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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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| === Monday === |
=== Monday, 10:00 === |
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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]] * [[attachment:Stochastic analysis of time series.ipynb]] [[attachment:Monte carlo.ipynb]] [[attachment:GISSTEMP netcdf.ipynb]] [[attachment:datetimeobjects.ipynb]] [[attachment:Station data seasonal cycle.ipynb]] [[/Projects2014 | Projects descriptions]] |
* [[DataIO|Access to data sets]] * [[GenMaps|Data Visualization: Generating maps]] |
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| Morning: Group presentations of methods and code implementations | |
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| Project work | Afternoon: Project work |
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| Morning: Group presentations of preliminary results | |
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| Presentation of results and discussion | Afternoon: Project work |
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| Morning: final presenation of results and discussion | |
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| Further analysis and preparation of final report | Afternoon: evaluation and preparation of final report = Topics for group work = The major topic of this course will deal with data gaps in climate and satellite records and their implications for the calculation of global statistics. The overall objectives of the course are: * raise awareness on the problem with data gaps in climate records * learn about methods for coping with data gaps * assess the effect of data gaps on statistics of climate related datasets * learn processing of data using a variety of methods Another major objective of this course is to train the so called soft-skills in practice, like * project management (planning ... final report) * joint code development * reporting |
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| == Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends == {{attachment:media_summary.png}} |
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| == Topics for group work == = Project groups = |
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: * http://onlinelibrary.wiley.com/doi/10.1002/qj.2297/abstract * http://www-users.york.ac.uk/~kdc3/papers/coverage2013/methods.html Surface temperatures are available from the International Arctic Buoy Programme (IABP) website: * http://iabp.apl.washington.edu/data_satemp.html === 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.) * Look at correlations with climate indices, e.g. ENSO, NAO, PDO |
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| === 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 there significant trends over different period of times? * [[http://iabp.apl.washington.edu/AirT/RigorEtal-SAT.pdf|Rigor, I., R. Colony, and S. Martin, 2000, Variations in Surface Air Temperature Observations in the Arctic, 1979 - 1997, J. Climate, Vol. 13, no 5, 896-914.]] |
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=== 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 * Was the data gap in the Arctic filled in reasonably? * Are there biases or jumps in the data? |
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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? * What is the impact of different land/sea masks and spatial grids (resolution, projections) on total mean global fluxes? [[/ProjectD]] |
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* prepare [[ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-concentration/data/|sea ice data]] (Lars) :-) (CMIP5 data available) * prepare CMIP5 data (Alex) :-) |
* CW2013 :-) |
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| * Stations :-) | * Bouy data :-) * HOAPS data :-) |
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| * Methods and data | * Methods and data |
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* Zwiers, von Storch * Jenkins and Watts, Spectral Analysis and its Application, MAT STAT J3 |
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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, 10:00
General Introduction
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
The major topic of this course will deal with data gaps in climate and satellite records and their implications for the calculation of global statistics. The overall objectives of the course are:
- raise awareness on the problem with data gaps in climate records
- learn about methods for coping with data gaps
- assess the effect of data gaps on statistics of climate related datasets
- learn processing of data using a variety of methods
Another major objective of this course is to train the so called soft-skills in practice, like
- project management (planning ... final report)
- joint code development
- reporting
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.)
- Look at correlations with climate indices, e.g. ENSO, NAO, PDO
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 there significant trends over different period of times?
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
- Was the data gap in the Arctic filled in reasonably?
- Are there biases or jumps in the data?
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?
- What is the impact of different land/sea masks and spatial grids (resolution, projections) on total mean global 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