of modeling or experiments, the following tasks are formulated: to study and classify existing models of the relation between surface parameters and brightness temperature, and to calculate and analyze the potential accuracy of estimating surface parameters by passive remote sensing systems. The following results were obtained: the impact of the atmosphere on the surface's brightness temperature, described by the electrodynamic flat model, for the operating frequencies 75-110 GHz of the radiometer was analyzed. The impact of the atmosphere on the brightness temperature appears at sighting angles greater than 50° at horizontal polarization and greater than 80° at vertical polarization. According to the results, it can be said that the estimation accuracy of the imaginary part of the complex permittivity decreases with an operating wavelength decrease, and the estimation accuracy of the real part is the opposite. Conclusions. The results of simulation modeling show that for radiometers with the above-mentioned operating frequencies, it is possible to estimate the real permittivity or only the real component separately in the case of describing the surface with a complex permittivity parameter. It is recommended that the study be conducted at vertical and horizontal polarizations, without and with the impact of the atmosphere. To estimate the surface conductivity (the imaginary part of the complex permittivity of a surface), systems with lower operating frequencies are required. Thus, this study presents an analysis of existing models, calculates the potential accuracy of parameters estimation when using these models, and provides recommendations for practical experiments or modeling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Weather radars play a crucial role in the monitoring of severe convective weather. However, due to their limited detection range, they cannot conduct an effective monitoring in remote offshore areas. Therefore, this paper utilized UNet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibited significant diurnal cycles, but there were notable differences in their variation characteristics. To address this, we established four comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results showed that adding the distribution map of the minimum brightness temperature at the corresponding time in the model could effectively improve the model's performance in both spatial and temporal dimensions, reduce the root-mean-square error (RMSE) of the model, and enhance the accuracy of severe convective weather monitoring. [ABSTRACT FROM AUTHOR]

On 5 July 2021, China launched the world's first early morning orbit meteorological satellite, which was equipped with China's newest-generation microwave instrument, the Microwave Temperature Sounder-3 (MWTS-3). MWTS-3 has 17 detection channels, which can detect temperature profiles from near the ground to approximately 2 hPa. However, similar to the newest-generation microwave temperature instruments in other countries, MWTS-3 may also suffer from striping noise. In this paper, a principal component analysis (PCA) is combined with ensemble empirical mode decomposition (EEMD) to extract the striping noise from the brightness temperatures observed by MWTS-3, and a comparative analysis is performed with its predecessor (MWTS-2). This paper analyzes the characteristics of the striping noise of both instruments and its possible impact on their observations. The results show that the striping noise of MWTS-3 has been significantly reduced in most of the 17 channels. In particular, the striping noise of MWTS-3's channel 3 is reduced by nearly half compared to the channel of MWTS-2 with the same frequency. Furthermore, the interchannel correlations for MWTS-3 are significantly lower than those for MWTS-2. After noise mitigation, the interchannel correlations of both MWTS-2 and MWTS-3 are obviously reduced. In addition, striping noise mitigation can reduce observation errors to some extent, especially for channels above the middle troposphere. [ABSTRACT FROM AUTHOR]

The Calibration Microwave Radiometer (CMR) is a three-band radiometer deployed on the HY-2C satellite in a near-Earth orbit, and since it launched, there are few studies presented on the performance of CMR to date. Therefore, this paper focuses on providing an assessment of HY-2C CMR brightness temperature and wet troposphere correction (WTC). CMR works at 18.7 GHz, 23.8 GHz and 37 GHz in a nadir-viewing direction, aligned with the HY-2C radar altimeter. The wet troposphere path delay of the radar altimeter signal caused by water vapour and cloud liquid water content can be monitored and corrected by CMR. In this paper, guided by the concept of antenna pattern correction algorithm and a purely statistical method, we directly establish the function between the CMR antenna temperature and the model-derived WTC calculated by the European Centre from Medium-Range Weather Forecasting (ECMWF) Reanalysis data, which can obtain the brightness temperature and the WTC of CMR simultaneously. Firstly, the algorithm principle of CMR to establish the function between the antenna temperature and the model-derived WTC is introduced, and then the brightness temperature of CMR is evaluated using reference brightness temperatures of the Advanced Microwave Radiometer 2 (AMR-2) on Jason-3 satellite at crossover points. Furthermore, the performance of the CMR WTC is validated in three ways: (1) directly comparing with the colocated WTC measured by Jason-3 AMR-2, (2) directly comparing with model-derived WTC from ECMWF, which allows a rapid check at a global scale, (3) comparing the standard deviation of the Sea Surface Height (SSH) difference at crossover points using different WTC retrieval methods. The linear fit with Jason-3 brightness temperature and WTC in all non-precipitation conditions demonstrated a good agreement with Jason-3. In addition, the WTC of CMR has an obvious decrease in the standard deviation of the SSH difference compared with model-derived WTC, indicating the CMR can significantly improve the accuracy of the HY-2C SSH measurements. All the assessments indicate that the CMR performances are satisfying the expectations and fulfilling the mission requirements. [ABSTRACT FROM AUTHOR]

