The extrapolation of weatherfeatures in quantitative nowcasting all start with the calculation of the so called "displacement vectors". These vectors refer to the forecast of the position of a weather feature based solely upon recent past motion of that feature. In other words it assumes that the systems in the atmosphere propogate at similar speeds as seen in the past at some distance into the future. Several methods are nowadays applied in extrapolation, they all have in common however that they work best over short periods of time.
A key product derived from SEVIRI observations is the Atmospheric Motion Vector (AMV) product. It depicts the atmospheric flow and is derived from the movement of clouds and water vapor motions using primarily the visible 0.6 or 0.8 μm channel, the infrared 10.8 μm channel, and the two water vapour 6.2 and 7.3 μm channels (Holmlund, 2001). The horizontal displacements of the clouds and water vapor features is used to derive a displacement vector, while the cloud top pressure derived from SEVIRI observations of IR12.0 is used to position this vector at a certain pressure level.
Atmospheric motion vectors also constitute one of the most important product for global data assimilation schemes. An important feature for these scheme is the improved automatic quality control of the AMVs using quality indicators (Holmlund 1997). The MSG algorithm now features the following concepts:
| a) selecting a feature to track or a candidate target | |
| b) tracking the target in a time sequence of images to obtain a relative motion | |
| c) assigning a pressure height (altitude) to the vector | |
| d) assessing the quality of the vector. |
Besides the derival of AMVs from satellite also CMVs (Cloud Motion Vectors) have found their way in operational nowcasting. Following an almost identical procedure to derive as AMVs, a Cloud Motion Vector (CMV) is obtained when joining successive positions of an identifiable cloud or group of clouds between two succesive IR images.
CMVs are a key products derived from current day operational geostationary satellites and continues to be a useful source of information for nowcasting. These fields result basically from the apparent cloud motion estimated from two IR satellite images and the corresponding cloud level is inferred from observed brightness temperatures (BT) in the infrared window channel.
Cloud Motion Vectors (CMVs) are calculated by a three-step objective procedure (WMO, 2006). The initial step selects targets, the second step assigns pressure altitude, and the third step derives motion. Altitude is assigned based on a temperature/pressure derived from radiative transfer calculations in the environment of the target. Motion is derived by a pattern recognition algorithm that matches a feature within the "target area" in one image within a "search area" in the second image. For each target two winds are produced representing the motion from the first to the second, and from the second to the third image.
An objective editing scheme is then employed to perform quality control: the first guess motion, the consistency of the two winds, the precision of the cloud height assignment, and the vector fit to an analysis are all used to assign a quality flag to the "vector" (which is actually the average of the two vectors).
However, clouds grow and decay with lifetimes which are related to their size. To qualify for tracking, the tracer cloud must have a lifetime that is long with respect to the time interval of the tracking sequence. The cloud must also be large in comparison to the resolution of the satellite images. This means that a match between the spatial and temporal resolution of the image sequence is feasible.
Water vapour images are found to hold synoptic scale features longer and are best tracked at hourly intervals (a longer time interval offers better accuracy of the tracer if the feature is not changing).
It also must be recognized that cloud winds represent a limited and meteorologically biased data set. The cloud winds generally reflect measurements from only one level (that of the cloudtop) and are only from regions where the air is going up (and thus producing clouds). Even with the water vapour motions enhancing the quality of CMVs, this meteorological bias persists.
Generation and utilisation of quality indicators for satellite-derived atmospheric motion vectors. Kenneth Holmlund. 2001. University of Helsinki.; report no. 51
The Utilization of Statistical Properties of Satellite-Derived Atmospheric Motion Vectors to Derive Quality Indicators. Kenneth Holmlund. 1997. Weather and Forecasting: Vol. 13, No. 4, pp. 1093–1104.