Robert J. Kuligowski

2.3k total citations
51 papers, 1.8k citations indexed

About

Robert J. Kuligowski is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Robert J. Kuligowski has authored 51 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Atmospheric Science, 26 papers in Global and Planetary Change and 15 papers in Environmental Engineering. Recurrent topics in Robert J. Kuligowski's work include Meteorological Phenomena and Simulations (47 papers), Precipitation Measurement and Analysis (45 papers) and Climate variability and models (14 papers). Robert J. Kuligowski is often cited by papers focused on Meteorological Phenomena and Simulations (47 papers), Precipitation Measurement and Analysis (45 papers) and Climate variability and models (14 papers). Robert J. Kuligowski collaborates with scholars based in United States, Puerto Rico and Australia. Robert J. Kuligowski's co-authors include Ana P. Barros, Roderick A. Scofield, Soroosh Sorooshian, B. Imam, Mark DeMaria, Robert E. Tuleya, Kuolin Hsu, Ali Behrangi, Brian Skahill and Efi Foufoula‐Georgiou and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Geophysical Research Letters and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Robert J. Kuligowski

51 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert J. Kuligowski United States 20 1.5k 1.1k 517 204 81 51 1.8k
Thomas I. Petroliagis United Kingdom 7 1.5k 1.0× 1.4k 1.3× 259 0.5× 149 0.7× 189 2.3× 9 1.7k
U. Damrath Germany 7 1.1k 0.7× 1.1k 1.0× 194 0.4× 124 0.6× 107 1.3× 8 1.4k
Éric Bazile France 20 1.3k 0.9× 1.1k 1.0× 358 0.7× 111 0.5× 70 0.9× 43 1.5k
Maurice Schmeits Netherlands 18 811 0.5× 570 0.5× 150 0.3× 86 0.4× 161 2.0× 40 1.1k
Laurence J. Wilson Canada 16 934 0.6× 847 0.8× 193 0.4× 71 0.3× 172 2.1× 26 1.2k
Geoff Pegram South Africa 17 579 0.4× 833 0.8× 265 0.5× 367 1.8× 23 0.3× 43 1.3k
James Correia United States 16 956 0.6× 982 0.9× 176 0.3× 79 0.4× 29 0.4× 31 1.2k
Jean‐François Geleyn France 17 1.4k 1.0× 1.4k 1.3× 242 0.5× 82 0.4× 145 1.8× 23 1.6k
Kenneth R. Mylne United Kingdom 13 892 0.6× 891 0.8× 196 0.4× 75 0.4× 87 1.1× 19 1.2k
Joseph T. Schaefer United States 14 1.5k 1.0× 1.4k 1.3× 286 0.6× 75 0.4× 74 0.9× 38 1.8k

Countries citing papers authored by Robert J. Kuligowski

Since Specialization
Citations

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

Fields of papers citing papers by Robert J. Kuligowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert J. Kuligowski. 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 Robert J. Kuligowski. The network helps show where Robert J. Kuligowski may publish in the future.

Co-authorship network of co-authors of Robert J. Kuligowski

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

All Works

20 of 20 papers shown
1.
Kirstetter, Pierre‐Emmanuel, et al.. (2022). Towards improved precipitation estimation with the GOES‐16 advanced baseline imager: Algorithm and evaluation. Quarterly Journal of the Royal Meteorological Society. 148(748). 3406–3427. 6 indexed citations
2.
Kirstetter, Pierre‐Emmanuel, et al.. (2022). Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 1 indexed citations
3.
Kirstetter, Pierre‐Emmanuel, et al.. (2021). Classifying precipitation from GEO satellite observations: Diagnostic model. Quarterly Journal of the Royal Meteorological Society. 147(739). 3318–3334. 7 indexed citations
4.
Kirstetter, Pierre‐Emmanuel, et al.. (2021). Classifying precipitation from GEO satellite observations: Prognostic model. Quarterly Journal of the Royal Meteorological Society. 147(739). 3394–3409. 3 indexed citations
5.
Kirstetter, Pierre‐Emmanuel, et al.. (2020). On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates. Journal of Hydrometeorology. 21(6). 1367–1381. 16 indexed citations
6.
Zhang, Yu, et al.. (2018). Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm. Remote Sensing. 10(1). 106–106. 10 indexed citations
7.
DeMaria, Mark, Ralph Ferraro, John L. Beven, et al.. (2015). Satellite tools to monitor and predict Hurricane Sandy (2012): Current and emerging products. Atmospheric Research. 166. 165–181. 5 indexed citations
8.
Lee, Haksu, et al.. (2014). Utility of SCaMPR Satellite versus Ground-Based Quantitative Precipitation Estimates in Operational Flood Forecasting: The Effects of TRMM Data Ingest. Journal of Hydrometeorology. 15(3). 1051–1069. 10 indexed citations
9.
Sorooshian, Soroosh, Amir AghaKouchak, Phillip A. Arkin, et al.. (2011). Advancing the Remote Sensing of Precipitation. Bulletin of the American Meteorological Society. 92(10). 1271–1272. 49 indexed citations
10.
Ebert, Elizabeth E., et al.. (2010). Ensemble Tropical Rainfall Potential (eTRaP) Forecasts. Weather and Forecasting. 26(2). 213–224. 33 indexed citations
11.
Yücel, İsmail, Robert J. Kuligowski, & David Gochis. (2009). Evaluation of the Hydro-Estimator satellite rainfall algorithm and its utility in hydrological prediction in a mountainous region. IAHS-AISH publication. 259–266. 2 indexed citations
12.
Kuligowski, Robert J., et al.. (2009). A satellite rainfall detection algorithm. 61–66. 2 indexed citations
13.
Kuligowski, Robert J., et al.. (2009). Warm rainy clouds and droplet size distribution. WSEAS TRANSACTIONS on SYSTEMS archive. 8(1). 75–85. 3 indexed citations
14.
Kuligowski, Robert J., et al.. (2009). A projection algorithm for satellite rainfall detection. WSEAS TRANSACTIONS on SYSTEMS archive. 8(6). 763–772. 1 indexed citations
15.
Harmsen, Eric W., et al.. (2008). Satellite sub-pixel rainfall variability. 4(8). 504–513. 14 indexed citations
16.
Kuligowski, Robert J., et al.. (2008). Rainfall estimation from convective storms using the hydro-estimator and NEXRAD. WSEAS TRANSACTIONS on SYSTEMS archive. 7(10). 1016–1027. 12 indexed citations
17.
Kuligowski, Robert J., et al.. (2008). Validation and strategies to improve the Hydro-Estimator and NEXRAD over Puerto Rico. International Conference on Systems. 125(42). 799–806. 5 indexed citations
18.
Kuligowski, Robert J.. (2004). Re-calibrating the operational Hydro-Estimator satellite precipitation algorithm. 1 indexed citations
19.
Kuligowski, Robert J. & Ana P. Barros. (1998). Experiments in Short-Term Precipitation Forecasting Using Artificial Neural Networks. Monthly Weather Review. 126(2). 470–482. 112 indexed citations
20.
Barros, Ana P. & Robert J. Kuligowski. (1998). Orographic Effects during a Severe Wintertime Rainstorm in the Appalachian Mountains. Monthly Weather Review. 126(10). 2648–2672. 60 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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