PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-real time, Quasi-Global Satellite Precipitation Dataset

Abstract:

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 4°x4° 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 an extreme event that occurred over the Netherlands where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as hourly precipitation rates with a correlation coefficient (CORR) of 0.64.