The FengYun (FY)-3G satellite is China’s first meteorological satellite in a low-inclination orbit, and the medium resolution spectral imager-rainfall mission (MERSI-RM) is one of its primary payloads. Due to the unique overpass times of FY-3G compared to most polar-orbiting meteorological satellites, such as FY-3D and Terra, the precipitable water vapor (PWV) data derived from FY-3G/MERSI-RM is critical for studies on weather systems and climate change. However, there is currently a lack of accessible MERSI-RM PWV data. To address this issue, we develop a semi-empirical PWV retrieval algorithm for the near-infrared (NIR) channels at 0.865 and 0.940 μm from FY-3G/MERSI-RM.

The relationship between the natural logarithm of the water vapor absorption transmittance (WVAT) in the NIR water vapor absorption (WVA) channel and the slant column water vapor content along the sun-earth-satellite path is closely correlated and can be expressed by a quadratic equation. The NIR PWV retrieval model for MERSI-RM is established based on this correlation. Initially, average ground-based PWV data from the Aerosol Robotic Network (AERONET) obtained within a 30-min window of satellite transit are matched with the average MERSI-RM data within a 10 km×10 km area centered on the ground stations. Subsequently, the three coefficients of the quadratic equation are solved based on these matching results, completing the construction of the MERSI-RM PWV retrieval model. To ensure that AERONET PWV data can be used both for establishing the PWV retrieval model and for the quality assessment of the retrieval results, the matching data are divided into two independent sets based on the locations of the ground stations: data from the eastern hemisphere are used to construct the MERSI-RM PWV retrieval model, while data from the western hemisphere are used to validate the MERSI-RM PWV retrieval results.

Validation results using ground-based data show that the root mean square error (RMSE) and relative error (RE) of MERSI-RM PWV data, developed using the semi-empirical algorithm, are 0.20 cm and 0.10, respectively. In contrast, the RMSE and RE of MERSI-RM PWV data, developed using the traditional retrieval algorithm based on a radiative transfer model, are 0.35 cm and 0.15, respectively. Meanwhile, the RMSE and RE of MODIS PWV data are 0.57 cm and 0.39, respectively. Compared to MODIS PWV data, MERSI-RM PWV data, developed based on the semi-empirical algorithm, exhibit a 65% reduction in absolute error and a 74% reduction in relative error. Given that MODIS PWV data are widely acknowledged for their high accuracy, it can be concluded that the MERSI-RM PWV data developed using the semi-empirical algorithm also exhibit high accuracy. In comparison to MERSI-RM PWV data developed using the retrieval algorithm based on a radiative transfer model, the absolute error of the MERSI-RM PWV data derived using the semi-empirical algorithm is reduced by 43%, while the relative error is reduced by 33%. The lower accuracy observed in MERSI-RM PWV data and MODIS PWV data developed based on the radiative transfer model is primarily attributed to noticeable systematic errors. In contrast, the MERSI-RM PWV data obtained using the semi-empirical algorithm do not exhibit this issue. The success of the semi-empirical algorithm is attributed to its PWV retrieval model, which is constructed based on matching results between satellite observations and ground-based data. In other words, the errors in satellite observations are considered in the retrieval model. To provide a more comprehensive evaluation of the semi-empirical algorithm and offer additional choices for model construction methods, we also assess the PWV retrieval model constructed based on randomly allocated data. Validation results based on ground-based data show that the retrieval accuracy of the model constructed using randomly allocated data is equivalent to that of the retrieval model constructed using data obtained from the eastern hemisphere.

We introduce a semi-empirical PWV retrieval algorithm tailored specifically for FY-3G/MERSI-RM. This algorithm effectively tackles the current challenge of unavailable PWV data from FY-3G satellite observations. It does not rely on complex radiative transfer models but instead utilizes a quadratic equation, resulting in remarkably efficient PWV retrieval. Compared to traditional methods based on radiative transfer models, this semi-empirical approach achieves notably higher retrieval accuracy. The errors in MERSI-RM PWV data, obtained using the algorithm, are reduced by at least 33% compared to those derived from models based on radiative transfer. Moreover, when contrasted with the widely utilized MODIS official PWV data (MOD05), this semi-empirical algorithm diminishes errors in MERSI-RM PWV data by a minimum of 65%. These results underscore the high accuracy and efficiency of the semi-empirical PWV retrieval algorithm for MERSI-RM, making it suitable for large-scale PWV data development.