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Maximilian Maahn

Head of research group clouD and pRecipitation Observations for Process Studies (drOPS)

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Contact

mmaahn_credit_stefanie_maahn.jpg
Phone: +49 341 97-32853
maximilian [dot] maahn [at] uni [dash] leipzig [dot] de
Institut für Meteorologie
Stephanstraße 3, Room 7
04103 Leipzig

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Maximilian Maahn joined the Leipzig Institute for Meteorology in July 2020 and leads the drOPS (clouD and pRecipitation Observations for Process Studies) group. His main research interests include enhancing radar observations of polar clouds and precipitation and understanding how clouds are influenced by aerosols through remote sensing and in-situ observations. He is currently developing a novel in situ sensor for snowfall, the Video In Situ Snowfall Sensor (VISSS). Read a feature about his background and work at the website of the ARM program at https://www.arm.gov/news/features/post/54283

Professional career

  • since 07/2020: Leipzig University, leader of the drOPS (clouD and pRecipitation Observations for Process Studies) group
  • 03/2016 - 05/2020: Cooperative Institute for Research in Environmental Sciences (CIRES) of the NOAA Earth System Research Laboratories and the University of Colorado Boulder
  • 05/2011 - 02/2016: University of Cologne
  • 01/2011 - 05/2011: Bonn University

Education

  • 01/2024-12/2027: DFG (AC)3 project E05. Process-level Understanding of Sublimation and Evaporation of Precipitation.
  • 09/2023-08/2024: SMWK (Saxon State Ministry for Science, Culture and Tourism) project for preparing Breathing Nature Excellence Initiative (80 k€).
  • 09/2023-08/2026: DFG project EMPOS Evaluating Microphysical Pathways Of Midlatitude Snow Formation EMPOS (256 k€).
  • 06/2022-06/2025: DFG SPP PROM project CORSIPP. Characterization of orography-influenced riming and secondary ice production and their effects on precipitation rates using radar polarimetry and Doppler spectra (349 k€).
  • 01/2021-12/2023: DFG (AC)3 project B08. Characterising the spatial variability of ice water content in and below mixed-phase clouds (166 k€).
  • 05/2019: CIRES Innovative Research Program. Development of a visual in-situ snowfall sensor (VISSS) for MOSAiC ($ 20k).
  • 07/2015-12/2015: Graduate School of Geosciences of the University of Cologne. Potential of higher order moments of the radar Doppler spectrum for retrieving microphysical and kinematic properties of Arctic ice clouds (13k€)

drOPS team members are underlined. Data (DXX) und code (CXX) publications are in italic.

Submitted/in review

38) Mahecha, M. D., and coauthors (including M. Maahn), 2023: Biodiversity and climate extremes: known interactions and research gaps. Earth’s Future, submitted, https://doi.org/10.22541/essoar.169462031.19744802/v1.
37) Lee, J., P. Seifert, T. Hashino, M. Maahn, F. Senf, and O. Knoth, 2023: Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars. ACP/EGUsphere, in review, https://doi.org/10.5194/egusphere-2023-1887.

2024

36) Maherndl, N., M. Moser, J. Lucke, M. Mech, N. Risse, I. Schirmacher, and M. Maahn, 2024: Quantifying riming from airborne data during HALO-(AC)3. AMT/EGUsphere, accepted, 1–32, https://doi.org/10.5194/egusphere-2023-1118.
35) Maahn, M., D. Moisseev, I. Steinke, N. Maherndl, and M. D. Shupe, 2024: Introducing the Video In Situ Snowfall Sensor (VISSS). Atmospheric Measurement Techniques, 17, 899–919, https://doi.org/10.5194/amt-17-899-2024.
D8) Maahn, M., and S. Wolter, 2024: Hardware design of the Video In Situ Snowfall Sensor v3 (VISSS3). https://doi.org/10.5281/zenodo.10526898.
D7) Maahn, M., V. Ettrichraetz, and I. Steinke, 2024: VISSS Raw data from SAIL at Gothic from November 2022 to June 2023, https://doi.org/10.5439/2278627.

