Climatological data

Climatology is the branch of meteorology dealing with climate formation, the distribution of climates over the globe, the analysis of the causes of differences in climate (physical climatology), and the application of climatic data to the solution of specific design or operational problems (applied climatology). In a similar way, meteorology is concerned with the atmosphere and its phenomena, including its structure, properties, and physical processes (American Meteorological Society, 1959). The main difference between the two disciplines is that meteorology describes the day to day state of the atmosphere, and its short-term variation (from minutes to weeks), whereas climatology is a collection of statistical meteorological information that describes the variation of weather at a given place and for a specified time interval, in general 30 years (American Meteorological Society, 1959), plus statistics of weather extremes. The basic atmospheric phenomena, which climatology and meteorology are interested in, are listed in Table 3.1. They play a primary role in several critical natural processes, particularly in all the processes concerning water resource assessment and hydrological modelling. The selected variables are those that best quantify the intensity of climatic phenomena in relation to water resource assessment. The required frequency and density of meteorological observations are strongly dependent on the scale of analysis to be performed. For this reason meteorological organisations sometimes distinguish between analyses at meso-scale and macro-scale. These terms are also used in this chapter. Meso-scale refers to studies with a time resolution of minutes to a day and areas from a hundred to several thousands of square kilometers. Macro-scale refers to analysis with a time resolution of days or coarser and regions larger than 10 thousand square kilometers. In this chapter, each section starts with a description of the uncertainty code and the type of empirical uncertainty associated with the variable in question. Then, it lists the users most likely to be interested in the specific variable, followed by suggestions with regard to time/space measurement scale and to instrument precision and accuracy. Finally, every section ends with a sketch of the state of the art of variable uncertainty characterisation, followed by several literature references. A useful reference presenting all the variables and related statistical tools is Maidment (1993).