Updates to a Global Precipitation Product
Center for Hydrometeorology and Remote Sensing
© University of California Irvine
by Maureen Cribb & Debra Baker, CISESS Coordinators
CISESS Consortium Scientists Kuo-lin Hsu, Phu Nguyen , and Soroosh Sorooshian from the University of California Irvine (UCI), recently published an article along with their colleagues from UCI’s Center for Hydrometeorology and Remote Sensing in the journal Scientific Data. It presents the new version of the original Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) near-global precipitation dataset covering almost four decades. This dataset has a spatial resolution of 0.04o and a temporal resolution of three hours. Version 2 of PERSIANN-CCS-CDR overcomes a number of problems identified in Version 1, mainly inconsistencies in input data and the incorrect application of this dataset specifically developed for tasks that required high spatiotemporal resolutions. The paper presents an in-depth evaluation of PERSIANN-CCS-CDR V2.0 and assesses the performance of the dataset for extreme weather events, such as Hurricane Michael (2018). Suggestions on how to best use this dataset and other PERSIANN datasets are also offered. All PERSIANN datasets can be accessed at the Center for Hydrometeorology and Remote Sensing data portal.
Figure 1: Sample visualization of daily mean precipitation amounts on 31 July 2024 over a selected region of the world generated from the PERSIANN data portal.
Hsu, Nguyen and Sorooshian have been working on a CISESS project to improve predictions of orographic precipitation for the last two years. They are developing machine learning algorithms to aid forecasts. They are collaborating with the CISESS Microwave Integrated Retrieval System (MiRS) team with the ultimate goal of integrating a post-processing module over orographically influenced terrains into MiRS. They receive funding from JPSS PGRR for this work.
Citation: Zadeh, Mohammad Bolboli, Phu Nguyen, Kuo-Lin Hsu, Amir AghaKouchak, Tu Thanh Ung, and Soroosh Sorooshian, 2026: A global high-resolution precipitation climate record: PERSIANN-CCS-CDR Version 2.0. Sci. Data, 13, 314, https://doi.org/10.1038/s41597-026-06625-5.
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