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection. [ABSTRACT FROM AUTHOR]

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm−1 and 2320 cm−1) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO2 EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions. [ABSTRACT FROM AUTHOR]

Enhancing the spatial resolution of real-aperture microwave radiometers is an essential research topic. The accuracy of the numerical values of brightness temperatures (BTs) observed using microwave radiometers directly affects the precision of the retrieval of marine environmental parameters. Hence, ensuring the accuracy of the enhanced brightness temperature values is of paramount importance when striving to enhance spatial resolution. A spectrum extension (SE) method is proposed in this paper, which restores the suppressed high-frequency components in the scene BT spectrum through frequency domain transformation and calculations, specifically, dividing the observed BT spectrum by the conjugate of the antenna pattern spectrum and applying a Taylor approximation to suppress error amplification, thereby extending the observed BT spectrum. By using a convolutional neural network to correct errors in the calculated spectrum and then reconstructing the BT through inverse fast Fourier transform (IFFT), the enhanced BTs are obtained. Since the extended BT spectrum contains more high-frequency components, namely, the spectrum is closer to that of the original scene BT, the reconstructed BT not only achieves an enhancement in spatial resolution, but also an improvement in the accuracy of BT values. Both the results from simulated data and satellite-measured data processing illustrate that the SE method is able to enhance the spatial resolution of real-aperture microwave radiometers and concurrently improve the accuracy of BT values. [ABSTRACT FROM AUTHOR]

Various aspects of tropical cyclones (TCs) fluctuate with the diurnal cycle. TC size is critical to the extent of its damage, but diurnal variations in the growth of the outer region size have largely remained unexplored. This paper examines the diurnal variations in the growth of the radius of gale‐force winds (34 kt; R34) by analyzing best‐track data from 2001 to 2019 for both North Atlantic TCs and global TCs outside of the North Atlantic region. Statistically significant diurnal variations was found for R34 growth, with the maximum 6‐h growth rate occurring at 2100–0300 local solar time (LST) for North Atlantic TCs, and at 0300–0900 LST for global TCs excluding the North Atlantic which experienced rapid intensification (≥30 knots within 24 h). The higher R34 6‐h growth rates during the night were linked to a larger extent of very deep convective clouds with infrared brightness temperatures <208 K during this time. [ABSTRACT FROM AUTHOR]

A partial solar eclipse occurred on 25 October 2022, in the central and northern parts of Europe. The partial solar eclipse was observed at Aalto University Metsähovi Radio Observatory, Finland at the radio wavelength of 8 mm (37 GHz). In Finland, the magnitude of the partial solar eclipse was 62.7 %. Solar radio maps at the time cadence of about 9 minutes were observed over the whole eclipse. The solar eclipse observations could be used for instrument calibration purposes. This paper investigates the solar brightness temperature, the limb brightening effect, the height of the chromosphere and the location of radio brightening using the aforementioned partial solar eclipse observations. We got the confirmation that our earlier results are consistent, e.g., the solar brightness temperature matches with 8100 K ± 300 K. It was also possible to detect limb brightening effect. However, the prevailing solar activity might have distorted the final conclusions. The Moon should operate as a focusing element and the location of radio brightenings could be defined more carefully than in the normal conditions. We investigated this feature. Our results are in some parts unexpected and need further investigations. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared. [ABSTRACT FROM AUTHOR]

