The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form of radiative transfer models. Significant efforts have been dedicated to enhancing the computational efficiency of these models. Computational cost remains a bottleneck, and a large fraction of available data goes unused for assimilation. To address this, we used machine learning to build an efficient neural network based probabilistic emulator of the Community Radiative Transfer Model (CRTM), applied to the GOES Advanced Baseline Imager. The trained NN emulator predicts brightness temperatures output by CRTM and the corresponding error with respect to CRTM. RMSE of the predicted brightness temperature is 0.3 K averaged across all channels. For clear sky conditions, the RMSE is less than 0.1 K for 9 out of 10 infrared channels. The error predictions are generally reliable across a wide range of conditions. Explainable AI methods demonstrate that the trained emulator reproduces the relevant physics, increasing confidence that the model will perform well when presented with new data.

Intelligence lies not only in the brain but in the body. The shape of our bodies can influence how we think and interact with the physical world. In robotics research, interacting with the physical world is crucial as it allows robots to manipulate objects in various real-life scenarios. Conventional robotic manipulation strategies mainly rely on finger-shaped end effectors. However, achieving stable grasps on fragile, deformable, irregularly shaped, or slippery objects is challenging due to difficulties in establishing stable force or geometric constraints. Here, we present surface-based manipulation strategies that diverge from classical grasping approaches, using with flat surfaces as minimalist end-effectors. By changing the position and orientation of these surfaces, objects can be translated, rotated and even flipped across the surface using closed-loop control strategies. Since this method does not rely on stable grasp, it can adapt to objects of various shapes, sizes, and stiffness levels, even enabling the manipulation the shape of deformable objects. Our results provide a new perspective for solving complex manipulation problems.