The World Weather Watch (WWW), established in 1963, is the core program of the World Meteorological Organization (WMO). It combines three systems to provide WMO members with meteorological data and forecast. First, the Global Observing System (GOS) which is tasked with ensuring the coordination of national observing facilities for the collection of meteorological and climatic data. Secondly, the Global Telecommunication System (GTS) which is defined as the coordinated global system of telecommunication guaranteeing the exchange and distribution of GOS data. Finally, the Global Data-processing and Forecasting System (GDPFS) which consists of world, regional and specialized meteorological centers that provide processed data, analysis and forecast products. The WWW is now a vital tool not only for the civil society but also for the military. We believe it became a self-powered dynamic thanks to several interdependent variables. The high complexity of the meteorology field and its interactions with the society make difficult the identification of the variables involved in this feedback loop. We therefore present a number of variables which we believe have an impact on the global meteorology machine.
The evolution of accuracy of GDPFS forecast products constitutes the touchstone variable for the successful development of the WWW. One can observe the evolution of accuracy for various forecast products for northern and southern hemispheres of one of the GDPFS meteorological centers, the European Center for Medium Weather Forecast (ECMWF). The increase of this accuracy has causal relations with several other variables. First, countries need to be member of the WMO to access meteorological data and forecast products. As a result, a better precision makes GDPFS product more attractive, so country gets involved as WMO members. Secondly, the imperfection in forecast is the source of economic losses as the meteorology field provides a service to the society on which relies many economic activities. Therefore, an improved accuracy reduces economic losses and provide the society with more funds for investment in meteorology-related infrastructures or research. One of the possible output of this investment is the number of stations on the surface of the globe collecting data. The number of meteorological land-based stations in the United States is a relevant proxy for this output and one can observe on the figure the continuous augmentation of their number during the 20th century.
An increase in the number of WMO members increases the spatial coverage of meteorological data. In the same way, an increase in the number of stations enhance data granularity. The resulting data from the combination of these improvements are thus more more distributed and cover a bigger area. The acceleration of international cooperation also plays a role in the reliability of transmission of data.
The improvement of processing capacities, approximated by the well known Moore’s Law, enables the development of continuously more complex models, capable of handling a wider spatial coverage and finer granularity of data and whose forecasts gain in precision. In the same time, the improvement of models creates the need for more data as models are not tolerant with missing values. It fosters the willingness to construct more stations on the surface of the globe to avoid missing values.
All these inter-dependencies tend to make the wheel turn in the same way and create a self powered dynamic, as the amelioration of forecast seems to lead to an even greater amelioration through complex mecanisms, some of which were presented previously.
A list of the datasets used in this visualization.