The present invention provides a method for inverting cloud / in-cloud clearance based on daily-scale
nitrate wet deposition, which belongs to the field of
remote sensing atmospheric environment applications. Based on
satellite data combined with a backward trajectory model,
pollutant concentration parameters in the cloud before a
precipitation event are calculated to quantify the long-range
pollution transport effect; a
random forest machine learning model is constructed based on multi-
source data such as
satellite observations and ground observations, integrating meteorological factors,
pollutant concentration parameters, cloud attribute parameters, socioeconomic factors, and topographic factors to estimate daily-
scale deposition; a cross-validation optimization model is used and bias correction is used to improve spatial generalization ability; the contribution of each factor is analyzed, and daily-scale wet deposition flux gridded data is output. The present invention adopts the above-mentioned inverting method for cloud / in-cloud clearance based on daily-scale
nitrate wet deposition, and for the first time achieves daily-resolution wet deposition
estimation, accurately captures the short-term deposition dynamics of heavy
precipitation events in summer, and quantifies the contribution of cloud clearance effects to clean areas.