Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias correction and spatial downscaling are fundamental components to develop operational weather forecast systems, as they seek to improve the consistency between coarse-resolution climate model simulations or satellite-based estimates and ground-based observations. In recent years, deep learning-based models have been increasingly replaced traditional statistical methods to generate high-resolution, bias free projections of climate variables. For example, Max-Average U-Net (MAUNet) architecture has been demonstrated for its ability to downscale precipitation estimates. The versatility and adaptability of these neural models make them highly effective across a range of applications, though this often come at the cost of high computational and memory requirements. The aim of this research is to develop light-weight neural network architectures for both bias correction and downscaling of precipitation, for which the teacher-student based learning paradigm is explored. This research demonstrates the adaptability of MAUNet to the task of bias correction, and further introduces a compact, lightweight neural network architecture termed MAUNet-Light.The proposed MAUNet-Light model is developed by transferring knowledge from the trained MAUNet, and it is designed to perform both downscaling and bias correction with reduced computational requirements without any significant loss in accuracy compared to state-of-the-art.

翻译：卫星反演数据产品和气候模型对降水等地球物理变量的模拟结果，相较于现场观测数据常存在系统性偏差。偏差校正与空间降尺度是发展业务化天气预报系统的基础环节，旨在提升粗分辨率气候模型模拟或卫星估计与地面观测之间的一致性。近年来，基于深度学习的模型已逐步取代传统统计方法，用于生成高分辨率、无偏差的气候变量预估。例如，最大平均U-Net（MAUNet）架构已被证实具备对降水估计进行降尺度的能力。这类神经模型的通用性与适应性使其在多种应用中表现卓越，但往往伴随着较高的计算与内存需求。本研究旨在开发轻量级神经网络架构，以同时实现降水的偏差校正与降尺度，并探索基于师生范式的学习方法。研究论证了MAUNet在偏差校正任务中的适应性，并进一步提出一种紧凑型轻量级神经网络架构，命名为MAUNet-Light。所提出的MAUNet-Light模型通过从训练好的MAUNet迁移知识而构建，其设计目标是在保持与前沿方法相当精度的前提下，以更低计算需求同时完成降尺度与偏差校正。