Zihan Wang

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University of Michigan

United States

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Hemispheric Asymmetries in Thermospheric Composition and Temperature: Characteristics and Drivers

Open Date: 2023-04-01

Close Date: 2026-03-31

Articles (14)

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2021

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2021

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2020

Collaborators (4)

Aaron Ridley

Professor

University of Michigan

UNITED STATES

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Shasha Zou

Associate professor

University of Michigan

UNITED STATES

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Yang Chen

University of Michigan

UNITED STATES

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Yuxi Chen

University of Michigan

UNITED STATES

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Zihan Wang

Research Interests

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Recent Grants

Articles (14)

Formation Mechanism and Interhemispheric Asymmetry of Storm‐Enhanced Density During April 2023 Storm Revealed by GITM

Electron Energization With Bursty Bulk Flows: MHD With Embedded Particle‐In‐Cell Simulation

Phase and Amplitude Scintillations Associated With Polar Cap Patches: Statistical and Event Analyses

Significant Midlatitude Bubble‐Like Ionospheric Super‐Depletion Structure (BLISS) and Dynamic Variation of Storm‐Enhanced Density Plume During the 23 April 2023 Geomagnetic Storm

Development of Super Plasma Bubbles During the 7 September 2017 Geomagnetic Storm Revealed by Coupled GITM‐SAMI3 Simulations

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

Hemispheric Asymmetries in Thermospheric Composition and Temperature: GOLD Observations

Investigation of Interhemispheric Asymmetry of the Thermospheric Composition Observed by GOLD During the First Strong Geomagnetic Storm in Solar‐Cycle 25, 1: IMF By Effects

Next generation magnetic field measurements from low-earth orbit satellites enable enhanced space weather operations

COMPASS: A New COnductance Model Based on PFISR And SWARM Satellite Observations

Impact of Storm-Enhanced Density (SED) on Ion Upflow Fluxes During Geomagnetic Storm

Hemispheric Asymmetries in the Mid‐latitude Ionosphere During the September 7–8, 2017 Storm: Multi‐instrument Observations

Forecasting Global Ionospheric TEC Using Deep Learning Approach

Collaborators (4)

Aaron Ridley

Shasha Zou

Yang Chen

Yuxi Chen

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