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.
Strictly Proper Scoring Rules, Prediction, and Estimation
20073.1k citationsTilmann Gneiting, Adrian E. RafteryJournal of the American Statistical Associationprofile →
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
20051.4k citationsAdrian E. Raftery, Tilmann Gneiting et al.Monthly Weather Reviewprofile →
Probabilistic Forecasts, Calibration and Sharpness
20071.1k citationsTilmann Gneiting, Fadoua Balabdaoui et al.Journal of the Royal Statistical Society Series B (Statistical Methodology)profile →
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
2005753 citationsTilmann Gneiting, Adrian E. Raftery et al.Monthly Weather Reviewprofile →
Making and Evaluating Point Forecasts
2011724 citationsTilmann GneitingJournal of the American Statistical Associationprofile →
Countries citing papers authored by Tilmann Gneiting
Since
Specialization
Citations
This map shows the geographic impact of Tilmann Gneiting'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 Tilmann Gneiting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tilmann Gneiting more than expected).
Fields of papers citing papers by Tilmann Gneiting
This network shows the impact of papers produced by Tilmann Gneiting. 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 Tilmann Gneiting. The network helps show where Tilmann Gneiting may publish in the future.
Co-authorship network of co-authors of Tilmann Gneiting
This figure shows the co-authorship network connecting the top 25 collaborators of Tilmann Gneiting.
A scholar is included among the top collaborators of Tilmann Gneiting 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 Tilmann Gneiting. Tilmann Gneiting is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vogel, Peter, Tilmann Gneiting, Peter Knippertz, Andreas H. Fink, & Andreas Schlüter. (2017). Statistical ensemble postprocessing for precipitation forecasting during the West African Monsoon. European geosciences union general assembly. 14208.1 indexed citations
10.
Gneiting, Tilmann. (2011). Statistical postprocessing for ensembles of numerical weather prediction models. Aisberg (University of Bergamo). 1–4.1 indexed citations
Berrocal, Veronica J., Adrian E. Raftery, & Tilmann Gneiting. (2008). PROBABILISTIC QUANTITATIVE PRECIPITATION FIELD FORECASTING USING A TWO-STAGE SPATIAL MODEL 1.62 indexed citations
14.
Gneiting, Tilmann & Adrian E. Raftery. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102(477). 359–378.3105 indexed citations breakdown →
15.
Gneiting, Tilmann, Fadoua Balabdaoui, & Adrian E. Raftery. (2007). Probabilistic Forecasts, Calibration and Sharpness. Journal of the Royal Statistical Society Series B (Statistical Methodology). 69(2). 243–268.1096 indexed citations breakdown →
16.
Raftery, Adrian E., Tilmann Gneiting, Fadoua Balabdaoui, & Michael Polakowski. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review. 133(5). 1155–1174.1415 indexed citations breakdown →
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.