HelioWebKristopher M. Bedka
flowchart
A[Kristopher M. Bedka]
AC["Associated Concepts (25)"]
AW["Authored Works (9)"]
CA["Linked Co-Authors (22)"]
CI["Linked Collaborating Institutions (21)"]
A== dcterms:relation ==>AC
AW== author ==>A
AW== author ==>CA
AW== affil ==>CI
click AC "#associated-concepts"
click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
NI["add incoming edge"]
NO["add outgoing edge"]
NI-- ? -->A
A-- ? -->NO
click NI "#add-incoming-edge"
click NO "#add-outgoing-edge"
style NI stroke-width:2px,stroke-dasharray: 5 5
style NO stroke-width:2px,stroke-dasharray: 5 5
- ORCiD
- https://orcid.org/0000-0003-3066-0555
- OpenAlex ID
- https://openalex.org/A5013805896 (API record)
Associated Concepts [?]
- Geology
- Geography
- Physics
- Meteorology
- Environmental science
- Engineering
- Aerospace engineering
- Astronomy
- Computer science
- Remote sensing
- Oceanography
- Climatology
- Satellite
- Atmospheric sciences
- Thermodynamics
- Quantum mechanics
- Mechanics
- Optics
- Chemistry
- Convection
- Telecommunications
- Operating system
- Precipitation
- Geostationary orbit
- Troposphere
Authored Works
sorted by decreasing year, and then by display-name
- Spaceborne Passive-Microwave and Visible/IR Observations of Severe Weather: Leveraging Multiple Perspectives for Detection, Validation, and Climatologies
- Global perturbation of stratospheric water and aerosol burden by the Hunga eruption
- Balloon-borne observations of infrasound waves from the January 2022 eruption of the Hunga volcano, Tonga
- Automated Detection and Analysis of Severe, Tropopause-Penetrating Convective Storm Patterns Using Remote Sensing Data Fusion and Deep Learning
- Use of Geostationary Infrared, Passive Microwave Imager, and Reanalysis Datasets to Assess Climatological Hailstorm Risk
- Microphysical Properties of Tropopause Cirrus Clouds during BATAL campaigns in India.
- Identification of the Unique Microphysical Signatures of Above Anvil Cirrus Plumes in GOES-16/-17 Infrared and Visible Imagery
- Critical Research Pathways in Satellite-Based Dense Optical Flow Techniques for Atmospheric Science
- Automated Detection of Severe Thunderstorm Signatures Within Geostationary Satellite Imagery Using Deep Learning
Linked Co-Authors
- Adam Bourassa
- Alexis Le Pichon
- Aurélien Podglajen
- Bernard Legras
- Bo Zhang
- Cameron R. Homeyer
- Hongyu Liu
- Isamu Morino
- Jean‐Paul Vernier
- John Barnes
- John R. Mecikalski
- Kyle F. Itterly
- Landon Rieger
- Melody Avery
- Richard Querel
- Sarah D. Bang
- Sergey Khaykin
- Sophie Godin‐Beekmann
- Steven D. Miller
- Tetsu Sakai
- Yoonjin Lee
- Yoo‐Jeong Noh
Linked Collaborating Institutions
- Central Michigan University
- Colorado State University
- Direction des Applications Militaires, Ile-de-France
- Ecole Nationale Superieure de l'Aeronautique et de l'Espace de Toulouse, France
- Eidgenossische Technische Hochschule, Zurich, Switzerland
- Helmholtz Research Center, Julich
- JMA, Meteorological Research Institute, Tsukuba
- Karlsruhe Institute of Technology, Germany
- NASA Langley Research Center
- NASA Marshall Space Flight Center
- National Atmospheric Research Laboratory, India
- National Center for Atmospheric Research, Colorado
- National Institute of Water and Atmospheric Research, New Zealand
- National Oceanic and Atmospheric Administration, Boulder
- Physical Research Laboratory, India
- Science Systems and Applications Inc., Maryland
- Tata Institute of Fundamental Research, India
- University of Alabama, Huntsville
- University of Oklahoma, Norman
- University of Saskatchewan, Canada
- University of Utah
Add Incoming Edge
Login via ORCiD to contribute.
Add Outgoing Edge
Login via ORCiD to contribute.