Lisa Milani

906 total citations
22 papers, 537 citations indexed

About

Lisa Milani is a scholar working on Atmospheric Science, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Lisa Milani has authored 22 papers receiving a total of 537 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Atmospheric Science, 4 papers in Environmental Engineering and 4 papers in Global and Planetary Change. Recurrent topics in Lisa Milani's work include Meteorological Phenomena and Simulations (21 papers), Precipitation Measurement and Analysis (20 papers) and Cryospheric studies and observations (15 papers). Lisa Milani is often cited by papers focused on Meteorological Phenomena and Simulations (21 papers), Precipitation Measurement and Analysis (20 papers) and Cryospheric studies and observations (15 papers). Lisa Milani collaborates with scholars based in United States, Italy and France. Lisa Milani's co-authors include Mark S. Kulie, Norman B. Wood, Paolo Sanò, Giulia Panegrossi, Federico Porcù, Stefano Dietrich, Daniele Casella, Tristan L’Ecuyer, Gail Skofronick‐Jackson and Marco Petracca and has published in prestigious journals such as Geophysical Research Letters, IEEE Transactions on Geoscience and Remote Sensing and Quarterly Journal of the Royal Meteorological Society.

In The Last Decade

Lisa Milani

21 papers receiving 527 citations

Peers

Lisa Milani

GPC

OCEAN

Gregori de Arruda Moreira Brazil

Montgomery L. Flora United States

Leonhard Gantner Germany

Aldo S. Moya-Álvarez Peru

Wangshu Tan China

Andrew I. Barrett United Kingdom

Vineeth Krishnan Valappil United Arab Emirates

Chuanhua Ren China

Jean‐Luc Deuzé France

Gregori de Arruda Moreira Brazil

Lisa Milani

384 ×0.7

382 ×1.6

GPC

114 ×1.4

10 ×0.9

OCEAN

13 ×1.2

Citations per year, relative to Lisa Milani Lisa Milani (= 1×) peers Gregori de Arruda Moreira

Countries citing papers authored by Lisa Milani

Since Specialization

Citations

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

Fields of papers citing papers by Lisa Milani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lisa Milani. 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 Lisa Milani. The network helps show where Lisa Milani may publish in the future.

Co-authorship network of co-authors of Lisa Milani

This figure shows the co-authorship network connecting the top 25 collaborators of Lisa Milani. A scholar is included among the top collaborators of Lisa Milani 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 Lisa Milani. Lisa Milani 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

Ebtehaj, Ardeshir, et al.. (2025). Advancing Passive Microwave Retrievals of Precipitation Using CloudSat and GPM Coincidences: Integration of Machine Learning with a Bayesian Algorithm. Journal of Hydrometeorology. 26(5). 537–553.

Milani, Lisa & C. Kidd. (2023). The State of Precipitation Measurements at Mid-to-High Latitudes. Atmosphere. 14(11). 1677–1677. 6 indexed citations

Milani, Lisa, et al.. (2022). DeepPrecip: a deep neural network for precipitation retrievals. Atmospheric measurement techniques. 15(20). 6035–6050. 5 indexed citations

You, Yalei, George J. Huffman, Veljko Petković, et al.. (2022). Evaluation of Snowfall Retrieval Performance of GPM Constellation Radiometers Relative to Spaceborne Radars. Journal of Hydrometeorology. 24(3). 389–405. 3 indexed citations

Ebtehaj, Ardeshir, et al.. (2022). Vulnerability of Passive Microwave Snowfall Retrievals to Physical Properties of Snowpack: A Perspective From Dense Media Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–13. 5 indexed citations

Milani, Lisa, Veljko Petković, Marko Orescanin, & Mark S. Kulie. (2021). Snowfall Type Classification for Improving Passive Microwave Snowfall Estimates. 1 indexed citations

Milani, Lisa, et al.. (2021). Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land. Remote Sensing. 13(13). 2641–2641. 2 indexed citations

Turk, F. Joseph, Sarah Ringerud, Daniele Casella, et al.. (2021). Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset. Remote Sensing. 13(12). 2264–2264. 30 indexed citations

Milani, Lisa & Norman B. Wood. (2021). Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations. Remote Sensing. 13(11). 2041–2041. 17 indexed citations

10.

Milani, Lisa, Mark S. Kulie, Daniele Casella, et al.. (2020). Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation. Journal of Atmospheric and Oceanic Technology. 38(2). 293–311. 17 indexed citations

11.

Rysman, Jean‐François, Giulia Panegrossi, Paolo Sanò, et al.. (2019). Retrieving Surface Snowfall With the GPM Microwave Imager: A New Module for the SLALOM Algorithm. Geophysical Research Letters. 46(22). 13593–13601. 26 indexed citations

12.

Milani, Lisa & Norman B. Wood. (2019). CloudSat Bias on Falling Snow Estimates Over the Daylight Only Operational Period (2012-2019).. AGU Fall Meeting Abstracts. 2019. 2 indexed citations

13.

Rysman, Jean‐François, Giulia Panegrossi, Paolo Sanò, et al.. (2018). SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager. Remote Sensing. 10(8). 1278–1278. 33 indexed citations

14.

Milani, Lisa, Mark S. Kulie, Daniele Casella, et al.. (2018). CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis. Atmospheric Research. 213. 121–135. 52 indexed citations

15.

Skofronick‐Jackson, Gail, S. Joseph Munchak, Mark S. Kulie, et al.. (2018). The Global Precipitation Measurement (GPM) Mission Status: Emphasis on Falling Snow Retrievals. Digital Commons - Michigan Tech (Michigan Technological University). 85281a. 8316–8319. 1 indexed citations

16.

Kulie, Mark S. & Lisa Milani. (2017). Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective. Quarterly Journal of the Royal Meteorological Society. 144(S1). 329–343. 39 indexed citations

17.

Kulie, Mark S., et al.. (2016). A Shallow Cumuliform Snowfall Census Using Spaceborne Radar. Journal of Hydrometeorology. 17(4). 1261–1279. 88 indexed citations

18.

Casella, Daniele, Giulia Panegrossi, Paolo Sanò, et al.. (2015). A novel algorithm for detection of precipitation in tropical regions using PMW radiometers. Atmospheric measurement techniques. 8(3). 1217–1232. 12 indexed citations

19.

Sanò, Paolo, Giulia Panegrossi, Daniele Casella, et al.. (2015). The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies. Atmospheric measurement techniques. 8(2). 837–857. 53 indexed citations

20.

Milani, Lisa, Federico Porcù, Daniele Casella, et al.. (2015). Analysis of long-term precipitation pattern over Antarctica derived from satellite-borne radar. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 6 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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