Infrared radiation (IR) features that are influenced by infrared emissivity ε and physical temperature T d have been successfully applied to the early-warning of landslides. Although the infrared emissivity of a rock is a key parameter to determine its thermal radiation properties, the effect of particle size on the infrared emissivity of rock fragments is unknown. So in this paper, granite, marble, and sandstone were used as examples to conduct infrared imaging experiments on rock fragments. Their equivalent emissivity was used to interpret the detected infrared emission, including that from indoor backgrounds. In addition, the characteristics of changes in equivalent emissivity were discussed with reference to changes in observation direction and zenith angle. Then, a computation model of equivalent emissivity based on multiple observation directions and zenith angles was built to reveal the change in equivalent emissivity with particle sizes. The result indicates that the indoor background radiation has a predominant direction just above the rock fragments. The maximum deviation of infrared brightness temperature (IBT) was 0.260 K, and the maximum deviation of equivalent emissivity among different observation directions and zenith angles was 0.0065. After eliminating the influence of directional and angle effects with the operation of normalization, the general law of equivalent emissivity for all rock fragments that change with particle size is consistent. The maximum equivalent emissivity occurs at particle size 5 mm in the condition of particle size larger than 1 mm, while the equivalent emissivity changes inversely with particle size in the condition of particle size smaller than 1 mm. Above all, this study contributes new cognitions to Remote Sensing Rock Mechanics, and provides valuable evidence for better thermal infrared remote sensing monitoring on loose slope landslides. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang'E-1 (CE-1) and Chang'E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar subsurface rock abundance (RA). In this paper, a thermal conductivity model with an undetermined parameter β of the mixture has been employed to estimate the physical temperature profile of the mixed layer (rock and regolith). Parameter β and the physical temperature profile of the mixed layer are constrained by the Diviner Channel 7 observations. Then, the subsurface RA on the 16 large (Diameter > 20 km) Copernican-age craters of the Moon is extracted from the average nighttime TB of the CE-2 37 GHz channel based on our previous rocky TB model. Two conclusions can be derived from the results: (1) the subsurface RA values are usually greater than the surface RA values retrieved from Diviner observations of the studied craters; (2) the spatial distribution of subsurface RA extracted from CE-2 MRM data is not necessarily consistent with the surface RA detected by Diviner data. For example, there are similar RA spatial distributions on both the surface and subsurface in Giordano Bruno, Necho, and Aristarchus craters. However, the distribution of subsurface RA is obviously different from that of surface RA for Copernicus, Ohm, Sharonov, and Tycho craters. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between the growth of the 34‐kt wind radius (R34) of tropical cyclones (TCs) and their fullness using best‐track data from 2001 to 2020. The storms were categorized into four groups based on a fullness scale: FS1 (fullness ≤ 0.4), FS2 (0.4 < fullness ≤ 0.6), FS3 (0.6 < fullness ≤ 0.8), and FS4 (fullness > 0.8). These groups exhibit unique spatial patterns of very deep convective clouds with infrared brightness temperatures <208 K. The mean R34 growth rates in 24 hr decrease from FS1 to FS4, which is linked to the higher coverage of very deep convection around R34 in storms with lower fullness. This study demonstrates that TC fullness can characterize the spatial distribution of deep convection in storms and serve as a representation of the growth of TC outer region size. These results have implications for understanding the mechanisms behind TC outer size growth. Plain Language Summary: The destructive potential of tropical cyclones (TCs) is primarily determined by two key factors: intensity and the size of the outer region. While the impact of TC wind structure on TC intensification rates has been extensively studied, little has been done to explore its effect on the growth of the outer region. This study finds that storms with lower fullness had larger growth rates in the radius of gale‐force winds (34 kt or 17 m/s; R34). TC fullness is defined as the ratio of the extent of the outer‐core wind skirt to the outer‐core size of the TC. This is associated with the higher coverage of very deep convective clouds with infrared brightness temperatures below 208 K around R34 in storms with lower fullness. These findings shed light on the importance of wind structure in the growth of the outer region in TCs, which helps improve our understanding of how these storms change in outer‐core size. Key Points: Tropical cyclone fullness can serve as a reliable indicator of the distribution of very deep convectionStorms with lower fullness have a higher mean R34 growth rate over a 24‐hr periodMore intense convection occurred around R34 in storms with lower fullness, which promotes the growth of R34 [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement: Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia. [ABSTRACT FROM AUTHOR]

