I’ve worked on atmospheric science — especially cloud and precipitation radar — data problems since about 2008 and I’m currently working on applications of machine learning to these problems.

I’m working as a Solution Architect at the Climate and Sustainability group of NVIDIA.

Education Experience Papers Presentations Successful proposals

Education

  • 2013: Doctor of Science (Tech.), Physics, Aalto University, Espoo, Finland.
    Advisors: Prof. Risto Nieminen (Aalto University) and Dr. Timo Nousiainen (University of Helsinki)
  • 2007: Master of Science (Tech.), Physics (major), Space Technology (minor), Helsinki University of Technology, Espoo, Finland

Experience

  • October 2023 – present: NVIDIA, Zurich, Switzerland (Remote)
    • Position: Senior Solution Architect
    • Responsibilities: Developing NVIDIA Earth-2 solutions for artificial intelligence in weather and representing them to customers
  • October 2020 – September 2023: MeteoSwiss, Locarno-Monti, Switzerland
    • Position: EUMETSAT Fellow
    • Responsibilities: Development of machine learning methods for thunderstorm nowcasting
  • April 2019 – September 2020: École polytechnique fédérale de Lausanne, Lausanne, Switzerland
  • March 2014 – March 2019: NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
    • Positions:
      • January 2018 – March 2019: Data Scientist, Science Data Understanding and Modeling group
      • September 2016 – January 2018: Assistant Researcher, JIFRESSE (jointly with University of California, Los Angeles, California, USA)
      • March 2014 – September 2016: Postdoctoral Scholar (advisor: Dr. Matthew D. Lebsock)
    • Responsibilities: Research of retrievals of clouds and precipitation from satellite measurements
  • November 2008 – March 2014: Finnish Meteorological Institute, Helsinki, Finland
    • Position: Graduate Researcher, Radar and Space Technology group
    • Responsibilities: Research on microphysics and radar remote sensing of hydrometeors, snow in particular
  • January 2008 – October 2008: Finnish Institute of Marine Research, Helsinki, Finland
    • Position: Civilian Service, Physical Oceanography group
    • Responsibilities: Data analysis and operational programming related to sea level and wave height measurements and forecasts
  • May 2005 – June 2007: Finnish Meteorological Institute, Helsinki, Finland
    • Position: Research Trainee, Space Technology group
    • Responsibilities: Analysis of the Huygens pressure measurements from Titan, including reconstruction of the temperature profile independently of temperature measurements
  • May 2004 – August 2004: Helsinki University of Technology, Espoo, Finland
    • Position: Research Assistant, Low Temperature Laboratory
    • Responsibilities: Design and use of computer software for the analysis of experiments on rotating Helium-3 superfluid

