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”.
The MiRS Science Team, composed of ESSIC/CISESS scientists Yong-Keun Lee and Christopher Grassotti, as well as NOAA STAR scientist Mark Liu, published a paper this week titled “In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets” in Earth and Space Science. Lee was the first author of the study.
On October 24, a powerful Category 5 (the maximum possible) atmospheric river (AR) occurred over the northern and central parts of California. The storm system featured record breaking precipitation, leading to flooding and mudslides in some locations, along with dangerous winds exceeding 70 miles per hour at higher elevations. San Francisco recorded its fourth highest single-day rainfall amount of over 4 inches. Satellite passive microwave measurements are one of the observational tools that allow depiction of these extreme events, since microwaves are less affected by clouds and precipitation.
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)”.