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse Gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to the longwave Thermal InfraRed radiation (TIR) with 0.2 cm -1 spectral sampling, and the corresponding spectral resolution (full width at half maximum, FWHM) of TIR region is less than 0.27 cm -1. TANSO-FTS-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version v210210 TIR products of TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the pointing mirror thermal emission. These correction parameters are characterized by an optimization that depends on the difference between the spectral radiance of TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for the simultaneous nadir overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1 σ) from 180 to 330 K for the 680 cm -1 CO 2 spectral range. The brightness temperatures between TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO 2 spectral ranges at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-orthogonal simultaneous off-nadir overpass (2O-SONO) is now done for TANSO-FTS-2 and IASI, TANSO-FTS-2 and AIRS, and TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO 2 spectral range for scenes with forward and backward viewing angles greater than 20 ∘. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products. [ABSTRACT FROM AUTHOR]

The short-term heavy rainfalls, thunderstorm gales, hail, squall lines, tornadoes, thunderstorms and other disastrous weather caused by deep convective clouds greatly threaten social and economic activities and the safety of people's lives and property. Therefore, it is of great value to study the recognition methods of deep convective clouds in the field of the weather forecast. Since deep convective clouds are characterized by a short life cycle, small spatial scale and complex structure, it is difficult to accurately monitor and identify deep convective clouds by traditional ground monitoring methods. In this paper, the semantic segmentation network SCNET based on attention mechanism was proposed and a deep learning network for the recognition of deep convective clouds was established with infrared and brightness temperature channels of FY4A stationary meteorological satellites as input features. The results showed that SCNET has a better recognition effect than meteorological and machine learning methods, such as single-band threshold method, SVM, NN, UNET and RESNET, and can effectively improve the recognition accuracy of deep convective clouds [ABSTRACT FROM AUTHOR]

Corner reflector, as a passive jammer, can generate a large radar cross section (RCS) to interfere with radar for target recognition. However, the impact of corner reflectors on passive millimetre-wave (PMMW) recognition is unclear. In this paper, based on ray tracing, the brightness temperature (TB) of corner reflectors is calculated. Ray tracing paths are used to analyze the impact of the sky, sea surface, and sun on the TB of corner reflectors. The antenna temperature of corner reflectors and their array at different distances is simulated and compared with that of a metal ship to evaluate their jamming performance. Four series of experiments measuring the antenna temperature of corner reflectors and the ship are conducted to verify the jamming performance. The results indicate that the contrast of corner reflectors is much smaller than that of ship target, causing little jamming for PMMW recognition. [ABSTRACT FROM AUTHOR]

Meteorological satellite data have been extensively utilized in global numerical weather prediction systems and have a positive impact to improve forecast accuracy. In order to correctly assimilate satellite radiance observations in data assimilation systems, the systematic observation biases must be corrected to conform to a Gaussian normal distribution with a mean of 0. By selecting appropriate air-mass predictors through correlation assessment, a two-step bias correction scheme (namely the scan-angle bias correction and the air-mass bias correction) is established in this paper based on radiation observations of FY-3E/ HIRAS-II from 1 to 31 January 2023. The results indicate that FY-3E/HIRAS-II O-B (observation-simulation) bias exhibits scanning angle bias dependence from nadir to limb field of view. Statistics have found that this scanning angle bias does not depend on latitude band. After scan-angle bias correction using statistical scan-angle correction coefficients, the dependence of the O-B biases on the scan angle can be eliminated. The second step is to perform air-mass correction. Our correction scheme is compared with the air-mass bias correction scheme in NCEP-GSI. Although the scan angle influence is also considered in NECP-GSI scheme, it does not account for the water vapor effect in the atmosphere. Consequently, the correction effect is not good for channels with lower peak height of weighting function, resulting in a slightly residual positive bias after correction. The combination of air-mass predictors (model surface skin temperature, model total column water vapor, thickness of 1000–300 hPa, and thickness of 200–50 hPa) selected through importance assessment in this study effectively eliminates the air-mass biases. The systematic biases between observed brightness temperature and background simulated brightness temperature from background atmospheric field for all HIRAS-II channels significantly decrease after bias correction, and the bias distribution essentially follows a Gaussian normal distribution with a mean of 0. The bias correction scheme has a significant improvement for the analysis at upper air and near surface. [ABSTRACT FROM AUTHOR]

This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave Radiative Transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down and up-welling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development especially by students and early career scientists. PyRTlib allows simulating observations from ground-based, airborne, and satellite microwave sensors in clear sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor for state-of-the-art atmospheric radiative transfer codes that excel for speed and/or versatility (e.g., ARTS, RTTOV). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built in modules to access popular open data archives. The paper also presents examples for computing simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

