Meteorological
and Climate
Modeling
Environmental modeling: meteorological and climate data inputs

Meteorology (and its long-term integral, climate) is by far the most important driving force of all environmental and ecological processes.

Key meteorological variables include:

  • solar radiation,
  • temperatures,
  • heat flux (latent and sensible)
  • atmospheric turbulence,
  • wind speed and direction,
  • humidity, clouds and precipitation
Together they directly or indirectly control most environmental processes. Meteorology (monitoring and forecasts) as well as climate systems analysis (including climate change) must therefore be considered as shared infrastructure (inputs) for any environmental analysis, planning and management, together with geology and soils/vegetation which, however, are strongly affected by climate and weather, as well as man-made structures and activities (agriculture, urban structures, industry, transportation systems, mining, etc.)

Typical examples of these controlling relationships include:

  • Air pollution (pollutant dispersion) as a function of temperature (plume rise), wind speed and direction, turbulence;
  • Water resources and quality, which very directly depend on precipitation but also temperature (evapo-transpiration), including the important consequences such as soil moisture and groundwater recharge;
  • Vegetation as a function of temperature and humidity/precipitation which again directly (physiology) and indirectly (trophic factors based on primary production of plant material) affects ecological nichesand thus species habitats and biodiversity;
  • Desertification, depending both on vegetation (see above) and wind driven sand movement;
  • Coastal water quality, driven by ocean currents (in turn driven by wind and temperature as well as by tides) that control flow and thus pollution dispersion;
  • Environmental emergency management that depends on (largely wind-driven) dispersion of pollutants, atmospheric or aquatic/oceanographic;
  • More indirect effects are energy use patterns as a function of temperature (cooling).

Any environmental monitoring (like ambient concentrations of pollutants) needs parallel meteorological data as explanatory (driving) variables to be properly interpreted.


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