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Fig. 3 Brazil local lightning safety day event.

Zhang Organized the 2024 International Lightning Safety Day Event

ESSIC/CISESS scientist Daile Zhang took the lead and organized the 2024 International Lightning Safety Day (ILSD) Event on June 28, 2024. The ILSD event serves as an annual virtual platform dedicated to the discussion of lightning safety strategies, educational initiatives, technological advancements, methodologies, progress, and challenges from around the world. With participants from over 25 countries spanning North and South Americas, Africa, Asia, and Europe, this event aims to foster global collaboration in lightning safety.

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The global ocean visualization

Mapping Ocean Acidification From 1998-2022

ESSIC scientists Li-Qing Jiang, Paige Lavin, and Hyelim Yoo are a part of a team of scientists that have developed detailed maps that track ocean acidification indicators from 1998 to 2022 for eleven large marine ecosystems (LMEs) in the U.S. The study was just published in Nature – Scientific Data for this work.

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Figure 1. LPSs installation at the St. Victor Mulabana Primary School. PHOTO: Daile Zhang

Zhang Visits Uganda to Help with Installation of Lightning Protection Systems

ESSIC/CISESS scientist, Daile Zhang, who also serves as a Board of Directors of an NGO – African Centres for Lightning and Electromagnetics Network (ACLENet) visited Uganda last week. She and the team helped install lightning protection systems (LPSs) at the St. Victor Mulabana Primary School in Kalangala district, an Island on the Lake Victoria. Uganda experiences more than two million lightning strikes per year. The installation will protect 400+ students, teachers, and the nearby community from these lightning strikes.

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An Air Quality Model That Is Evolving with the Times

ESSIC Scientist Min Huang is first author on a new article published in Eos, the American Geophysical Union’s science magazine. The article, titled “An Air Quality Model That Is Evolving with the Times”, discusses how the Sulfur Transport and Deposition Model (STEM) continues to find new applications and value in environmental science and policy making.

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Figure 1. Biases of accumulated precipitation (mm) relative to the MRMS ground-based analysis of the five machine learning models studied during the period of 1 May to 30 September, 2022. Biases of the operational MiRS algorithm are also shown in the bottom right panel.

Using Machine Learning to Improve Microwave-Based Precipitation Estimates

ESSIC/CISESS scientist Chris Grassotti along with CIRA and NOAA researchers Shuyan Liu and Quanhua (Mark) Liu, recently published a paper in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing titled “Warm-Season Microwave Integrated Retrieval System (MiRS) Precipitation Improvement Using Machine Learning Methods”.

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