Abstract. Precipitable water vapor (PWV) is a key component of Earth’s climate and hydrological systems, yet its accurate and continuous observation under varying sky conditions remains challenging. This study demonstrates the strong potential of surface-based spectral irradiance measurements for PWV retrieval across a range of atmospheric conditions using deep neural network (DNN) models trained on water vapor absorption bands. Global, direct, and diffuse spectral irradiances observed at water vapor absorption bands of 929.0–997.3 nm, 800.9–840.5 nm, and 708.1–744.6 nm by a spectroradiometer (MS-700; EKO Instruments Co., Ltd., Japan) equipped with a rotating shadow-band system were used as test data, while PWV observed by a microwave radiometer (MP-1500; Radiometrics Corporation, USA) served as reference data for model training and validation. Models incorporating global, direct, and diffuse irradiances achieved the highest accuracy, exhibiting minimal errors and closely capturing seasonal PWV variations. Notably, even models using only global irradiance—an easier and more accessible measurement—maintained high predictive performance, with low errors and robust seasonal tracking. In contrast, models trained solely on clear-sky direct irradiance with limited data showed relatively higher errors and weaker generalization, underscoring the importance of data volume and diversity in DNN models. These results highlight the effectiveness of spectral irradiance-based approaches for continuous PWV estimation across a range of atmospheric conditions. Future research should incorporate additional spectral bands sensitive to constituents like aerosols and ozone to expand retrieval capability.
Khatri et al. (Fri,) studied this question.
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