Jesse Ross
(He/him)Jesse Ross (He/him) is a Senior Data Scientist for the USGS Water Resources Mission Area
Science and Products
National-scale remotely sensed lake trophic state from 1984 through 2020
Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used La
Authors
Michael Frederick Meyer, Simon Nemer Topp, Tyler Victor King, Robert Ladwig, Rachel M. Pilla, Hilary A. Dugan, Jack R. Eggleston, Stephanie E. Hampton, Dina M Leech, Isabella Oleksy, Jesse Cleveland Ross, Matthew V Ross, Iestyn R Woolway, Xiao Yang, Matthew R. Brousil, Kate Colleen Fickas, Julie C Padowski, Amina Pollard, Jianning Ren, Jacob Aaron Zwart
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White
Science and Products
National-scale remotely sensed lake trophic state from 1984 through 2020
Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used La
Authors
Michael Frederick Meyer, Simon Nemer Topp, Tyler Victor King, Robert Ladwig, Rachel M. Pilla, Hilary A. Dugan, Jack R. Eggleston, Stephanie E. Hampton, Dina M Leech, Isabella Oleksy, Jesse Cleveland Ross, Matthew V Ross, Iestyn R Woolway, Xiao Yang, Matthew R. Brousil, Kate Colleen Fickas, Julie C Padowski, Amina Pollard, Jianning Ren, Jacob Aaron Zwart
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Authors
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White