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Welcome to the Meteorological Development Laboratory (MDL)! MDL conducts and sponsors applied research and development for the improvement of diagnostic and prognostic weather information; data depiction and utilization; warning and forecast product preparation; and data and forecast evaluation. Projects involve modern physical, dynamical, and statistical techniques implemented on operational computers. Emphasis is placed on integrated suites of guidance and final products for Weather Forecast Offices, River Forecast Centers, and National Centers.

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  • National Blend of Models

    Spatially-continuous forecast products calculated using a statistical blend of error-weighted data from deterministic and ensemble global model outputs and observations.


    Forecast guidance products developed by relating NWP model data to observations using statistical methods. MOS provides general guidance for stations and gridded data; LAMP provides hourly aviation station-based forecast products.

  • NDFD

    National Digital Forecast Database: a national seamless mosaic of gridded observable forecast products (max temp, cloud cover, etc.) produced by local forecast offices and integrated by NWS and NCEP.


    Development of models and forecast products to help predict and warn about storm surges and other coastal flooding events.


    Development of systems to collect and assess the quality of a variety of NWS public and aviation forecasts, including forecasts from the National Digital Forecast Database.


    Development of products to better communicate and evaluate weather, water, and climate events with the general public as well as applications for data-sharing and model-validating interactions.

  • NOAA VLab

    NOAA Virtual Laboratory: a set of services and IT framework which enables collaboration, software development, applied research, and project management.

  • IRIS/Impacts Catalog

    A centralized database of known societal impacts of climate, water, and weather-dependent events and interactive system to create user-specific warnings based on location and user-input weather events.