Abstract: A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada (2017 AGU Fall Meeting)

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

Adam Coogan¹, Francesco Avanzi², Ram Akella¹, Martha H Conklin³, Roger C Bales^4,5 and Steven D Glaser², (1)University of California Santa Cruz, Santa Cruz, CA, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)University of California Merced, Merced, United States, (4)University of California Merced, Merced, CA, United States, (5)University of California Berkeley, Berkeley, United States

Back to: Data and Tools for Knowledge Discovery Around the Energy-Water Nexus I Posters