The database can store these kinds of data:

  • Daily observed data and daily data measured by automatic stations
  • Observation of meteorological phenomena
  • Monthly data (which has not been calculated from daily data)
  • Upper air data (ascent data)
  • Long term rainfall gauge measurements (rainfall accumulations)
  • Normals (which have not been calculated from daily data)
  • One minute precipitation totals (rainfall intensity)
  • Long term extreme values
  • Interpolation of missing values
  • Inventory of missing data

Stored, Measured and Derived Data

  • Daily observed data – the data are observed by observers or are measured by automatic stations during the day. These are stored into the daily data table with the information on the day and specific time. An example is the daily measurement of temperature done by an observer three times a day at 0700, 1400 and 2100 or the temperature measured by the automatic thermometer each 15 minutes. These are the basis for the derived daily, monthly, yearly or long term characteristics. The data can be keyed manually or can be imported directly from data files into the database.
  • Daily aggregated values – are daily minima, maxima, sums or averages calculated via a defined calculation formula. For example, the calculation of average daily temperature or daily precipitation total.
  • Meteorological phenomena – are observed by observers over a certain period of the day, for example, a thunderstorm, rain or dew. The observation can contain additional information, e.g. its intensity or progress. For key entry and for displaying of the phenomena the special pictorial symbols are used. These symbols have been adopted from the WHO Cloud Atlas. The meteorological phenomena are post processed so that occurrence day counts of each phenomenon are calculated for month and year. The data are keyed manually or they can be imported from data files.
  • Upper air data (ascent data) – the data measured by meteorological balloons. The measurement is carried out at certain levels. The levels are specified by means of the height, the pressure or both. If only one of the pressure or height is available the other is automatically calculated. At the same time the standard pressure levels of 1000, 950, 850, 700, 500, 400 300, 250, 200, 150, 100, 70, 50, 30 a 10 hPa are calculated. The other measured values (wind speed and direction, temperature etc.) for the standard pressure levels are interpolated.
  • Storage precipitation gauge measurements (accumulations) – the system can store data which are measured for more than one day by means of storage precipitation gauges. This data can be apportioned to daily precipitation values by means of the correlation relationship between the storage gauge and surrounding (regular) daily stations.
  • Intensity of rainfall (One minute precipitation) – one minute precipitation can be stored into the database. This data is measured automatically in Czech Republic, thus it is imported directly from data files. This is subsequently used for computation of the intensity rainfall chart; here the cumulative and moving sums can be used.
  • Monthly data – these monthly values are calculated from daily data. The maximum, minimum, sum and the average is calculated not only for the month but also for the year. Optionally you can calculate the values for shorter 1-3. decade or 1-6. pentade periods. Besides these values the number of days satisfying certain condition can be calculated (e.g. number of days with temperature above 25 °C). For meteorological phenomena the number of days for which the phenomenon occurs is calculated (e.g. number of days with rainfall). All are automatically calculated by system.
  • Normal data – normal values calculated from the monthly data. This data represents long term month or year normals; again calculated automatically by the system.
  • Extreme values – derived automatically from daily data, these represent long term maximum, minimum or average. The date of the occurrence of the extreme is stored for the maximum and minimum value.
  • Inventory – the system calculates the inventory of missing data. For each month the number of missing values is stored.
data_aggregated.png
  Example of aggragated data stored in database.
data_daily.png
  Example of daily data stored in database.
data_extremes.png
  Example of extreme data stored in database.
data_inventory.png
   In red colour the missed data are displayed.
data_monthly.png
  Example of monthly data stored in database.
data_normals.png
  Example of normals stored in database.
data_one_min_precipitation.png
  Example of one minute precipitation data stored in database.
data_phenomena.png
  Example of meteorological phenomena stored in database.
data_upper_air.png
  Example of upper air data stored in database.