Satellite rainfall retrieval algorithms that use infrared (IR) data typically lack sensitivity to rainfall from relatively warm cloud tops (e.g., above 240 K) because these algorithms are typically designed for detecting deep convection rather than shallow convection or stratiform rainfall. Additional information is needed to address these warm-cloud events, which form an important component of precipitation climatology in many parts of the world. It is known that precipitation processes in clouds with warm tops are very sensitive to the microphysical structure of their tops. Specifically, precipitation processes are more efficient when water droplets or/and ice particles grow to larger sizes. It has also been observed that clouds with small droplets scatter and reflect much of the incoming solar radiation in the near-infrared region (3.7-4.0 microns) while clouds with large particle size are less reflective at this wavelength. This allows the effective radius of cloud-top particles to be derived and then used to determine whether or not a given warm-topped cloud is producing rainfall.

As an extension of previous work in this area, an algorithm for rainy cloud detection over Puerto Rico is being developed based on the GOES-12 near infrared band (3.9 microns). The algorithm estimates the reflectance, which can be used to estimate the effective radius of droplets and thus identify warm-top rainy clouds that would be missed by a longwave IR-only algorithm. The performance of this algorithm, which is limited to daytime applications, will be described and discussed.