README.md

Solar-Radiation-Balance-maps

• High resolution Solar Radiation Balance maps from ECMWF 40-year reanalysis model data
• Folders contain maps (pdfs and gifs), model data and python code to create the maps
• You are free to use the maps or run the code to generate your own.
• Used in a lab activity to teach about climate in Geology/Astronomy 106L at Iowa State University:
  • The learning objective for this lab is for students to understand how short- and long-wave radiation budgets affect monthly and yearly temperature fluctuations.
  • Students work in pairs, and are given a map that shows either annual mean incoming solar radiation or annual mean outgoing thermal radiation.
  • Students answer a series of questions about these maps, and then combine with another group in order to compare the differences between incoming and outgoing radiation values.
  • By doing this, they develop an understanding of which parts of the world experience an energy surplus or deficit.
  • Students are then asked to hypothesize why the energy deficit (surplus) areas don’t continue to get colder (warmer)

Folder content

data/ECMWF40_moda_Sep1957_Aug2002_SSR_STR_TSR_TTR.nc

• netcdf file from http://apps.ecmwf.int/datasets/data/era40-moda
• Monthly means of Daily means, moda, 6, 2.5°, 1957-09-01 to 2002-08-01, Forecast, 40 years reanalysis
• Variables: Surface net solar radiation, Surface net thermal radiation, Top net solar radiation, Top net thermal radiation (SSR, STR, TSR, TTR)
• Radiation Quantities in the ECMWF model and MARS.pdf documents the variables and gives some example maps

src/Radiation_maps_from_ECMWF40_data_monthly.py

src/Radiation_maps_from_ECMWF40_data_yearly.py

• Python 2 scripts for generating monthly or yearly maps from the Variables
• reads in the netcdf file and generates a bunch of geotiffs using GDAL so they could be used in a GIS
• uses numpy to store data in arrays
• data values (energy) is converted from J to W, duration is number of secs per month
• for TTR and STR, the absolute values are used (otherwise they would be negative)
• calculates the yearly or monthly averages
• uses Matplotlib/Basemap to create the maps
• lots of plot parameters you could change for the maps:
  • variable (currently ttr but can be a list for batch processing)
  • area (currently global)
  • projection (currently Robinson)
  • visual center (median) of the map but can be a list for batch processing
  • grids (currently 30 degree spacings)
  • colormap/colorramp: currently CMRmap but can be a list for batch processing
  • contours (on/off)
  • for Radiation_maps_from_ECMWF40_data_yearly.py only one map is created
  • Radiation_maps_from_ECMWF40_data_monthly.py creates a map for each month
  • if batch processing is used (e.g. if a list of medians is given), a map is produced for each median, this can be combined with a list of colormaps
  • monthly maps at different medians can be used as frames to create and animation showing the changes over the year, plus a slow rotation around the globe

geotiffs:

• rasters for monthly and annual means for ssr, str, ttr and tsr
• Radiation_data.mxd - ArcMap document for use in ArcGIS 10.3 or later
• shapefile for continents (continents.shp) and countries (world30.shp)

SSR: Surface net solar radiation maps (pdfs)

TSR: Top net solar radiation maps (pdfs and animated gif)

TTR: Top net thermal radiation (pdfs and animated gif)

( Surface net thermal radiation (STR) is in the netcdf file but no maps were created)

Creating your own maps

• use ArcGIS with the rasters in the geotiffs folders (load Radiation_data.mxd to get started)

or:

• get python 2.7, install numpyt, netCDF4 osgeo (for gdal) matplotlib and Basemap
• decide if you want to plot yearly or monthly maps, edit the corresponding python script
• change this for the variable(s) to plot:

# variables to plot
#varnames = [ "ttr", "tsr", "str", "ssr"] 
varnames = [ "ttr"] 

• change this for the medians/meridians to plot:

    # plot at median      
    #medians = range(-90,181,90) # must be -180 to +180
    medians = [0]

• pick your colormap(s), here are some suggestions

	cmaps  = [plt.cm.CMRmap]
        """
                  plt.cm.gist_ncar,
                  plt.cm.gist_rainbow,
                  plt.cm.gist_earth,
                  plt.cm.gnuplot2,
                  plt.cm.CMRmap,
                  plt.cm.cubehelix,
                  plt.cm.BuPu_r,
                  plt.cm.YlGnBu_r,
        """

• fiddle with the basemap and matplotlob plot parameters
• run the code
• maps will end up in their variable folder (e.g. TTR),
• map filenames will contain the meridian (_mrd=XXX) and the colormap (_cmap=XXX) used.
• monthly maps will contain the month as number (_month=01 to 12)
• Radiation_maps_from_ECMWF40_data_monthly.py has some provisions for creating lower res jpgs which can be used to create animated gifs

Header info for netdf file

netcdf ECMWF40_moda_Sep1957_Aug2002_SSR_STR {
dimensions:
	longitude = 144 ;
	latitude = 73 ;
	time = UNLIMITED ; // (540 currently)
variables:
	float longitude(longitude) ;
		longitude:units = "degrees_east" ;
		longitude:long_name = "longitude" ;
	float latitude(latitude) ;
		latitude:units = "degrees_north" ;
		latitude:long_name = "latitude" ;
	int time(time) ;
		time:units = "hours since 1900-01-01 00:00:0.0" ;
		time:long_name = "time" ;
		time:calendar = "gregorian" ;
	short ssr(time, latitude, longitude) ;
		ssr:scale_factor = 125.433476263867 ;
		ssr:add_offset = 4109953.28326187 ;
		ssr:_FillValue = -32767s ;
		ssr:missing_value = -32767s ;
		ssr:units = "J m**-2" ;
		ssr:long_name = "Surface net solar radiation" ;
		ssr:standard_name = "surface_net_downward_shortwave_flux" ;
	short str(time, latitude, longitude) ;
		str:scale_factor = 67.7184777135184 ;
		str:add_offset = -1765199.35923886 ;
		str:_FillValue = -32767s ;
		str:missing_value = -32767s ;
		str:units = "J m**-2" ;
		str:long_name = "Surface net thermal radiation" ;
		str:standard_name = "surface_net_upward_longwave_flux" ;

// global attributes:
		:Conventions = "CF-1.0" ;
		:history = "2015-09-03 20:49:00 GMT by grib_to_netcdf-1.13.1: grib_to_netcdf /data/data04/scratch/netcdf-atls13-a562cefde8a29a7288fa0b8b7f9413f7-aVlFGs.target -o /data/data04/scratch/netcdf-atls13-a562cefde8a29a7288fa0b8b7f9413f7-L2Furv.nc -utime" ;
}