About

Matthew West is a Professor in the Department of Climate, Meteorology & Atmospheric Sciences at the Illinois School of Earth, Society & Environment. He holds additional appointments as a Professor in Mechanical Science and Engineering and is an Affiliate at the National Center for Supercomputing Applications (NCSA). His research focuses on atmospheric sciences, with recent publications covering topics such as scalable performance-portable multiphase atmospheric chemistry, the role of liquid-liquid phase separation in cloud droplet activation, and the microphysics of aerosol-cloud interactions. West's work contributes to understanding complex atmospheric processes, leveraging computational and modeling techniques to advance the field.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Programming language
• Mathematics education
• Medicine
• Medical education
• Information Retrieval
• Psychology
• Engineering
• Mathematics
• Multimedia
• Linguistics
• Software engineering
• Pedagogy
• Statistics
• Atmospheric sciences
• Geology
• World Wide Web
• Environmental science
• Climatology
• Geography
• Meteorology
• Discrete mathematics

Selected publications

• Compensating biases in CCN predictions from composition averaging and neglected surfactant effects

  2026-05-20

  articleOpen access

  Abstract. Accurate predictions of cloud condensation nuclei (CCN) activation are essential for reducing uncertainties in aerosol-cloud interactions and climate projections. Most large-scale aerosol models represent particles as compositionally averaged internal mixtures and assume constant surface tension of water, neglecting particle-level compositional variability and surfactant-driven reductions in surface tension. Here we use the particle-resolved model WRF-PartMC to quantify how these simplifications affect CCN predictions by comparing particle-resolved (PR) and composition-averaged (Comp) aerosol populations under constant surface tension (CST) and effect surface tension (EST) treatments. Within this framework, PR-EST case provides the most physically detailed reference, and Comp-CST case represents a modal-like aerosol representation in large-scale models. We find this modal-like representation underpredicts CCN by ~19% on average relative to PR-EST reference. This bias reflects two opposing effects: neglecting surfactants suppresses activation, whereas composition averaging shifts activation in both directions depending on particle size and composition. A particle-level decomposition shows that Comp-EST modifies activation through coupled changes in hygroscopicity and surface tension that oppose each other, producing compensating shifts in particle critical supersaturation. These responses produce opposing biases across particle size ranges, with enhanced activation in Aitken mode and suppressed activation in accumulation mode. When EST is included, the remaining bias from composition averaging is substantially reduced, with Comp-EST case differing from the PR-EST reference by ~6% in domain-mean. These results demonstrate simplified aerosol schemes can produce apparently reasonable CCN predictions through compensating errors, even when underlying activation physics is misrepresented. Incorporating effective surface tension therefore offers a practical pathway to reduce structural biases in large-scale models.

  PublisherDOI

• Data for The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations

  Illinois Data Bank · 2026-01-01

  datasetOpen access

  This dataset contains the values directly shown in the figures of the article "The impact of aerosol mixing state on immersion freezing: Insights from classical nucleation theory and particle-resolved simulations". This article is in preparation for submission to the journal Atmospheric Chemistry and Physics. The dataset consists of 12 NetCDF files processed from the raw output of the PartMC model. It does not include the theoretical values of frozen fraction, which can be computed using the equations provided in the paper. *New in V2: adding data for a newly included figure (INP_spectrum.nc), removing files that are no longer used in the revised manuscript figures (e.g., UNC_A_ratio=0.9_Dp=0.1.nc, UNC_A_ratio=0.9_Dp=10.0.nc, UNC_A_ratio=0.1_Dp=0.1.nc, and UNC_A_ratio=0.1_Dp=10.0.nc), and updating README.pdf accordingly.

  PublisherDOI

• Idealized particle-resolved large-eddy simulations to evaluate the impact of emissions spatial heterogeneity on CCN activity

  Atmospheric chemistry and physics · 2026-04-14

  articleOpen access

  Abstract. Aerosol-cloud interactions remain a large source of uncertainty in global climate models (GCMs) due to complex, nonlinear processes that alter aerosol properties and the inability to represent the full compositional complexity of aerosol populations within large-scale modeling frameworks. The spatial resolution of GCMs is often coarser than the scale of the spatially varying emissions in the modeled geographic region. This results in diffuse, uniform concentration fields of primary aerosol and gas-phase species instead of spatially heterogeneous concentrations. Aerosol processes such as gas-particle partitioning and coagulation are concentration-dependent in a non-linear manner, and thus the representation of spatially heterogeneous emissions impacts aerosol aging and properties. This includes climate-relevant quantities key to aerosol-cloud interactions including particle hygroscopicity and cloud condensation nuclei (CCN) activity. We investigate the impact of emissions spatial heterogeneity on aerosol properties including CCN activity via a series of first-of-a-kind particle-resolved large-eddy simulations with the modeling framework WRF-PartMC-MOSAIC-LES. CCN concentrations within the planetary boundary layer (PBL) are compared across numerous scenarios ranging in emissions spatial heterogeneity. CCN concentrations at low supersaturations (Senv=0.1 %–0.3 %) increase in the upper PBL by up to 25 % for emissions scenarios with high spatial heterogeneity when compared to a uniform emissions base case. Process level analysis indicates that this increase is due to enhanced nitrate formation among scenarios with high emissions spatial heterogeneity.

