Analysis of the Polar Jet with the EUMETSAT Geostationary Atmospheric Motion Vectors Climate Data Record
EUMETSAT owns a unique archive covering 36 years of meteorological geostationary satellite data, going back to the beginning of the 1980s. These data are processed at EUMETSAT to derive Climate Data Records (CDRs) of geophysical parameters. One of these is the Atmospheric Motion Vectors (AMV) product. EUMETSAT has a long history in retrieving atmospheric winds, and has developed a very robust algorithm for near real time generation of AMVs from Meteosat Second Generation (MSG) imagery, which was recently extended to the first generation of Meteosat (MFG). The latest geostationary AMV CDR consists of almost four decades (1981-2017) of consistently retrieved wind data. AMVs are generated at EUMETSAT mainly for assimilation in numerical weather prediction models at the European Centre for Medium-Range Weather Forecasts (ECMWF) and at the Japan Meteorological Agency (JMA) to produce climate reanalysis.
Time Series of AMVs can also be exploited to follow the temporal evolution of climate variation patterns such as the North Atlantic Oscillation (NAO). The NAO is a large-scale atmospheric pressure see-saw steering the direction, location and strength of the polar Jet Streams (JS), which represent the outer edge of the polar vortex. The NAO and the JS directly influence the weather evolution in US and Europe, particularly in wintertime. The jet streams are geostrophic winds; they only depend on the pressure gradient and the Coriolis force. They flow from west to east at a speed greater than 30 m/s and are located in the upper level of the troposphere, between 100 and 400 hPa. A positive NAO is linked with stronger winds and moving the JS northward. A negative NAO is generally responsible for weaker JS and for pushing them southward.
The objective of this talk is to present an application derived from the geostationary AMV CDR. It is a first study of the northern hemisphere JS over the 36 years long satellite data record. The presentation will focus on analysing the JS spatial distribution and strength in order to detect potential seasonal trends over the decades. Finally, we will show seasonal comparison of all upper level winds against model data.
