Thomas N. Nipen

876 total citations
25 papers, 585 citations indexed

About

Thomas N. Nipen is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Thomas N. Nipen has authored 25 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Atmospheric Science, 14 papers in Global and Planetary Change and 5 papers in Environmental Engineering. Recurrent topics in Thomas N. Nipen's work include Meteorological Phenomena and Simulations (20 papers), Climate variability and models (13 papers) and Precipitation Measurement and Analysis (6 papers). Thomas N. Nipen is often cited by papers focused on Meteorological Phenomena and Simulations (20 papers), Climate variability and models (13 papers) and Precipitation Measurement and Analysis (6 papers). Thomas N. Nipen collaborates with scholars based in Norway, Canada and United States. Thomas N. Nipen's co-authors include Roland B. Stull, Luca Delle Monache, Yubao Liu, Gregory Roux, Ivar A. Seierstad, Cristian Lussana, Xingxiu Deng, Jørn Kristiansen, Øystein Hov and Patrik Bohlinger and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Water Resources Research and Monthly Weather Review.

In The Last Decade

Thomas N. Nipen

23 papers receiving 568 citations

Peers

Thomas N. Nipen

GPC

HTM

EEE

Dongjin Cho South Korea

Scarlet Stadtler Germany

Jan D. Keller Germany

Sabrina Wahl Germany

Bogdan Bochenek Poland

William Y. Y. Cheng United States

Xingxiu Deng Canada

Lukas Hubert Leufen Germany

Felix Kleinert Germany

Mariano Sastre Spain

Dongjin Cho South Korea

Thomas N. Nipen

286 ×0.7

224 ×0.6

GPC

309 ×1.5

83 ×1.1

HTM

34 ×0.5

EEE

Citations per year, relative to Thomas N. Nipen Thomas N. Nipen (= 1×) peers Dongjin Cho

Countries citing papers authored by Thomas N. Nipen

Since Specialization

Citations

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

Fields of papers citing papers by Thomas N. Nipen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas N. Nipen. 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 Thomas N. Nipen. The network helps show where Thomas N. Nipen may publish in the future.

Co-authorship network of co-authors of Thomas N. Nipen

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas N. Nipen. A scholar is included among the top collaborators of Thomas N. Nipen 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 Thomas N. Nipen. Thomas N. Nipen 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

Nipen, Thomas N., Ivar A. Seierstad, Jørn Kristiansen, et al.. (2025). Regional data-driven weather modeling with a global stretched-grid. 2 indexed citations

Bremnes, John Bjørnar, Thomas N. Nipen, & Ivar A. Seierstad. (2024). Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations. Nonlinear processes in geophysics. 31(2). 247–257.

Nipen, Thomas N., Roland B. Stull, Cristian Lussana, & Ivar A. Seierstad. (2023). Verif: A Weather-Prediction Verification Tool for Effective Product Development. Bulletin of the American Meteorological Society. 104(9). E1610–E1618.

Lussana, Cristian, et al.. (2023). Exploratory analysis of citizen observations of hourly precipitation over Scandinavia. Advances in science and research. 20. 35–48. 4 indexed citations

Lussana, Cristian, et al.. (2022). Optimizing Spatial Quality Control for a Dense Network of Meteorological Stations. Journal of Atmospheric and Oceanic Technology. 39(7). 973–984. 8 indexed citations

Lussana, Cristian, et al.. (2021). Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation. Nonlinear processes in geophysics. 28(1). 61–91. 5 indexed citations

Kelder, Timo, Malte Müller, Louise Slater, et al.. (2020). Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes. npj Climate and Atmospheric Science. 3(1). 67 indexed citations

Nipen, Thomas N., et al.. (2020). Citizen Observations for Weather Forecasts. Bulletin of the American Meteorological Society. 101(2). 137–142. 2 indexed citations

Båserud, Line, et al.. (2020). TITAN automatic spatial quality control of meteorological in-situ observations. Advances in science and research. 17. 153–163. 31 indexed citations

10.

Nipen, Thomas N., Ivar A. Seierstad, Cristian Lussana, & Jørn Kristiansen. (2019). Private Observations Improve MET Norway's Operational Weather Forecasts. EGU General Assembly Conference Abstracts. 14985. 1 indexed citations

11.

Lussana, Cristian, et al.. (2019). Spatial interpolation of two‐metre temperature over Norway based on the combination of numerical weather prediction ensembles and in situ observations. Quarterly Journal of the Royal Meteorological Society. 145(725). 3626–3643. 13 indexed citations

12.

Benedict, Imme, et al.. (2019). Large-Scale Flow Patterns Associated with Extreme Precipitation and Atmospheric Rivers over Norway. Monthly Weather Review. 147(4). 1415–1428. 23 indexed citations

13.

Engeland, Kolbjørn, et al.. (2018). Use of precipitation radar for improving estimates and forecasts of precipitation estimates and streamflow. EGU General Assembly Conference Abstracts. 12207. 3 indexed citations

14.

West, Gregory, et al.. (2017). Calibrated Probabilistic Hub-Height Wind Forecasts in Complex Terrain. Weather and Forecasting. 32(2). 555–577. 7 indexed citations

15.

Zhang, Yongxin, Yubao Liu, & Thomas N. Nipen. (2016). Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS. Advances in Meteorology. 2016. 1–13. 2 indexed citations

16.

Frogner, Inger‐Lise, Thomas N. Nipen, Andrew Singleton, John Bjørnar Bremnes, & Ole Vignes. (2016). Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches. Weather and Forecasting. 31(6). 1833–1851. 9 indexed citations

17.

Nipen, Thomas N., et al.. (2014). Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system. Water Resources Research. 50(4). 3108–3130. 28 indexed citations

18.

Monache, Luca Delle, Thomas N. Nipen, Yubao Liu, Gregory Roux, & Roland B. Stull. (2011). Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions. Monthly Weather Review. 139(11). 3554–3570. 170 indexed citations

19.

Nipen, Thomas N., Greg L. West, & Roland B. Stull. (2011). Updating Short-Term Probabilistic Weather Forecasts of Continuous Variables Using Recent Observations. Weather and Forecasting. 26(4). 564–571. 4 indexed citations

20.

Nipen, Thomas N. & Roland B. Stull. (2011). Calibrating probabilistic forecasts from an NWP ensemble. Tellus A Dynamic Meteorology and Oceanography. 63(5). 858–858. 20 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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