PMID- 34158807
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210625
IS  - 1525-7541 (Electronic)
IS  - 1525-755X (Print)
IS  - 1525-7541 (Linking)
VI  - 21
IP  - 12
DP  - 2020 Dec 1
TI  - PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global 
      Satellite Precipitation Dataset.
PG  - 2893-2906
LID - 10.1175/jhm-d-20-0177.1 [doi]
AB  - This study presents the Precipitation Estimation from Remotely Sensed Information 
      Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) 
      near-real-time precipitation dataset. This dataset provides hourly, quasi-global, 
      infrared-based precipitation estimates at 0.04 degrees  x 0.04 degrees  spatial resolution with a 
      short latency (15-60 min). It is intended to supersede the PERSIANN-Cloud 
      Classification System (PERSIANN-CCS) dataset previously produced as the 
      near-real-time product of the PERSIANN family. We first 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 subdaily scales. Last, 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 2017-18, demonstrates 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 subdaily precipitation rates with a correlation coefficient 
      (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.
FAU - Nguyen, Phu
AU  - Nguyen P
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Ombadi, Mohammed
AU  - Ombadi M
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Gorooh, Vesta Afzali
AU  - Gorooh VA
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Shearer, Eric J
AU  - Shearer EJ
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Sadeghi, Mojtaba
AU  - Sadeghi M
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Sorooshian, Soroosh
AU  - Sorooshian S
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Hsu, Kuolin
AU  - Hsu K
AD  - Center for Hydrometeorology and Remote Sensing, Department of Civil and 
      Environmental Engineering, University of California, Irvine, Irvine, California.
FAU - Bolvin, David
AU  - Bolvin D
AD  - Mesoscale Atmospheric Processes Laboratory, NASA GSFC, Greenbelt, Maryland.
FAU - Ralph, Martin F
AU  - Ralph MF
AD  - Center for Western Weather and Water Extremes, Scripps Institution of 
      Oceanography, University of California, San Diego, La Jolla, California.
LA  - eng
GR  - 80NSSC19K0726/ImNASA/Intramural NASA/United States
PT  - Journal Article
DEP - 20201125
PL  - United States
TA  - J Hydrometeorol
JT  - Journal of hydrometeorology
JID - 101722982
PMC - PMC8216223
MID - NIHMS1702041
OTO - NOTNLM
OT  - Neural networks
OT  - Precipitation
OT  - Rainfall
OT  - Remote sensing
OT  - Satellite observations
EDAT- 2021/06/24 06:00
MHDA- 2021/06/24 06:01
PMCR- 2021/06/21
CRDT- 2021/06/23 07:18
PHST- 2021/06/23 07:18 [entrez]
PHST- 2021/06/24 06:00 [pubmed]
PHST- 2021/06/24 06:01 [medline]
PHST- 2021/06/21 00:00 [pmc-release]
AID - 10.1175/jhm-d-20-0177.1 [doi]
PST - ppublish
SO  - J Hydrometeorol. 2020 Dec 1;21(12):2893-2906. doi: 10.1175/jhm-d-20-0177.1. Epub 
      2020 Nov 25.