Results 31 to 40 of about 2,560 (196)
Object-based assessment of satellite precipitation products [PDF]
, 2016 An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN ...
AghaKouchak, A +3 more
core +11 more sources
Remote Sensing, 2022 The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations ...
Muhammad Umer Nadeem +7 more
doaj +1 more source
PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset. [PDF]
J Hydrometeorol, 2020 AbstractThis 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° × 0.04° spatial resolution with a short latency (15–60 min). It
Nguyen P +8 more
europepmc +6 more sources
Atmosphere, 2023 Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the ...
Claudia Jimenez Arellano +4 more
doaj +1 more source
Advances in Meteorology, 2021 This study evaluated three satellite precipitation products, namely, TRMM, CMORPH, and PERSIANN, over the Three Gorges Reservoir area in China at multiple timescales.
Tianyu Zhang +3 more
doaj +1 more source
Satellite-Based Precipitation Measurement Using PERSIANN System [PDF]
, 2008 PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) is a satellite-based rainfall estimation algorithm. It uses local cloud textures from longwave infrared images of the geostationary environmental satellites to estimate surface rainfall rates based on an artificial neural network algorithm.
Hsu, Kuo-Lin, Sorooshian, Soroosh
openaire +2 more sources
Hydrologic evaluation of satellite precipitation products over a mid-size basin [PDF]
, 2011 Since the past three decades a great deal of effort is devoted to development of satellite-based precipitation retrieval algorithms. More recently, several satellite-based precipitation products have emerged that provide uninterrupted precipitation time ...
AghaKouchak, A +5 more
core +3 more sources
Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS [PDF]
, 2020 Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation.
Ganguly, S +6 more
core +1 more source
Rainfall frequency analysis for ungauged regions using remotely sensed precipitation information [PDF]
, 2018 Rainfall frequency analysis, which is an important tool in hydrologic engineering, has been traditionally performed using information from gauge observations.
Faridzad, M +4 more
core +1 more source
Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China
Remote Sensing, 2021 Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations.
Shanlei Sun +4 more
doaj +1 more source