scope:

Introduction

The totality of the meteorological parameters which completely describe the state of the atmosphere at a given location - commonly described as "weather" - can only be determined at the expense of very great effort. To be able to describe the weather pattern during a measurement campaign, e. g. for interpretation of sampling in the field of air pollution prevention or sound immission and the like, there are two options:

a) Information and measured values, which have been obtained using stationary observational networks designed for an operational period of many years and for specific tasks, can be gathered subsequently. However, this can lead to unusable results if the temporal and spatial representation of the data acquired at such stations is not sufficient or does not comply with the question prescribed by the sampling.

b) In addition to in-situ-measurements, meteorological parameters can be determined, for example, using a measuring station - according to guideline VDI 3786 Part 13 - which can be ideally adapted to the measurement programme. To keep the then still considerable economic and logistic effort to a minimum, there is the descriptive collection of visual observations and estimates, particularly in the case of programmes that employ staff; the present guideline provides the necessary specifications.

The results of visual weather observations contain individual, subjective errors and can therefore deviate considerably from each other if there are several people at the same time. In order to be able to compare the results in spite of that, i. e. to keep the subjective effect to a minimum, strict rules are necessary for observation and reporting in written form (record). In this field, the weather services have developed a comprehensive system [1]. The following sections are based on this system. Thus, the system is made available, in a highly simplified form, to a wide circle of users. The results obtained on this basis are then comparable to corresponding weather service material, e. g. to relate long-term averages or long data series, or to explore an observed weather event by using weather service data.