# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 6 Conclusions  
To solve the precipitation detection problem for FY-3C/D MWTS2, this paper simulates the nonlinear relationships between VISSR cloud products and AMSU-A SIs based on the random forest algorithm. By using GEO-LEO satellite pixel fusion technology, along with the introduction of data preprocessing techniques and model hyperparameter adjustments, this study proposes a novel ocean-surface precipitation detection approach called _RF_SI_ by using four data products derived from the FY-2F VISSR (cloud classification, cloud-top temperature, humidity profile, and water vapor content of the upper troposphere) as input features and NOAA-19 AMSU-A _SI_ values as the learning labels. The precipitation detection performance of the _RF_SI_ method is evaluated through single-case and long-term series observations, which leads us to the following conclusions.  
1. Data augmentation and downsampling techniques are employed to transform the given imbalanced dataset into a balanced dataset, thereby ensuring a high degree of model fit. An evaluation of the machine learning algorithm reveals that the ranking of the sensitivities of the input features to the particle scattering of microwave spectrum hydrometeors is highly consistent with the existing meteorological knowledge.  
Figure 17: Time series of (**a**) the retained pixel numbers and (**b**) RMSEs of O-B for channels 6–9 after performing AMSU-A precipitation detection via the _SI_ and _RF_SI_ methods from 06:00 UTC on June 6 to 18:00 UTC on 8 June 2017.  
Similar to the \(SI\) and \(CLWP\), \(RF\_SI\) is associated with deep convective cloud regions and therefore is a good indicator of the horizontal distribution of microwave scattering.
3. In the time series analysis, the precision of \(RF\_SI\) is stable, with little change observed over time. Compared with those of the NOAA-19 AMSU-A-based traditional \(SI\) and \(CLWP\) precipitation detection algorithms, the accuracy and detection rates of the \(RF\_SI\) method exceed 94% and 92%, respectively, and the error rate is less than 3%.  
In future work, high-resolution \(RF\_SI\) precipitation data obtained through simulations can be matched with FY-3C/3D MWTS2 pixels to address the rapid precipitation detection problem encountered in the WRFDA data assimilation system.  
Conceptualization, T.L. and Y.Y.; methodology, G.M.; writing--original draft preparation, T.L.; writing--review and editing, W.Z., L.Q., W.S., Q.D. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the National Key Research and Development Program of China (2021 YFC3101500), the National Natural Science Foundation of China (Grants 42075149, 42405161, 62372460, and 42430607), and the Postdoctoral Fellowship Program of CPSF (GZC20233526).  
The satellite dataset is available at [[http://www.nsmc.org.cn/NSMC/Home/Index.html](http://www.nsmc.org.cn/NSMC/Home/Index.html)]([http://www.nsmc.org.cn/NSMC/Home/Index.html](http://www.nsmc.org.cn/NSMC/Home/Index.html)) (accessed on 14 April 2025).  
The utilized AMSU-A, MWTS, and VISSR cloud data were obtained from the National Satellite Meteorological Centre of China ([[http://www.nsmc.org.cn/NSMC/Home/Index.html](http://www.nsmc.org.cn/NSMC/Home/Index.html)]([http://www.nsmc.org.cn/NSMC/Home/Index.html](http://www.nsmc.org.cn/NSMC/Home/Index.html))), and their support is acknowledged.  
The authors declare no conflicts of interest.