Abstract. Precipitation forecasts from numerical weather prediction models are often compared to rain gauge observations to make inferences as to model performance and the "best" resolution needed to accurately capture the structure of observed precipitation. A common approach to quantitative precipitation forecast (

[1] Precipitation is a highly heterogeneous process with considerable natural variability at scales ranging from a few meters to several hundreds of kilometers. This process is monitored with a variety of sensors (e.g., rain gauges, radars, and satellites) which provide direct or indirect measurements of precipitation at different scales. At the same time, physically based models at the storm, regional, continental, and global scales are used to predict precipitation and rely on the observed data for model verification, model initialization, and data assimilation. Because of the tremendous scale-dependent variability of precipitation fields, merging or comparing observations at different scales, or comparing model outputs at one scale with observations at one or more different scales, is not straightforward. This study explores the use of a recently developed scale-recursive estimation (SRE) framework for the problem of Quantitative Precipitation Forecast (QPF) verification using observations from multiple sensors. SRE can explicitly account for the multiscale variability of precipitation and the scale-dependent uncertainty associated with the model output and the multisensor observations. Special emphasis is placed on the specification of the multiscale structure of precipitation under sparse or noisy data. The results demonstrate the potential of SRE as a powerful tool for assessment of QPFs and also for multisensor data fusion and network design studies.

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