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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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SatERR is a bottom-up approach, where the four types of errors including measurement, preprocessing, observation operator, and representativeness errors are generated from sources and forward propagate through radiances, science products, and data assimilation systems. This approach can quantify and partition errors and uncertainties in science products, and capture leading features of the most important errors in a statistical sense for data assimilation.

Leveraging Satellite Observations with a Comprehensive Simulator

Satellite observations are vital for weather forecasts, climate monitoring, and environmental studies. In recent years, there has been a concerted effort to develop methods for quantifying and representing errors associated with satellite observations. ESSIC scientist John Xun Yang has led a team of scientists in the creation of an error inventory simulator, the Satellite Error Representation and Realization (SatERR).

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