Xiaolan L. Wang

11.4k total citations · 1 hit paper
87 papers, 6.9k citations indexed

About

Xiaolan L. Wang is a scholar working on Global and Planetary Change, Atmospheric Science and Oceanography. According to data from OpenAlex, Xiaolan L. Wang has authored 87 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Global and Planetary Change, 56 papers in Atmospheric Science and 32 papers in Oceanography. Recurrent topics in Xiaolan L. Wang's work include Climate variability and models (71 papers), Meteorological Phenomena and Simulations (31 papers) and Ocean Waves and Remote Sensing (22 papers). Xiaolan L. Wang is often cited by papers focused on Climate variability and models (71 papers), Meteorological Phenomena and Simulations (31 papers) and Ocean Waves and Remote Sensing (22 papers). Xiaolan L. Wang collaborates with scholars based in Canada, United States and United Kingdom. Xiaolan L. Wang's co-authors include Val R. Swail, Yang Feng, Francis W. Zwiers, Yuehua Wu, Mark Hemer, Robert Lund, Jaxk Reeves, Qi Lu, Yalin Fan and Qiuzi Han Wen and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Journal of Climate and Geophysical Research Letters.

In The Last Decade

Xiaolan L. Wang

83 papers receiving 6.7k citations

Hit Papers

Projected changes in wave climate from a multi-model ense... 2013 2026 2017 2021 2013 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaolan L. Wang Canada 43 4.6k 4.3k 2.1k 689 675 87 6.9k
Hans Hersbach United Kingdom 22 3.7k 0.8× 3.9k 0.9× 1.8k 0.9× 551 0.8× 1.2k 1.7× 42 6.7k
Kenneth R. Knapp United States 28 4.8k 1.0× 5.3k 1.2× 2.0k 0.9× 278 0.4× 327 0.5× 57 6.2k
Joaquim G. Pinto Germany 57 7.3k 1.6× 6.4k 1.5× 1.3k 0.6× 372 0.5× 589 0.9× 212 9.6k
Alex Hall United States 57 8.5k 1.8× 7.6k 1.7× 1.4k 0.6× 256 0.4× 567 0.8× 147 10.8k
Suranjana Saha United States 15 5.6k 1.2× 5.4k 1.2× 2.1k 1.0× 206 0.3× 403 0.6× 26 6.8k
Jean‐Noël Thépaut United Kingdom 36 6.5k 1.4× 6.4k 1.5× 1.3k 0.6× 167 0.2× 1.3k 1.9× 79 8.8k
Uwe Ulbrich Germany 43 5.4k 1.2× 4.7k 1.1× 933 0.4× 285 0.4× 613 0.9× 144 6.8k
Jack Woollen United States 10 7.2k 1.5× 6.7k 1.5× 2.2k 1.0× 135 0.2× 605 0.9× 12 8.8k
Wilco Hazeleger Netherlands 43 4.4k 0.9× 3.6k 0.8× 1.8k 0.8× 261 0.4× 248 0.4× 140 5.6k
Kevin I. Hodges United Kingdom 59 11.4k 2.5× 11.7k 2.7× 3.3k 1.5× 350 0.5× 313 0.5× 230 13.1k

Countries citing papers authored by Xiaolan L. Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xiaolan L. Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaolan L. Wang. 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 Xiaolan L. Wang. The network helps show where Xiaolan L. Wang may publish in the future.

