Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part II: Retrieval Study on Daytime Half-Hour Rain Rate

Abstract

To implement continuous and reliable rainfall retrieval, based on the satellite retrieval algorithm of 10-min rain rate, this study proposes an immediate tracking and continuous accumulation technique (ITCAT) of halfhour rainfall retrieval by further combining the cross-correlation method. The ITCAT includes two steps. 1) The cross-correlation method is applied to track cloud-motion currents and establish 10-min-interval image sequences. 2) A continuous retrieval of 10-min rain rates is conducted with the image sequences, and finally a total half-hour rainfall is determined by accumulations. The satellite retrieval tests on the typical precipitation processes in the summer of 2008 show that, compared with the previous direct rainfall retrieval for half-hour to one-hour, this rainfall retrieval technique significantly improves the retrieval accuracy of rainfall scope and rainfall intensity ranging from slight rain to rainstorm for both real-time monitoring or nowcasting processes. This technique is more effective than the previous algorithm, and the fundamental reason lies in its consideration of the movement of cloud clusters. On this basis, coverage duration of rainfall clouds can be reliably estimated. It is of significance to the retrieval of deep convective cloud rainfall with rapid movement speed and drastic intensity variation. This technique also provides a feasible idea for improving the accuracy of rainfall nowcasting.

Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate

“…At present, the commonly used geostationary satellite observation data are basically obtained every half hour, and therefore, the half-hour rainfall retrieval should be achieved based on the assurance of rainfall retrieval accuracy. In part two of this study (Yu et al 2011) the 10-min rainfall retrieval method proposed here will be used to further conduct half-hour satellite rainfall retrieval tests by combining the cross-correlation cloud tracking technique, and a continuous retrieval and nowcasting method for halfhour rainfall will be proposed.…”

“…For example, the threshold combination of IR brightness temperature and effective radius of cloud droplets was used by Rosenfeld and Gutman (1994), and that of VIS albedo and effective radius was used by Ba and Gruber (2001). The twodimensional RPIM determined by the unit-feature-space classification method is a more suitable scheme to identify rainfall areas (Cheng et al 1993;Yu 1998Yu , 2003Wang et al 2008;Behrangi et al 2009;Nauss and Kokhanovsky 2006).…”

“…The recent typical multicurve-fitting scheme was to divide precipitation clouds on satellite cloud images into six categories by means of clustering analysis, and rainfall was fitted with brightness temperature of each type of cloud (e.g., Hong et al 2004). IR-VIS two-dimensional rainfall intensity category matrix based on a large number of statistical samples reveals the statistical fact that the lower the brightness temperature and the greater the albedo is, the greater the rain rate will be, which can be used to distinguish rainfall with different intensities (Yu 1998(Yu , 2003Luque et al 2006;Wang et al 2008). For different VIS albedo, rainfall estimation could also be conducted by fitting the reflectance of 1.38 mm (R 1.38 ), thus forming a dual-spectrum multicurve-fitting algorithm (Yan and Yang 2007).…”

“…Except for the VIS channel providing information of albedo, other channels provide brightness temperatures. Existing studies indicate that the daytime rainfall is most closely related to the IR brightness temperature and VIS albedo (Cheng et al 1993;Hsu et al 1999;Behrangi et al 2009;Ba and Gruber 2001;Yu 1998Yu , 2003Wang et al 2008); and they are finally selected for the retrieval algorithm in this study.…”

“…The results are relatively consistent with Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) rainfall. Yu (1998Yu ( , 2003 and Wang et al (2008) used IR and VIS multispectral information from geostationary meteorological satellites (GMS) and multifunctional transport satellites (MTSAT) to generate two-and three-dimensional rainfall probability and rainfall intensity category matrices, and achieved relatively favorable results on the estimation of rainfall intensity distribution with five grades-namely, no rain, light rain, moderate rain, heavy rain, and rainstorm.…”