Historical and Current Data

STATION DATA

Station data capture local conditions best as they are in situ (“in position”) measurements of meteorological variables. Station data, or information

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Weather Station

based on such data, can be obtained from the National Meteorological Services (NMS) but is not always free of charge. In addition, in most of African countries spatial (and often temporal) coverage of such data may not be sufficient to meaningfully capture local details important for adaptation projects. In a lot of countries a systematic drop in the number of operating and reporting stations has been observed since the 1980s (Figure 3.1). The quality of the data may also vary and quality checks are always necessary.  Additional data may have been collected by businesses, farmers’ associations, agricultural extension services and academic institutions but may not be integrated in the main database. A subset of station data is available on line through the GTS system but the completeness of that data and their spatial and temporal coverage are usually less extensive than the data archived at the NMS.

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Figure 3.1 Number of rainfall stations operating in Mali since early 20th Century. Source: Samake (AGRHYMET)

SATELLITE DATA

Since the early 1980s satellites have been recording some meteorological variables at high spatial and temporal resolution and these archives are now long enough for climatic analyses. Satellite-based estimates of rainfall and temperature can be accessed online in different data repositories and have continuous spatial coverage in the form of a grid of values. They are usually global in nature. However, those data rely on indirect measurements (proxies) of rainfall quantities and temperature, and thus can  present a biased vision of the conditions on the ground. For example, satellite rainfall estimates are based on the temperature at the top of the clouds, which is then correlated to temperature and rainfall records from stations on the ground. In the tropics, this relationship is strongly affected by the availability of in-situ data, while in the extratropics estimates are closer to reality.
Rainfall and temperature estimated from satellite recordings are available from the following datasets:

  • CPC Morphing Technique (CMORPH) – a high spatial and temporal resolution dataset in real time. Only available since 2002.
  • Tropical Rainfall Measurement Mission (TRMM) focuses on rainfall estimates in the Tropics and is available since 1998. There is however a delay of about a month in data availability and monthly aggregates are more accurate than higher temporal resolution products.
  • Land Surface Temperature (LST) provides land-surface estimates for Africa and Latin America and is available since July 2002 for Africa.

In addition to meteorological variables, satellites also capture useful indicators such as vegetation cover with the Global Normalized Difference Vegetation Index (NDVI), available for 1981-2006 period and the TERRA-MODIS NDVI and Enhanced Vegetation Index (EVI) since 2000. More recently, soil moisture data have also become available.  When using satellite rainfall data it is important to remember that precipitation estimates have different validity in different regions, and thus need to be validated with in-situ observations for local applications.

GRIDDED DATA

Another class of data providing continuous spatial coverage are so called ‘gridded data.’ From a user’s perspective they are similar to satellite data in that data are available on a regular grid and for a given period of time. Most of these datasets are based on in-situ (station) information interpolated to a regular grid. Some datasets merge in-situ observations with satellite records to compensate for biases in satellite data. The quality of these merged products depends on the quantity of the in-situ data incorporated. Gridded datasets are usually global in nature and their spatial resolution is variable. Among the gridded datasets are:

Another class of gridded data is called ‘reanalysis’ data, which uses observations from sources at the surface and  higher in the atmosphere (planes, radio soundings, etc.) to interpolate meteorological variables using physical models,  ensuring stronger consistency between variables and values. It provides a wider array of meteorological variables, including atmospheric measurements above the surface.  Reanalysis products are usually high-resolution and date further back in time than other data types. However, this method relies strongly on dynamical models and some variable types are more reliable than others; for example, wind and pressure are fairly accurately captured through reanalysis while rainfall data are often biased. It is most useful for researchers studying the causes of climatic phenomena rather than for direct applications in adaptation.

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