92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012: 5:00 PM
The TIGGE Model Validation Portal: An Improvement in Data Interoperability
Room 348/349 (New Orleans Convention Center )
Thomas A. Cram, NCAR, Boulder, CO; and D. Schuster, H. Wilcox, and S. Worley

The THORPEX Interactive Grand Global Ensemble (TIGGE), a major component of the World Weather Research Programme, was created to help foster and accelerate the accuracy of 1-day to 2-week high-impact weather forecasts for the benefit of humanity. A key element of this effort is the ability of weather researchers to perform model forecast validation, a statistical procedure by which observational data is used to evaluate how well a numerical model forecast performs as a function of forecast time and model fields.

The current methods available for obtaining model forecast verification data can be time-consuming. For example, a user may need to obtain observational, in-situ, and model forecast data from multiple providers and sources in order to carry out the verification process. In most cases, the user is required to download a set of data covering a larger domain and over a longer period of time than is necessary for the user's research. The data preparation challenge is exacerbated if the requested data sets are provided in inconsistent formats, requiring the user to convert the multiple datasets into a preferred common data format.

The TIGGE model validation portal, a new product developed for the NCAR Research Data Archive (RDA), strives to solve this data interoperability problem by bringing together and providing observational, model forecast, and in-situ data into a single data package, and in a common data format. Developed to help augment TIGGE research and facilitate researchers' ability to validate TIGGE model forecasts, the portal allows users to submit a delayed-mode data request for the observational and model parameters of their choosing. Additionally, users have the option of requesting a temporal and spatial subset from the global dataset to fit their research needs. This convenience saves both time and storage resources, and allows users to focus their efforts on model verification and research.

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