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High‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m (T2m$$ {T}_{2\mathrm{m}} $$) and wind speed at 10 m (WS10m$$ W{S}_{10\mathrm{m}} $$) over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance. [ABSTRACT FROM AUTHOR]

Developing non‐noble metal catalysts for the oxygen evolution reaction (OER) is one of the main challenges in the production of hydrogen energy by water splitting reaction. Herein, taking advantage of the fact that microorganisms have high element contents and metabolic activities, we used them to recycle Ni and Fe from electroplating sludge. Based on the above, we designed a highly efficient oxygen evolution catalyst (Fe‐Ni2P@NPC) through the in situ synthesis of Fe‐doped Ni2P nanoparticles and N, P dual‐doped carbon materials coupled together. The material exhibited excellent catalytic activity, with a low overpotential of 282 mV at the current density of 10 mA cm−2 along with a fast reaction kinetics, with a small Tafel slope of 57.1 mV dec−1. This outstanding OER performance is mainly attributed to the in situ doping of Fe in Ni2P nanoparticles, resulting in a strong synergistic effect between them. Besides, the strong coupling interaction between Fe‐Ni2P and Fe‐NiOOH formed during the OER can adjust the electron distribution and optimize the adsorption capacity of the intermediates. This work provides a reference for the recycling of valuable Ni and Fe metals from electroplating sludge, as well as new insight into the preparation of low‐cost and high‐performance OER catalysts. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Numerical weather prediction models require a lot of computational resources and time to run. Calculating the atmospheric radiative transfer processes is one of the most computationally expensive parts of the NWP model. One alternative is to model the radiative transfer using deep learning (DL) models, but the DL models do not involve physical laws and may have physically inconsistent outputs. This paper proposes a DL model framework to ensure the thermal equilibrium between fluxes and heating rates, which are outputs of radiative transfer models. Also, the accuracy of DL‐based radiative transfer prediction is improved when using the framework. Various DL models have been trained and compared. The results demonstrate that model structures with global receptive fields work best for emulating radiative transfer calculations. Key Points: A physics‐incorporated deep learning (DL) framework for parameterization of atmospheric radiative transfer is proposedThe DL model structures with global receptive fields are more suitable for the radiative transfer problem [ABSTRACT FROM AUTHOR]