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Figure: ESSIC/CISESS Scientist Christopher Smith presenting his research as the Enterprise Proving Ground Satellite Liaison for the NWS Weather Prediction Center (WPC) and Ocean Prediction Center (OPC) to maximize satellite capabilities for weather forecasts.

ESSIC Hosts Annual UMD-NOAA Mini-Conference

The National Weather and Climate Prediction Center (NCWCP) and UMD’s Earth System Science Interdisciplinary Center (ESSIC) held a three-day mini-conference from February 27 to 29, a hybrid event held both at the ESSIC and online. The conference brought together ESSIC, CISESS, and NOAA scientists to share their presentations and posters from the recent AGU and AMS conferences. Peter Beierle was the conference organizer for UMD.

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WOA23 annual mean sea water temperature for the 2015-2022 time period on 0.25 grid.

World Ocean Atlas 2023 Released

ESSIC/CISESS scientist Alexey Mishonov is one of the authors of the newly released World Ocean Atlas 2023. The World Ocean Atlas 2023 (WOA23) is a set of temperature, salinity, oxygen, phosphate, silicate, and nitrate means based on profile data from the World Ocean Database (WOD). WOA23 includes approximately 1.8 million new oceanographic casts added to the WOD since WOA18’s release, as well as renewed and updated quality controls. The database can be used to create boundaries and/or initial conditions for a variety of ocean models, verify numerical simulations of the ocean, and corroborate satellite data.

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Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

ESSIC/CISESS scientists Yong-Keun Lee and Christopher Grassotti are co-authors on a new paper in Remote Sensing led by first author Zichao Liang, a student who interned with the MiRS team during the summer of 2023. NOAA scientists Lin Lin and Quanhua Liu also co-authored the paper. The paper, titled “Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm”, assesses the use of the U-Net model to estimate surface wind speed and surface pressure over pure ocean conditions.

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Figure: Field experiments with the RHG-BRDF system over grass, soil and sand scenes and its calibration with a reflectance reference board.

CISESS Seed Grant Project Develops a Robotic RHG-BRDF Measurement System

During its one-year funding period, this CISESS Seed Grant project expanded the work of the student-oriented CISESS Remote Sensing Laboratory by building equipment for post-launch radiometric validation using in situ measurements of reflective solar band calibration. ESSIC/CISESS Scientist Xi Shao, along with Sirish Uprety, Tung-Chang Liu, and Xin Jin, developed a Robotic Hyperspectral Ground Bi-directional Reflectance Distribution Function (RHG-BRDF) measurement system. Once built, they worked with three undergraduate students to perform field hyperspectral measurements of different ground targets. The student also developed python modules for converting measurements to hyperseptral reflectance, data visualization and analysis.

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Photo by David R. Gonzalez of the Minnesota Department of Transportation

Sujay Kaushal Hosts Reddit Ask Me Anything

ESSIC scientist Sujay Kaushal and Ph.D. candidate Sydney Shelton hosted an “Ask Me Anything” (AMA) thread on Reddit on the /r/askscience subreddit. For two and a half hours, Kaushal and Shelton answered questions from the public regarding salinization and its impact on our planet.

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