Nathan Snook

1.3k total citations
27 papers, 1.0k citations indexed

About

Nathan Snook is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Nathan Snook has authored 27 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Atmospheric Science, 23 papers in Global and Planetary Change and 2 papers in Environmental Engineering. Recurrent topics in Nathan Snook's work include Meteorological Phenomena and Simulations (27 papers), Climate variability and models (22 papers) and Precipitation Measurement and Analysis (11 papers). Nathan Snook is often cited by papers focused on Meteorological Phenomena and Simulations (27 papers), Climate variability and models (22 papers) and Precipitation Measurement and Analysis (11 papers). Nathan Snook collaborates with scholars based in United States. Nathan Snook's co-authors include Ming Xue, Youngsun Jung, William A. Gallus, Bryan J. Putnam, Guifu Zhang, Keith Brewster, Amy McGovern, David John Gagne, Corey K. Potvin and Jerald A. Brotzge and has published in prestigious journals such as Geophysical Research Letters, Monthly Weather Review and Bulletin of the American Meteorological Society.

In The Last Decade

Nathan Snook

26 papers receiving 1000 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nathan Snook United States	16	979	875	178	24	21	27	1.0k
Patrick S. Skinner United States	18	801 0.8×	704 0.8×	172 1.0×	20 0.8×	29 1.4×	51	855
Andrew R. Dean United States	14	919 0.9×	831 0.9×	205 1.2×	14 0.6×	39 1.9×	29	959
Bryan T. Smith United States	15	1.1k 1.2×	1.0k 1.2×	246 1.4×	18 0.8×	56 2.7×	30	1.2k
Donghai Wang China	9	644 0.7×	580 0.7×	90 0.5×	36 1.5×	13 0.6×	16	694
Glen S. Romine United States	20	1.2k 1.2×	1.1k 1.2×	242 1.4×	41 1.7×	15 0.7×	44	1.3k
Michael P. Foster United States	7	556 0.6×	490 0.6×	121 0.7×	13 0.5×	30 1.4×	11	619
Dustan M. Wheatley United States	15	1.1k 1.2×	1.1k 1.2×	218 1.2×	19 0.8×	44 2.1×	18	1.2k
Thibaut Montmerle France	17	880 0.9×	791 0.9×	189 1.1×	89 3.7×	26 1.2×	37	923
Youngsun Jung United States	24	1.7k 1.7×	1.5k 1.7×	244 1.4×	67 2.8×	33 1.6×	51	1.7k
Louisa Nance United States	13	673 0.7×	482 0.6×	120 0.7×	66 2.8×	28 1.3×	24	726

Countries citing papers authored by Nathan Snook

Since Specialization

Citations

This map shows the geographic impact of Nathan Snook'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 Nathan Snook with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Snook more than expected).

Fields of papers citing papers by Nathan Snook

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan Snook. 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 Nathan Snook. The network helps show where Nathan Snook may publish in the future.

Co-authorship network of co-authors of Nathan Snook

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Snook. A scholar is included among the top collaborators of Nathan Snook 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 Nathan Snook. Nathan Snook is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

McGovern, Amy, Randy J. Chase, Montgomery L. Flora, et al.. (2023). A Review of Machine Learning for Convective Weather. NOAA Institutional Repository. 2(3). 24 indexed citations

Hu, Xiao‐Ming, Timothy A. Supinie, Nathan Snook, et al.. (2022). Diagnosing Near-Surface Model Errors with Candidate Physics Parameterization Schemes for the Multiphysics Rapid Refresh Forecast System (RRFS) Ensemble during Winter over the Northeastern United States and Southern Great Plains. Monthly Weather Review. 151(1). 39–61. 4 indexed citations

McGovern, Amy, Ann Bostrom, Phillip Davis, et al.. (2022). NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Bulletin of the American Meteorological Society. 103(7). E1658–E1668. 11 indexed citations

Xu, Qin, et al.. (2022). A Vortex Relocation Method for Improving Initial Conditions and Subsequent Predictions of Tornadoes. Weather and Forecasting. 38(3). 391–399.

