This event has passed. See the seminar recording here: Dr. William Collins Director, Climate and Ecological Sciences Division, Lawrence Berkeley National Laboratory Professor in Residence,
Tag: machine learning
This event has passed. See the seminar recording here: Prof. Paul O’Gorman Professor of Atmospheric Science Massachusetts Institute of Technology Tuesday October 12, 2021,
This event has passed. See the seminar recording here: Dr. Yongzhen Fan Earth System Science Interdisciplinary Center University of Maryland Monday November 22, 2021, 2
Prof. Pierre Gentine Columbia University Monday December 19, 2022, 2 PM ET Abstract: Over the last couple of years, we have witnessed an explosion in
Dr. Chris Bretherton’s seminar flyer Dr. Christopher Bretherton Senior Director of Climate Modeling Allen Institute for Artificial Intelligence (AI2) Monday April 3, 2023, 2 PM
The Microwave Integrated Retrieval System (MiRS) Science Team has published a paper in IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) titled “Improvement of MiRS Sea Surface Temperature Retrievals Using a Machine Learning Approach”.
ESSIC/CISESS scientists Soni Yatheendradas and Sujay Kumar are co-authors on a paper in Journal of Hydrometeorology titled, “A novel Machine Learning-based gap-filling of fine-resolution remotely sensed snow cover fraction data by combining downscaling and regression”.
ESSIC/CISESS scientists Huan Meng and Yongzhen Fan have recently developed a new machine learning snowfall detection (SD) algorithm, based on eXtreme Gradient Boosting (XGB). The algorithm was developed for the Advanced Technology Microwave Sounder (ATMS) onboard NPP and NOAA-20 as well as the MHS/AMSU-A onboard Metop-A, Metop-B, Metop-C and NOAA-19.
To embrace the rapidly evolving field of AI/ML, ESSIC launches the ESSIC AI Forum and a one-day workshop: “On the Pathway to a Digital Earth”.
Zhou and Grassotti Published on Development of Machine Learning-Based Radiometric Bias Correction for MiRS
ESSIC/CISESS scientists Post-doctoral Associate Yan Zhou and Senior Faculty Specialist Chris Grassotti have recently published in article in Remote Sensing titled “Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)”.