Science
Recent
Presentation | Kuma (2024), Climate Modelling and Current Research Topics in Climate Science 
(Lecture Series in High Performance Computing, National Competence Center for HPC, Bratislava, Slovakia [online])
Article | McDonald et al. (2024), Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations with ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator (in review in JGR: Atmospheres)
Report | Kuma et al. (2024), NAISS Activity Report 2023/22-202
Poster | Kuma and Bender (2024), Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON (Swedish Climate Symposium, Norrköping, Sweden, 15–17 May 2024)
Poster | Kuma and Bender (2023), Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON (The 15th Bolin Days, Stockholm, Sweden, 29–30 November 2023)
Article | Pei et al. (2023), Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model (Atmospheric Chemistry and Physics)
Presentation | Kuma et al. (2023), Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity (FORCeS Annual Meeting, Patras, Greece, 21 September 2023)
Articles
McDonald et al. (2024), Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations with ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator (in review in JGR: Atmospheres)
Pei et al. (2023), Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model (Atmospheric Chemistry and Physics)
Kuma et al. (2023), Climate model code genealogy and its relation to climate feedbacks and sensitivity (Journal of Advances in Modeling of Earth Systems)
Kuma et al. (2023), Machine learning of cloud types in satellite observations and climate models (Atmospheric Chemistry and Physics)
Guyot et al. (2022), Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals (Atmospheric Measurement Techniques)
Revell et al. (2021), Direct radiative effects of airborne microplastics (Nature)
Kremser et al. (2021), Southern Ocean Cloud and Aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage (Earth System Science Data)
Dale et al. (2021), The winter 2019 air pollution (PM2.5) measurement campaign in Christchurch, New Zealand (Earth System Science Data)
Hartery et al. (2021), Classification of the Below-Cloud Mixing State Over the Southern Ocean Using In-Situ and Remotely-Sensed Measurements (withdrawn)
Kuma et al. (2021), Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0) (Geoscientific Model Development)
Kuma et al. (2020), Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations (Atmospheric Chemistry and Physics)
Klekociuk et al. (2020), The state of the atmosphere in the 2016 southern Kerguelen Axis campaign region (Deep-Sea Research Part II: Topical Studies in Oceanography)
Hartery et al. (2020), Constraining the Surface Flux of Sea Spray Particles from the Southern Ocean (Journal of Geophysical Research: Atmospheres)
Jolly et al. (2018), An analysis of the cloud environment over the Ross Sea and Ross Ice Shelf using CloudSat/CALIPSO satellite observations: The importance of synoptic forcing (Atmospheric Chemistry and Physics)
Geleyn et al. (2017), Single interval longwave radiation scheme based on the net exchanged rate decomposition with bracketing (Quarterly Journal of the Royal Meteorological Society)
Mašek et al. (2015), Single interval shortwave radiation scheme with parameterized optical saturation and spectral overlaps (Quarterly Journal of the Royal Meteorological Society)
Posters
Kuma and Bender (2024), Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON (Swedish Climate Symposium, Norrköping, Sweden, 15–17 May 2024)
Kuma and Bender (2023), Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON (The 15th Bolin Days, Stockholm, Sweden, 29–30 November 2023)
Kuma et al. (2022), Climate model code genealogy and its relation to sensitivity and feedbacks (FORCeS Annual Meeting, Oslo, Norway, 14 September 2022)
Kuma and Bender (2021), Using deep learning cloud classification in cloud feedback and climate sensitivity determination (FORCeS Annual Meeting, Stockholm University, Stockholm, Sweden, 27 October 2021)
Kuma and Bender (2021), Climate sensitivity and the Southern Ocean: the effect of the “too few, too bright” model cloud problem (EGU General Assembly, Vienna, Austria, 19–30 April 2021)
Kuma et al. (2020), Ground-based lidar processing and simulator framework for comparing models and observations (Gateway Antarctica Conference, Christchurch, New Zealand, 30–31 January 2020)
Kuma et al. (2019), Automatic Lidar and Ceilometer Framework (ALCF) (CFMIP 2019 Meeting, Mykonos, Greece, 30 September–4 October 2019)
Kuma et al. (2019), Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations (Deep South Challenge Conference, Auckland, New Zealand, 6–8 May 2019)
