There are many advantages Himawari-8 (and hence GOES-R) will offer in CI nowcasting. The improved spatial resolution (0.5 km and 2 km for visible and infrared channels, respectively) provides an opportunity to detect developing cumulus clouds in greater detail. Having extra spectral channels (specifically, 1.6, 2.3, 6.2, 6.9, 7.3, 8.5, and 10.4 µm) will help gain better understanding of cloud-top properties within growing cumulus clouds. While the temporal resolution does not seem to make much difference – 15 min for GOES and MSG vs. 10 min for Himawari-8, it provides critical background information on the future use of CI algorithm in GOES-R, especially in cases where cloud development in advance of CI is rapid. As noted, given the similarities between Himawari-8 and GOES-R, this study will provide solid information for CI detection within the GOES-R era.
The areas of interest in this study are within the Himawari-8 coverage: Guam and Japan. Initially, 154 potential satellite-based CI “interest fields” (139 infrared and 15 visible reflectance) are developed, with correlation and principal component analysis then used to distinguish the most unique and relevant “CI fields” when using Himawari-8 data for CI nowcasting. Similar to Mecikalski et al. (2010a, b), three processes during CI are examined: cloud depth and its changes, updraft strength, and cloud-top glaciation. The addition of 2.3 µm channel in Himawari-8 also enables us to evaluate the cloud particle size distribution and its influence in CI nowcasting; the potential application of this process will be discussed in this study. Other aspects of this study include:
1) The utility of improved spatial resolution of Himawari-8 compared to the current GOES series (2 km vs. 4-8 km)
2) The role of added channels – e.g., 2.3, 6.2, 6.9, 7.3, 8.5, and 10.4 µm – in CI nowcasting
3) Comparison of cloud-top properties in tropical (Guam) and mid-latitude (Japan) regions
Thanks to the NOAA Satellite and Information Service (NESDIS) and Japan Meteorological Agency (JMA), we are able to examine the results from this study in the aforementioned regions for improvement in current GOES-R CI algorithm. The importance of this study in future GOES-R applications will also be discussed.