GOES Multispectral Rainfall Algorithm (GMSRA)

Abstract

A multispectral approach is used to optimize the identification of raining clouds located at a given altitude estimated from the cloud-top temperature. The approach combines information from five channels on the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellite (GOES): visible (0.65 ?m), near infrared (3.9 ?m), water vapor (6.7 ?m), and window channels (11 and 12 ?m). The screening of nonraining clouds includes the use of spatial gradient of cloud-top temperature for cirrus clouds (this screening is applied at all times) and the effective radius of cloud-top particles derived from the measurements at 3.9 ?m during daytime. During nighttime, only clouds colder than 230 K are considered for the screening; during daytime, all clouds having a visible reflectance greater than 0.40 are considered for the screening, and a threshold of 15 ?m in droplet effective radius is used as a low boundary of raining clouds. A GOES rain rate for each indicated raining cloud group referenced by its cloud-top temperature is obtained by the product of probability of rain (Pb) and mean rain rate (RRmean) and is adjusted by a moisture factor that is designed to modulate the evaporation effects on rain below cloud base for different moisture environments. The calibration of the algorithm for constants Pb and RRmean is obtained using collocated instantaneous satellite and radar data and hourly gauge-adjusted radar products collected during 17 days in June and July 1998. A comparison of the combined visible and a temperature threshold of 230 K (e.g., previous infrared/visible algorithms) with the combined visible and a threshold of 15 ?m demonstrates that the latter improves the detection of rain from warm clouds without lowering the skill of the algorithm. The quantitative validation shows that the algorithm performs well at daily and monthly scales. At monthly scales, a comparison with GOES Precipitation Index (GPI) shows that GOES Multispectral Rainfall Algorithm's performance against gauges is much better for September and October 1999.