The atmospheric radiative transfer calculations are among the most time‐consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL‐based parameterizations. Therefore, we propose a physics‐incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic‐incorporated layer. In addition, we trained and compared various types of DL model architectures, including fully connected (FC) neural networks (NNs), convolutional‐based NNs (CNNs), bidirectional recurrent‐based NNs (RNNs), transformer‐based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer‐based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere.

GNSS radio occultation (RO) measurements are essential to global Earth observation systems. The YUNYAO constellation offers dense global coverage, high vertical resolution, and all-sky capabilities. This study proposes an improved bending-angle observation operator and presents a comprehensive evaluation of YUNYAO’s bending-angle observations against those from COSMIC-2 for December 2024. Our results indicate that YUNYAO achieves near-global coverage and provides substantially more observations than COSMIC-2. Within their region of overlap, the bending-angle quality of YUNYAO is broadly comparable to that of COSMIC-2, though YUNYAO shows a more pronounced negative relative bias above 30 km. In the vertical profile obtained by horizontally averaging the relative bias, YUNYAO exhibits a smaller bias than COSMIC-2 below 30 km. For both RO constellations, bending angles retrieved from Galileo signals exhibit a smaller relative bias than those retrieved from GLONASS in the upper atmosphere. Finally, performance differences are also evident among YUNYAO’s individual receivers.

\justifying The atmospheric radiative transfer calculations are among the most time-consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL-based parameterizations. Therefore, we propose a physics-incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic-incorporated layer. In addition, we trained and compared various types of deep learning model architectures, including fully connected (FC) neural networks (NNs), convolutional-based NNs (CNNs), bidirectional recurrent-based NNs (RNNs), transformer-based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer-based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere.

Ocean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much the atmospheric internal signals contribute to climate variabilities is not yet known. Here, we develop an interactive ensemble coupled model (called Hydra-SINTEX) to investigate the influences of atmospheric internal variations (AIV) on the mean-states and variability of the climate system. The results show that, while the climatological mean-states are little affected, the AIV can largely influence the variabilities of the climate over the globe. We pay particular attention to two regions, i.e., the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that the variabilities of sea surface temperature (SST) in these two regions are much reduced in the absence of the AIV but with distinct mechanisms. Without the AIV, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of the SST variability in the subtropical North Pacific, where local air–sea interaction itself is weak, is caused by the absence of the AIV that is generated by both the mid-latitude atmospheric processes and the weakened remote influence of the tropical SST in accordance with the reduced SST signals there.

The calculations of atmospheric radiative transfer are among the most time-consuming components of the numerical weather prediction (NWP) models. Therefore, using deep learning to achieve fast radiative transfer has become a popular research direction. We propose a physics-incorporated framework for the radiative transfer model training, in which the thermal relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. Based on this framework, we compared various types of neural networks and found that the model structures with global receptive fields are more suitable for the radiative transfer problem, among which the Bi-LSTM model has the best performance.

Land surface temperature (LST) plays a vital role in controlling the water and energy fluxes at the interface between the land and atmosphere, and the main aim of assimilating LST observations into Land Surface Models (LSMs) is to not only provide better initial conditions for the LSM itself, but also yield more accurate land–atmosphere interactions. While observation systems provide a vast amount of satellite-derived LST observations in recent years, they are not as widely used as soil-moisture observations in land data assimilation (DA), in research or in operations, owing to the fast temporally varying nature of LST. To effectively improve the impact of LST assimilation, this study proposes a new scheme by jointly updating the soil temperature and soil moisture within the upper surface layers. The DA method and LSM used in this study are the Local Ensemble Transform Kalman Filter (LETKF) and the Common Land Model (CoLM), respectively. Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST is assimilated into CoLM every 3 hours using the proposed scheme. The assimilation and open-loop experiments are conducted for one year with a global resolution of 0.5° × 0.5°. The LST shows marginal enhancement after assimilation, owing to its fast-varying nature dominated by atmospheric forcings. However, the BIAS in soil temperature over Northeast Asia is reduced significantly, with a magnitude of 1.0 K, 1.5 K, and 2.0 K for the layers within 0–10 cm, 40–100 cm, and 100–200 cm, respectively. Prominent improvements in snow temperature and snow depth are observed over Northeast Asia, with a reduction in root mean square difference (RMSD) of approximately 4 K and 150 mm, respectively. The improvements in soil water content are also notable, particularly over humid tropical regions. The largest reductions in unbiased RMSD of soil water content over the Amazon Rainforest are approximately 0.06, 0.12, 0.15, and 6.00 kg/m² for the layers within 0–10 cm, 10–0 cm, 40–100 cm, and 100–200 cm, respectively. These consistent improvements in both the energy and water components of CoLM demonstrate the effectiveness of the proposed scheme and the importance of LST assimilation for land-surface-process modeling.