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).

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[1] We analyzed the pattern of large forest disturbances or blow-downs apparently caused by severe storms in a mostly unmanaged portion of the Brazilian Amazon using 27 Landsat images and daily precipitation estimates from NOAA satellite data. For each Landsat a spectral mixture analysis (SMA) was applied. Based on SMA, we detected and mapped 279 patches (from 5 ha to 2,223 ha) characteristic of blow-downs. A total of 21,931 ha of forest were disturbed. We found a strong correlation between occurrence of blow-downs and frequency of heavy rainfall (Spearman's rank, r 2 = 0.84, p < 0.0003). The recurrence intervals of large disturbances were estimated to be 90,000 yr for the eastern Amazon and 27,000 yr for the western Amazon. This suggests that weather patterns affect the frequency of large forest disturbances that may produce different rates of forest turnover in the eastern and western Amazon basin.

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