The assimilation of two surface-sensitive channels of the AMSU-A instruments on board the NOAA-15/NOAA-18/NOAA-19 and MetOp-A/MetOp-B satellites over land was achieved in the China Meteorological Administration Global Forecast System (CMA_GFS). The land surface emissivity was calculated by 1) the window channel retrieval method and 2) the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM2). Quality controls for these satellite microwave observations over land were conducted. The predictors and regression coefficients used for oceanic satellite data were retained during the bias correction over land and found to perform well. Three batch experiments were implemented in CMA_GFS with 4D-Var: 1) assimilating only the default data, and adding the above data over land with land surface emissivity obtained from 2) TELSEM2 and 3) the window channel retrieval method. The results indicated that the window channel retrieval method can better reduce the departure between the observed and simulated brightness temperature. Over most land types, the positive impacts of this method exceed those of TELSEM2. Both TELSEM2 and the window channel retrieval method improve the humidity analysis near the ground, as well as the forecast capability globally, particularly in those regions where the land coverage is greater, such as in the Northern Hemisphere. The data utilization of the two surface-sensitive channels increase by 6% and 12%, respectively, and the additional data every 6 h can cover most land, where there was no surface-sensitive data assimilated before. This study marks the beginning of near-surface channel assimilation over land in CMA_GFS and represents a breakthrough in the assimilation of other surface-sensitive channels in other satellite instruments. Significance Statement: Surface-sensitive microwave channels are difficult to assimilate in NWP due to the lack of both direct measurement and appropriate modeling for instantaneous land surface emissivity. This paper discusses a method that improves the surface emissivity estimates, which has allowed the utilization of surface-sensitive microwave channels in CMA_GFS. Those capabilities have resulted in better data utilization, improved forecasts of temperature, geopotential height, and winds in the Northern Hemisphere at 3–7 days, and represent an incremental and important improvement to CMA_GFS. [ABSTRACT FROM AUTHOR]

Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters. [ABSTRACT FROM AUTHOR]

Microwave radiometer (MRM) is one of the important payloads on the Chang'e-2 (CE-2) Lunar satellite. In the Chang'e satellite's observation of the microwave radiation brightness temperature (TB) on the lunar surface, there are some "cold spots" of microwave thermal radiation at night containing the Jackson crater. In order to compare the diurnal radiation TB differences of "cold spots" on the lunar surface, two typical craters at similar latitudes on the northern hemisphere on the lunar farside were selected: Jackson, which represents the new craters with a large number of discrete rocks on their surfaces; and Morse, which no longer has a large number of rocks after long-term meteorite impact and lunar evolution. In this paper, the diurnal variation of CE-2 MRM data in the two craters is presented, and a comparative analysis is made with the (FeO + TiO 2 ) abundance (FTA) obtained by Clementine UV-VIS data and the rock abundance (RA) data of LRO Diviner. We find that the variation of the "cold spots" of lunar surface thermal radiation is closely related to the RA distribution in the newly formed craters on the lunar surface, and also has a certain correlation with the FTA. [ABSTRACT FROM AUTHOR]

Firn density plays a crucial role in assessing the surface mass balance of the Antarctic ice sheet. However, our understanding of the spatial and temporal variations in firn density is limited due to i) spatial and temporal limitations of in situ measurements, ii) potential modelling uncertainties, and iii) lack of firn density products driven by satellite remote sensing data. To address this gap, this paper explores the potential of satellite microwave radiometer (SMISS) and scatterometer (ASCAT) observations for assessing spatial and temporal dynamics of dry firn density over the Antarctic ice sheet. Our analysis demonstrates a clear relation between density anomalies at a depth of 4 cm and fluctuations in satellite observations. However, a linear relationship with individual satellite observations is insufficient to explain the spatial and temporal variation of snow density. Hence, we investigate the potential of a non-linear Random Forest (RF) machine learning approach trained on radiometer and scatterometer data to derive the spatial and temporal variations in dry firn density. In the estimation process, ten years of SSMIS observations (brightness temperature), ASCAT observations (backscatter intensity), and polarisation and frequency ratios derived from SSMIS observations are used as input features to a random forest (RF) regressor. The regressor is first trained on time series of modelled density and satellite observations at randomly sampled pixels, and then applied to estimate densities in dry firn areas across Antarctica. The RF results reveal a strong agreement between the spatial patterns estimated by the RF regressor and the modelled densities. The estimated densities exhibit an error of ± 10 kg m−3 in the interior of the ice sheet and ± 20 kg m−3 towards the ocean. However, the temporal patterns show some discrepancies, as the RF regressor tends to overestimate summer densities, except for high-elevation regions in East Antarctica and specific areas in West Antarctica. These errors may be attributed to underestimations of short-term (or seasonal) variations in the modelled density and the limitation of RF in extrapolating values outside the training data. Overall, our study presents a potential method for estimating unknown Antarctic firn densities using known densities and satellite parameters. [ABSTRACT FROM AUTHOR]