2023

34) Rizik, A., A. Battaglia, F. Tridon, F. E. Scarsi, A. Kötsche, H. Kalesse-Los, M. Maahn, and A. Illingworth, 2023: Impact of Crosstalk on Reflectivity and Doppler Measurements for the WIVERN Polarization Diversity Doppler Radar. IEEE Trans. Geosci. Remote Sens., 61, 1–14, https://doi.org/10.1109/TGRS.2023.3320287.
33) Maherndl, N., M. Maahn, F. Tridon, J. Leinonen, D. Ori, and S. Kneifel, 2023: A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh–Gans approximation. Q.J.R. Meteorol. Soc., https://doi.org/10.1002/qj.4573.
32) Wendisch, M., and Coauthors (including M. Maahn), 2023: Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project. Bull. Amer. Meteor. Soc., 1, https://doi.org/10.1175/BAMS-D-21-0218.1.
C5) Maahn, M., 2023: Video In Situ Snowfall Sensor (VISSS) data processing library V2023.1.6. https://doi.org/10.5281/zenodo.7650394.
C4) Maahn, M., 2023: Video In Situ Snowfall Sensor (VISSS) data acquisition software V0.3.1. https://doi.org/10.5281/zenodo.7640801.
D6) Maherndl. N., Maahn, M., F. Tridon, J. Leinonen, D. Ori, and S. Kneifel, 2023: Data set of simulated rimed aggregates for “A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation.” https://doi.org/10.5281/zenodo.7757034. https://zenodo.org/records/7757034.
D5) Maahn, M., and N. Maherndl, 2023: Video In Situ Snowfall Sensor (VISSS) data for Ny-Ålesund (2021-2023). https://doi.org/10.1594/PANGAEA.958537.
D4) Maahn, M., and D. Moisseev, 2023: Video In Situ Snowfall Sensor (VISSS) data for Hyytiälä (2021-2022). https://doi.org/10.1594/PANGAEA.959046.
D3) Maahn, M., C. J. Cox, M. R. Gallagher, J. K. Hutchings, M. D. Shupe, and U. Taneil, 2023: Video In Situ Snowfall Sensor (VISSS) data from MOSAiC expedition with POLARSTERN (2019-2020). https://doi.org/10.1594/PANGAEA.960391.
D2) Maahn, M., R. Haseneder-Lind, and P. Krobot, 2023: Hardware design of the Video In Situ Snowfall Sensor v2 (VISSS2). https://doi.org/10.5281/zenodo.7640821.

2022

31) Schimmel, W., H. Kalesse-Los, M. Maahn, T. Vogl, A. Foth, P. Saavedra Garfias, and P. Seifert, 2022: Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks. Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022.
30) Shupe, M. D., and Coauthors (including M. Maahn), 2022: Overview of the MOSAiC expedition—Atmosphere. Elem. Sci. Anth., 10, 00060, https://doi.org/10.1525/elementa.2021.00060.
29) Vogl, T., M. Maahn, S. Kneifel, W. Schimmel, D. Moisseev, and H. Kalesse-Los, 2022: Using artificial neural networks to predict riming from Doppler cloud radar observations. Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022.

2021

28) Kulie, M. S., Pettersen, C., Merrelli, A. J., Wagner, T. J., Wood, N. B., Dutter, M., Beachler, D., Kluber, T., Turner, R., Mateling, M., Lenters, J., Blanken, P., Maahn, M., Spence, C., Kneifel, S., Kucera, P. A., Tokay, A., Bliven, L. F., Wolff, D. B., and Petersen, W. A., 2021: Snowfall in the Northern Great Lakes: Lessons Learned from a Multi-Sensor Observatory. Bull. Amer. Meteor. Soc., 1, 1–61, https://doi.org/10.1175/BAMS-D-19-0128.1.
27) Luke, E. P., F. Yang, P. Kollias, A. M. Vogelmann, and M. Maahn, 2021: New insights into ice multiplication using remote-sensing observations of slightly supercooled mixed-phase clouds in the Arctic. PNAS, 118, https://doi.org/10.1073/pnas.2021387118.
26) Maahn, M., T. Goren, M. D. Shupe, and G. de Boer, 2021: Liquid containing clouds at the North Slope of Alaska demonstrate sensitivity to local industrial aerosol emissions. Geophys. Res. Lett., e2021GL094307, https://doi.org/10.1029/2021GL094307.

