Environmental processes are driven by weather, land, and water variables and their interactions that change continuously in space and time. A complete process description considers both spatio-temporal dependencies and associations between those variables. Describing the dependencies is challenging because natural phenomena are often observed at a discrete set of locations and times. This research focuses on weather data provided by ECMWF reanalysis that are being used increasingly for those process descriptions. Major dilemmas locally are that observations are sparse, and the use of ECMWF reanalysis data is prone to uncertainty because of the coarse spatial resolution and systematic bias. The complete study of dependencies will also lead to an increase in the number of the involved variables. To address these problems, this research demonstrates the potentials of copulas. It uses two datasets: daily mean air temperature collected from weather stations in the Qazvin Plain, Iran, and daily air temperature and precipitation retrieved from weather stations in the Netherlands. The European Centre for Medium-range Weather Forecasts (ECMWF) provided gridded weather data.
|Date made available||14 Feb 2019|
|Date of data production||31 Oct 2018|