Using Satellite-Era Reanalysis Datasets for Synoptic Pattern Recognition of South Texas Heavy Rainfall Events

Floods due to heavy rainfall, not associated with tropical cyclones, have been one of the greatest threats to lives and property in South Texas for decades.  Moisture originating from both the Gulf of Mexico and Pacific Ocean and its interaction with synoptic and mesoscale weather systems have produced many heavy rainfall events in the climatological record.  In order to accurately forecast such events, operational forecasters must use a mix of their own education and experience, numerical weather model output, anomalies, past analogs, and synoptic pattern recognition.  Synoptic pattern recognition associated with non-tropical cyclone heavy rainfall events was first introduced by Maddox in the late 1970s.  A further enhancement to synoptic pattern recognition associated with heavy rainfall events can be achieved through the use of reanalysis datasets.

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.