Tag: Numerical Modeling and Data Assimilation

Isaac Moradi smiles for the camera, wearing a red gridded button-up and a red tie

Isaac Moradi Joins UMD Research Council

ESSIC /CISESS Research Scientist Isaac Moradi was recently elected as a member of the University of Maryland Research Council. The Research Council is task force deployed by the University Senate that provides guidance to the UMD Vice President for research on matters such as:

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A screenshot of the virtual attendees at the summer intern party

CISESS Welcomes Summer Interns in 2022

This past summer, ESSIC’s Cooperative Institute for Satellite Earth System Studies (CISESS) welcomed 22 interns to assist in the research of 24 ESSIC/CISESS scientists. Of the interns, three were graduate students, 14 were undergraduates and five were high school students. Several of these students were returning interns from previous semesters.

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Figure: Vertical profiles of co-located LEO AMVs and RAY (red) and MIE (blue) winds. The top row shows the Arctic (north of 60° N), (a) mean AMV HLOSV (solid lines), Aeolus HLOSV (long dashed lines; m s−1), and mean AMV wind speed (short dashed lines; m s−1), (b) MCDs (solid), SDCDs (short dashed), and AMV HLOSV error, as represented by SDCD–Aeolus L2B uncertainty (long dashed; m s−1), and (c) co-location counts. Panels (d–f) are as in panels (a–c) but for the Antarctic (south of 60° S). Colored open circles indicate levels where MCDs are statistically significant at the 95 % level (p value < 0.05), using the paired Student’s t test. Vertical zero lines are displayed in the center panels in black. Levels with observation counts > 25 are plotted.

Atmospheric Motion Vector Bias and Uncertainty

ESSIC/CISESS Scientists Katherine Lukens (a former CISESS grad student), Kayo Ide, Hui Liu, and Ross Hoffman have a new article in the journal Atmospheric Measurement Techniques about their work with the NOAA/NESDIS Office of Projects, Planning, and Acquisition (OPPA) Technology Maturation Program (TMP). The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty.

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A New NOAA Blended Soil Moisture Product that Does Not Rely on Model Climatology

ESSIC/CISESS Scientists Jifu Yin, Jicheng Liu and Ralph Ferraro published a new article last month that discussed their work with NOAA’s Soil Moisture Operational Product System (SMOPS). SMOPS is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real time blended soil moisture (SM) for Numeric Weather Prediction and National Water Model applications.

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