2020

25) Maahn, M., D. D. Turner, U. Löhnert, D. J. Posselt, K. Ebell, G. G. Mace, and J. M. Comstock, 2020: Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know. Bull. Amer. Meteor. Soc., 101, E1512–E1523, https://doi.org/10.1175/BAMS-D-19-0027.1. Three page summary of the article
24) Matrosov, S. Y., A. V. Ryzhkov, M. Maahn, and G. de Boer, 2020: Hydrometeor Shape Variability in Snowfall as Retrieved from Polarimetric Radar Measurements. J. Appl. Meteor. Climatol., 59, 1503–1517, https://doi.org/10.1175/JAMC-D-20-0052.1.
23) Mech, M., M. Maahn, S. Kneifel, D. Ori, E. Orlandi, P. Kollias, V. Schemann, and S. Crewell, 2020: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere. Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020.
C3) Maahn, M., 2020: “pyOptimalEstimation” Package. https://github.com/maahn/pyOptimalEstimation.

2019

22) Acquistapace, C., U. Löhnert, M. Maahn, and P. Kollias, 2019: A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing. J. Atmos. Oceanic Technol., 36, 781–801, https://doi.org/10.1175/JTECH-D-18-0158.1.
21) Ghate, V. P., P. Kollias, S. Crewell, A. M. Fridlind, T. Heus, U. Löehnert, M. Maahn, G. M. McFarquhar, D. Moisseev, M. Oue, M. Wendisch, and C. Williams, 2019: The Second ARM Training and Science Application Event: Training the Next Generation of Atmospheric Scientists. Bull. Amer. Meteor. Soc., 100, ES5–ES9, https://doi.org/10.1175/BAMS-D-18-0242.1.
20) Maahn, M., F. Hoffmann, M. D. Shupe, G. de Boer, S. Y. Matrosov, and E. P. Luke, 2019: Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration? Atmos. Meas. Tech., 12, 3151–3171, https://doi.org/10.5194/amt-12-3151-2019.
19) Matrosov, S. Y., M. Maahn, and G. de Boer, 2019: Observational and Modeling Study of Ice Hydrometeor Radar Dual-Wavelength Ratios. J. Appl. Meteor. Climatol., 58, 2005–2017, https://doi.org/10.1175/JAMC-D-19-0018.1.
C2) Maahn, M., and D. Ori, 2019: maahn/pamtra2: calibrationPaper_v1. https://doi.org/10.5281/zenodo.2552448.
D1) Maahn, M., 2019: MASC Snowparticle Images. https://doi.org/10.5439/1497701.

2018

18) de Boer, G., M. Ivey, B. Schmid, D. Lawrence, D. Dexheimer, F. Mei, J. Hubbe, A. Bendure, J. Hardesty, M. D. Shupe, A. McComiskey, H. Telg, C. Schmitt, S. Y. Matrosov, I. Brooks, J. Creamean, A. Solomon, D. D. Turner, C. Williams, M. Maahn, B. Argrow, S. Palo, C. N. Long, R.-S. Gao, and J. Mather, 2018: A Bird’s-Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska. Bull. Amer. Meteor. Soc., 99, 1197–1212, https://doi.org/10.1175/BAMS-D-17-0156.1.
17) Creamean, J. M., R. M. Kirpes, K. A. Pratt, N. J. Spada, M. Maahn, G. de Boer, R. C. Schnell, and S. China, 2018: Marine and terrestrial influences on ice nucleating particles during continuous springtime measurements in an Arctic oilfield location. Atmos. Chem. Phys., 18, 18023–18042, https://doi.org/10.5194/acp-18-18023-2018.
16) Creamean, J. M., M. Maahn, G. de Boer, A. McComiskey, A. J. Sedlacek, and Y. Feng, 2018: The influence of local oil exploration and regional wildfires on summer 2015 aerosol over the North Slope of Alaska. Atmos. Chem. Phys., 18, 555–570, https://doi.org/10.5194/acp-18-555-2018.
15) Solomon, A., G. de Boer, J. M. Creamean, A. McComiskey, M. D. Shupe, M. Maahn, and C. Cox, 2018: The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds. Atmos. Chem. Phys., 18, 17047–17059, https://doi.org/10.5194/acp-18-17047-2018.
14) Williams, C. R., M. Maahn, J. C. Hardin, and G. de Boer, 2018: Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra. Atmos. Meas. Tech., 11, 4963–4980, https://doi.org/10.5194/amt-11-4963-2018.
13) Acquistapace, C., S. Kneifel, U. Löhnert, P. Kollias, M. Maahn, and M. Bauer-Pfundstein, 2017: Optimizing observations of drizzle onset with millimeter-wavelength radars. Atmos. Meas. Tech., 10, 1783–1802, https://doi.org/10.5194/amt-10-1783-2017.

