JTTI shepherds promising research into operations.

JTTI transitions latest scientific and technological advances into operations of our major stakeholder, the National Weather Service.

The Joint Technology Transfer Initiative focuses on Unified Forecast System by advancing coupled data assimilation techniques, stochastic physics, post-processing of ensembles, and verification and validation through development, testing and evaluation for regional and global models on hourly to sub-seasonal time scales. In addition, JTTI also focuses on improving water prediction capabilities, extreme and high impact weather forecasting, and communicating forecast uncertainty using social and behavioral science.

JTTI Provides the Latest Advancements for Weather Forecasts to Stakeholders

Transitioning Matured Research into Operations

The Joint Technology Transfer Initiative (JTTI) Program was first created by the U.S. Congress in 2016 to accelerate the transition of matured research from the American Weather Enterprise to NWS and later it was included in the Weather Research and Forecasting Act of 2017 (The Weather Act). The mission of the JTTI is to ensure continuous, cost effective development and transition of the latest scientific and technological advancements into NWS operations therefore WPO manages and implements the JTTI program in close collaboration with the NWS.

JTTI meets WPO’s objectives through funding matured transition projects that can transition to operations within 3-5 years. Pursuant to NOAA Administrative Order 216-105B, JTTI funded projects are required to have a research to operations transition plan. In order to achieve successful transitions, JTTI program activities are closely coordinated with NWS’ Science and Technology integration Office.

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Current Focus Areas

Data Assimilation

Advancing data assimilation of new observations and data assimilation techniques including Artificial Intelligence (AI)/Machine Learning (ML) techniques to support Unified Forecast System (UFS).

Artificial Intelligence

Developing, testing and evaluating AI/ML methods to post-process ensemble weather model data on global and regional-scale predictions out to sub-seasonal time scale.