Hauke Schulz

Hauke Schulz

Climate scientist, PhD @Max Planck Institute for Meteorology and @University of Hamburg

Observing the atmosphere from space and ground, from land and sea, active and passive.

Trying to understand ☁️.

Open-source software, machine learning, data versioning, large-eddy simulations.

Projects

Note: Searching for a student project, like a master thesis? Please reach out to me! You can find some ideas here as a starting point but I am open to discuss your own ideas as well.

Process-understanding of meso-scale cloud organization

Why do clouds organize on the mesoscale? Why do they appear in different formations? Which role does precipitation and aerosols play? How will these pattern behave in a warmer climate? These and further questions are part of different efforts to better understand the processes behind cloud morphologies. These efforts are linked to field campagins like EUREC⁴A/ATOMIC and the simulation efforts following them (EUREC⁴A-MIP)

Relevant links

Relevant publications

Schulz, H. ; Stevens, B. (2023). Evaluating large-domain, hecto-meter, large-eddy simulations of trade-wind clouds using EUREC4A data. JAMES , https://doi.org/10.1029/2023MS003648

Vogel, R.; Konow, H.; Schulz, H. ; Zuidema, P. A Climatology of Trade-Wind Cumulus Cold Pools and Their Link to Mesoscale Cloud Organization. Atmospheric Chemistry and Physics 2021 , 21 (21), 16609–16630. https://doi.org/10.5194/acp-21-16609-2021 .

Schulz, H. ; Eastman, R.; Stevens, B. Characterization and Evolution of Organized Shallow Convection in the Trades. Journal of Geophysical Research: Atmospheres   2021 , 126 (17). doi:10.1029/2021JD034575

Vial, J.; Vogel, R.; Schulz, H. . On the Daily Cycle of Mesoscale Cloud Organization in the Winter Trades. Q.J Royal Met. Soc. 2021 https://doi.org/10.1002/qj.4103 .

Detection of meso-scale cloud patterns

In a Kaggle Competition we challenge the machine-learning community to develop a classification algorithm that matches the human labels best.

Relevant publications

Schulz, H. (2022). C3ONTEXT: A Common Consensus on Convective OrgaNizaTion during the EUREC4A eXperimenT. Earth System Science Data, 14 (3), 1233–1256. https://doi.org/10.5194/essd-14-1233-2022

Rasp, S.; Schulz, H.; Bony, S.; Stevens, B. Combining Crowd-Sourcing and Deep Learning to Explore the Meso-Scale Organization of Shallow Convection. Bull. Amer. Meteor. Soc.2020. https://doi.org/10.1175/BAMS-D-19-0324.1.

Observing the atmosphere in moisture space

Simulations of radiative equilibrium (RCE) have shown that clouds can self-organize. Randomly distributed clouds can end up in a large cloud-cluster. This redistribution of cloudiness alters the state of the atmosphere as the moist regions get moister and the dry regions get drier. The work here presents observational evidence that circulations driving this mechanism also exist in the real world.

Relevant publications

Schulz, H.; Stevens, B. Observing the Tropical Atmosphere in Moisture Space. J. Atmos. Sci.2018, 75 (10), 3313–3330. doi.org10.1175/JAS-D-17-0375.1.

Sea-ice study in Antarctica

Sea-ice plays a critical role in the Earth's climate system. However, data about sea-ice thickness is still sparse, especially in Antarctica. While satellite retrivals are getting better, the spatial variability and composition of the sea-ice are still challenging. With various measurement techniques from simple snow-depths readings to drillings to ground-radars pulled behind a snow-cat, we captured the composition of the sea-ice and its environment. The deployment of snow-buoys and thermistor chains provided additional information about temporal evolution of the sea-ice. This data now helps to improve satellite retrivals and our understanding of land-fast sea ice.

Relevant dataset

Arndt, S., Schulz, H., and Nicolaus, M. Thickness and properties of sea ice and snow of land-fast sea ice in Atka Bay in 2017. Data set. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. 10.1594/PANGAEA.889250.

Meteorological climate record in Antarctica

Long-term observations are crucial to analyse climate change. At the Neumayer Station they have a long tradition dating back to 1981, when the Georg-von-Neumayer Station was build. As an overwinterer at the Neumayer Station III, I took over the responsibility for over a year and contributed to this long-term record with:

  • Radiosoundings
  • Ozone soundings
  • Synoptic observations
  • Radiation measurements
  • Standard meteorology measurements

Relevant datasets

Schmithüsen, H. and Schulz, H.Meteorological synoptical observations from Neumayer Station (Feb. 2017- Jan. 2018). Dataset collection. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. URL.

Schmithüsen, H. and Schulz, H.Radiosonde measurements from Neumayer Station (Feb. 2017- Jan. 2018).Dataset collection. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. URL.

Schmithüsen, H. and Schulz, H.Ozone measurements from Neumayer Station (Feb. 2017- Jan. 2018).Dataset collection. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. URL.

Schmithüsen, H. and Schulz, H.Meteorological synoptical observations from Neumayer Station (Feb. 2017- Jan. 2018).Dataset collection. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. URL.

Schmithüsen, H. and Schulz, H.High resolved snow height measurements at Neumayer Station, Antarctica, 2017.Dataset collection. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 2018. 10.1594/PANGAEA.887689.