Peter Kuma Software and Science

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Science

Article | Kuma et al. (2022), Climate model code genealogy and its relation to climate feedbacks and sensitivity (manuscript in preparation)

Poster | Kuma et al. (2022), Climate model code genealogy and its relation to sensitivity and feedbacks (FORCeS Annual Meeting, Oslo, Norway, 14 September 2022)

Article | Kuma et al. (2022), Machine learning of cloud types in satellite observations and climate models (in review in Atmospheric Chemistry and Physics)

Article | 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)

Presentation | 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)

Presentation | Kuma (2022), Software for climate sciences (Research seminar, Stockholm University, Stockholm, Sweden, 9 February 2022)

Lecture | Global Climate System: Clouds and aerosols in the climate system (Stockholm University, Stockholm, Sweden, 25 January 2022)

Presentation | 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)

Poster | Kuma and Bender (2021), Using deep learning cloud classification in cloud feedback (FORCeS Annual Meeting, Stockholm University, Stockholm, Sweden, 27 October 2021)

Presentation | 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)

Presentation | 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)

Media | How Airborne Microplastics Affect Climate Change (Scientific American, 2021)

Media | Microplastics May Be Cooling—and Heating—Earth’s Climate (WIRED, 2021)

Media | Microplastics May Be Cooling—and Heating—Earth’s Climate (Ars Technica, 2021)

Media | Microplastics in the air have a small cooling effect on our climate (New Scientist, 2021)

Media | Microplastics are in the air we breathe and in Earth’s atmosphere, and they affect the climate (The Conversation, 2021)

Article | Revell et al. (2021), Direct radiative effects of airborne microplastics (Nature)

Article | 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)

Article | Dale et al. (2021), The winter 2019 air pollution (PM2.5) measurement campaign in Christchurch, New Zealand (Earth System Science Data)

Poster | Kuma and Bender (2021), Climate sensitivity and the Southern Ocean: the effect of the “too few, too bright” model cloud problem (EGU General Assembly, 19–30 April 2021)

Article | Hartery et al. (2021), Classification of the Below-Cloud Mixing State Over the Southern Ocean Using In-Situ and Remotely-Sensed Measurements (manuscript in preparation)

Article | Kuma et al. (2021), Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0) (Geoscientific Model Development)

Dataset | 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)

Presentation | 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)

Article | 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)

Thesis | Kuma (2020), Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations (University of Canterbury, Christchurch, New Zealand)

Article | 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)

Poster | 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)

Article | Hartery et al. (2020), Constraining the Surface Flux of Sea Spray Particles from the Southern Ocean (Journal of Geophysical Research: Atmospheres)

Poster | Kuma et al. (2019), Automatic Lidar and Ceilometer Framework (ALCF) (CFMIP 2019 Meeting, Mykonos, Greece, 30 September–4 October 2019)

Poster | 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)

Presentation | 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)

Article | 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)

Poster | Kuma et al. (2018), Shipborne and ground-based observations of clouds in the Southern Ocean (POLAR 2018, Davos, Switzerland, 19–23 June 2018)

Presentation | 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)

Media | New Zealand’s Next Top Model (New Zealand Geographic, 2018)

Presentation | 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)

Poster | 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)

Poster | Kuma et al. (2017), Shipborne and ground-based observations of clouds in the Subantarctic the the Southern Ocean (New Zealand Antarctic Science Conference, Dunedin, New Zealand, 26–28 June 2017)

Article | 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)

Article | Mašek et al. (2015), Single interval shortwave radiation scheme with parameterized optical saturation and spectral overlaps (Quarterly Journal of the Royal Meteorological Society)

Thesis | Kuma (2015), Broadband approach as a framework for implementation of radiative transfer scheme with selective intermittency: Cost versus accuracy study (Comenius University, Bratislava, Slovakia)

Thesis | Kuma (2010), Visualising Data from CloudSat and CALIPSO Satellites (Comenius University, Bratislava, Slovakia)

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