Zobrazeno 1 - 10 of 11 pro vyhledávání: '"E. J. Shearer"'
1
PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset
Autor: E. J. Shearer, Soroosh Sorooshian, Phu Nguyen, Kuolin Hsu, Vesta Afzali Gorooh, David T. Bolvin, Mohammed Ombadi, Martin F. Ralph, Mojtaba Sadeghi
Publikováno v: Journal of hydrometeorology, vol 21, iss 12
J Hydrometeorol
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-base
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ae96b86e4db184b6deebc4b2db5ec33
https://doi.org/10.1175/jhm-d-20-0177.1
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2
An Overview of ARTMIP's Tier 2 Reanalysis Intercomparison: Uncertainty in the Detection of Atmospheric Rivers and Their Associated Precipitation
Autor: A. B. Marquardt Collow, C. A. Shields, B. Guan, S. Kim, J. M. Lora, E. E. McClenny, K. Nardi, A. Payne, K. Reid, E. J. Shearer, R. Tomé, J. D. Wille, A. M. Ramos, I. V. Gorodetskaya, L. R. Leung, T. A. O’Brien, F. M. Ralph, J. Rutz, P. A. Ullrich, M. Wehner
Publikováno v: Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres, 2022, 127, ⟨10.1029/2021JD036155⟩
International audience; Atmospheric rivers, or long but narrow regions of enhanced water vapor transport, are an important component of the hydrologic cycle as they are responsible for much of the poleward transport of water vapor and result in preci
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f1fb79458f6de714a2404ee66868b42
https://doi.org/10.1029/2021jd036155
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4
PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation
Autor: W. S. Logan, Mohammed Ombadi, Marty Ralph, E. J. Shearer, Kuolin Hsu, Vesta Afzali Gorooh, Phu Nguyen, Soroosh Sorooshian
Publikováno v: Bulletin of the American Meteorological Society. 101:E286-E302
Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time prec
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::887dbb71fabd9b22e9aa280c97e02320
https://doi.org/10.1175/bams-d-19-0118.1
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5
Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment
Autor: Allison B. Marquardt Collow, Paul A. Ullrich, E. J. Shearer, Elizabeth McClenny, Colin M. Zarzycki, Alan M. Rhoades, Huanping Huang, B. Loring, Christine A. Shields, Yang Zhou, Jonathan J. Rutz, L. Ruby Leung, Héctor Inda Díaz, Kyle M. Nardi, Ashley E. Payne, F. Martin Ralph, Travis A. O'Brien, Irina Gorodetskaya, Chandan Sarangi, Michael Wehner, Bin Guan, Juan M. Lora, Ricardo Tomé, Alexandre M. Ramos
Publikováno v: Journal of geophysical research. Atmospheres : JGR, vol 127, iss 6
The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tie
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07419a7c260a096b3580c7e57264b732
https://pubmed.ncbi.nlm.nih.gov/35859545
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6
Four Decades of Intensifying Precipitation from Tropical Cyclones
Autor: Kuolin Hsu, E. J. Shearer, Soroosh Sorooshian, Phu Nguyen, Vesta Afzali Gorooh
In this study, the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Dynamic Infrared Rain-rate model (PDIR) product, using a consistently measured 40-year archive of satellite-measured cloud-top inf
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3b1be0e5b58ec4bbac94ae754c8e5247
https://doi.org/10.21203/rs.3.rs-424061/v1
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8
Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)
Autor: Negin Hayatbini, Phu Nguyen, Vesta Afzali Gorooh, Pari-Sima Katiraie-Boroujerdy, Soroosh Sorooshian, Matin Rahnamay Naeini, Hamidreza Mosaffa, E. J. Shearer, Bita Analui, Mojtaba Sadeghi
Publikováno v: Journal of Hydrology. 600:126569
In recent years, the number of floods following unprecedented rainfall events have increased in Iran during early spring (March 21st to April 20th, referred to in Iran as the month of “Farvadin”). While numerous studies have addressed changes in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::412d5ac3aa1cfe57e2fd4fa04de66834
https://doi.org/10.1016/j.jhydrol.2021.126569
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9
The Evolution of Bits and Bottlenecks in a Scientific Workflow Trying to Keep Up with Technology: Accelerating 4D Image Segmentation Applied to NASA Data
Autor: John Graham, Scott Sellars, Joulien Tatar, F. Martin Ralph, Kyle Marcus, Frank Wuerthwein, Ilkay Altintas, Larry Smarr, Dmitry Mishin, Phu Nguyen, Thomas A. DeFanti, E. J. Shearer, Camille Crittenden, Soroosh Sorooshian
Publikováno v: eScience
In 2016, a team of earth scientists directly engaged a team of computer scientists to identify cyberinfrastructure (CI) approaches that would speed up an earth science workflow. This paper describes the evolution of that workflow as the two teams bri
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::56cabcdb6d50c92718f6b283cc6a8308
https://doi.org/10.1109/escience.2019.00016
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10
The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data
Autor: Phu Nguyen, E. J. Shearer, Kuolin Hsu, Negin Hayatbini, Bob Kuligowski, Phat Huynh, Soroosh Sorooshian, Will S. Logan, Dan Braithwaite, Hoang Tran, Garr Updegraff, Thanh Palacios, Mohammed Ombadi
Publikováno v: Scientific Data
Nguyen, Phu; Shearer, Eric J; Tran, Hoang; Ombadi, Mohammed; Hayatbini, Negin; Palacios, Thanh; et al.(2019). The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data.. Scientific data, 6, 180296. doi: 10.1038/sdata.2018.296. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/6s18j1q8
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
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43d38f1c7da669528c5daf93226fdce9
http://europepmc.org/articles/PMC6326112
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