Daniel Hodyss

1.6k total citations
58 papers, 1.2k citations indexed

About

Daniel Hodyss is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Daniel Hodyss has authored 58 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Atmospheric Science, 40 papers in Global and Planetary Change and 16 papers in Oceanography. Recurrent topics in Daniel Hodyss's work include Meteorological Phenomena and Simulations (44 papers), Climate variability and models (40 papers) and Tropical and Extratropical Cyclones Research (18 papers). Daniel Hodyss is often cited by papers focused on Meteorological Phenomena and Simulations (44 papers), Climate variability and models (40 papers) and Tropical and Extratropical Cyclones Research (18 papers). Daniel Hodyss collaborates with scholars based in United States, United Kingdom and Vietnam. Daniel Hodyss's co-authors include Craig H. Bishop, David R. Ryglicki, William F. Campbell, James D. Doyle, Joshua H. Cossuth, Terrence R. Nathan, Nancy Nichols, Matthias Morzfeld, David S. Nolan and Yi Jin and has published in prestigious journals such as Physical Review Letters, Journal of Fluid Mechanics and Journal of the Atmospheric Sciences.

In The Last Decade

Daniel Hodyss

57 papers receiving 1.1k citations

Peers

Daniel Hodyss

GPC

OCEAN

Ross Bannister United Kingdom

Bruce Macpherson United Kingdom

Hiromu Seko Japan

Drasko Vasiljevic United Kingdom

Meta Sienkiewicz United States

Bernard Chapnik France

Altuğ Aksoy United States

Rolf H. Langland United States

Lucas Harris United States

D. Zupanski United States

Ross Bannister United Kingdom

Daniel Hodyss

886 ×0.8

792 ×0.9

GPC

117 ×0.4

OCEAN

196 ×0.9

30 ×0.7

Citations per year, relative to Daniel Hodyss Daniel Hodyss (= 1×) peers Ross Bannister

Countries citing papers authored by Daniel Hodyss

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Hodyss

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Hodyss

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

All Works

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20 of 20 papers shown

McDonald, S. E., K. F. Dymond, A. G. Burrell, et al.. (2024). ANCHOR: Global Parametrized Ionospheric Data Assimilation. Space Weather. 22(7). 1 indexed citations

Morzfeld, Matthias, et al.. (2024). High‐Dimensional Covariance Estimation From a Small Number of Samples. Journal of Advances in Modeling Earth Systems. 16(9). 6 indexed citations

Allen, Douglas, et al.. (2023). Low latitude monthly total electron content composite correlations. Journal of Space Weather and Space Climate. 13. 7–7.

Hodyss, Daniel & Matthias Morzfeld. (2023). How Sampling Errors in Covariance Estimates Cause Bias in the Kalman Gain and Impact Ensemble Data Assimilation. Monthly Weather Review. 151(9). 2413–2426. 1 indexed citations

Allen, Douglas, Daniel Hodyss, K. W. Hoppel, & Gerald E. Nedoluha. (2022). The Ensemble Tangent Linear Model Applied to Nonorographic Gravity Wave Drag. Monthly Weather Review. 151(1). 193–210. 1 indexed citations

Kochendorfer, John, Michael E. Earle, Daniel Hodyss, et al.. (2020). Undercatch Adjustments for Tipping-Bucket Gauge Measurements of Solid Precipitation. Journal of Hydrometeorology. 21(6). 1193–1205. 19 indexed citations

Ryglicki, David R., James D. Doyle, Daniel Hodyss, et al.. (2019). The Unexpected Rapid Intensification of Tropical Cyclones in Moderate Vertical Wind Shear. Part III: Outflow–Environment Interaction. Monthly Weather Review. 147(8). 2919–2940. 46 indexed citations

Morzfeld, Matthias & Daniel Hodyss. (2019). Gaussian approximations in filters and smoothers for data assimilation. Tellus A Dynamic Meteorology and Oceanography. 71(1). 1600344–1600344. 18 indexed citations

Ryglicki, David R., James D. Doyle, Yi Jin, Daniel Hodyss, & Joshua H. Cossuth. (2018). The Unexpected Rapid Intensification of Tropical Cyclones in Moderate Vertical Wind Shear. Part II: Vortex Tilt. Monthly Weather Review. 146(11). 3801–3825. 47 indexed citations

10.

Hodyss, Daniel, et al.. (2018). Observation-Informed Generalized Hybrid Error Covariance Models. Monthly Weather Review. 146(11). 3605–3622. 6 indexed citations

11.

Hodyss, Daniel, Craig H. Bishop, & Matthias Morzfeld. (2016). To what extent is your data assimilation scheme designed to find the posterior mean, the posterior mode or something else?. Tellus A Dynamic Meteorology and Oceanography. 68(1). 30625–30625. 5 indexed citations

12.

Hodyss, Daniel & William F. Campbell. (2013). Square Root and Perturbed Observation Ensemble Generation Techniques in Kalman and Quadratic Ensemble Filtering Algorithms. Monthly Weather Review. 141(7). 2561–2573. 13 indexed citations

13.

Bishop, Craig H. & Daniel Hodyss. (2011). Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation. Monthly Weather Review. 139(4). 1241–1255. 55 indexed citations

14.

Bishop, Craig H., Daniel Hodyss, Peter Steinle, et al.. (2010). Efficient Ensemble Covariance Localization in Variational Data Assimilation. Monthly Weather Review. 139(2). 573–580. 29 indexed citations

15.

Bishop, Craig H. & Daniel Hodyss. (2008). Ensemble covariances adaptively localized with ECO-RAP. Part 2: a strategy for the atmosphere. Tellus A Dynamic Meteorology and Oceanography. 61(1). 97–111. 87 indexed citations

16.

Hodyss, Daniel & David S. Nolan. (2007). Linear Anelastic Equations for Atmospheric Vortices. Journal of the Atmospheric Sciences. 64(8). 2947–2959. 16 indexed citations

17.

Hodyss, Daniel & Terrence R. Nathan. (2004). Effects of Topography and Potential Vorticity Forcing on Solitary Rossby Waves in Zonally Varying Flow. Geophysical & Astrophysical Fluid Dynamics. 98(3). 175–202. 8 indexed citations

18.

Hodyss, Daniel & Terrence R. Nathan. (2004). Long Waves in Streamwise Varying Shear Flows: New Mechanisms for a Weakly Nonlinear Instability. Physical Review Letters. 93(7). 74502–74502. 7 indexed citations

19.

Hodyss, Daniel & Terrence R. Nathan. (2002). Solitary Rossby Waves in Zonally Varying Jet Flows. Geophysical & Astrophysical Fluid Dynamics. 96(3). 239–262. 12 indexed citations

20.

Hodyss, Daniel & Richard Grotjahn. (2001). Diagnosing cyclogenesis by partitioning energy and potential enstrophy in a linear quasi-geostrophic model. Tellus A Dynamic Meteorology and Oceanography. 53(5). 567–577. 4 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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