Stream in forest

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.

Water Prediction

Improving water prediction capabilities through enhancements to National Water Model.

Extreme Events

Improving forecasts of extreme weather and high impact weather events

Social and Behavioral Sciences

Utilizing social and behavioral science to Communicate forecast uncertainty.

Project Vignettes

Successfully Implementing Funded Research into NWS Operations

Taking on Operational Challenges

Currently Funded Projects

Evaluating Alternative Risk Communication Strategies

For the Storm Prediction Center’s Convective Outlook

PI: Patrick Marsh, FY22 Internal

Accelerating Transition to JEDI Ensemble Solvers

For coupled data assimilation in the GEFS system

PI: Sergey Frolov, FY22 Internal

Hazard Services:

Modernizing Weather Prediction Center, Ocean Prediction Center, National Hurricane Center/Tropical Analysis and Forecasting Branch, and Honolulu Weather Forecast Office Workflows

PI: Nathan Hardin, FY22 Internal

Transition and Evaluation of Snow-to-Liquid Ratio Algorithms

Into the Unified Post Processor for UFS medium range and short range applications

PI: Hui-Ya Chuang, FY22 Internal

A Machine Learning Model

For Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty

PI: Mark DeMaria, Colorado State University

Transitioning HYSPLIT

To Operations

PI: Alice Cawford, FY22 Internal

The Impact of Targeted Synoptic Dropsondes

On Tropical Cyclone Forecasts in HAFS

PI: Sarah D. Ditchek, University of Miami

Deep Learning Hybrid Dynamical-statistical model

For US Precipitation Subseasonal Forecasting

PI: Hyemi Kim, FY22 External

Probabilistic Prediction of Thunderstorm Hazards

Using the NOAA Warn-on-Forecast System and Machine Learning

PI: Montgomery Flora, FY22 External

A Wind-Wave-Current Data Assimilation Scheme

For the 3D-Real Time Mesoscale Analysis

PI: Malaquías Peña, FY22 External

A Weather-Ready Nation Para Todos:

Evaluating Current Practices in Communicating Hazardous Weather Risks to Spanish Speakers

PI: Justin Reedy, FY22 External 

Advancement of Background Ensemble Covariance

At the air-sea interface toward the UFS HAFS fully coupled data assimilation

PIs: Xuguang Wang, Jun Zhang, FY22 External

We Provide Technology Transfer Support for the Weather Program Office

Contact Our Team

Chandra Kondragunta
Chandra Kondragunta, PhD

JTTI Program Manager

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Valbona Kunkel
Valbona Kunkel, PhD

JTTI Program Coordinator

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Aaron Pratt
Aaron Pratt, PhD

JTTI Deputy Program Manager

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