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| = 63-953 Climate and Satellite Data Analysis = | = 63-953 Climate and Satellite Data Analysis = Lars Kaleschke, Alexander Loew |
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| Place: Geom 1536c | Place: Geom 1536c |
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The participants will learn to practically work with climate model, reanalysis, in-situ station and satellite data. Organized as a group project, the participants will further learn the principles of project management and shared software development. |
The participants will learn to practically work with climate model, reanalysis, in-situ station and satellite data. Organized as a group project, the participants will further learn the principles of project management and shared software development. |
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| * [[/PM|Project management and documentation]] | * [[Climate_and_Satellite_Data_Analysis_2014/PM|Project management and documentation]] |
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| * [[DataIO|Access to data sets]] * [[/Plotting|Plotting]] [[attachment:Seawater plot.ipynb]] * [[GenMaps|Data Visualization: Generating maps]] * [[http://ipython.org/ipython-doc/dev/interactive/htmlnotebook.html|IPython Notebooks]] [[https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks|Gallery of interesting IPython Notebooks]] |
* [[DataIO|Access to data sets]] * [[Climate_and_Satellite_Data_Analysis_2014/Plotting|Plotting]] [[attachment:Seawater plot.ipynb]] * [[GenMaps|Data Visualization: Generating maps]] * [[http://ipython.org/ipython-doc/dev/interactive/htmlnotebook.html|IPython Notebooks]] [[https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks|Gallery of interesting IPython Notebooks]] |
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| Afternoon: evaluation and preparation of final report | Afternoon: evaluation and preparation of final report |
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| Kevin Cowtan and Robert Way fill the gaps of the HadCRUT temperature data set by using satellite data. Compare their new reconstruction of surface temperature data to independent in-situ observations and reanalysis data. | Kevin Cowtan and Robert Way fill the gaps of the HadCRUT temperature data set by using satellite data. Compare their new reconstruction of surface temperature data to independent in-situ observations and reanalysis data. |
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| Cowtand and Way (2013) methods and data are freely available: | Cowtan and Way (2013) methods and data are freely available: |
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| Surface temperatures are available from the International Arctic Buoy Programme (IABP) website: | Surface temperatures are available from the International Arctic Buoy Programme (IABP) website: |
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| * Review methods of Cowtan and Way (2013) * Write code to read and plot the data * Calculate trends and variabilites |
* Review methods of Cowtan and Way (2013) * Analyse their reconstructed dataset of surface air temperature (seasonal cycle, anomalies, trends etc.) * Are the trends significant? |
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| * Review methods of Rigor et al. (2000) * Write code to read and plot the gridded buoy data * Calculate trends and variabilites |
* Review methods of Rigor et al. (2000) * Analyse the surface air temperature measured by the drift buoys (seasonal cycle, anomalies, trends etc.) * Are the trends significant? |
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| Reference: | |
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| Use the buoy measurements of surface air temperature as ground truth | |
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| (advanced programming skills needed) * Write code to interpolate the different datasets in a common grid * Compare CW2013, buoy and reanalysis data (variability and trends) |
* Write code to interpolate the different datasets in a common grid * Compare CW2013, buoy and reanalysis data * How large is the correlation of the datasets? * Are there biases or jumps in the data? * Was the data gap in the Arctic filled reasonably? |
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| == HOAPS ocean flux sampling bias == * [[http://www.hoaps.org|HOAPS climatology]] of ocean surface fluxes * HOAPS is sampled twice a day * What is the impact of undersampling the dirnal cycle on monthly means? * What is the impact of sea ice gaps on monthly means? * How do HOAPS surface flux estimates compare to literature values of global mean ocean surface fluxes? |
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| == Soil moisture == * Sampling bias |
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| * Bouy data :-) | * Bouy data :-) * HOAPS data :-) |
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| * Methods and data | * Methods and data |
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= Examples from the past = == How significant are observations of Arctic temperature trends? == * https://wiki.zmaw.de/lehre/Climate_and_Satellite_Data_Analysis_2013/ProjectA |
Contents
63-953 Climate and Satellite Data Analysis
Lars Kaleschke, Alexander Loew
MS Integrated Climate System Sciences
Date: 3.2.2014-7.2.2014
Place: Geom 1536c
Course objectives
The participants will learn to practically work with climate model, reanalysis, in-situ station and satellite data. Organized as a group project, the participants will further learn the principles of project management and shared software development.
Schedule
Monday
General Introduction
Stochastic analysis of time series.ipynb Monte carlo.ipynb GISSTEMP netcdf.ipynb datetimeobjects.ipynb Station data seasonal cycle.ipynb
Group work: develop a project plan and write a short technical proposal for your project.
Final report due by 15. March 2014
Obtain data and do preliminary analysis (e.g. data coverage).
Tuesday
Morning: Group presentations of project plan and preliminary analysis.
Afternoon: implementation and project work
Wednesday
Morning: Group presentations of methods and code implementations
Afternoon: Project work
Thursday
Morning: Group presentations of preliminary results
Afternoon: Project work
Friday
Morning: final presenation of results and discussion
Afternoon: evaluation and preparation of final report
Topics for group work
Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends
Kevin Cowtan and Robert Way fill the gaps of the HadCRUT temperature data set by using satellite data. Compare their new reconstruction of surface temperature data to independent in-situ observations and reanalysis data.
Cowtan and Way (2013) methods and data are freely available:
Surface temperatures are available from the International Arctic Buoy Programme (IABP) website:
Cowtan and Way (CW2013) reconstruction
- Review methods of Cowtan and Way (2013)
- Analyse their reconstructed dataset of surface air temperature (seasonal cycle, anomalies, trends etc.)
- Are the trends significant?
Variations in Surface Air Temperature Observations in the Arctic
- Review methods of Rigor et al. (2000)
- Analyse the surface air temperature measured by the drift buoys (seasonal cycle, anomalies, trends etc.)
- Are the trends significant?
Data intercomparison
Use the buoy measurements of surface air temperature as ground truth
- Write code to interpolate the different datasets in a common grid
- Compare CW2013, buoy and reanalysis data
- How large is the correlation of the datasets?
- Are there biases or jumps in the data?
- Was the data gap in the Arctic filled reasonably?
HOAPS ocean flux sampling bias
HOAPS climatology of ocean surface fluxes
- HOAPS is sampled twice a day
- What is the impact of undersampling the dirnal cycle on monthly means?
- What is the impact of sea ice gaps on monthly means?
- How do HOAPS surface flux estimates compare to literature values of global mean ocean surface fluxes?
TODOs
Data
CW2013
ERA-Interim
NCEP
Bouy data
HOAPS data
Final report
Template structure:
- Abstract
- Introduction: state of the art (literature), statement of the problem
- Methods and data
- Results
- Discussion
- Conlcusion
References
- Python Scripting for Computational Science, Hans Petter Langtangen, Springer (available in the ZMAW library)
Examples from the past