6. ConclusionsThe aim of this paper

6. ConclusionsThe aim of this paper is to apply a deep neural network framework to satellite-based precipitation estimation products to correct the estimation bias in a data-driven manner by extracting more useful features from satellite imagery. More specifically, SDAE, a popular technique in image recognition, is employed to improve the PERSIANN-CCS product. The model is trained in 2012–13 and evaluated during the 2013 summer and 2013/14 winter seasons.Verification studies show improved results in both R/NR detection and precipitation intensity over the validation period for both seasons. Binary R/NR detection resulted in the correction of a significant number of false alarm pixels, especially in the cold season. For precipitation intensity, the averaged daily biases are corrected by as much as 98% and 78% in the validation warm and cold seasons, respectively. These results are also illustrated for a specific rainfall event on 4 August 2014, for which visualization of the cumulative rainfall amount demonstrates the model’s ability to correct false alarms and overestimation.The results verify that useful information is available in IR imagery and can help improve the quality of satellite precipitation products with respect to detecting R/NR pixels and quantifying the precipitation rates. More important, such useful information for precipitation estimation can be extracted automatically by deep neural networks. Moreover, the methodology can be easily integrated into near-real-time operational precipitation estimation products and can help extract additional features from satellite datasets to reduce bias. Meanwhile, the application of the technique is not limited to IR imagery, but should be extendable to multiple satellite datasets because of its ability to automatically extract information. The case study of PERSIANN-CCS proves its advantage compared to a few manually designed features.In addition, our results suggest that GOES cloud IR imagery still contains valuable information that has not been utilized by most satellite precipitation retrieval algorithms. Our experiment demonstrates that the cloud IR image from a 15 × 15 pixel window is more informative than the nine IR statistic features used in PERSIANN-CCS as the input data for precipitation estimation. Such information can be extracted automatically by a well-designed deep neural network. The next step for this work will be to explore the possibility of using deep learning techniques to produce a precipitation estimation product directly instead of bias correction. Moreover, we believe that these data-driven methodologies can benefit many fields of weather forecasting, climate variability, hydrology, and water resources management.