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June 2012 Jörg Trentmann Satellite Application Facility on Climate Monitoring (CM SAF) Deutscher Wetterdienst Technical Introduction

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Technical Introduction. Jörg Trentmann Satellite Application Facility on Climate Monitoring (CM SAF) Deutscher Wetterdienst. Overview. Introduction to CM SAF data Software tools ( cdo , R ) Example scripts Suggestions/ideas for exercises. Introduction. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: Jörg Trentmann

June 2012

Jörg TrentmannSatellite Application Facility on

Climate Monitoring (CM SAF)

Deutscher Wetterdienst

Technical Introduction

Page 2: Jörg Trentmann

June 2012

•Introduction to

•CM SAF data

•Software tools (cdo, R)

•Example scripts

•Suggestions/ideas for exercises

Overview

Page 3: Jörg Trentmann

June 2012

Are you planning to use CM SAF data during the

CM SAF Event Week?

Introduction

Yes

No(but will use

CM SAF data later)

No(no plans to useCM SAF data)

Page 4: Jörg Trentmann

June 2012

Are you a registered user of CM SAF?

Yes No

Have you ever ordered CM SAF data through the Web

User Interface (WUI: www.cmsaf.eu/wui)?

Yes No

Introduction

Page 5: Jörg Trentmann

June 2012

CM SAF data files

How does a CM SAF data file look like, have you ever

seen a CM SAF data file??

Yes No

Page 6: Jörg Trentmann

June 2012

CM SAF data files

The file names of CM SAF data follow a certain naming

convention:

Page 7: Jörg Trentmann

June 2012

CM SAF data files

The file names of CM SAF data follow a certain naming

convention:

Page 8: Jörg Trentmann

June 2012

Example:

CM SAF data files

Page 9: Jörg Trentmann

June 2012

hdf(http://www.hdfgroup.org/HDF5/)

Data Formats

+ supports additional data compression

+ commonly used in satellite data community

+ provides a standard

– not very common– only limited software

available to access the data

– complex to read data

Page 10: Jörg Trentmann

June 2012

hdf(http://www.hdfgroup.org/HDF5/)

Data Formats

+ supports additional data compression

+ commonly used in satellite data community

+ provides a standard

– not very common– only limited software

available to access the data

– complex to read data

netcdf (http://www.unidata.ucar.edu/software/netcdf

+ growing number of meteorological / climatological users

+ wider range of software available to access the data, incl. GIS

+ standard for climate data available

– no data compression, larger files

Page 11: Jörg Trentmann

June 2012

hdf(http://www.hdfgroup.org/HDF5/)

Data Formats

+ supports additional data compression

+ commonly used in satellite data community

+ provides a standard

– not very common– only limited software

available to access the data

– complex to read data

netcdf (http://www.unidata.ucar.edu/software/netcdf

+ growing number of meteorological/climatological users

+ wider range of software available to access the data, incl. GIS

+ standard for climate data available

– no data compression, larger files

It is recommended to order CM SAF data in netcdf-format via the Web

User Interface (WUI)!

Page 12: Jörg Trentmann

June 2012

Software tools

The free, open-source climate data operators (cdo)

and the statistical software tool R are the main tools

supported to analyse CM SAF data.

•cdo: https://code.zmaw.de/projects/cdo

•R: http://www.r-project.org

Page 13: Jörg Trentmann

June 2012

Software tools

Have you ever used the

climate data operators (cdo) / R?

Yes

No

Very little

Yes

No

Very little

cdo R

Page 14: Jörg Trentmann

June 2012

Climate Data Operators• developed at Max Planck Institute for Meteorology, Hamburg

• freely available including extensive documentation:

https://code.zmaw.de/projects/cdo

• works with multiple file formats, including grib, netcdf etc.

• collection of operators for processing climate (model) data e.g.spatial interpolationdata selectionSubsamplingstatistical and arithmetical functions…

• is accessible from the ‘command line’ (‘prompt’)

• cdo does currently not include a graphical user interface and can

not visualize data

tools: cdo

Page 15: Jörg Trentmann

June 2012

R• programming language and software environment for

statistical computing and graphics: http://www.r-project.org/• includes an interface to netcdf

• has extensive statistical and graphical capabilities, mainly through additional packages

• supports the use of scripts

• extensive online-documentation available on the official webpage and on other webpages

• has been developed for use in statistics (biology, medicine, etc.);

• has not been developed for use in meteorology / climatology:-> terminology sometimes not intuitive -> rather slow for large data sets

tools: R

Page 16: Jörg Trentmann

June 2012

tools: cdo + R

Which operating system are you using?

