Marcus van Lier-Walqui

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    A[Marcus van Lier-Walqui]
    AC["Associated Concepts (25)"]
    AW["Authored Works (13)"]
    CA["Linked Co-Authors (15)"]
    CI["Linked Collaborating Institutions (14)"]
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ORCiD

https://orcid.org/0000-0002-0628-0045

OpenAlex ID

https://openalex.org/A5083288725 (API record)

Associated Concepts [?]

• Computer science
• Physics
• Geography
• Meteorology
• Environmental science
• Engineering
• Geology
• Mathematics
• Aerospace engineering
• Telecommunications
• Radar
• Statistics
• Remote sensing
• Quantum mechanics
• Mechanical engineering
• Operating system
• Artificial intelligence
• Political science
• Law
• Statistical physics
• Programming language
• Cloud computing
• Biology
• Bayesian probability
• Astronomy

Authored Works

sorted by decreasing year, and then by display-name

• Unsupervised learning of microphysical process rates using generative machine learning models
• Radar polarimetry and flash rate variability in varying thermodynamic and aerosol environments in Houston, TX
• Machine learning optimal prognostic moments for single-category cloud microphysics parameterizations
• Learning from JEFE: Using an Adjoint Model to Compare Error Sensitivities of Different Bulk Representations of Cloud Microphysics
• Exploring the Feasibility of a Single Liquid Category Bulk Microphysics Scheme
• Bulking Up with the BOSS: Accurate Emulation of the TAU Bin Model's Liquid Microphysics Using Simple Bulk Schemes
• Unifying top-down and bottom-up constraints on Earth system model physics via a Bayesian statistical-physical approach
• Understanding Sensitivity of Climate with a Perturbed Parameter Ensemble of the Community Earth System Model (CESM)
• The Impact of Observational Errors on the Optimization of GCM Free Parameters
• Radar-based Bayesian Estimation of Ice Aggregate Parameters Within a Microphysics Model
• One is Greater than Two for Collision-Coalescence in Bulk Microphysics Schemes
• Learning about microphysical processes from polarimetric radar observations with BOSS
• Bin, bulk, or BOSS? Producing bulk microphysics schemes that emulate bin microphysics using Bayesian inference

Linked Co-Authors

• Adele L. Igel
• Andrew Gettelman
• Arthur Z. Hu
• Duncan Watson-Parris
• Eric C. Bruning
• Gregory S. Elsaesser
• Hugh Morrison
• Israel Silber
• Kara D. Lamb
• Matthew R. Kumjian
• Peter Caldwell
• Po‐Lun Ma
• Sean Patrick Santos
• Timothy Logan
• Trude Eidhammer

Linked Collaborating Institutions

• Columbia University, New York
• Lawrence Livermore National Laboratory, California
• NASA Goddard Space Flight Center, Maryland
• National Center for Atmospheric Research, Colorado
• Pacific Northwest National Laboratory
• Pennsylvania State University
• Purdue University, Indiana
• Tel Aviv University, Israel
• Texas A&M University, College Station
• Texas Technological University
• University of California, Davis
• University of Oxford, UK
• University of Washington, Seattle
• Western Washington University

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