7.2 Probabilistic Forecasting: From Scientific Understanding to Societal Use

Wednesday, 13 January 2016: 10:45 AM
Room 333-334 ( New Orleans Ernest N. Morial Convention Center)
Zoltan Toth, NOAA, Boulder, CO; and R. Krzysztofowicz, M. S. Antolik, M. Pena, and B. J. Etherton

Keeping to the “Science to Service” theme of the 2016 Annual Meeting, this presentation will review (a) the scientific discoveries pertaining to uncertainties in Earth System forecasts as well as how the users of forecasts can best act under such uncertainties, and (b) based on the physical and social science discoveries what operational practices can provide the most valuable service to society.

The socioeconomic realization of the full value of environmental predictions requires a significant change or expansion in operational services from the past “single value” to the future “probabilistic” forecast paradigm. This will involve changes across the end-to-end forecast process that introduce new or additional requirements, metrics, formats, and methods for the generation, dissemination, and use of forecast information.

In particular, some toolsets critical to maximizing socioeconomic value related to the statistical calibration of numerical model forecast output, the derivation of additional user variables, and the storing, human manipulation (editing), and retrieval of probabilistic information will be discussed. At the core of such a wide array of tools will be a 6-dimensional (6D) forecast datacube, an expansion of the current NWS National Digital Forecast Database (a “6D-NDFD”), capturing realistic forecast scenarios (e.g., 1D across ensemble member forecasts) for each geographical location (3D), lead time (1D), and environmental variable (1D).

The challenges associated with this paradigm shift offer fertile grounds for collaborative work across the weather enterprise. Some cutting edge probabilistic forecast tools developed in academia are being transitioned to public sector operations that in partnership with the private sector will provide expanded probabilistic forecast services for their wide range of government, public, and private sector user bases.

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