Reanalysis data is the output of a variety of observational datasets that are assimilated into a numerical model that simulates the Earth system, usually run over a period of many decades. The resulting gridded multivariate reanalysis data often spans globally and extends from the surface up to as high as the mesosphere. A consistent assimilation scheme used by the model provides a consistent snapshot of the past state of the atmosphere. With the emergence of high resolution and sophisticated assimilation and modelling reanalysis datasets, a more accurate representation of the past state of the atmosphere can be obtained. These “satellite-era” reanalysis datasets all incorporate satellite observations dating back to 1979, which is when space-based remote sensing first began.
This presentation will discuss the techniques used to derive the synoptic pattern recognition maps using five “satellite-era” reanalysis datasets. The main goal of this project will be to produce new synoptic pattern recognition maps that could be used by operational forecasters to help better predict future heavy rainfall events in South Texas, not associated with tropical cyclones.