Orographic gap features near coastlines can concentrate regional winds, resulting in increased wind speeds that can affect the local climate. When these focused jets occur at sea level, they can additionally produce localized cold-water upwelling events that can be of interest to research, commercial and military users. The DISCOVER team are developing automated algorithms to detect gap wind and ocean upwelling events at gaps and other jet-producing locations globally, using Cross-Calibrated, Multi-Platform (CCMP) ocean surface wind product and Optimally Interpolated Sea Surface Temperature (OISST) product. The team has utilized analysis techniques such as hierarchical thresholding and region growing, resulting in automated capabilities to identify and catalog mountain gap jet wind events and SST upwelling events at known coastal locations worldwide. This algorithm is being used to process historical data with the goal of generating a climatology of past and current identified events. The initial study has included locations in Central America, Somalia and the Mediterranean, but the reusable techniques will be applied to other locations around the world. A web application has been prototyped to provide users with tools to investigate and retrieve the gap wind and SST upwelling results.
Tropical Storm Cold Wakes
As tropical storms strengthen they often result in ocean turbulence that mixes the top warmer layers with underlying cooler layers, bringing the colder water to the surface, and leaving cold water anomalies in the wake of the moving storm. SST measurements commonly show a wake, 2 – 5°C cooler than the pre-storm waters behind the right-hand side of hurricanes. These cold wake anomalies can be important to researchers as indications of the strength of the storms, as well as potential predictor of storm intensity. The DISCOVER team is working on automated algorithms that will detect these resulting anomalies in historical and new data, and preserve them for researchers to utilize for further studies.
RFI (Radio Frequency Interference)
Radio frequency interference (RFI) is a form of electromagnetic interference that in general disturbs other electrical circuits resulting, in the case of remote sensing signals, in a degradation or loss of data. This type of interference is increasingly a problem for both passive and active Earth remote sensing. The interference can emanate from natural or manmade sources. In either case the effect can be observations that are not reliable. Our efforts are to develop an automated way to identify RFI occurrences at known locations and possibly look for new events at previously unknown locations around the globe. A database of these events will be constructed to provide researchers with information about possible times and locations of likely unreliable data observations.
Posters and Presentations