Comparison of Cloud Properties from Himawari-8 and FengYun-4A Geostationary Satellite Radiometers with MODIS Cloud Retrievals

Abstract

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration.

Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm

“…Generally, very strong winds, as well as the widespread of mixed-phase clouds, such as those in dataset 5 (Figure 8e,f), for instance, can lead to values of POD that are significantly smaller than the values that are seen under the conditions of homogeneous clouds (e.g., dataset 1). The lower POD of mixed-phase clouds, as compared to homogeneous clouds, is also found in another study [29].…”

“…This is exemplified in Figure 7d,e, in that the total percentage of the cloud pixels is 60% for Terra, which is higher than the value of 54% for AHI (dataset #9). This difference is ascribable to the fact that the MODIS sensor is more affected by sun glint, especially in the summer season, as also indicated in a previous study on the MODIS cloud mask [8,29]. Table 5 is the summary of the results for all the datasets, whichshowed that the PODs of cloud mask (columns 4 and 5) are more than 80% on average.…”

“…Additionally, Yamamoto et al [26] built a cloud detection method, which the JMA cloud mask product verified. The remaining problem with these previous researches is that the use of a visible band is indispensable for constructing the cloud mask [25][26][27][28][29][30] and, hence, the analysis has so far been limited to the daytime data. Nighttime cloud masks, on the other hand, have been provided in the MODIS standard product (MYD35/MOD35) [5,31] as well as in other studies, including the polar region [31][32][33][34], although all of these studies did not yield products with high temporal coverage, as seen in Himawari-8 AHI.…”

“…Nighttime cloud masks, on the other hand, have been provided in the MODIS standard product (MYD35/MOD35) [5,31] as well as in other studies, including the polar region [31][32][33][34], although all of these studies did not yield products with high temporal coverage, as seen in Himawari-8 AHI. Recently, Lai et al [29] reported on the study of cloud properties while using the data from AHI, FengYun-4A, and MODIS, but they only treated daytime images. In this context, it is desirable to introduce a method that enables both daytime and nighttime cloud detection while using the very high temporal data of Himawari-8.…”

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

“…Such a R ei discrepancy can be caused by the known large biases in model R ei as well as the uncertainty in satellite retrievals (Lai et al, 2019). MODIS cannot clearly distinguish ice and snow defined in the model, as long as they are floated near the cloud top.…”