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.
EARLINET: towards an advanced sustainable European aerosol lidar network
2014376 citationsGelsomina Pappalardo, Aldo Amodeo et al.Atmospheric measurement techniquesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Ina Mattis'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 Ina Mattis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ina Mattis more than expected).
This network shows the impact of papers produced by Ina Mattis. 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 Ina Mattis. The network helps show where Ina Mattis may publish in the future.
Co-authorship network of co-authors of Ina Mattis
This figure shows the co-authorship network connecting the top 25 collaborators of Ina Mattis.
A scholar is included among the top collaborators of Ina Mattis 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 Ina Mattis. Ina Mattis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rüfenacht, Rolf, Maxime Hervo, Lucia Mona, et al.. (2019). The European network of automatic lidars and ceilometers E-PROFILE: Validation through EARLINET/ACTRIS measurements and potential for satellite cal/val. EGU General Assembly Conference Abstracts. 9430.1 indexed citations
4.
Hervo, Maxime, Martial Haeffelin, Simone Kotthaus, et al.. (2018). Smoke Long range transport: monitoring with the new European automatic Lidar and ceilometer network: E-PROFILE. AGU Fall Meeting Abstracts. 2018.1 indexed citations
Mattis, Ina, Patric Seifert, Detlef Müller, et al.. (2010). Volcanic aerosol layers observed with multi-wavelength Raman lidar over Europe since summer 2008. EGU General Assembly Conference Abstracts. 9760.2 indexed citations
11.
Tegen, Ina, et al.. (2010). Model initialization and validation with ground- and space-based lidar measurements and sun photometer measurements. EGUGA. 9733.
12.
Pappalardo, Gelsomina & Ina Mattis. (2010). Dispersion and evolution of the Eyjafjallajökull ash plume over Europe: vertically resolved measurements with the European LIDAR network EARLINET. EGUGA. 15731.7 indexed citations
13.
Schneebeli, Marc, Ulla Wandinger, Ina Mattis, & Christian Mätzler. (2009). Optimal combination of Raman lidar and microwave radiometer data for tropospheric water and temperature profiling. Bern Open Repository and Information System (University of Bern).1 indexed citations
Arshinov, Yuri, et al.. (2004). Optic-fiber scramblers and a fourier transform lens as a means to tackle the problem on the overlap factor of lidar. University of Hertfordshire Research Archive (University of Hertfordshire). 561. 227.3 indexed citations
Ansmann, Albert, Ina Mattis, H. Jäger, & Ulla Wandinger. (1998). Stratospheric aerosol monitoring with lidar. Conventional backscatter versus raman lidar observations of Pinatubo aerosol. Fraunhofer-Publica (Fraunhofer-Gesellschaft).2 indexed citations
20.
Ansmann, Albert, et al.. (1998). Role of Lidar in Climate-related Aerosol Characterization Experiments.. 7–10.1 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.