PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

Geophysical Research Letters

et al. 2021

Self Cite

Precipitation is not only a key variable in climate studies, but also a crucial forcing factor of the hydrological cycle (Do et al., 2020;Jiang & Bauer-Gottwein, 2019;Yao et al., 2020). Currently, various precipitation products are developed through different technologies, such as gauge interpolation, radar observation, satellite retrieval, and assimilation reanalysis (Foufoula-Georgiou et al., 2020;Sun et al., 2018). However, despite the significant advances of these products, there still exists errors associated with them due to many factors (e.g., algorithms, data sources, bias correction). Therefore, it is important to identify and quantify these error sources before proceeding to applications, which is also beneficial to the iterative updates of products (Alijanian et al., 2019;Sorooshian et al., 2000).Many efforts have been done to address this task, including the comparison of gauge observations, intercomparison, and hydrological validation (

Section: Datamentioning

confidence: 99%

New Insights Into Error Decomposition for Precipitation Products

Zhang

Geophysical Research Letters

et al. 2021

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“…PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now, hereafter, PDIR) [48,49] is the latest generation of the PERSIANN family of products. It incorporates more highfrequency sampled IR imagery and provides near real-time precipitation estimates with a very short time latency (15-60 min).…”

Section: Pdirmentioning

confidence: 99%

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Zhang

et al. 2021

Remote Sensing

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Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

“…PERSIANN-Climate Data Record (CDR) (Ashouri et al 2015) belongs to the PERSIANN family of precipitation datasets (Nguyen et al 2019). It provides daily precipitation for the period (1983-delayed present) over land and oceans at (608S-608N); precipitation estimates from PERSIANN-CDR are bias-adjusted using GPCP V2.3 monthly 2.58 3 2.58.…”

Section: ) Persiann-cdrmentioning

confidence: 99%

“…PDIR-Now dataset is available through two web-based interfaces operated by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine: the CHRS iRain interface (https://irain.eng.uci.edu/), a website that provides a user-friendly interface to visualize global precipitation dataset for the last 72 h, and the CHRS Data Portal (https://chrsdata.eng.uci.edu/), which is intended as an accessible interface for the download of PDIR-Now dataset as well as other PERSIANN family datasets. The latter is described in detail in Nguyen et al (2019). Here, we provide a brief description of the two interfaces.…”

Section: Data Dissemination and Web-based Interfacementioning

confidence: 99%

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PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

Journal of Hydrometeorology

Ombadi

Gorooh

et al. 2020

Self Cite

This study presents the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Dynamic Infrared Rain Rate near real-time (PDIR-Now) precipitation dataset. This dataset provides hourly, quasi-global, Infrared-based precipitation estimates at 0.04°x0.04° spatial resolution with a short latency (15 – 60 minutes). It is intended to supersede PERSIANN- Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near real-time product of the PERSIANN family. We firstly provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and sub-daily scales. Lastly, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period (2017-2018), demonstrate the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a Critical Success Index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as sub-daily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.