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The statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the physical mechanisms of wave transmission through the atmosphere. We develop a deep neural network-based RTM (DNN-based RTM) to calculate the simulated BTs for the Microwave Temperature Sounder-II onboard the Fengyun-3D satellite under different weather conditions. The DNN-based RTM is compared in detail with the physical-based RTM in retrieving the atmospheric temperature profiles by the statistical retrieval scheme. Compared to the physical-based RTM, the DNN-based RTM can obtain higher accuracy for simulated BTs and enables the statistical retrieval scheme to achieve higher accuracy in temperature profile retrieval in clear, cloudy, and rainy sky conditions. Due to its ability to simulate microwave observations more accurately, the DNN-based RTM is valuable for the theoretical study of microwave remote sensing and the application of passive microwave observations.

Accurate modeling of hydrometeor bulk scattering properties (BSPs) is essential for the effective assimilation of satellite microwave observations in cloudy and precipitating conditions. Nevertheless, current radiative transfer models use oversimplified hydrometeor BSP parameterizations, leading to significant simulation errors and biases that limit the full potential of all‐sky assimilation. To address this challenge, this study developed PhySCAT‐Net, a physics‐informed deep learning (DL) framework that integrates forward and Jacobian operators of a physical radiative transfer model into the neural network training, enabling efficient optimization of the BSP models against satellite observations. Applied to vertically and horizontally polarized radiances from the Global Precipitation Measurement Microwave Imager 166.5 GHz channels, the framework selectively fine‐tunes the snow BSP model while temporarily fixing other hydrometeor types. Results demonstrate that the optimized DL model substantially improves agreement between simulated and observed brightness temperatures. Across most regions globally, mean observation‐minus‐background (O‐B) biases are reduced to within ±1K, and the Jensen‐Shannon divergence decreases by orders of magnitude. The error distributions, which were previously highly skewed and therefore problematic for data assimilation, are now roughly symmetrical. Furthermore, PhySCAT‐Net enables the DL model to extract polarimetric information of non‐spherical ice particles directly from observed radiances, demonstrating superior performance compared to existing empirical schemes. It successfully reproduces the distributions of polarization differences and their non‐monotonic relationship with brightness temperature. Plain Language Summary Simulating satellite microwave observations in all‐sky conditions requires precise knowledge of how hydrometeors scatter and emit radiation. Current radiative transfer models (RTMs) use simplified assumptions about cloud particle shapes, sizes, and other microphysical properties, leading to significant simulation errors and biases, particularly for frozen hydrometeors with diverse morphologies. To address these limitations, this study explored the application of deep learning (DL) techniques to parameterize and optimize hydrometeor bulk scattering properties (BSPs). The key innovation lies in embedding the forward and Jacobian operators of a physical RTM into the DL training process, creating a differentiable pathway between satellite radiances and BSPs. This approach enables the DL model to automatically learn optimal scattering properties by minimizing differences between simulated and observed satellite radiances. The framework achieves substantial improvements in radiative transfer simulations, with simulated brightness temperature distributions showing much closer agreement with satellite observations. Moreover, the optimized DL model successfully captures complex polarization signals by learning directly from observed radiances. The model accurately reproduces the observed distributions of polarization differences and their characteristic curved relationship with brightness temperature. This demonstrates that the developed DL framework provides a promising pathway to improve the assimilation of satellite observations in cloudy and precipitating conditions. Key Points We developed PhySCAT‐Net, a physics‐informed deep learning framework to optimize hydrometeor bulk scattering properties using satellite observations PhySCAT‐Net significantly improved brightness temperature simulations and made skewed error distributions nearly symmetrical PhySCAT‐Net learned complex polarization signals directly from observed radiances, outperforming existing empirical methods

Forward radiative transfer models (RTM) are an indispensable tool for quantitative applications of satellite radiometers, e.g., for data calibration, instrument development, retrieval, and so on. In this study, we develop an accurate and efficient RTM for radiometers onboard Fengyun satellites, namely FYRTM (RTM for Fengyun Radiometers). Correlated k -distribution models are developed to improve the computational efficiency for gas absorption, and the effects of cloud and aerosol multiple scattering and emission are accelerated with pre-computed look-up tables. FYRTM is evaluated with a rigorous simulation based on discrete ordinate radiative transfer model (DISORT) as well as a popular fast forward model, i.e., the Community Radiative Transfer Model (CRTM). Results indicate that FYRTM-based simulations are two to three orders of magnitudes faster than the DISORT-based simulations. Compared to the rigorous model, FYRTM relative errors are within 2% at solar channels, and brightness temperatures (BT) differences are within 1 K at infrared channels. Compared with CRTM, FYRTM is computationally similar at solar channels, but three times faster at infrared channels. Furthermore, simulated reflectances/BTs using FYRTM are in a good agreement with the satellite observations. Overall, FYRTM is capable to simulate satellite observations under different atmospheric conditions, and can be extended to other radiometers onboard the Fengyun satellites (both geostationary and polar-orbiting satellites). It is expected to play important roles in future applications with Fengyun observations.

