2017 IBS Workshop

This page provides material for participants of the Workshop offered on January 9 2017 to the International Biogeography Society (IBS) Conference participants.  It also provides links to much of the material presented during the workshop.  (Post-workshop note:  many items were not covered adequately during the workshop, this material will eventually be expanded and re-organized here.  Please stand by!)




  •     Review WorldClim and its problems
  •     Introduce available satellite products
  •     Discuss MODIS, IR and TRMM climatologies and their limitations
  •     Provide overview of global and regional climate model products and their use
  •     Discuss development of new products for biogeography community using satellite- and data assimilation products.


  • Why Worldclim?  (nothing much available before it on global scale and gridded resolution).  GHCN observations.
  • Where does it work well?  (Temp values, flat, good data regions – but Amazonia? Derived quantities may be reasonable even if original observations have systematic errors)
  • Where does it fail?  (data-poor regions, irregular topography). See comments by Hijmans.
  • How to improve it?  (end of workshop discussion).  CHELSA is one example – incorporate winds from global reanalyses.


PRISMS  (a simple explanation and history of this technique is here)  Procedure does poorly if station density is low compared with major topographic features.

DAYMET See here for 2012 annual precipitation over North America.  You judge the “issues”.

CHIRPS  An infrared (satellite-based) precipitation climatology – quasi-real time.

CHELSA  Importance of including topography and orientation of mean winds (coastal California winter storm example and here and a loop is here of a later part of the event)


GEOSTATIONARY (INCLUDING GOES-R AND HIMIWARA)  GOES-R has a better imager and more frequent scanning than current GOES satellites.

GOES-East and West, Meteosat and IO, China and Japan geostationary satellites.  Good site for full-resolution imagery for past 10 days is here.  Many other websites for weather-related activities.


Operational satellites

AVHRR (NOAA series, METOP etc)  Primarily for weather forecasting activities.

Research satellites

TRMM (non-synchronous, radar)  15-yr record  and GPM.  TRMM averages can be seen here.

MODIS (~15 years ongoing) and more details here   Good perusal of the daily data here. We have used sectors cut for near real-time use as these are also archived.  Where MODIS should be most helpful (oceanic islands and coasts immersed in stratus).


Satellite data is remotely-sensed data.  To add value and improve its usefulness it needs to be converted to quantities that are of user-interest.

What do biogeographers need?

Better observations.  If your results depend importantly on the analysis scheme you use then your observations are not good enough!

Precipitation (amount, frequency, intensity),

radiation reaching surface of earth

surface temperature, humidity and wind (why?)  E= f(T, RH and V)

temperature (daily maximum, diurnal cycle, daily minimum and means and extremes of these quantities)

What does WorldClim lack?

Daily maxima and minima, mean winds, humidity, solar radiation.

Daily data difficult to obtain globally – many met services do not provide it.

Humidity data not readily available, though evaporation estimates are available

Wind data is difficult to interpret and extrapolate to representative scales.

Solar radiation not measured very densely compared with precip or temperature.

Global and regional model data assimilation products

Biogeography community needs require products of data assimilation systems.  Such quantities include:

  • global wind fields
  • fine-scale windfields in vicinity of topography
  • cloud base information on global scale (?)
  • ???

Global models and their products

  • Model characteristics and limitations See here and here for some examples.
  • Products of biogeographical applicability
  • availability of data

Regional models and their use

  • Model characteristics and downscaling
  • scale considerations – can you resolve what you need?
  • data availability


Is a community needed to develop a consensus product for the biogeography community?  (Is a Black-box product like WorldClim best for the user community?)  Or are investigator-driven one-by-one products better?


Identifying Worldclim problems (observation distribution, unrepresentative data, interpolation problems)

MODIS cloud detection issues (examples of cirrus, stratus etc)

MODIS cloud detection procedures (comparison of both procedures,

TRMM Data (examples of large- and small-scale features)

Cloudiness from MODIS imagery (examples of individual days and inferring processes)

Diurnal effects (GOES examples to illustrate diurnal processes)

Compare a Terra and Aqua image of Luzon on the same day.  How do the clouds change in ~3 hr?  Where do they increase?  Where do they decrease?

Compare the change in cloudiness over and around Lake Maracaibo.  Why is this important?  Terra and Aqua

Compare these two Terra and Aqua images for the same day.  What is the implication of this?

Comparing different data assimilation products (how similar are different global products?  How about mesoscale fields?  Value near topography)

Develop a consensus about where MODIS validation is most critical.  Consider the provided  global mean cloudiness provided by Wilson and Jetz and a basic source.

Links relevant to Workshop material


MODIS-related:  Douglas et al 2016, Wilson and Jetz, 2016 and website

GOES-IR climatology: CHIRPS


Surface historical data:  WeatherUnderground

MODIS QUICK-LOOK: https://earthdata.nasa.gov/labs/worldview/

Satellite data and imagery:  CIMMS, U of Wiconsin, Monterey US Navy, RAMSDIS, EUMETSAT,

General weather-related products:  Dupage College

Climate forecasts: Climate Prediction Center (NOAA)

Climate data: NCDC, Climate.gov