Kuma et al. (2018), Shipborne and ground-based observations of clouds in the Southern Ocean (POLAR 2018, Davos, Switzerland, 19–23 June 2018)
Kuma et al. (2017), Assessment of Southern Ocean clouds and aerosols in the New Zealand Earth System Model using shipborne and ground-based observations (Deep South Challenge Symposium, Wellington, New Zealand, 4–6 September 2017)
Kuma et al. (2017), Shipborne and ground-based observations of clouds in the Subantarctic and the Southern Ocean (New Zealand Antarctic Science Conference, Dunedin, New Zealand, 26–28 June 2017)
Presentations
Kuma (2024), Climate Modelling and Current Research Topics in Climate Science (Lecture Series in High Performance Computing, National Competence Center for HPC, Bratislava, Slovakia [online])
Kuma et al. (2023), Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity (FORCeS Annual Meeting, Patras, Greece, 21 September 2023)
Kuma et al. (2022), Machine learning of cloud types in satellite observations and climate models (The 13th Annual SeRC Meeting, Bro, Sweden, 13 May 2022)
Kuma (2022), Software for climate sciences (Research seminar, Stockholm University, Stockholm, Sweden, 9 February 2022)
Kuma (2021), Clouds in climate models and atmospheric observations (Research seminar, Stockholm University, Stockholm, Sweden, 14 December 2021); video 1 (weather balloon launch), video 2 (UAV flight)
Kuma and Bender (2021), Machine learning of cloud types for evaluation of climate models and constraining climate sensitivity (FORCeS Annual Meeting, Stockholm University, Stockholm, Sweden, 25 October 2021)
Kuma and Bender (2021), Using deep learning cloud classification for cloud feedback and climate sensitivity determination (FORCeS WP5 & WP6 Science Meeting, Stockholm University, Stockholm, Sweden, 9 September 2021)
Kuma et al. (2020), Doctoral thesis presentation: Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations (University of Canterbury, Christchurch, New Zealand, 7 October 2020)
Kuma et al. (2018), Evaluation of HadGEM3 Southern Ocean cloud using observations and reanalyses (NZ Hydrological Society & Meteorological Society of NZ Joint Conference, Christchurch, New Zealand, 4–7 December 2018)
Kuma et al. (2018), Doctoral Confirmation Presentation: Assessment of Southern Ocean Clouds and Aerosol in General Circulation Models (University of Canterbury, Christchurch, New Zealand, 9 April 2018)
Kuma et al. (2017), Assessment of Southern Ocean clouds and aerosols in the New Zealand Earth System Model using shipborne and ground-based observations (Meteorological Society of New Zealand Annual Conference, Dunedin, New Zealand, 13–15 November 2017)
Theses
Kuma (2020), Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations (University of Canterbury, Christchurch, New Zealand)
Kuma (2015), Broadband approach as a framework for implementation of radiative transfer scheme with selective intermittency: Cost versus accuracy study (Comenius University, Bratislava, Slovakia)
Kuma (2010), Visualising Data from CloudSat and CALIPSO Satellites (Comenius University, Bratislava, Slovakia)
Media
Family Trees Clarify Relationships Among Climate Models (Eos, 2023)
How Airborne Microplastics Affect Climate Change (Scientific American, 2021)
Microplastics May Be Cooling—and Heating—Earth’s Climate (WIRED, 2021)
Microplastics May Be Cooling—and Heating—Earth’s Climate (Ars Technica, 2021)
Microplastics in the air have a small cooling effect on our climate (New Scientist, 2021)
Microplastics are in the air we breathe and in Earth’s atmosphere, and they affect the climate (The Conversation, 2021)
New Zealand’s Next Top Model (New Zealand Geographic, 2018)
Datasets
Kremser et al. (2020), Southern Ocean Cloud and Aerosol data set: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage (Zenodo)
Lectures
Kuma (2023), Global Climate System: Clouds and aerosols in the climate system (Stockholm University, Stockholm, Sweden, 2022–2023)
Reports
Kuma et al. (2024), NAISS Activity Report 2023/22-202
Open source software
ALCF – Automatic Lidar and Ceilometer Processing Framework
ccbrowse – Web application for browsing data from active Earth observation satellites
ccplot – Command-line application for visualizing data from CloudSat and CALIPSO satellites
cl2nc – Convert Vaisala CL51 and CL31 ceilometer dat files to NetCDF
ds-format – Python interface for reading and writing NetCDF and a custom dataset format
mpl2nc – Convert Sigma Space Micro Pulse Lidar (MPL) data files to NetCDF
mrr2c – Convert Metek MRR-2 micro rain radar data files to HDF
rstool – Command-line program for converting native radiosonde data to NetCDF and calculation of derived quantities, supporting InterMet Systems (iMet) and Windsond radiosondes