Brian M. Griffin

1.7k total citations
12 papers, 255 citations indexed

About

Brian M. Griffin is a scholar working on Atmospheric Science, Global and Planetary Change and Earth-Surface Processes. According to data from OpenAlex, Brian M. Griffin has authored 12 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Atmospheric Science, 12 papers in Global and Planetary Change and 2 papers in Earth-Surface Processes. Recurrent topics in Brian M. Griffin's work include Meteorological Phenomena and Simulations (10 papers), Climate variability and models (8 papers) and Atmospheric aerosols and clouds (6 papers). Brian M. Griffin is often cited by papers focused on Meteorological Phenomena and Simulations (10 papers), Climate variability and models (8 papers) and Atmospheric aerosols and clouds (6 papers). Brian M. Griffin collaborates with scholars based in United States, China and Netherlands. Brian M. Griffin's co-authors include Vincent E. Larson, D. P. Schanen, Jean‐Christophe Golaz, James A. Hansen, Minghuai Wang, R. L. Storer, Philip J. Rasch, M. C. Wyant, Huan Guo and Zhun Guo and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Monthly Weather Review and Quarterly Journal of the Royal Meteorological Society.

In The Last Decade

Brian M. Griffin

12 papers receiving 254 citations

Peers

Brian M. Griffin
D. P. Schanen United States
Matthew F. Garvert United States
M. S. Yao United States
Alyssa Matthews United States
Fernando Prates United Kingdom
Mark Dixon United Kingdom
D. P. Schanen United States
Brian M. Griffin
Citations per year, relative to Brian M. Griffin Brian M. Griffin (= 1×) peers D. P. Schanen

Countries citing papers authored by Brian M. Griffin

Since Specialization
Citations

This map shows the geographic impact of Brian M. Griffin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Brian M. Griffin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian M. Griffin more than expected).

Fields of papers citing papers by Brian M. Griffin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian M. Griffin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Brian M. Griffin. The network helps show where Brian M. Griffin may publish in the future.

Co-authorship network of co-authors of Brian M. Griffin

This figure shows the co-authorship network connecting the top 25 collaborators of Brian M. Griffin. A scholar is included among the top collaborators of Brian M. Griffin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian M. Griffin. Brian M. Griffin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Wang, Minghuai, Zhibo Zhang, Vincent E. Larson, et al.. (2022). Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2. Journal of Advances in Modeling Earth Systems. 14(9). 1 indexed citations
3.
Larson, Vincent E., et al.. (2019). Momentum Transport in Shallow Cumulus Clouds and Its Parameterization by Higher‐Order Closure. Journal of Advances in Modeling Earth Systems. 11(11). 3419–3442. 18 indexed citations
4.
Griffin, Brian M. & Vincent E. Larson. (2016). A new subgrid-scale representation of hydrometeor fields using a multivariate PDF. Geoscientific model development. 9(6). 2031–2053. 7 indexed citations
5.
Griffin, Brian M. & Vincent E. Larson. (2016). Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS). Geoscientific model development. 9(11). 4273–4295. 9 indexed citations
6.
Storer, R. L., et al.. (2015). Parameterizing deep convection using the assumed probability density function method. Geoscientific model development. 8(1). 1–19. 36 indexed citations
7.
Thayer‐Calder, Katherine, Andrew Gettelman, Cheryl Craig, et al.. (2015). A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model. Geoscientific model development. 8(12). 3801–3821. 29 indexed citations
8.
Larson, Vincent E. & Brian M. Griffin. (2012). Analytic upscaling of a local microphysics scheme. Part I: Derivation. Quarterly Journal of the Royal Meteorological Society. 139(670). 46–57. 33 indexed citations
9.
Griffin, Brian M. & Vincent E. Larson. (2012). Analytic upscaling of a local microphysics scheme. Part II: Simulations. Quarterly Journal of the Royal Meteorological Society. 139(670). 58–69. 20 indexed citations
10.
Guo, Huan, Jean‐Christophe Golaz, Leo J. Donner, et al.. (2010). Multi-variate probability density functions with dynamics for cloud droplet activation in large-scale models: single column tests. Geoscientific model development. 3(2). 475–486. 21 indexed citations
11.
Golaz, Jean‐Christophe, Vincent E. Larson, James A. Hansen, D. P. Schanen, & Brian M. Griffin. (2007). Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework. Monthly Weather Review. 135(12). 4077–4096. 34 indexed citations
12.
Wyant, M. C., Christopher S. Bretherton, Andreas Chlond, et al.. (2007). A single‐column model intercomparison of a heavily drizzling stratocumulus‐topped boundary layer. Journal of Geophysical Research Atmospheres. 112(D24). 39 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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