The Operational GOES Infrared Rainfall Estimation Technique

Abstract

This paper presents a description, sensitivity analyses, sample results, validation, and the recent progress done on the development of a new satellite rainfall estimation technique in the National Environmental Satellite Data and Information Service (NESDIS) at the National Oceanic and Atmospheric Administration (NO A A). The technique, called the auto-estimator, runs in real time for applications to flash flood forecasting, numerical modeling, and operational hydrol-ogy. The auto-estimator uses the Geoestationary Operational Environmental Satellite-8 and-9 in the infrared (IR) 10.7-^m band to compute real-time precipitation amounts based on a power-law regression algorithm. This regression is derived from a statistical analysis between surface radar-derived instantaneous rainfall estimates and satellite-derived IR cloud-top temperatures collocated in time and space. The rainfall rate estimates are adjusted for different moisture regimes using the most recent fields of precipitable water and relative humidity generated by the National Centers for Environmental Prediction Eta Model. In addition, a mask is computed to restrict rain to regions satisfying two criteria: (a) the growth rate of the cloud as a function of the temperature change of the cloud tops in two consecutive IR images must be positive, and (b) the spatial gradients of the cloud-top temperature field must show distinct and isolated cold cores in the cloud-top surface. Both the growth rate and the gradient corrections are useful for locating heavy precipitation cores. The auto-estimator has been used experimentally for almost 3 yr to provide real-time instantaneous rainfall rate estimates , average hourly estimates, and 3-, 6-, and 24-h accumulations over the conterminous 48 United States and nearby ocean areas. The NOAA/NESDIS Satellite Analyses Branch (SAB) has examined the accuracy of the rainfall estimates daily for a variety of storm systems. They have determined that the algorithm produces useful 1-6-h estimates for flash flood monitoring but exaggerates the area of precipitation causing overestimation of 24-h rainfall total associated with slow-moving, cold-topped mesoscale convective systems. The SAB analyses have also shown a tendency for underestimation of rainfall rates in warm-top stratiform cloud systems. Until further improvements, the use of this technique for stratiform events should be considered with caution. The authors validate the hourly rainfall rates of the auto-estimator using gauge-adjusted radar precipitation products (with radar bias removed) in three distinct cases. Results show that the auto-estimator has modest skill at 1-h time resolution for a spatial resolution of 12 km. Results improve with larger grid sizes (48 by 48 km or larger).

Climatological Behavior of Precipitating Clouds in the Northeast Region of Brazil

“…The different techniques using visible and infrared spectra enable analyzing only the cloud tops (brightness and temperature) and thus are a limiting factor for precipitation estimates [24]. References [25,26] found that techniques using infrared and passive microwave radiation are not very effective for estimate precipitation in NEB, in view of the predominance of shallow clouds with relatively warm tops in this region.…”

IR‐based satellite and radar rainfall estimates of convective storms over northern Italy

“…Pure IR techniques are also still used in operational configuration, though they require calibration by independent instruments. Vicente et al (1998) use the IR channels of the Geostationary Operational Environmental Satellite (GOES-8/9) to compute real-time precipitation amounts based on a power law algorithm. The regression is derived from a statistical analysis between surface instantaneous radar rainfall estimates and satellite IR cloud top temperatures collocated in time and space.…”

“…Barrett & Michell (1991) give a clear idea of the problem in the Mediterranean basin. Flash flood monitoring in the US, a crucial issue for the national weather prediction system, is becoming operational (Scofield & Naimeng, 1994;Vicente & Scofield, 1996;Vicente et al, 1998). Applications to Italy's precipitation systems have been attempted on several occasions over various time scales and for different precipitation systems.…”

Retrieval of Rainfall by Combining Rain Gauge, Ground-Based Radar and Satellite Measurements over Phimai, Thailand

“…3a. We also performed data conversion from MTSAT TBB to rainfall intensity (Rm) through a non-linear regression equation that expressed their relationship (Vicente et al 1998). We investigated the relationship between the TBB11 of the classified cloud types and the rain gauge measurements.…”