Windows

Linux/Unix

MAC OSX

XP 7

2000 Vista

Page 17: Jörg Trentmann

June 2012

tools: cdo + R

Is cdo / R available on your computer system?

Yes

No

Yes

No

cdo R

Page 18: Jörg Trentmann

June 2012

•A collection of software scripts including example CM SAF data is provided on the Course page

•scripts are based on Windows, rearranging them for Unix/Linux/Mac-operating systems should be straight forward.

•Before running these scripts, install cdo and R on your local computer system (see further instructions on the Course Page)!

•Suggestion: install panoply (visualize netcdf-files) and Notepad++ (text-editor).

•Have a look at the screencasts (provided on the Course page) that describe the use of the scripts!

tools: cdo + R

Page 19: Jörg Trentmann

June 2012

1. Order data in netcdf-format on a regular lon-lat grid from the Web User Interface

Step-by-step

Page 20: Jörg Trentmann

June 2012

1. Order data in netcdf-format on a regular lon-lat grid from the CM SAF Web User Interface

2. Combine the individual time steps into one netcdf-file (cdo)

Step-by-step

Page 21: Jörg Trentmann

June 2012

1. Order data in netcdf-format on a regular lon-lat grid from the CM SAF Web User Interface

2. Combine the individual time steps into one netcdf-file (cdo)

3. Extract the time series for a single location (cdo)

4. Calculate the temporal / spatial means (cdo)

5. Calculate the multi-year monthly averages (cdo)

6. Calculate the monthly anomalies (cdo)

Step-by-step

Page 22: Jörg Trentmann

June 2012

1. Order data in netcdf-format on a regular lon-lat grid from the CM SAF Web User Interface

2. Combine the individual time steps into one netcdf-file (cdo)

3. Extract the time series for a single location (cdo)

4. Calculate the temporal / spatial means (cdo)

5. Calculate the multi-year monthly averages (cdo)

6. Calculate the monthly anomalies (cdo)

7. Plot time series data (R)

8. Analyse the time series data, e.g., calculate average annual cycles, trends (R)

9. Visualize the 2D Data, e.g., monthly means / temporal means / anomalies (R)

Step-by-step

Page 23: Jörg Trentmann

June 2012

Step-by-step

Examples

Time Series Analysis

Regional Plot

Surface Solar Radiation, Offenbach, Germany

Page 24: Jörg Trentmann

June 2012

• Use the scripts and the data provided• Order more data that fits your purpose using the WUI• Best way of learning new tools is using them!

Now it is your turn!

Page 25: Jörg Trentmann

June 2012

• Use the scripts and the data provided• Order more data that fits your purpose using the WUI• Best way of learning new tools is using them!

• Some ideas to start with:- Have a look at your home town:

What is the average annual cycle of cloud cover, solar radiation, humidity?

Is there a trend in solar surface radiation? Are there temporal/spatial correlations between solar

radiation, cloud cover, humidity in your country?Which was the ‘best’ summer / winter in the last five years?

Now it is your turn!

Page 26: Jörg Trentmann

June 2012

• Use the scripts and the data provided• Order more data that fits your purpose using the WUI• Best way of learning new tools is using them!

• Some ideas to start with:- Have a look at your home town:

What is the average annual cycle of cloud cover, solar radiation, humidity?

Is there a trend in solar surface radiation? Are there temporal/spatial correlations between solar

radiation, cloud cover, humidity in your country?Which was the ‘best’ summer / winter in the last five years?

- Which region in your country has the highest cloud cover (e.g., in July)? Is this the region with the highest solar radiation?

Now it is your turn!

Page 27: Jörg Trentmann

June 2012

• Use the scripts and the data provided• Order more data that fits your purpose using the WUI• Best way of learning new tools is using them!

• Some ideas to start with:- Have a look at your home town:

What is the average annual cycle of cloud cover, solar radiation, humidity?

Is there a trend in solar surface radiation? Are there temporal/spatial correlations between solar

radiation, cloud cover, humidity in your country?Which was the ‘best’ summer / winter in the last five years?

- Which region in your country has the highest cloud cover (e.g., in July)? Is this the region with the highest solar radiation?

Now it is your turn!

Create your own project, we are curious to learn from you!

Page 28: Jörg Trentmann

June 2012

Now it is your turn!

Have fun playing with the data!

Any Questions?

We will be back later today (14 UTC) in Centra for further comments / questions.

See also the course page:

•Download CM SAF Sample File including instructions (screencasts).

•Forum at Course page for further discussions.