Welcome to our Lab's website!
We are a group of climate scientists, hydrologists, environmental engineers and data scientists who work on better understanding hydroclimate and its variability across scales, better capture and quantify predictability of the Earth system, and assess/explore adaptation strategies in the face of climate change. In doing so, we use, develop and assess state-of-the-art data science techniques, such deep learning models and expainable artificial intelligence (AI).
Past and current areas of research include improving predictive skill of hydroclimate and extreme events, understanding climate teleconnections and predictability, advancing climate attribution and causal discovery, climate downscaling, among others.
Some representative papers from our Lab have garnered international attention and have been highlighted by publishers. Examples include "A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US," published in Nature Communications; "Zonally contrasting shifts of the tropical rain belt in response to climate change," published in Nature Climate Change; "Underestimated MJO variability in CMIP6 models," published in Geophysical Research Letters; and "Climate-driven changes in the predictability of seasonal precipitation" published in Nature Communications.
Read more about our research here:
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News
New study published in AI4ES
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
September 2022
New study published in Env. Data Science
Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset
June 2022
New study published in Water Resources Research
Identifying regions of high precipitation predictability at seasonal timescales from limited time series observations
May 2022
New Book chapter published
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
March 2022
New study published in Geophysical Research Letters
Underestimated MJO variability in CMIP6 models
June 2021
New study published in Nature Climate Change
Zonally contrasting shifts of the tropical rain belt in response to climate change
January 2021
New study published in Journal of Climate
Graph-Guided Regularized Regression to Increase Predictive Skill of Winter Precipitation
January 2021
New study published in Journal of Climate
Rotated Spectral Principal Component Analysis (rsPCA) for Identifying Dynamical Modes of Variability in Climate Systems
January 2021
Study included in the top 50 articles of Nature Comm:
A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US
July 2019
Highlighted Research
Attribution Benchmarks to introduce objectivity in the XAI assessment
Zonally contrasting shifts of the tropical rain belt in response to climate change
New teleconnection increases predictability of precipitation in southwestern US
Simultaneous Bias Correction and spatial Downscaling of climate model Rainfall |