Radiative transfer models of the Earth’s atmosphere play a critical role in supporting Earth Observation applications such as vicarious calibration. In the solar reflective spectral domain, these models usually account for the scattering and absorption processes in the atmosphere and the underlying surface as well as the radiative coupling between these two media. A range of models is available to the scientific community with built-in capabilities making them easy to operate by a large number of users. These models are usually benchmarked in idealised but often unrealistic conditions such as monochromatic radiation reflected by a Lambertian surface. Four different 1D radiative transfer models are compared in actual usage conditions corresponding to the simulation of satellite observations. Observations acquired by six different space-borne radiometers over the pseudo-invariant calibration site Libya-4 are used to define these conditions. The differences between the models typically vary between 0.5 and 3.5% depending on the spectral region and the shape of the sensor spectral response.

We present results from Phase I of the Continual Intercomparison of Radiation Codes (CIRC), intended as an evolving and regularly updated reference source for evaluation of radiative transfer (RT) codes used in global climate models and other atmospheric applications. CIRC differs from previous intercomparisons in that it relies on an observationally validated catalog of cases. The seven CIRC Phase I baseline cases, five cloud free and two with overcast liquid clouds, are built around observations by the Atmospheric Radiation Measurements program that satisfy the goals of Phase I, namely, to examine RT model performance in realistic, yet not overly complex, atmospheric conditions. Besides the seven baseline cases, additional idealized “subcases” are also employed to facilitate interpretation of model errors. In addition to quantifying individual model performance with respect to reference line‐by‐line calculations, we also highlight RT code behavior for conditions of doubled CO2, issues arising from spectral specification of surface albedo, and the impact of cloud scattering in the thermal infrared. Our analysis suggests that improvements in the calculation of diffuse shortwave flux, shortwave absorption, and shortwave CO2 forcing as well as in the treatment of spectral surface albedo should be considered for many RT codes. On the other hand, longwave calculations are generally in agreement with the reference results. By expanding the range of conditions under which participating codes are tested, future CIRC phases will hopefully allow even more rigorous examination of RT codes. Key Points There is need to continuously evaluate GCM radiation codes Intercomparisons of radiation codes should be based on validated LBL calculation Our intercomparison shows aspects of radiation codes that need improvement

The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m2 m−2 when 5% noise was added compared to inversions with 0% absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m2 m−2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m2 m−2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

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

A high-resolution dual-band terahertz (THz) radiometer was designed to measure vertical distributions of chemical elements in the middle atmosphere of the Tibetan Plateau. A forward simulation, which always should be conducted firstly for the development of a matching retrieval algorithm, has not been done before. We use two radiative transfer models, ARTS and AM, to simulate the water vapor, ozone and carbon monoxide spectra on the plateau based on the spectral design of the THz radiometer. The emission line characteristics of the three gases in this spectral band are identified. Reasons for the differences in the spectral simulations between the two models are analyzed for individual gases. The impact of several different spectral parameter settings on the simulations are evaluated through a series of sensitivity experiments. This study suggests that the ARTS is more suitable for the development of the THz radiometer retrieval algorithm. An optimal parameter setting of the ARTS for the three elements are given.

We provide a complete classification of the radial solutions to a class of reaction diffusion equations arising in the study of thermal structures such as plasmas with thermal equilibrium or no flux at the boundary. In particular, our study includes

We present the Spectroscopic Classification of Astronomical Transients (SCAT) survey, which is dedicated to spectrophotometric observations of transient objects such as supernovae and tidal disruption events. SCAT uses the SuperNova Integral-Field Spectrograph (SNIFS) on the University of Hawai’i 2.2 m (UH2.2m) telescope. SNIFS was designed specifically for accurate transient spectrophotometry, including absolute flux calibration and host-galaxy removal. We describe the data reduction and calibration pipeline including spectral extraction, telluric correction, atmospheric characterization, nightly photometricity, and spectrophotometric precision. We achieve ≲5% spectrophotometry across the full optical wavelength range (3500–9000 Å) under photometric conditions. The inclusion of photometry from the SNIFS multi-filter mosaic imager allows for decent spectrophotometric calibration (10%–20%) even under unfavorable weather/atmospheric conditions. SCAT obtained ≈640 spectra of transients over the first 3 yr of operations, including supernovae of all types, active galactic nuclei, cataclysmic variables, and rare transients such as superluminous supernovae and tidal disruption events. These observations will provide the community with benchmark spectrophotometry to constrain the next generation of hydrodynamic and radiative transfer models.