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A two-dimensional satellite rainfall error model ( SREM2D) is developed for simulating ensembles of satellite rain fields on the basis of "reference" rain fields derived from higher accuracy sensor estimates. With this model we aim at characterizing the multidimensional stochastic error structure of satellite rainfall estimates as a function of scale. The pertinent error dimensions we seek to address are: 1) the joint probability density function for characterizing the spatial structure of the successful delineation of rainy and nonrainy areas; 2) the temporal dynamics of rain estimation bias; and 3) the spatial variability of rain rate estimation error. Ground radar rain fields in the Southern plains of the United States are used as reference to evaluate SREM2D error parameters at 0.25-deg and hourly spatiotemporal resolution for an infrared (IR) rain retrieval algorithm (IR-3B41RT) developed at NASA. Comparison of SREM2D simulated satellite rainfall with actual IR-3B41RT data showed that the error modeling technique can preserve the estimation error characteristics across scales with marginal deviations. The model performance is compared against two simpler, but widely used, approaches of error modeling that do not account for uncertainty in rainy/nonrainy area delineation. It is shown that both of these approaches fare poorly with regards to preserving the error structure across scales. They underestimated the sensor retrieval error standard deviation by more than 100% upon aggregation, which, for SREM2D, was found to be below 30%. SREM2D is modular in design-it can be applied for any satellite rainfall algorithm to consistently characterize its error structure.