Constructing Records of Storminess
AbstractStorms are characterized by high wind speeds; often large precipitation amounts in the form of rain, freezing rain, or snow; and thunder and lightning in the case of a thunderstorm. Many different types exist, ranging from tropical cyclones and large storms of the mid-latitudes to small polar lows, medicanes (Mediterranean tropical cyclones), thunderstorms, and tornadoes. They can lead to extreme weather events such as storm surges, flooding, high snow quantities, and bushfires. Storms often pose a threat to human lives and properties, agriculture, forestry, shipping, and offshore and onshore industries. Thus it is of great interest to gain knowledge about changes in storm frequency and intensity. Future storm predictions are important and they depend to a great extent on the evaluation of changes in wind statistics of the past. For reliable statistics, long and homogeneous time series extending over at least several decades are needed. But wind measurements are frequently influenced by changes in the synoptic station, its location or surroundings, instruments, and measurement practice. These factors deteriorate the homogeneity of wind records. Storm indices derived from sea level pressure measurements are less prone to such changes as pressure does not show very large spatial variability in contrast to wind speed. Long-term historical pressure measurements exist that enable us to deduce changes in storminess for more than the last 140 years. But storm records are not just compiled from measurement data; they may also be inferred from climate model data. The first numerical weather forecasts were performed in the 1950s. These predictions served as a basis for the development of atmospheric circulation models, which constituted the first generation of climate models or general circulation models. Soon afterward, model data were analyzed for storm events, and cyclone tracking algorithms were programmed. Climate models nowadays have reached high resolution and reliability and can be run not just for the past, but also for future emission scenarios to provide possible future changes of storm activity.