Co-authorship network of co-authors of Xiaolan L. Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaolan L. Wang. A scholar is included among the top collaborators of Xiaolan L. Wang 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 Xiaolan L. Wang. Xiaolan L. Wang 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.
Qian, Budong, et al.. (2025). Observed Changes in Canada’s Snowfall as Inferred from Precipitation and Daily Mean Temperatures. ATMOSPHERE-OCEAN. 64(2). 132–146.
2.
Zwiers, Francis W., et al.. (2025). Constrained Estimates of Externally Forced Past and Future Warming for Canada. Earth s Future. 13(10).
3.
Cheng, Vincent Y. S., Xiaolan L. Wang, & Yang Feng. (2024). A Quality Control System for Historical In Situ Precipitation Data. ATMOSPHERE-OCEAN. 62(4). 271–287. 1 indexed citations
4.
Casas‐Prat, Mercè, Mark Hemer, Guillaume Dodet, et al.. (2024). Author Correction: Wind-wave climate changes and their impacts. Nature Reviews Earth & Environment. 5(2). 152–152. 1 indexed citations
6.
Casas‐Prat, Mercè, et al.. (2023). A 100-member ensemble simulations of global historical (1951–2010) wave heights. Scientific Data. 10(1). 362–362. 2 indexed citations
7.
Wang, Xiaolan L., Yang Feng, Vincent Y. S. Cheng, & Xu Hong. (2023). Observed Precipitation Trends Inferred from Canada’s Homogenized Monthly Precipitation Dataset. Journal of Climate. 36(22). 7957–7971. 10 indexed citations
8.
Wang, Xiaolan L., et al.. (2021). Historical Changes in the Davis Strait Baffin Bay Surface Winds and Waves, 1979–2016. Journal of Climate. 34(22). 8879–8896. 2 indexed citations
9.
Min, Seung‐Ki, et al.. (2021). Changes in extreme ocean wave heights under 1.5 °C, 2 °C, and 3 °C global warming. Weather and Climate Extremes. 33. 100358–100358. 13 indexed citations
10.
Kaur, Sukhwinder, et al.. (2021). CMIP5 model evaluation for extreme ocean wave height responses to ENSO. Climate Dynamics. 59(5-6). 1323–1337. 2 indexed citations
11.
Li, Qingxiang, Yun Xiang, Boyin Huang, et al.. (2021). An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST. Climate Dynamics. 56(1-2). 635–650. 31 indexed citations
12.
Morim, Joao, Claire Trenham, Mark Hemer, et al.. (2020). A global ensemble of ocean wave climate projections from CMIP5-driven models. Scientific Data. 7(1). 105–105. 70 indexed citations
13.
Xu, Wenhui, Qingxiang Li, P. D. Jones, et al.. (2017). A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900. Climate Dynamics. 50(7-8). 2513–2536. 69 indexed citations
14.
Wu, Yuehua, et al.. (2014). Bayesian spatiotemporal modeling for blending in situ observations with satellite precipitation estimates. Journal of Geophysical Research Atmospheres. 119(4). 1806–1819. 17 indexed citations
15.
Wang, Xiaolan L., et al.. (2013). Is the storminess in the Twentieth Century Reanalysis really inconsistent with observations? - A reply to the comment by Krueger et al. (2013). EGUGA. 1 indexed citations
16.
Hemer, Mark, Yalin Fan, Nobuhito Mori, Álvaro Semedo, & Xiaolan L. Wang. (2013). Projected changes in wave climate from a multi-model ensemble. Nature Climate Change. 3(5). 471–476. 431 indexed citations breakdown →
17.
Vincent, Lucie A., Xiaolan L. Wang, Ewa J. Milewska, et al.. (2012). A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. Journal of Geophysical Research Atmospheres. 117(D18). 294 indexed citations
18.
Wang, Xiaolan L.. (2005). Climatology and Changes of Extra-Tropical Storm Tracks and Cyclone Activity: Comparison of ERA-40 with NCEP/NCAR Reanalysis for 1958-2001. 12 indexed citations
19.
Wang, Xiaolan L. & Francis W. Zwiers. (1999). Interannual Variability of Precipitation in an Ensemble of AMIP Climate Simulations Conducted with the CCC GCM2. Journal of Climate. 12(5). 1322–1335. 20 indexed citations
20.
Wang, Xiaolan L., João Corte‐Real, & Xuebin Zhang. (1996). Intraseasonal oscillations and associated spatial‐temporal structures of precipitation over China. Journal of Geophysical Research Atmospheres. 101(D14). 19035–19042. 14 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026