The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data

Phu Nguyen,Soroosh Sorooshian,Mohammed Ombadi,Negin Hayatbini,Phat Huynh,Hoang Tran,Dan Braithwaite,Will S Logan,Kuolin Hsu,Eric J Shearer,Garr Updegraff,Thanh Palacios,Bob Kuligowski

doi:10.1038/sdata.2018.296

Abstract

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.

Highlights

The growth of the field of remotely sensed hydrometeorology has benefited the world with the first nearglobal analyses of rainfall distribution
PERSIANN uses the machine learning technique known as artificial neural networks (ANNs) to determine the relationship between remotely sensed cloud-top temperature, measured by long-wave infrared (IR) sensors on geostationary orbiting (GEO) satellites, and rainfall rates, with bias correction from passive microwave (PMW) readings measured by low Earth-orbiting (LEO) satellites[1,2]
The spatiotemporal characteristics of the global regular-interval satellite rainfall data for the three PERSIANN products are accessible from the Center for Hydrometeorology and Remote Sensing (CHRS) Data Portal web system, dependent on data availability

Summary

Introduction

The growth of the field of remotely sensed hydrometeorology has benefited the world with the first nearglobal analyses of rainfall distribution. The northern and southern latitudes of approximately 60° bound most quasi-global products owing to the increasing unreliability of satellite data readings as geostationary orbiting (GEO) satellites scan nearer to the poles. In 1997, researchers from the University of Arizona— situated at the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine—developed one of the first satellite precipitation estimating systems, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). PERSIANN uses the machine learning technique known as artificial neural networks (ANNs) to determine the relationship between remotely sensed cloud-top temperature, measured by long-wave infrared (IR) sensors on GEO satellites, and rainfall rates, with bias correction from passive microwave (PMW) readings measured by low Earth-orbiting (LEO) satellites[1,2]

Methods

Results

Conclusion