Accurate estimates of the unperturbed state of upwelling radiation from the earth's surface are vital to the detection and classification of anomalous radiation values. Determining radiative anomalies in the landscape is critical for isolating change, a key application being wildfire detection, which is reliant upon knowledge of a location's radiation budget sans fire. Most techniques for deriving the unperturbed background state of a location use that location's spatial context, that is, the pixels immediately surrounding the target. Spatial contextual estimation can be subject to error due to occlusion of the pixel's spatial context and issues such as land cover heterogeneity. This paper proposes a new method of deriving background radiation levels by decoupling the set of prediction pixels used for estimation from the target location in a Spatio-Temporal Selection (STS) process. The process selects training pixels for predictive purposes from a target-centred search area based on their similarity with the target pixel in terms of brightness temperature over a prescribed time period. The proposed STS process was applied to images from the AHI-8 geostationary sensor centred over the Asia-Pacific, and comparisons were made to both brightness temperature estimates from the spatial context and to sensor measurements. This comparison showed that the STS method provided between 10–40% reduction in estimation error over the commonly utilised contextual estimator; in addition, the STS method increased the availability of estimates in comparison to the spatial context by between 12–31%. Image reconstruction using the method resulted in high-fidelity reproductions of the examined landscape, with standing geographic features and areas experiencing thermal anomalies readily identifiable on the resulting images. [ABSTRACT FROM AUTHOR]

The underlying mechanism that couples the Quasi-Biennial Oscillation (QBO) and the Madden-Julian oscillation (MJO) has remained elusive, challenging our understanding of both phenomena. A popular hypothesis about the QBO-MJO connection is that the vertical extent of MJO convection is strongly modulated by the QBO. However, this hypothesis has not been verified observationally. Here we show that the cloud-top pressure and brightness temperature of deep convection and anvil clouds are systematically lower in the easterly QBO (EQBO) winters than in the westerly QBO (WQBO) winters, indicating that the vertical growth of deep convective systems within MJO envelopes is facilitated by the EQBO mean state. Moreover, the deeper clouds during EQBO winters are more effective at reducing longwave radiation escaping to space and thereby enhancing longwave cloud-radiative feedback within MJO envelopes. Our results provide robust observational evidence of the enhanced MJO activity during EQBO winters by mean state changes induced by the QBO. This paper shows that the vertical growth of deep convective systems within Madden-Julian oscillation envelopes is facilitated by mean state changes in the upper-troposphere and lower-stratosphere during easterly Quasi-Biennial Oscillation winters. [ABSTRACT FROM AUTHOR]

Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To solve this ill-posed problem, classical algorithms often require the input of external parameters such as land surface emissivity and atmospheric profiles, which are often difficult to obtain accurately and timely, and this may introduce additional errors and limit the applicability of the LST retrieval algorithms. To reduce the dependence on external parameters, this paper proposes a new algorithm to directly estimate the LST from the top-of-atmosphere brightness temperature in Landsat-9 two-channel TIR data (channels 10 and 11) without external parameters. The proposed algorithm takes full advantage of the adeptness of the ensemble learning method to solve nonlinear problems. It considers the physical radiance transfer process and adds the leaving-ground bright temperature and atmospheric water vapor index to the input feature set. The experimental results show that the new algorithm achieves accurate LST estimation results compared with the ground-measured LST and is consistent with the Landsat-9 LST product. In subsequent work, further studies will be undertaken on developing end-to-end deep learning models, mining more in-depth features between TIR channels, and reducing the effect of spatial heterogeneity on accuracy validation. [ABSTRACT FROM AUTHOR]

