# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
## 4 Results
### Comparison Between FY-3F Retrieval Results and ERA5 Data  
To assess the discrepancies between IWP retrievals from FY-3F satellite data and ERA5 reanalysis data, and to examine their specific characteristics within the cyclone region, this study focuses on retrieval results on 1 October 2024, when Super Ty  
Figure 5: Time series of IWP variations in oceanic regions across different latitude bands: **(top)** 30\({}^{\circ}\)N–60\({}^{\circ}\)N, **(middle)** 30\({}^{\circ}\)N–30\({}^{\circ}\)S, and **(bottom)** 30\({}^{\circ}\)S–60\({}^{\circ}\)S. The blue lines and shaded areas represent daily mean values and standard deviations derived from FY-3F observational data, the orange lines and shaded areas correspond to values from METOP-C observational data, and the green lines and shaded areas represent corresponding values from ERA5 reanalysis data. its peak intensity over the Luzon Strait. Bilinear interpolation was used for spatiotemporal matching between the 24-hourly ERA5 reanalysis data and the FY-3F data for the day, resulting in the global IWP distribution shown in Figure 4. To minimize errors caused by excessively high latitudes during the IWP retrieval process, the analysis was limited to regions between 60\({}^{\circ}\)N and 60\({}^{\circ}\)S.  
Figure 6 illustrates that the IWP retrieved using FY-3F data exhibits a high degree of consistency with ERA5 in spatial distribution. However, ERA5 data contains a larger proportion of low IWP values. This discrepancy can be attributed to the resolution differences: ERA5 has a spatial resolution of 25 km, whereas the retrieval algorithm employed in this study offers higher resolution. Additionally, it may result from the physical processes representing clouds and precipitation in numerical models, which include grid-scale cloud microphysics parameterization and sub-grid-scale convective parameterization schemes. Notably, the convective parameterization schemes for cumulus convection do not explicitly represent ice clouds in terms of water content, as these processes manifest primarily as precipitation. Consequently, ERA5's ice cloud content excludes ice clouds formed through sub-grid-scale convective processes. In contrast, the retrieval algorithm used in this study captures IWP information more accurately on a global scale. [PERSON] et al. noted that ERA5 struggles to effectively simulate atmospheric ice conditions in mid-to-high latitudes, underestimating high IWP values and overestimating low IWP values compared to satellite observations [23]. These findings align with the observations from this study's retrieval products, supporting the validity of using FY-3F data for IWP retrieval. Moreover, the higher spatial resolution of FY-3F data allows for more detailed information, making it a valuable tool for global IWP studies. Figure 6: Global distribution of IWP on October 01, 2024: **(a)** IWP retrieved from FY-3F observation data; **(b)** IWP from ERA5 reanalysis dataset. Figure 7 presents the IWP retrieved from FY-3F satellite data on 1 October 2024, in both tropical and extratropical cyclones, alongside the corresponding IWP data from ERA5. The results demonstrate that the spatial distribution of IWP retrieved from FY-3F data closely aligns with ERA5 data in both regions. Moreover, the FY-3F retrievals provide higher spatial resolution, particularly in capturing fine-scale typhoon structures, enabling more detailed and accurate observations of IWP. These findings indicate that the retrieval method used in this study not only effectively retrieves high-resolution global IWP information but also captures finer details in localized regions. To compare retrieval results across different latitudes, this study analyzed marine region data from 1 October 2024 and compared the results with ERA5 cloud ice data to examine the IWP distribution across various latitudes. Probability density distribution maps and cumulative distribution function curves were generated.