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Figure 1. Global and regional validation of the logistic regression (blue), deep neural network (orange), random forest(green) and XGboost (red) snowfall detection (SD) model for S-NPP.

Fan and Meng Develop New Machine Learning Snowfall Detection Algorithm

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.

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A view of a city at night

Retired Satellites: A Chance to Shed Light

ESSIC/CISESS Assistant Research Scientist Zhuosen Wang is a co-author on a letter in Science titled “Retired Satellites: A Chance to Shed Light” alongside researchers from the Universities Space Research Association, German Research Centre for Geosciences, Cooperative Institute for Research in the Atmosphere, Earth Observation Center, and Northern Arizona University.

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Graphic abstract, all text is in the abstract

Improving Flood and Drought Management in Agricultural River Basins

ESSIC Post-doctoral Associate Natthachet Tangdamrongsub is a co-author on a new study in Water Policy that aims at the solutions to mitigate flood and drought damage to agriculture. For this international paper, Tangdamrongsub worked alongside researchers from the Rajamangala University of Technology Thanyaburi in Thailand and the IHE Delft Institute for Water Education in the Netherlands.

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Figure: Total transmittance from surface to satellite (black line). The red line is the accumulated CRTM radiance Jacobian to ozone profile. Symbol “c” is the position at 331 nm used to estimate surface reflectance. The symbol “o” are the two channels, that we propose, to estimate the surface reflectance. The surface reflectance for other channels is either interpolated or extrapolated from the two reflectance at 347.6 nm and 371.8 nm.

UV Surface Reflectance from OMPS Nadir Mapper (NM) Radiance—Simulation and Assimilation

ESSIC/CISESS scientists Christopher Grassotti and Xingming Liang are co-authors in a recently published study that documents the first ultraviolet radiance assimilation for atmospheric ozone in the troposphere and stratosphere. The paper, titled “Experimental OMPS Radiance Assimilation through One-Dimensional Variational Analysis for Total Column Ozone in the Atmosphere”, was published in Remote Sensing and includes co-authors from the NOAA/NESDIS Center for Satellite Applications and Research.

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The researchers smile in front of a plane

UMD, ESSIC Researchers Find Evidence of New CFC Production

In 1986, chlorofluorocarbons (CFCs), chemical compounds known to break down ozone in the atmosphere, were banned by the Montreal Protocol. This led to an immediate rapid decline in emissions. However, recent studies have shown that CFC-11 emissions have increased, suggesting a contribution from eastern Asia.

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A NOAA visualization of Tropical Storm Fred's path up the East Coast, which spreads from Alabama and Mississippi to northern New York and western Massachussets

Performance of NOAA Satellite-based Precipitation Products During Tropical Storm Fred

On August 18, 2021, the western part of North Carolina suffered a catastrophic flash flood caused by Tropical Storm Fred. As part of a NOAA/STAR precipitation validation project, CISESS science team Malar Arulraj, Veljko Petković, Ralph Ferraro, and Huan Meng evaluated the performance of different satellite-based precipitation products during this event using Multi-Radar/Multi-Sensor (MRMS) observations.

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A tall iceberg melts in ocean waters

State of the Climate in 2020: The Arctic

ESSIC/CISESS scientist Sinéad Farrell is one of the contributors to the new “State of the Climate in 2020: The Arctic”, released by the American Meteorological Society (AMS) in the August 2021 issue of the monthly bulletin (BAMS). She co-authored the section on sea ice extent.

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