Meteorological measurements and weather forecasts presentation for researchers and higher level industrial users

Researchers in the field of meteorology often have the task of identifying new patterns in measured or forecasted data. Once these patterns are discovered, we must present them in a way that is understandable to researchers from related fields and to industrial users who require specifically processed data.

For such purposes, merely numerical data presentation is not sufficient. We also need graphical representation. When we want to exchange results with users from other fields outside meteorology, the first choice is usually a program for graphs and spreadsheets like Excel (or other similar packages). However, for more complex data, the capabilities of these packages quickly become insufficient. In our research group, which mainly focuses on atmospheric pollution modeling, we have therefore developed a dedicated database and accompanying graphical representations that address at least some of the challenges in this area.

Many atmospheric phenomena exhibit 24-hour cycles. The source of these cycles is, of course, the influence of solar radiation, both directly and indirectly, on other meteorological variables. However, since there are no specialized programs available for graphical representation of 24-hour cycles, we developed an algorithm for statistical analysis of variables with a 24-hour cycle and an appropriate graphical representation of the results – a diagram we named "sunflower." The idea behind the algorithm and the implementation of the graphical representation will be explained in detail during the presentation.

From the graphical representation "sunflower," we developed an additional graphical representation called "weather flower" which allows for an innovative graphical display of weather forecasts based on a similar principle.

In the second part of the presentation, we will present several graphical solutions for presenting vertical profiles of wind, temperature, and relative humidity. These graphical representations are useful for both measurements and weather model forecasts. Users from the nuclear power plant also utilize these solutions, as profiles data are crucial input for air pollution dispersion models in the event of accidental atmospheric releases. In addition, they aim to understand dedicated weather forecasts with fine temporal and spatial resolution, both for dispersion prediction and general use. This is an example of good practice where research knowledge in meteorology is directly applied in industry.