Supinie, Timothy A., Nathan Snook, Xiao‐Ming Hu, et al.. (2022). Cool-Season Evaluation of FV3-LAM-Based CONUS-Scale Forecasts with Physics Configurations of Experimental RRFS Ensembles. Monthly Weather Review. 150(9). 2379–2398. 4 indexed citations

Snook, Nathan, Fanyou Kong, Adam J. Clark, et al.. (2020). Comparison and Verification of Point‐Wise and Patch‐Wise Localized Probability‐Matched Mean Algorithms for Ensemble Consensus Precipitation Forecasts. Geophysical Research Letters. 47(12). 4 indexed citations

Snook, Nathan, et al.. (2020). Evaluating Ensemble Kalman Filter Analyses of Severe Hailstorms on 8 May 2017 in Colorado: Effects of State Variable Updating and Multimoment Microphysics Schemes on State Variable Cross Covariances. Monthly Weather Review. 148(6). 2365–2389. 5 indexed citations

Snook, Nathan, Fanyou Kong, Keith Brewster, et al.. (2019). Evaluation of Convection-Permitting Precipitation Forecast Products Using WRF, NMMB, and FV3 for the 2016–17 NOAA Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiments. Weather and Forecasting. 34(3). 781–804. 26 indexed citations

Putnam, Bryan J., Ming Xue, Youngsun Jung, Nathan Snook, & Guifu Zhang. (2019). Ensemble Kalman Filter Assimilation of Polarimetric Radar Observations for the 20 May 2013 Oklahoma Tornadic Supercell Case. Monthly Weather Review. 147(7). 2511–2533. 28 indexed citations

10.

Snook, Nathan, et al.. (2019). Explicit Ensemble Prediction of Hail in 19 May 2013 Oklahoma City Thunderstorms and Analysis of Hail Growth Processes with Several Multimoment Microphysics Schemes. Monthly Weather Review. 147(4). 1193–1213. 25 indexed citations

11.

Zhang, Chunxi, Ming Xue, Timothy A. Supinie, et al.. (2019). How Well Does an FV3‐Based Model Predict Precipitation at a Convection‐Allowing Resolution? Results From CAPS Forecasts for the 2018 NOAA Hazardous Weather Test Bed With Different Physics Combinations. Geophysical Research Letters. 46(6). 3523–3531. 15 indexed citations

12.

Putnam, Bryan J., Ming Xue, Youngsun Jung, Nathan Snook, & Guifu Zhang. (2017). Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation. Monthly Weather Review. 145(6). 2257–2279. 23 indexed citations

13.

Snook, Nathan, Youngsun Jung, Jerald A. Brotzge, Bryan J. Putnam, & Ming Xue. (2016). Prediction and Ensemble Forecast Verification of Hail in the Supercell Storms of 20 May 2013. Weather and Forecasting. 31(3). 811–825. 49 indexed citations

14.

Snook, Nathan, Ming Xue, & Youngsun Jung. (2014). Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System. Monthly Weather Review. 143(4). 1035–1057. 62 indexed citations

15.

Putnam, Bryan J., Ming Xue, Youngsun Jung, Nathan Snook, & Guifu Zhang. (2013). The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter. Monthly Weather Review. 142(1). 141–162. 66 indexed citations

16.

Snook, Nathan. (2013). Impacts of Assumed Observation Errors in EnKF Analyses and Ensemble Forecasts of a Tornadic Mesoscale Convective System. 5 indexed citations

17.

Snook, Nathan, Ming Xue, & Youngsun Jung. (2012). Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data. Monthly Weather Review. 140(7). 2126–2146. 59 indexed citations

18.

Stensrud, David J., Louis J. Wicker, Ming Xue, et al.. (2012). Progress and challenges with Warn-on-Forecast. Atmospheric Research. 123. 2–16. 173 indexed citations

19.

Gallus, William A., et al.. (2008). Spring and Summer Severe Weather Reports over the Midwest as a Function of Convective Mode: A Preliminary Study. Weather and Forecasting. 23(1). 101–113. 158 indexed citations

20.

Snook, Nathan & Ming Xue. (2008). Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophysical Research Letters. 35(24). 99 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.

Explore authors with similar magnitude of impact