I am a Remote Sensing Research Scientist employed by [the University Corporation at] Cal State Monterey Bay. I work at NASA Ames Research Center (ARC) in the Biospheric Science Branch under the ARC-CREST agreement. I was previously a NASA Postdoctoral Program (NPP) fellow affiliated with the NASA Earth Exchange. I did a PhD in environmental engineering at Stanford University (graduated March 2023), where I was a member of the WE3 Lab. I was a Stanford Data Science Scholar in 2020-2021. I also have a MS in public policy and management from Carnegie Mellon and a BS in mathematics from Stanford. Between undergrad and grad school, I worked at SingleStore (fka MemSQL).
I contribute to OpenET, for which I am currently working on partitioning evapotranspiration (ET) into ET of precipitation versus ET of applied water (i.e., irrigation). My NPP project focused on statistical methods for quantifying uncertainty in interpolated near-surface meteorological data products. During my PhD, I worked on various theoretical and applied topics in remote sensing of agriculture and irrigation. I have written many thousands of lines of Python but I am now a vociferous advocate for Julia.
Contact: doherty.conor@gmail.com
Doherty, C.T., et al., (in revision). [preprint] "A Method for Quantifying Uncertainty in Spatially Interpolated Meteorological Data with Application to Daily Maximum Air Temperature."
Doherty, C.T., Wong, C.A., Mauter, M.S., (in preparation). "Satellite-based classification of irrigated fields without labeled training data."
Doherty, C.T., Mauter, M.S., (2025) [link]. "Fisher Discriminant Analysis for Extracting Interpretable Phenological Information from Multivariate Time Series Data." JSTARS.
Volk, J.M., [...], Doherty, C.T., [...], (2024) [link]. "Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management." Nat Water.
Doherty, C.T., (tutorial). [link] "The Uses and Limitations of the Discrete Fourier Transform for Analyzing Intra-Annual Environmental Time Series."
Doherty, C.T., et al., (2022) [link]. "Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS." Irrig Sci.
Melton, F.S., Huntington, J., [...], Doherty, C.T., [...], (2021). [link] "OpenET: Filling a Critical Data Gap in Water Management for the Western United States." JAWRA.
Data Assimilation (CEE 261D) Winter 2023: Dynamic systems and state-space representation, Kalman Filter (KF), Ensemble and Compressed-State KF for large systems, optimal filter tuning (TA).
Imaging with Incomplete Information (CEE 362G/CME 262) Spring 2022: Bayesian approach to inverse problems with applications in hydrologic imaging (TA).
Inclusive Mentoring in Data Science (BIODS 360) Winter 2022: Course for graduate students who serve as mentors for undergraduates at 2- and 4-year colleges (TA and curriculum development).
Remote Sensing of Hydrology (CEE 260D/ESS 224) Spring 2021: Survey of principles and methods used in remote sensing of hydrology (TA and curriculum development).
Environmental Policy Analysis (CEE 275D) Fall 2019: Introduction to policy analysis for environmental engineering graduate students (TA).
Inclusive Mentoring in Data Science Program (2021-2022): Weekly mentorship session with students enrolled at colleges with limited opportunities for research and mentorship. Activities include homework help, resume/application review, discussions about careers and graduate school. Participated for two years including assisting with administration and curriculum development in second year. [program website]