Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A local ensemble Kalman filter for atmospheric data assimilation
2004546 citationsEdward Ott, Brian R. Hunt et al.Tellus A Dynamic Meteorology and Oceanographyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of M. Corazza'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 M. Corazza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Corazza more than expected).
This network shows the impact of papers produced by M. Corazza. 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 M. Corazza. The network helps show where M. Corazza may publish in the future.
Co-authorship network of co-authors of M. Corazza
This figure shows the co-authorship network connecting the top 25 collaborators of M. Corazza.
A scholar is included among the top collaborators of M. Corazza 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 M. Corazza. M. Corazza is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Corazza, M., et al.. (2007). An implementation of the Local Ensemble Kalman Filter in a quasi geostrophic model and comparison with 3D-Var. SHILAP Revista de lepidopterología.1 indexed citations
Corazza, M., et al.. (2006). Comparison of ensemble-based and variational-based data assimilation schemes in a quasi-geostrophic model.11 indexed citations
Ott, Edward, Brian R. Hunt, Istvan Szunyogh, et al.. (2004). A local ensemble Kalman filter for atmospheric data assimilation. Tellus A Dynamic Meteorology and Oceanography. 56(5). 415–415.546 indexed citations breakdown →
14.
Corazza, M., Eugenia Kalnay, D. J. Patil, et al.. (2003). Use of the breeding technique to estimate the structure of the analysis "errors of the day". SHILAP Revista de lepidopterología.58 indexed citations
15.
Buzzi, A., et al.. (2003). Three dimensional forecast verification of the limited area model BOLAM using radiosoundings of MAP-SOP dataset. EGS - AGU - EUG Joint Assembly. 9588.1 indexed citations
Ott, Edward, Brian R. Hunt, Istvan Szunyogh, et al.. (2002). Exploiting Local Low Dimensionality of the Atmospheric Dynamics for Efficient Ensemble Kalman Filtering. arXiv (Cornell University).17 indexed citations
19.
Kalnay, Eugenia, M. Corazza, & Ming Cai. (2002). Are Bred Vectors The Same As Lyapunov Vectors. EGSGA. 6820.16 indexed citations
20.
Corazza, M., et al.. (2001). Use of the breeding technique to estimate the shape of the analysis "errors of the day". AGUSM. 2001.8 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.