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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 12 Apr 2018

Review article | 12 Apr 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

The PERSIANN Family of Global Satellite Precipitation Data: A Review and Evaluation of Products

Phu Nguyen, Mohammed Ombadi, Soroosh Sorooshian, Kuolin Hsu, Amir AghaKouchak, Dan Brathwaite, Hamed Ashouri, and Andrea Rose Thorstensen Phu Nguyen et al.
  • Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, USA

Abstract. Over the past two decades, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products have been incorporated in a wide range of studies. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely, PERSIANN, PERSIANN-CCS and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the Contiguous United States at different spatial and temporal scales using Climate Prediction Center (CPC) Unified gauge-based analysis as a benchmark. Finally, the available products are intercompared at a quasi-global scale. Furthermore, we highlight strength and limitations of the PERSIANN products and briefly discuss the expected future developments.

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Phu Nguyen et al.
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Phu Nguyen et al.
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