Publications and appearances

Papers

Journal articles

  1. N. Rombeek, J. Leinonen and U. Hamann. Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning. Natural Hazards and Earth System Sciences, 24, 133–144, 2024. doi:10.5194/nhess-24-133-2024
  2. N. Maherndl, M. Maahn, F. Tridon, J. Leinonen, D. Ori and S. Kneifel. A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation. Quarterly Journal of the Royal Meteorological Society, 149, 3562–3581, 2023. doi:10.1002/qj.4573
  3. J. Leinonen, U. Hamann, U. Germann and I. V. Sideris. Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model. Geophysical Research Letters, 50, e2022GL101626, 2023. doi:10.1029/2022GL101626
  4. J. Leinonen, U. Hamann and U. Germann. Seamless lightning nowcasting with recurrent-convolutional deep learning. Artificial Intelligence for the Earth Systems, 1, e220043, 2022. doi:10.1175/AIES-D-22-0043.1
  5. J. Leinonen, U. Hamann, U. Germann and J. R. Mecikalski. Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance. Natural Hazards and Earth System Sciences 22, 577–597, 2022. doi:10.5194/nhess-22-577-2022
  6. J. Leinonen, J. Grazioli, and A. Berne. Reconstruction of the mass and geometry of snowfall particles from multi angle snowflake camera (MASC) images. Atmospheric Measurement Techniques 14, 6851–6866, 2021. doi:10.5194/amt-14-6851-2021
  7. J. Leinonen, D. Nerini and A. Berne. Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network. IEEE Transactions on Geoscience and Remote Sensing, 59, 7211–7223, 2021. doi:10.1109/TGRS.2020.3032790, preprint available at ArXiv
  8. J. Leinonen and A. Berne. Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification. Atmospheric Measurement Techniques, 13, 2949–2964, 2020. doi:10.5194/amt-13-2949-2020
  9. A. Protat, C. Klepp, V. Louf, W. Petersen, S. Alexander, A. Barros, J. Leinonen, and G. Mace. The Latitudinal Variability of Oceanic Rainfall Properties and Its Implication for Satellite Retrievals: 2. The Relationships Between Radar Observables and Drop Size Distribution Parameters. Journal of Geophysical Research: Atmospheres, 124, 13312–13324, 2019. doi:10.1029/2019JD031011
  10. A. Protat, C. Klepp, V. Louf, W. Petersen, S. Alexander, A. Barros, J. Leinonen, and G. Mace. The Latitudinal Variability of Oceanic Rainfall Properties and Its Implication for Satellite Retrievals: 1. Drop Size Distribution Properties. Journal of Geophysical Research: Atmospheres, 124, 13291–13311, 2019. doi:10.1029/2019JD031010
  11. F. Tridon, A. Battaglia, R. J. Chase, F. J. Turk, J. Leinonen, S. Kneifel, K. Mroz, J. Finlon, Bansemer A., S. Tanelli, A. J. Heymsfield, and S. W. Nesbitt. The Microphysics of Stratiform Precipitation During OLYMPEX: Compatibility Between Triple‐Frequency Radar and Airborne In Situ Observations. Journal of Geophysical Research: Atmospheres, 124, 8764–8792, 2019. doi:10.1029/2018JD029858
  12. J. Leinonen, A. Guillaume, and T. Yuan. Reconstruction of cloud vertical structure with a generative adversarial network. Geophysical Research Letters, 46, 7035–7044, 2019. doi:10.1029/2019GL082532, preprint available at EarthArXiv.
  13. M. Richardson, J. Leinonen, H. Q. Cronk, J. McDuffie, M. D. Lebsock, and G. L. Stephens. Liquid marine cloud geometric thickness retrieved from OCO-2’s oxygen A-band spectrometer. Atmospheric Measurement Techniques, 12, 1717–1737, 2019. doi:10.5194/amt-12-1717-2019
  14. A. Seifert, J. Leinonen, C. Siewert, and S. Kneifel. The geometry of rimed aggregate snowflakes: A modeling study. Journal of Advances in Modeling Earth Systems, 11, 2019. doi:10.1029/2018MS001519
  15. J. Leinonen, M. D. Lebsock, S. Tanelli, O. O. Sy, B. Dolan, R. J. Chase, J. A. Finlon, A. von Lerber, and D. Moisseev. Retrieval of snowflake microphysical properties from multifrequency radar observations. Atmospheric Measurement Techniques, 11, 5471–5488, 2018. doi:10.5194/amt-11-5471-2018
  16. J. Leinonen and A. von Lerber. Snowflake melting simulation using smoothed particle hydrodynamics. Journal of Geophysical Research: Atmospheres, 123, 1811–1825, 2018. doi:10.1002/2017JD027909
  17. J. Leinonen, S. Kneifel, and R. J. Hogan. Evaluation of the Rayleigh–Gans Approximation for microwave scattering by rimed snowflakes. Quarterly Journal of the Royal Meteorological Society, 144, 77–88, 2018. doi:10.1002/qj.3093
  18. A.-M. Harri, K. Pichkadze, L. Zeleny, L. Vazquez, W. Schmidt, S. Alexashkin, O. Korablev, H. Guerrero, J. Heilimo, M. Uspensky, V. Finchenko, V. Linkin, I. Arruego, M. Genzer, A. Lipatov, J. Polkko, M. Paton, H. Savijärvi, H. Haukka, T. Siili, V. Khovanskov, B. Ostesko, A. Poroshin, M. Diaz-Michelena, T. Siikonen, M. Palin, V. Vorontsov, A. Polyakov, F. Valero, O. Kemppinen, J. Leinonen, and P. Romero. The MetNet vehicle: a lander to deploy environmental stations for local and global investigations of Mars. Geoscientific Instrumentation, Methods and Data Systems, 6, 103–124, 2017. doi:10.5194/gi-6-103-2017
