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STAR Satellite Rainfall Estimates
Validation Algorithms

Since April 2001, personnel at the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Resarch (STAR) have been archiving data from six different algorithms from estimating precipitation using data from the Geostationary Operational Environmental Satellites (GOES):

  • Operational Auto-Estimator (AE):

    The AE was originally developed by Vicente et al. (1998) as an automation of its semi-manual predecessor, the Interactive Flash Flood Forecaster (Scofield 1987; Borneman 1988). It is based on a radar-calibrated relationship between 10.7-micron (T10.7) brightness temperature and rainfall rate. Time changes in T10.7 are used to identify nonraining cirrus clouds, while adjustments are made for subcloud evaporation (using the product of the precipitable water (PW) in inches and the low-level relative humidity (RH) expressed as a decimal fraction) and for warm-top convection (by adjusting T10.7 according to the Eta-derived convective equilibrium level (EL) prior to rain rate computation). Adjustments for parallax and for orographic effects on precipitation have been added to the algorithm (Vicente et al. 2002), along with a correction for limb effects on brightness temperature using the scheme of Joyce et al. (2001). Because of the tendency of the AE to incorrectly identify cirrus shields as raining clouds, a screen that uses radar data to identify nonraining clouds has also been added.

  • Hydro-Estimator (HE):

    The Hydro-Estimator (Kuligowski et al. 2003) was developed in response to the need to eliminate the dependence on radar that the AE had. This was done by considering the conditions of the surrounding pixels, rather than only of the pixel itself, when determining the presence and rate of rain. In the former case, pixels with a brightness temperature above the regional average are considered to be nonraining. Other changes include separate PW and RH adjustments and a scheme for adjusting the rain rate curves based on the condition of surrounding pixels. The HE has shown a substantially improved ability to differentiate raining clouds from cirrus compared to the AE, but performs similarly to the AE when the AE uses a radar screen.

  • Hydro-Estimator with Radar (HE-R):

    The HE is run with a radar screen similar to the HE in order to demonstrate the mitigated impact of the radar screen on the HE relative to that on the HE.

  • Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR):

    SCaMPR (Kuligowski 2002) is an effort to combine the higher accuracy of microwave rainfall estimates (relative to IR) with the more frequent availability and higher spatial resolution of IR estimates. This is done by using microwave rainfall estimates from the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU) to calibrate an algorithm that uses GOES IR data and derived parameters as input. Calibrations are performed for both rain/no rain separation (using discriminant analysis) and rainfall rate (using multiple linear regression).

  • GOES Multi-Spectral Rainfall Algorithm (GMSRA):

    Unlike the AE and HE, the GMSRA (Ba and Gruber 2001) uses data from all five GOES channels to produce rainfall estimates. The visible data and the difference between T10.7 and T6.9 are used to differentiate cirrus from raining clouds, while a combination of T3.9, T10.7, and T12.0 are used during the daytime to retrieve estimates of cloud particle size according to the algorithm of Rosenfeld and Gutman (1994). Separate probability of precipitation and conditional rain rates have been related to brightness temperature via calibration with radar data, and separate calibrations are used for different portions of the country. The PW*RH adjustment of the AE is also applied to the GMSRA. A nighttime rain/no rain screen that is analogous to the 3.9-micron correction that was developed by Rosenfeld and Lensky (1998) has been applied to the GMSRA.

  • IR/Microwave Blended Algorithm (Blend):

    Contains many features of the AE, but instead of a fixed relationship between IR brightness temperature and rain rate, uses microwave-based rain rates from the Special Sensor Microwave/Imager (SSM/I), Advanced Microwave Sounding Unit (AMSU), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) to perform a real-time recalibration of the brightness temperature- rain rate relationship (Turk et al. 1998).


Ba, M. B., and A. Gruber, 2001: GOES multi-spectral rainfall algorithm (GMSRA). J. Appl. Meteor., 40, 1500-1514.

Borneman, R., 1988: Satellite rainfall estimation program of the NOAA/NESDIS synoptic analysis branch. Natl. Wea Dig., 13, 7-15.

Joyce, R. J., J. Janowiak, and G. Huffman, 2001: Latitudinally and seasonally dependent zenith-angle corrections for geostationary satellite IR brightness temperatures. J. Appl. Meteor., 40, 689-703.

Kuligowski, R. J., 2002: A self-calibrating GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.

Kuligowski, R. J., J. C. Davenport, and R. A. Scofield, 2003: The Hydro-Estimator technique for high-resolution geostationary satellite rainfall estimates. Submitted to Mon. Wea. Rev..

Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multi spectral analysis of AVHRR data. Atmos. Res., 34, 259-283.

Rosenfeld, D., and I. Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bull. Amer. Meteor. Soc., 79, 2457-2476.

Scofield, R. A., 1987: The NESDIS operational convective precipitation technique. Mon. Wea. Rev., 115, 1773-1792.

Turk, F. J., F. S. Marzano, and E. A. Smith, 1998: Combining geostationary and SSM/I data for rapid rain rate estimation and accumulation. Preprints, Seventh Conf. on Satellite Meteorology and Oceanography, Monterey, CA, Amer. Meteor. Soc., 34-37.

Vicente, G. A., J. C. Davenport, and R. A. Scofield, 2002: The role of orographic and parallax corrections on real time high resolution satellite rainfall estimation. Int. J. Remote Sens., 23, 221-230.

Vicente, G. A., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898.