Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net longwave radiation. Its estimation in cloudy conditions has always been a challenge due to lack of instrumentation and regular measurements at different spatial scales. In this study, two artificial neural network (ANN) multi-layer perceptron (MLP) models were developed for LWin flux estimation under cloudy-sky during daytime and nighttime using half-hourly flux measurements over different agro-climatic settings and several atmospheric parameters from measurements, satellite-based observations, and model outputs. A comparative evaluation was made between existing or newly developed multivariate linear regression (MVR) models and ANN-based models. The latter set of models were found to be superior to the best MVR model during both daytime and nighttime. The ANN models were found to have consistent performance across different sites and cloud types except less accuracy in sub-humid or humid climate and in deep convection cloud. The ANN models showed overall accuracies of 2.7% and 3.3% of measured mean and R2 of 0.86 and 0.85 for daytime and nighttime, respectively, when compared with independent data of in-situ measurements.

中文翻译：

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基于神经网络的多云条件下下行长波辐射通量估计方法

净地表辐射定义了地表上和附近的辐射能的可用性，以驱动许多物理和生理过程，例如潜热、显热通量和蒸散。模拟辐射预算的主要挑战之一是估计净长波辐射。传入或下行长波辐射 (LWin) 通量是净长波辐射的两个关键组成部分之一。由于缺乏仪器和不同空间尺度的定期测量，它在多云条件下的估计一直是一个挑战。在这项研究中，开发了两个人工神经网络 (ANN) 多层感知器 (MLP) 模型，用于白天和夜间多云天空下的 LWin 通量估计，使用不同农业气候设置下的半小时通量测量以及来自测量、卫星-基于观察和模型输出。对现有或新开发的多元线性回归 (MVR) 模型和基于 ANN 的模型进行了比较评估。发现后一组模型在白天和夜间都优于最佳 MVR 模型。发现 ANN 模型在不同地点和云类型之间具有一致的性能，但在半湿润或潮湿气候和深对流云中精度较低。ANN 模型显示总体准确度为测量平均值的 2.7% 和 3.3%，R2 为 0.86 和 0。