Jan Walter Schroeder1496095200
Jan Walter Schroeder
Energy providers are very dependent on accurate weather forecasting. Mid-term trends can be estimated quite accurately using prediction models and allow good scheduling (e.g. in case of expected high temperatures and the associated use of airconditioning units and power consumption). A major problem are peak loads which have not been predicted. They force energy providers to procure capacity from other sources whereby the cost has a quasi-exponential function with time. In other words, the earlier such peak load can be known, the lower the cost of procuring energy from third sources to compensate this peak load. A typical example is the prediction of clouds around 17.00, when a considerable number of people come daily back from work. At certain periods in time during the year heavy clouds can lead to rather simultaneous switching on of lights, which causes a considerable peak load.
Power companies need to provide the right amount of electricity each day, each hour. This means they have to predict power consumption. Any miss-prediction
means they have to buy more electricity on the spot market at high prices, or sell surplus electricity at low prices. The overall cost they incur here is called “variance charge”. There is therefore considerable interest from energy providers to have more accurate prediction models on weather conditions, with a very high granularity such as hourly updated predictions.
94% of weather forecasting relevant data comes from satellites. However, it is still highly limited due to the small number of satellites. Weather is a classic application where a large number of satellites have a sustained competitive advantage in describing the starting condition of the atmosphere more accurately than fewer satellites. The better the starting conditions are known, the better the forecast. Especially GNSS-RO is a highly promising modality of earth observation which can - when created in large quantities - dramatically increase the accuracy of weather forecasts.