  19. J. Leinonen, M. D. Lebsock, L. Oreopoulos, and N. Cho. Interregional differences in MODIS- derived cloud regimes. Journal of Geophysical Research: Atmospheres, 121, 11648–11665, 2016. doi:10.1002/2016JD025193
  20. J. Leinonen, M. D. Lebsock, G. L. Stephens, and K. Suzuki. Improved retrieval of cloud liquid water from CloudSat and MODIS. Journal of Applied Meteorology and Climatology, 55, 1831–1844, 2016. doi:10.1175/JAMC-D-16-0077.1
  21. S. Kneifel, P. Kollias, A. Battaglia, J. Leinonen, H. Kalesse, and F. Tridon. First observations of triple frequency radar Doppler spectra in snowfall: Interpretation and applications. Geophysical Research Letters, 43, 2225–2233, 2016. doi:10.1002/2015GL067618
  22. J. Leinonen, M. D. Lebsock, S. Tanelli, K. Suzuki, H. Yashiro, and Y. Miyamoto. Performance assessment of a triple-frequency spaceborne cloud–precipitation radar concept using a global cloud-resolving model. Atmospheric Measurement Techniques, 8, 3493–3517, 2015. doi:10.5194/amt-8-3493-2015
  23. J. Leinonen and W. Szyrmer. Radar signatures of snowflake riming: a modeling study. Earth and Space Science, 2, 346–358, 2015. doi:10.1002/2015EA000102
  24. S. Kneifel, A. von Lerber, J. Tiira, D. Moisseev, P. Kollias, and J. Leinonen. Observed relations between snowfall microphysics and triple-frequency radar measurements. Journal of Geophysical Research: Atmospheres, 120, 6034–6055, 2015. doi:10.1002/2015JD023156
  25. J. Leinonen and D. Moisseev. What do triple-frequency radar signatures reveal about aggregate snowflakes? Journal of Geophysical Research: Atmospheres, 120, 223–239, 2015. doi:10.1002/2014JD022072
  26. A. von Lerber, D. Moisseev, J. Leinonen, J. Koistinen, and M. Hallikainen. Modeling attenuation of a low melting layer with optimized model parameters at C-band. IEEE Transactions on Geoscience and Remote Sensing, 53, 724–727, 2015. doi:10.1109/TGRS.2014.2327148
  27. J. Leinonen. High-level interface to T-matrix scattering calculations: architecture, capabilities and limitations. Optics Express, 22, 1655–1660, 2014. doi:10.1364/OE.22.001655
  28. J. Tyynelä, J. Leinonen, D. Moisseev, T. Nousiainen, and A. von Lerber. Modeling radar backscattering from melting snowflakes using spheroids with nonuniform distribution of water. Journal of Quantitative Spectroscopy and Radiative Transfer, 133, 504–519, 2014. doi:10.1016/j.jqsrt.2013.09.013
  29. J. Leinonen, D. Moisseev, and T. Nousiainen. Linking snowflake microstructure to multi- frequency radar observations. Journal of Geophysical Research: Atmospheres, 118, 3259–3270, 2013. doi:10.1002/jgrd.50163
  30. J. Tyynelä, J. Leinonen, C. Westbrook, D. Moisseev, and T. Nousiainen. Applicability of the Rayleigh–Gans approximation for scattering by snowflakes at microwave frequencies in vertical incidence. Journal of Geophysical Research: Atmospheres, 118, 1826–1839, 2013. doi:10.1002/jgrd.50167
  31. J. Leinonen, S. Kneifel, D. Moisseev, J. Tyynelä, S. Tanelli, and T. Nousiainen. Evidence of nonspheroidal behavior in millimeter-wavelength radar observations of snowfall. Journal of Geophysical Research: Atmospheres, 117, D18205, 2012. doi:10.1029/2012JD017680
  32. J. Leinonen, D. Moisseev, M. Leskinen, and W. Petersen. A climatology of disdrometer measurements of rainfall in Finland over five years with implications for global radar observations. Journal of Applied Meteorology and Climatology, 51, 392–404, 2012. doi:10.1175/JAMC-D-11-056.1
  33. J. Tyynelä, J. Leinonen, D. Moisseev, and T. Nousiainen. Radar backscattering from snowflakes: comparison of fractal, aggregate and soft-spheroid models. Journal of Atmospheric and Oceanic Technology, 28, 1365–1372, 2011. doi:10.1175/JTECH-D-11-00004.1
  34. J. Leinonen, D. Moisseev, V. Chandrasekar, and J. Koskinen. Mapping radar reflectivity values of snowfall between frequency bands. IEEE Transactions on Geoscience and Remote Sensing, 49, 3047–3058, 2011. doi:10.1109/TGRS.2011.2117432
  35. J. Leinonen and K. K. Kahma. Estimating optimal weights for combinations of multiple forecasts. Geophysica, 46, 21–32, 2010. http://www.geophysica.fi/pdf/geophysica_2010_46_1-2_047_leinonen.pdf
  36. J. Leinonen, T. Mäkinen, and A.-M. Harri. A method to determine the atmospheric temperature profile from in situ pressure data: Application to Titan. Planetary and Space Science, 55, 2071– 2076, 2007. doi:10.1016/j.pss.2007.06.001