  PublisherDOI

• Do Centralized Testing Centers Influence Test Anxiety forEngineering Students?

  2025-08-21

  article
  PublisherDOI

• Outcomes Following First-Line Immune Checkpoint Inhibitors With or Without Chemotherapy Stratified by KRAS Mutational Status—A Real-World Analysis in Patients With Advanced NSCLC

  Clinical Lung Cancer · 2025-05-28 · 2 citations

  article
  PublisherDOI

• Reflecting on Ten Years of Building a Community of Practice for Teaching Innovations in Fundamental Mechanics Courses

  2025-08-21

  article
  PublisherDOI

• A statistical model for predicting water temperature in temperate rivers and streams

  Marine and Freshwater Research · 2025-08-06 · 1 citations

  articleOpen access

  Context Water temperature affects the biology and ecology of many freshwater species. However, in situ water temperature measurements are not always available because of spatial or temporal gaps in observations. Aims We evaluated the importance of different environmental variables in predicting water temperature in temperate Australian rivers and streams and developed a water temperature model for use in these environments. Methods We used linear mixed models that incorporated combinations of air temperature, stream flow and catchment variables to predict daily water temperatures. Key results Air temperature integrated over the preceding 7 days, in conjunction with elevation, were very good predictors of water temperature. However, stream flow did not significantly improve model predictions. Conclusions Air temperature explained the most variation in water temperature, and elevation also improved model predictions. Implications Our approach demonstrated that water temperature in temperate rivers and streams can be readily modelled using elevation and air temperature across large spatial and temporal scales. Our work has provided an easily implementable method to fill gaps in monitoring networks in temperate climate zones exhibiting warm summers. The predictions created by our model will have most use in studies where researchers want to explore the impacts of relative changes in water temperature.

  PublisherOA PDFDOI

• The effects of a pandemic on community college faculty work life

  Sociological Spectrum · 2025-03-20

  article1st authorCorresponding
  PublisherDOI

• Immersion Freezing in Particle‐Based Aerosol‐Cloud Microphysics: A Probabilistic Perspective on Singular and Time‐Dependent Models

  Journal of Advances in Modeling Earth Systems · 2025-04-01 · 1 citations

  articleOpen access

  Abstract Cloud droplets containing immersed ice‐nucleating particles (INPs) may freeze at temperatures above the homogeneous freezing threshold temperature in a process referred to as immersion freezing. In modeling studies, immersion freezing is often described using either so‐called “singular” or “time‐dependent” parameterizations. Here, we compare both approaches and discuss them in the context of probabilistic particle‐based (super‐droplet) cloud microphysics modeling. First, using a box model, we contrast how both parameterizations respond to idealized ambient cooling rate profiles and quantify the impact of the polydispersity of the immersed surface spectrum on the frozen fraction evolution. Presented simulations highlight that the singular approach, constituting a time‐integrated form of a more general time‐dependent approach, is only accurate under a limited range of ambient cooling rates. The time‐dependent approach is free from this limitation. Second, using a prescribed‐flow two‐dimensional cloud model, we illustrate the macroscopic differences in the evolution in time of ice particle concentrations in simulations with flow regimes relevant to ambient cloud conditions. The flow‐coupled aerosol‐budget‐resolving simulations highlight the benefits and challenges of modeling cloud condensation nuclei activation and immersion freezing on insoluble ice nuclei with super‐particle methods. The challenges stem, on the one hand, from heterogeneous ice nucleation being contingent on the presence of relatively sparse immersed INPs, and on the other hand, from the need to represent a vast population of particles with relatively few so‐called super particles (each representing a multiplicity of real particles). We discuss the critical role of the sampling strategy for particle attributes, including the INP size, the freezing temperature (for singular scheme) and the multiplicity.

  PublisherOA PDFDOI

• Making space for curiosity, connection, and creating value by integrating real-world examples into engineering education

  2025-08-21

  article
  PublisherDOI

Recent grants

• CAREER: Particle Methods for High-Dimensional Integro-Differential Simulation with Application to Aerosol Modeling

  NSF · $419k · 2012–2018

Frequent coauthors

• Craig Zilles

  University of Illinois Urbana-Champaign

  95 shared

• Geoffrey Herman

  Lahore University of Management Sciences

  83 shared

• Nicole Riemer

  University of Illinois Urbana-Champaign

  65 shared

• Nicolas Nytko

  University of Illinois Urbana-Champaign

  44 shared

• Mariana Silva

  39 shared

• Mariana Silva

  Universidade Estadual Paulista (Unesp)

  38 shared

• Jacob Bailey

  Kwantlen Polytechnic University

  37 shared

• Jeffrey H. Curtis

  University of Illinois Urbana-Champaign

  24 shared

Education

• Ph.D. in Control and Dynamical Systems

  California Institute of Technology

  2003

• B.Sc. in Pure and Applied Mathematics

  University of Western Australia

  1996

Awards & honors

• William H. Severns Faculty Scholar, Mechanical Science and E…