Noemi Vergopolan

Noemi Vergopolan

Computational Hydrology

Princeton University

About Me

My research aims to aid water resource decision-making by improving monitoring and forecasting of hydrological hazards and their impacts from global to local spatial scales. To this aim, I work developing scalable solutions for high-resolution hydrological predictions by leveraging satellite remote sensing, land surface modeling, machine learning, data fusion, and high-performance computing.

I obtained a PhD in Civil and Environmental Engineering from Princeton University. Currently, I am a Postdoctoral Research Associate at the Atmospheric and Ocean Science Program at Princeton University working on Earth System Modeling at the NOAA Geophysical Fluid Dynamics Laboratory.

Learn more about my interests in research and publications.

Interests

  • Hydrology
  • Agriculture
  • Satellite Remote Sensing
  • Artificial Intelligence
  • High Performance Computing
  • Big Data

Education

  • PhD in Civil & Environmental Engineering

    Princeton University, 2021

  • Statistics & Machine Learning Certificate

    Princeton University, 2021

  • Computational Science & Engineering Certificate

    Princeton University, 2019

  • MA in Civil & Environmental Engineering

    Princeton University, 2017

  • BS Environmental Engineering

    Federal University of Paraná, 2014

Research

SMAP-HydroBlocks
The first hyper-resolution satellite-based surface soil moisture dataset at 30-m resolution over the continental United States
SMAP-HydroBlocks
HydroBlocks
Development & applications of a field-scale resolving land surface model over continental extents
HydroBlocks
Crop Yield Prediction
Combining hyper-resolution land surface modeling and machine learning for predicting crop yields at field scale
Crop Yield Prediction

Recent Publications

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors
PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Social