Conference papers

  1. J. Leinonen, Improvements to short-term weather prediction with recurrent-convolutional networks. In 2021 IEEE International Conference on Big Data, pages 5764–5769 (proceedings), 2021. doi:10.1109/BigData52589.2021.9671869
  2. J. Leinonen, Spatiotemporal weather data predictions with shortcut recurrent-convolutional networks: A solution for the Weather4cast challenge. In Proceedings of the CIKM2021 Workshops, 2021. http://ceur-ws.org/Vol-3052/short15.pdf
  3. J. Leinonen, T. Yuan, and A. Berne. Generative adversarial network for climate data field generation. In 9th International Workshop on Climate Informatics, Paris, France, pages 37–42 (proceedings), 2019. doi:10.5065/y82j-f154
  4. J. Leinonen, M. D. Lebsock, Sy. O. O., S. Tanelli, B. Dolan, R. J. Chase, and Finlon. J. A. Multi-frequency radar retrieval of snowfall microphysics. In ERAD 2018, Ede-Wageningen, The Netherlands, pages 586–593 (book of abstracts), 2018. doi:10.18174/454537
  5. J. Leinonen, V. Chandrasekar, and D. Moisseev. A Bayesian algorithm for tangential deconvolution of weather radar images. In ERAD 2012, Toulouse, France, 2012. http://www.meteo.fr/cic/meetings/2012/ERAD/extended_abs/DQ_210_ext_abs.pdf
  6. A. von Lerber, D. Moisseev, J. Leinonen, J. Tyynelä, V. Chandrasekar, and M. Hallikainen. Modeling melting layer radar observations at GPM frequencies: comparison to measurements. In International Geoscience & Remote Sensing Symposium 2011, Vancouver, British Columbia, Canada, 2011, 2507–2510. doi:10.1109/IGARSS.2011.6049721
  7. J. Leinonen and D. Moisseev. Snowfall characterization with combined ground and space radar. In ERAD 2010, Sibiu, Romania, 2010. https://www.erad2010.com/pdf/oral/monday/03_ERAD2010_0117.pdf

Book chapters

  1. S. Kneifel, J. Leinonen, J. Tyynelä, D. Ori, and A. Battaglia. Scattering of hydrometeors. In Satellite Precipitation Measurement, chapter 15, pages 249–276. Springer, 2020. doi:10.1007/978-3-030-24568-9_15
  2. A. Battaglia, S. Tanelli, F. Tridon, S. Kneifel, J. Leinonen, and P. Kollias. Triple-frequency radar retrievals. In Satellite Precipitation Measurement, chapter 13, pages 211–229. Springer, 2020. doi:10.1007/978-3-030-24568-9_13

Papers in review

  1. J. Leinonen, U. Hamann, D. Nerini, U. Germann and G. Franch. Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification. 2023. In review.
  2. J. C. Hardin, N. Guy. J. Leinonen and V. Chandrasekar. PyDSD - A Python Library for Working with Disdrometers, Particle Probes, and Drop Size Distribution Data. Journal of Open Research Software, 2022. In review.