In passive microwave remote sensing of the Earth's surface, it is essential to relate the emission to geophysical parameters. The emissivity ranges between 0 and 1. Hence, a slight emissivity variation leads to a significant change in brightness temperature. Many sources of error contribute to such tiny variations in emission. This paper quantifies microwave emission variability from a rough surface through model simulation due to the non-Gaussianity in height probability density (HPD) and power spectrum density (PSD). We considered Gaussian and exponential distributions for surface height and correlation functions, representing two extremes of asperity and skewness. Additionally, the surface under consideration contains multiscale roughness. The impact of the HPD and multiscale roughness on the polarization index of the emissivity is evaluated as a function of frequency and roughness. In general, assuming that Gaussian-distributed height leads to an underestimation of the emissivity, with V polarization being less sensitive to the non-Gaussian HPD and PSD effects than H polarization, the emissions are enhanced at high roughness with small look angles but are reduced for smooth surfaces at large look angles under non-Gaussian PSD. In a specific scenario, the dynamic range of the difference between exponential and Gaussian HPD is 0~10%, and the difference in emissivity caused by non-Gaussian PSD ranges from −2% to 16%. These results should be helpful in interpreting the radiometric measurements that exhibit fluctuations and differences with model predictions. [ABSTRACT FROM AUTHOR]

In studying the mass balance of polar ice sheets, fluctuations in firn density near the surface is a major uncertainty. In this paper, we explore these variations at locations on the Greenland Ice Sheet and at the Dome C location in Antarctica. Borehole in situ measurements, snow radar echoes, microwave brightness temperatures, and modeling results from the Community Firn Model (CFM) are used. It is shown that firn density profiles can be represented using three processes: "long-scale" and "short-scale" density variations and "refrozen layers". Consistency with this description is observed in the dynamic range of airborne 0.5–2 GHz brightness temperatures and snow radar echo peaks in measurements performed in Greenland in 2017. Based on these insights, a new analytical partially coherent model is implemented to explain the microwave brightness temperatures using the three-scale description of the firn. Short- and long-scale firn processes are modeled as a 3D continuous random medium with finite vertical and horizontal correlation lengths as opposed to past 1D randomly layered medium descriptions. Refrozen layers are described as deterministic sheets with planar interfaces, with the number of refrozen-layer interfaces determined by radar observations. Firn density and correlation length parameters used in forward modeling to match measured 0.5–2 GHz brightness temperatures in Greenland show consistency with similar parameters in CFM predictions. Model predictions also are in good agreement with multi-angle 1.4 GHz vertically and horizontally polarized brightness temperature measured by the Soil Moisture and Ocean Salinity (SMOS) satellite at Dome C, Antarctica. This work shows that co-located active and passive microwave measurements can be used to infer polar firn properties that can be compared with predictions of the CFM. In particular, 0.5–2 GHz brightness temperature measurements are shown to be sensitive to long-scale firn density fluctuations with density standard deviations in the range of 0.01–0.06 gcm-3 and vertical correlation lengths of 6–20 cm. [ABSTRACT FROM AUTHOR]

Sentinel-3 (S3) satellites are equipped with microwave radiometers that perform brightness temperature (TB) measurements at 23.8 and 36.5 GHz to determine the wet tropospheric correction (WTC). The analysis of the two MWR-derived WTC present in S3 products, retrieved from three- and five-input neural network (NN) algorithms, suggest the need for their improvement. Focusing on the inputs and physical component, this paper aims at improving the WTC retrieval for open ocean, considering a suitable learning for S3 and a better accounting for the surface contribution to the WTC retrieval. Adopting a purely empirical approach, the learning database has been built using 1 year (2017) of valid S3A measurements, ERA5-derived WTC, and dynamic sea surface temperature (SST) from ERA5. The proposed approach is a similar NN with four inputs: TB at 23.8 and 36.5 GHz, altimeter backscattering coefficient and SST. Results show that the use of a dynamic SST, instead of static tables as currently adopted in S3, makes the fifth input (vertical temperature decrease) redundant. Comparisons with reference and independent WTC sources show that the WTC derived from this algorithm, when compared with those available in the S3 products, leads to a decrease in the RMS values of WTC differences, with respect to these independent WTC, by about 1 mm globally, that locally can reach almost 1 cm. This study proposes a new approach for the WTC of Sentinel-3 by considering a suitable set of dynamic inputs that better characterize the atmosphere, which is a significant enhancement over the current algorithms. [ABSTRACT FROM AUTHOR]