Ali Behrangi

About

Ali Behrangi is a Professor of Hydrology and Atmospheric Sciences and an Associate Professor of Civil Engineering and Architectural Engineering and Mechanics. He is also an Associate Professor of Geosciences and a member of the Graduate Faculty at The University of Arizona. His research focuses on hydrology, atmospheric sciences, and related geosciences, contributing to the understanding of water and atmospheric processes. He is actively involved in academic and research activities within the Department of Civil & Architectural Engineering & Mechanics, engaging in teaching, research, and service to the university community.

Research topics

• Computer Science
• Meteorology
• Geology
• Environmental science
• Machine Learning
• Remote sensing
• Geography
• Data Mining
• Atmospheric sciences
• Physics

Selected publications

• Precipitation Merging Based on the Triple Collocation Method Across Mainland China

  IEEE Transactions on Geoscience and Remote Sensing · 2020 · 89 citations

  Triple collocation (TC) is a novel method for quantifying the uncertainties of three data sets with mutually independent errors and has been widely used over different geographical fields. Researches in recent years report that TC shows potential in merging multiple data sets from different sources, while the TC-based merging method has not been used over precipitation. Using the TC formulation, this study merges precipitation from the Climate Prediction Center's morphing technique (CMORPH), Pre…

  DOI

• Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP ² Ex aircraft campaign: chemical composition, size distributions, and the impact of convection

  Atmospheric chemistry and physics · 2021 · 71 citations

  Abstract. The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integra…

  DOI

• Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

  Journal of Hydrometeorology · 2021 · 46 citations

  Abstract Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously coll…

  PublisherOA PDFDOI

• Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

  Journal of Advances in Modeling Earth Systems · 2025-04-01 · 16 citations

  articleOpen access

  Abstract A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top‐of‐atmospher…

  PublisherOA PDFDOI

• Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: Comparison with ECOSTRESS, MODIS, and OpenET

  Journal of Hydrology · 2025-05-08 · 14 citations

  article
  PublisherDOI

Frequent coauthors

• Kuolin Hsu

  University of California, Irvine

  54 shared

• Soroosh Sorooshian

  University of California, Irvine

  41 shared

• Yang Hong

  University of Oklahoma

  36 shared

• Bjorn Lambrigtsen

  Jet Propulsion Laboratory

  33 shared

• Robert J. Kuligowski

  29 shared

• B. Imam

  29 shared

• George J. Huffman

  Goddard Space Flight Center

  28 shared

• Mohammad Reza Ehsani

  University of Arizona

  22 shared

Education

• Postdoctoral scholar

  California Institute of Technology

  2012

• PhD, Civil Eng., Remote sensing, hydrology, water resources

  University of California Irvine

  2009

• MSc, Civil and Environmental Engineering

  Sharif University of Technology

  2004

• Bsc, Civil and Environmental Engineering

  Sharif University of Technology

  2003

Similar researchers at University of Arizona

• Jennifer G. Duanh-27
• George N. Frantziskonish-22
• Dominic Boccellih-20