Presentations

Invited talks and keynotes

  1. Latent diffusion models for generative nowcasting and uncertainty quantification of precipitation fields. At EGU General Assembly 2023, Vienna, Austria, 2023.
  2. Predicting hazards from convective systems with deep learning. At EGU General Assembly 2023, Vienna, Austria, 2023.
  3. Time-Consistent Downscaling of Atmospheric Fields with Generative Adversarial Networks. At MAELSTROM dissemination workshop, online, 2022.
  4. Keynote: Seamlessly nowcasting lightning with convolutional-recurrent deep learning. At 4th European Nowcasting Conference, online, 2022.
  5. Stochastic machine learning for atmospheric fields with generative adversarial networks. At Joint IS-ENES3/ESiWACE2 Virtual Workshop on New Opportunities for ML and AI in Weather and Climate Modelling, online, 2021
  6. Keynote: Snowflake models for ice microphysics retrievals with multi-frequency radars. At 38th AMS Conference on Radar Meteorology, Chicago, Illinois, USA, 2017
  7. Keynote: Physics-based simulation of snowflakes. At First International Summer Snowfall Meeting, Cologne, Germany, 2017

Other conference talks

  1. Seamless prediction of multiple thunderstorm hazards with deep learning. In ECMWF-ESA Workshop on Machine Learning for Earth Observation and Prediction, Reading, United Kingdom, 2022.
  2. Deep learning based seamless nowcasting of thunderstorm hazards from multi-source data. In EUMETSAT Meteorological Satellite Conference 2022, Brussels, Belgium, 2022.
  3. Nowcasting thunderstorm hazards with neural networks from multi-source data. In ERAD 2022, Locarno, Switzerland, 2022.
  4. Improvements to short-term weather prediction with recurrent-convolutional networks. In 2021 IEEE International Conference on Big Data, 2021.
  5. Spatiotemporal weather data predictions with shortcut recurrent-convolutional networks: A solution for the Weather4cast challenge. In Workshop on Complex Data Challenges in Earth Observation (CDCEO), 2021.
  6. Hazard-specific Thunderstorm Nowcasting Using Machine Learning with Satellite, Radar and NWP Data. In EUMETSAT Meteorological Satellite Conference, 2021.
  7. Machine Learning for Seamless Thunderstorm Nowcasting from Multiple Data Sources. In ESA-ECMWF Workshop, online, 2021.
  8. Stochastic generation of climate and weather data fields with generative adversarial networks. In Workshop on Data Science in Climate and Climate Impact Research, online, 2020
  9. Highlight talk: Generative adversarial networks for climate data field generation. In 9th International Workshop on Climate Informatics, 2019
  10. Snowflake microphysics studies from deep-learning analysis of in-situ imagery. At 2nd International Summer Snowfall Workshop, Hyytiälä, Finland, 2019
  11. Multi-frequency radar retrieval of snowfall microphysics. In ERAD 2018, Ede-Wageningen, The Netherlands, 2018
  12. The release 05 CloudSat CWC-RVOD data product. At 2018 CloudSat-CALIPSO Science Team Meeting, Boulder, Colorado, USA, 2018
  13. A multi-instrument satellite view of the global three-dimensional distribution of cloud liquid water. At XVII International Conference on Clouds and Precipitation, Manchester, United Kingdom, 2016
  14. Improved CloudSat–MODIS cloud liquid water content retrieval. At 2016 CloudSat Science Team Meeting, Newport News, Virginia, USA, 2016
  15. Localized analysis of cloud optical depth and droplet radius from MODIS observations. At Asia Oceania Geosciences Society 12th Annual Meeting, Singapore, 2015
  16. Linking snowflake microphysics and radar scattering models. At URSI Commission F Specialist Symposium on Microwave Remote Sensing of the Earth, Oceans, and Atmosphere, Espoo, Finland, 2013
  17. GPM activities in Finland. At 5th International Workshop for GPM Ground Validation, Toronto, Ontario, Canada, 2012
  18. Statistical parametrization of the backscattering properties of snowflakes. At ERAD 2012, Toulouse, France, 2012
  19. Snowfall characterization with combined ground and space radar. At ERAD 2010, Sibiu, Romania, 2010
  20. DPR simulations using multi-parameter radar data. At 4th GPM International GV Workshop, Helsinki, Finland, 2010
  21. Simulating GPM DPR snowfall observations by using combined weather radar and CloudSat measurements. At Nordic Remote Sensing Days, Helsinki, Finland, 2009

