Sven Löffler
2. June 2015 0

Big Data – The new Weather Forecast Announcer

Everyone knows it, you watch the forecast for the next few days on TV, but the announced weather does not occur as promised. Big data is increasingly used for a more detailed prognosis. More and more data is achieving more accurate assumptions. The European Centre for Medium-Range Weather Forecasts (ECMWF) announced recently that today the 7-day forecasts are approximate to the 5-day forecasts of the 90s.

The data is coming from many satellites and monitoring stations, which are distributed throughout the world and constantly provide information. However, the analysis of this data with the aim of an accurate prediction is not so easy because very fine mesh grids are needed for physical weather models. For each grid point of the grid, approximation formulas must be calculated. The finer the grid, the more the processing steps. Result: A greater computing power is required, but at the same time, the forecasts are more accurate than without the use of Big Data.

Win-win situation

The accurate analysis achieves a benefit for consumers, but also for companies. Imagine, you are a person who suffers from allergies and pollen count increases earlier than expected through the air and your medicine cabinet is empty. You may already be a little nervous that the nearest supermarket or the pharmacy next door has empty shelves because all allergy sufferers already stocked up on medicine. A scenario like this luckily belongs to the past thanks to Big Data. For example, the US grocery chain Wal-Mart increased its stock of Claritin, a medicine against allergic Rhinitis, before the rush started in order to provide it to all customers. However, the increase in inventories was only possible because Merck as the producer of the active substance knew the pollen situation ten months earlier. This was due to a large-scale big data analysis of complex weather information supplied by diverse services.

Network operators can also benefit from Big Data

Weather conditions can change within a few minutes – a problem for network operators, which focus on renewable energies such as solar or wind power. The threat: unstable power grids, even blackouts as the weather is difficult to calculate. But Big Data also creates mathematical calculation methods, and the analysis of weather data remedy here so that the stability of the network is predictable at certain periods. Thus, the network operators can have a better plan when to have their installations, for example, at times when there is no sun or no wind.

Big Data helps to position the once difficult task to assess the weather as a more and more stable factor.

Big Data Greetings
Sven Löffler

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