Distributed Information Services for Climate and Ocean Products and Visualizations for Earth Research
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Mining Solutions
 for Climatologies

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NASA Contact: Michael Goodman
Site Curator: Michael Goodman

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Mining Solutions for Climatologies

The DISCOVER team is working on the construction of climatologies for a number of features  that would typically be reflected as anomalies in remote sensing data.  We are currently working on SST upwelling resulting from mountain gap wind events.  We are also beginning to investigate tropical storm cold wakes and radio frequency interference.  Our efforts involve the development of algorithms to automate the identification of features in the DISCOVER data sets.  The resulting climatologies of these analyses will be preserved at the Global Hydrology Resource Center (GHRC) and made publicly available to researchers and other users, such as commercial and military interests. Science expertise on the interpretation of the wind and sea surface temperatures and development of the event detection algorithm is being provided by DISCOVER team members at Remote Sensing Systems in Santa Rosa, CA, and the University of Alabama in Huntsville. Once the algorithms are verified, a climatology of the features is being created with associated tools and services to allow users to access and view the information.  From there we are putting in place daily processing of ongoing data to identify new features and services, allowing users to subscribe for notification of events in their areas of interest.  We hope these services will be useful and we appreciate feedback and suggestions from users.  More information on these features types are provided below, as well as links to ongoing efforts in these areas.

 

  • SST
    Upwelling
  • Tropical Storm
    Cold Wakes
  • RFI (Radio Frequency
    Interference)
  • Posters and
    Presentations

SST Upwelling

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.

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