Soroosh Sorooshian

About

Professor Soroosh Sorooshian is a Distinguished Professor in the Department of Civil & Environmental Engineering at UC Irvine, holding the Henry Samueli Endowed Chair in Engineering. His academic background includes a Ph.D. from UCLA in 1978, with prior degrees in systems engineering, operations research, and mechanical engineering. His research primarily focuses on surface hydrology, especially rainfall-runoff modeling, model identification and calibration, remote sensing applications for hydrologic parameters, and the implications of climate variability and change on water resources. He has developed special estimation criteria to address uncertainties in calibration data and has contributed significantly to hydrologic modeling and water resources management. Professor Sorooshian has been recognized with numerous awards and honors, including election to the U.S. National Academy of Engineering, the American Geophysical Union Robert Horton Medal, and the Prince Sultan Bin Abdulaziz International Prize for Water Resources Management. He has served on various national and international advisory committees, including the NOAA Science Advisory Board, NRC committees, and UNESCO panels. His leadership roles include past-president of the AGU Hydrology Section, former editor of Water Resources Research, and chair of the GEWEX Scientific Steering Group. He has been an invited and keynote speaker at over 240 events and has actively contributed to the scientific community through…

Research topics

• Geography
• Meteorology
• Geology
• Environmental science
• Climatology
• Machine Learning
• Computer Science
• Artificial Intelligence
• Data Mining
• Mathematics

Selected publications

• Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG)

  Advances in global change research · 2020 · 692 citations

  DOI

• PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

  Scientific Data · 2021 · 151 citations

  Senior authorCorresponding

  Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation produ…

  DOI

• Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

  Environmental Modelling & Software · 2020 · 110 citations

  Senior authorCorresponding
  DOI

• Hydrological Projections under CMIP5 and CMIP6: Sources and Magnitudes of Uncertainty

  Bulletin of the American Meteorological Society · 2024-01-01 · 67 citations

  articleOpen accessSenior author

  Abstract Projections of future hydrological conditions rely largely on global climate models, but model performance varies greatly. In this study, we investigated projected changes in runoff ( R ), precipitation ( P ), evapotranspiration (ET), and soil moisture (SM) based on the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) and quantified the uncertainties of their projected changes on annual and seasonal scales. The results indicate that all four hydrolog…

  PublisherOA PDFDOI

• Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

  Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2022-10-23 · 65 citations

  articleOpen accessSenior author

  Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experimen…

  PublisherOA PDFDOI

Recent grants

• CyberSEES:Type 2: Precipitation Estimation from Multi-Source Information using Advanced Machine Learning

  NSF · $1.1M · 2013–2018

Frequent coauthors

• Kuolin Hsu

  University of California, Irvine

  505 shared

• Phu Nguyen

  University of California, Irvine

  166 shared

• Xiaogang Gao

  152 shared

• Hoshin V. Gupta

  University of Arizona

  122 shared

• B. Imam

  91 shared

• Dan Braithwaite

  Samueli Institute

  89 shared

• Hamed Ashouri

  University of California, Irvine

  81 shared

• Amir AghaKouchak

  University of California, Irvine

  75 shared

Education

• PhD, civil engineering

  ucla

  1978

Awards & honors

• U.S. National Academy of Engineering (NAE) (2003)
• Robert Horton Medal from AGU (recipient of AGU Robert Horton…
• Prince Sultan Bin Abdulaziz International Prize for Water Re…
• UNESCO’s 2007 Great Man-Made River International Water Prize…
• American Geophysical Union (AGU) Fellow

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