2017

12) Bühl, J., S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch, 2017: Remote Sensing. Meteor. Mon., 58, 10.1-10.21, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0015.1.
11) Maahn, M., and U. Löhnert, 2017: Potential of Higher-Order Moments and Slopes of the Radar Doppler Spectrum for Retrieving Microphysical and Kinematic Properties of Arctic Ice Clouds. J. Appl. Meteor. Climatol., 56, 263–282, https://doi.org/10.1175/JAMC-D-16-0020.1.
10) Maahn, M., G. de Boer, J. M. Creamean, G. Feingold, G. M. McFarquhar, W. Wu, and F. Mei, 2017: The observed influence of local anthropogenic pollution on northern Alaskan cloud properties. Atmos. Chem. Phys., 17, 14709–14726, https://doi.org/10.5194/acp-17-14709-2017.
9) Matrosov, S. Y., C. G. Schmitt, M. Maahn, and G. de Boer, 2017: Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ Observations. J. Atmos. Oceanic Technol., 34, 2569–2587, https://doi.org/10.1175/JTECH-D-17-0111.1.
8) Souverijns, N., A. Gossart, S. Lhermitte, I. V. Gorodetskaya, S. Kneifel, M. Maahn, F. L. Bliven, and N. P. M. van Lipzig, 2017: Estimating radar reflectivity - Snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations. Atmos. Res., 196, 211–223, https://doi.org/10.1016/j.atmosres.2017.06.001.

2016

7) Kneifel, S., P. Kollias, A. Battaglia, J. Leinonen, M. Maahn, H. Kalesse, and F. Tridon, 2016: First observations of triple-frequency radar Doppler spectra in snowfall: Interpretation and applications. Geophys. Res. Lett., 43, 2225–2233, https://doi.org/10.1002/2015GL067618.

2015

6) Gorodetskaya, I. V., S. Kneifel, M. Maahn, K. Van Tricht, W. Thiery, J. H. Schween, A. Mangold, S. Crewell, and N. P. M. Van Lipzig, 2015: Cloud and precipitation properties from ground-based remote-sensing instruments in East Antarctica. Cryosphere, 9, 285–304, https://doi.org/10.5194/tc-9-285-2015.
5) Löhnert, U., J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O’Connor, C. Simmer, A. Wahner, and S. Crewell, 2015: JOYCE: Jülich Observatory for Cloud Evolution. Bull. Amer. Meteor. Soc., 96, 1157–1174, https://doi.org/10.1175/BAMS-D-14-00105.1.
4) Maahn, M., U. Löhnert, P. Kollias, R. C. Jackson, and G. M. McFarquhar, 2015: Developing and Evaluating Ice Cloud Parameterizations for Forward Modeling of Radar Moments Using in situ Aircraft Observations. J. Atmos. Oceanic Technol., 32, 880–903, https://doi.org/10.1175/JTECH-D-14-00112.1.

2011 - 2014

3) Maahn, M., C. Burgard, S. Crewell, I. V. Gorodetskaya, S. Kneifel, S. Lhermitte, K. Van Tricht, and N. P. M. van Lipzig, 2014: How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions? J. Geophys. Res. Atmos., 119, 13604–13620, https://doi.org/10.1002/2014JD022079.
2) Maahn, M., and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012.
C1) Maahn, M., 2012: IMProToo - Improved Mrr Processing Tool. https://github.com/maahn/IMProToo.
1) Kneifel, S., M. Maahn, G. Peters, and C. Simmer, 2011: Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar). Meteorol. Atmos. Phys., 113, 75–87, https://doi.org/10.1007/s00703-011-0142-z.

  • What Every Atmospheric Scientist Should Know About Inverse Modelling, Seminar in Microwave Physics, University of Bern, 2021
  • Spatial variability of cloud properties at the North Slope of Alaska. Colloquium, Leipzig University, 2019.
  • Spatial variability of cloud properties at the North Slope of Alaska. Colloquium, Karlsruhe Institute of Technology, 2019
  • Investigating the impact of anthropogenic pollution on cloud properties derived from ground based remote sensors at the North Slope of Alaska. European Geosciences Union General Assembly, Vienna, Austria, 2018.
  • Anthropogenic pollution and cloud properties at the North Slope of Alaska. AOS Colloquium, University of Wisconsin Madison, 2018.
  • Using vertically pointing Doppler radar to study clouds (and precipitation). Seminar talk at Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA, 2015.