GOES Multispectral Rainfall Algorithm (GMSRA)
Open Access
- 1 August 2001
- journal article
- Published by American Meteorological Society in Journal of Applied Meteorology and Climatology
- Vol. 40 (8), 1500-1514
- https://doi.org/10.1175/1520-0450(2001)040<1500:gmrag>2.0.co;2
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 μm), near infrared (3.9 μm), water vapor (6.7 μm), and window channels (11 and 12 μm). 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 μm 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 μm 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 μm 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.Keywords
This publication has 26 references indexed in Scilit:
- Satellite techniques yield insight into devastating rainfall from Hurricane MitchEos, 1999
- Validation and Intercomparison of SSM/I Rain-Rate Retrieval Methods over the Continental United StatesJournal of Applied Meteorology and Climatology, 1998
- Special sensor microwave imager derived global rainfall estimates for climatological applicationsJournal of Geophysical Research: Solid Earth, 1997
- Estimation of Precipitation Area and Rain Intensity Based on the Microphysical Properties Retrieved from NOAA AVHRR DataJournal of Applied Meteorology and Climatology, 1997
- Assessment of Rainfall Estimates Using a StandardZ-RRelationship and the Probability Matching Method Applied to Composite Radar Data in Central FloridaJournal of Applied Meteorology and Climatology, 1996
- Satellite-Derived Interannual Variability of West African Rainfall during 1983?88Journal of Applied Meteorology and Climatology, 1995
- Retrieval of precipitation from satellite microwave measurement using both emission and scatteringJournal of Geophysical Research: Solid Earth, 1992
- A Satellite Infrared Technique to Estimate Tropical Convective and Stratiform RainfallJournal of Applied Meteorology and Climatology, 1988
- The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-84Monthly Weather Review, 1987
- Retrieval of Cloud Cover Parameters from Multispectral Satellite ImagesJournal of Climate and Applied Meteorology, 1985