BUCSS22

Course materials from the 2022 Bochum Urban Climate Summer School (Germany).

Context

The first three editions of BUCSS took place in Bucharest (Romania) (see e.g. here for 2019). This 4th edition took place from 26 to 29 September 2022 at Ruhr-University Bochum (Germany), and aimed at 1) providing a general introduction to different facets of urban climatology with a special focus on urban climate informatics, 2) providing structured information and skill-building capabilities related to urban climate monitoring, remote sensing and modelling, and 3) strengthening an active pool of young scientists to tackle the major urban sustainability challenges of future generations.

Official website: https://www.climate.ruhr-uni-bochum.de/bucss/

Note

The crowdsourced and LST/SUHI data used in some hands-on sessions are developed by the Bochum Urban Climate Lab as part of the ENLIGHT project. These dataset excerpts are only a small fraction of a global dataset, that spans 2000-2018 for LST/SUHI and 2019-now for the crowdsourced data. Feel free to reach out in case you are interested to use these datasets for your own reach project, we are always open for new collaborations.

Course materials

The programme consists out of state-of-the-art lectures and hands-on tutorials on remote sensing in urban areas, crowdsourcing and urban climate modelling. All of these teaching resources are available below.

Lectures

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Monday 26.09

• Student self-presentations: a compressed archive of all presentations is available here
• Gerald Mills: Urban climate science (including notes)
• Andreas Christen: Urbanization, human dynamics and and climate change
• Benjamin Bechtel: Introduction to Satellite Remote Sensing (of Urban Climates)
• Simone Kotthaus: Introduction to ground-based remote sensing of the urban boundary layer
• Matthias Demuzere: Introduction to Google Colaboratory
• Panagiotis Sismanidis: Thermal remote sensing - exercise

Tuesday 27.09

• Ariane Middel: Urban Climate Informatics
• Negin Nazarian: Personal exposure to urban climate: Infilling the urban climate networks with IoT, ubiquitous, and crowdsourced sensing
• Daniel Fenner: Crowdsourcing Crowd (Citizen) Weather Station Data
• Matthias Demuzere: Introduction to Google Earth Engine

Wednesday 28.09

• Leena Järvi: General introduction to urban climate modelling
• Robert Rauterkus: Urban Climate Modeling With PALM
• Helen Claire Ward: Evaluating urban climate models

Thursday 29.09

• Andrea Zonato: Introduction to (urban climate modelling with) WRF
• Matthias Demuzere and Andrea Zonato: Guidelines for the WRF hands-on session

Hands-on sessions

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During the school, all Python notebooks were executed in Google Colaboratory, reading and writing information from a dedicated Google Drive. As you will probably be using these notebooks outside this environment, make sure to take care of your own Python environment and directory definitions.

1. Process ESA CCI LST/SUHI data

This hands-on is composed out of two parts:

1. Process MODIS LST data and calculate zonal statistics (Python notebook)
2. Open the SUHI database for Mexico, developed in the ENLIGHT project, and process it in a number of ways, leading to the SUHII seasonal hysteresis as published in Sismanidis et al. 2022 (Python notebook).

These hands-ons require the following files that can be downloaded from here, under LST/:

• ESACCI-LST-MODIST-RUBMEX-2018-D.nc: MODIS LST data
• mexico_cities.tif: raster layer with urban boundaries
• SUHI_DB-MOD11A1.061-v1.0.1.db: excerpt of SUHI database developed in the ENLIGHT project
• SUHII_monthly_means.db: excerpt of SUHI database developed in the ENLIGHT project

2. Quality control CWS data with CrowdQC+

Goal of this workshop is to learn how to apply the quality-control package CrowdQC+ on an excerpt of the global CWS database compiled in the ENLIGHT project.

Via this Python notebook you learn how to:

• use R in a Python environment
• get hands-on experience with real crowdsourced CWS data
• investigate issues with such data
• use CrowdQC+ to quality-control such data
• do some first analyses with the quality-controlled data

This hands-on requires the cws_data_cqcp_hands_on.csv file available from here under CWS/

3. Greenest German city in Google Earth Engine

In this interactive newspaper article, published in the Berliner Morgenpost May 10 2016, the authors used the Landsat archive in Google Earth Engine to estimate the "greenest" large (>100 000 inhabitants) city in Germany.

The goal of this hands-on is the replicate this work, using Sentinel-2 information instead. The procedure consist out of three steps:

1. Create a database with relevant cities (Python notebook)
2. Add greennees (derived from Sentinel-2) to this database (GEE javascript)
3. Display the results (Python notebook)

This hands-on requires the 05-staedte.xlsx and gadm41_DEU_4.json files available from here under GEE/

4. WRF evaluation and roottop mitigation strategies

For this hands-on, 6 WRF simulations are pre-computed, covering the Ruhr-Area (Germany) and a heatwave period between 20 - 28 July 2019. Further details on the model's configuration and simulation details are provided in the WRF hands-on slides.

Two notebooks are developed to complete this exercise:

• WRF_functions.ipynb: a notebook that contains a set of pre-scripted functions to read, manipulate and evalute all WRF input and output, the latter in combination with CWS observations (no changes needed in this one).
• WRF_evaluations.ipynb: a notebook to read, manipulate and evalute all WRF input and output, the latter in combination with CWS observations. It also allows you to assess the impact of the rooftop mitigation strategies (RMS) compared to the baseline simulation without RMS.

These hands-ons require a bunch of files available from here under:

• WRF_CWS/:

  • cws_heatwave_2019_bucss.csv: quality-controlled timeseries of 5000+ CWS stations in the Ruhr-area, for the period 21-28 July 2019.
  • cws_heatwave_2019_bucss_metadata.csv: metadata for all above-mentioned CWS stations

• WRF/:

  • INPUT/: the required geo_em*.nc files to run the various WRF simulations. Note that:

    • the Local Climate Zone map for the Ruhr area was extracted from the global map of Local Climate Zones (Demuzere et al., 2022).
    • the W2W package was used to insert the LCZ-based urban description.

  • OUTPUT/: output files for all WRF simulations.

Please see WRF hands-on slides for further details.