Guest talks at universities and research institutes

  1. Deep Generative Models for Weather and Climate Applications. At IDSIA Dalle Molle Institute for Artificial Intelligence, Oct 2022.
  2. Stochastic machine learning for atmospheric fields with generative adversarial networks. At Ludwig Maximilians Universität Munich (online talk), Jun 2021.
  3. From snowflake models to space-based radar retrievals of atmospheric ice. At NASA Goddard Space Flight Center, Greenbelt, Maryland, USA, Sep 2018
  4. Snowflake models for retrievals with multi-frequency radars. At Pacific Northwest National Laboratory, Richland, Washington, USA, Aug 2017
  5. Multi-frequency radars for cloud and precipitation science: the ”how”, ”why”, and ”what now”. At Colorado State University, Fort Collins, Colorado, USA, Apr 2017
  6. Improved CloudSat–MODIS cloud liquid water content retrieval. At University of Cologne, Cologne, Germany, Nov 2016
  7. Multi-frequency radars for cloud and precipitation science: the “how”, “why”, and “what now”. At University of Cologne, Cologne, Germany, Oct 2015
  8. Multi-frequency radar signatures of snowflakes. At McGill University, Montreal, Quebec, Canada, Nov 2014
  9. What can multi-frequency radars tell us about snowflake microstructure? At University of Wisconsin, Madison, Wisconsin, USA, Aug 2013
  10. What can multi-frequency radars tell us about snowflake microstructure? At Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, Aug 2013
  11. Inferring atmospheric vertical profiles from observations and vehicle responses. At Summer course: The Exploration of Mars, Complutense University, Madrid, Spain, Jul 2009

Posters presented

  1. J. Leinonen and A. von Lerber. Physical simulations of melting snowflakes with smoothed particle hydrodynamics. At 2018 American Geophysical Union Fall Meeting, Washington, DC, USA, 2018.
  2. J. Leinonen, M. D. Lebsock, Sy. O. O., S. Tanelli, B. Dolan, R. J. Chase, Finlon. J. A., D. Moisseev, and A. von Lerber. Triple-frequency radar retrievals of snowfall properties from the OLYMPEX/RADEX field campaign. At 2017 American Geophysical Union Fall Meeting, New Orleans, Louisiana, USA, 2017.
  3. J. Leinonen, M. D. Lebsock, and G. L. Stephens. Combined CloudSat–Aqua–CALIPSO observations of cloud liquid water. At A-Train Symposium 2017, Pasadena, California, USA, 2017.
  4. J. Leinonen, M. D. Lebsock, and G. L. Stephens. The three-dimensional distribution of cloud liquid water observed by the A-Train. At 2016 American Geophysical Union Fall Meeting, San Francisco, California, USA, 2016.
  5. J. Leinonen, D. Moisseev, W. Szyrmer, and S. Kneifel. Simulations of microwave scattering using realistic snowflake models. At 8th IPWG & 5th IWSSM Joint Workshop, Bologna, Italy, 2016.
  6. J. Leinonen, M. D. Lebsock, L. Oreopoulos, and N. Cho. Evaluation of interregional variability in MODIS cloud regimes. At 2015 American Geophysical Union Fall Meeting, San Francisco, California, USA, 2015.
  7. J. Leinonen, M. D. Lebsock, K. Suzuki, H. Yashiro, and Y. Miyamoto. CloudSat observations simulated from a global cloud system-resolving model. At 2014 CloudSat Science Team Meeting, Alexandria, Virginia, USA, 2014.
  8. J. Leinonen, J. Tyynelä, D. Moisseev, and T. Nousiainen. Linking snowflake microphysics and radar scattering models. At Fourth International Workshop on Space-based Snowfall Measurement, Mammoth Mountain, California, USA, 2013.
  9. J. Leinonen, J. Tyynelä, D. Moisseev, and T. Nousiainen. Identification of non-spheroidal scattering effects from snowfall simulations and observations. At Third International Workshop on Space-based Snowfall Measurement, Grainau, Germany, 2011.
  10. J. Leinonen, T. Mäkinen, and A.-M. Harri. Pressure-based determination of Titan’s temperature profile using the Huygens HASI/PPI instrument. At 36th COSPAR Scientific Assembly, Beijing, China, 2006.
  11. J. Leinonen, T. Mäkinen, and A.-M. Harri. The atmospheric temperature profile of Titan reconstructed from pressure data. At 4th International Planetary Probe Workshop, Pasadena, California, USA, 2006.

Successful proposals