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Outcomes of the S2S Prediction Task Force

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This report details the activities, projects, and outcomes from the Subseasonal-to-Seasonal Task Force. The report can be accessed here.

Mission

The OAR Climate Program Office’s Modeling, Analysis, Predictions and Projections (MAPP) Program has organized the Subseasonal to Seasonal (S2S) Prediction Task Force to advance NOAA’s and the Nation’s capability to model and predict sources of S2S predictability. The ultimate goal of this initiative is to help close the gap in prediction skill and products between traditional weather and seasonal lead times.

The core membership of the Task Force is comprised of MAPP-supported scientists from universities, research laboratories, and NOAA centers and laboratories funded through the fiscal year 2016 MAPP–National Weather Service (NWS) Science and Technology Integration (STI) grant competition "Research to Advance Prediction of Subseasonal to Seasonal Phenomena". Members of the Task Force also include MAPP-funded scientists participating in the Subseasonal Experiment, SubX, an interagency research project to test subseasonal prediction models for NWS operations selected via a MAPP-NOAA Climate Test Bed competition, as well as invited scientists from across the community with interest and expertise in the S2S prediction problem.

Through monthly teleconferences, the Task Force provides a formal mechanism for MAPP-supported PIs to share new datasets, methodologies, and results, as well as to ultimately synthesize their collective efforts through technical reports, review articles, journal special collections and engage with the rest of the community via workshops and meeting sessions. The Task Force collaborates and coordinates with ongoing national and international S2S prediction, research, and applications efforts, such as the NOAA NWS/STI Weeks 3-4: Improving Mid-range Weather Outlooks Initiative, the US Climate Variability and Predictability (CLIVAR) Predictability, Predictions, and Applications Interface Panel, and World Meteorological Organization Subseasonal to Seasonal Prediction Project.

The S2S Prediction Task Force is a three-year effort starting September 2016.

Key research questions and activities (two-page pdf); prepared March 2017

Members

➜ Relevant MAPP Program PIs and selected additional invitees.

Elizabeth Barnes (Lead), Colorado State University
Edmund Chang (Co-Lead), Stony Brook University
Paul Dirmeyer (Co-Lead), George Mason University/COLA
Andrea Lang (Co-Lead), University at Albany
Kathleen Pegion (Co-Lead), George Mason University

To view the full Participants list, please visit the Participants page.

Projects

For the abstracts of S2S and SubX projects funded from MAPP's FY16 competitions, please click here.

S2S Prediction Task Force Terms of Reference

  • The MAPP Program Management has selected one lead scientist and four co-leads for the Task Force.
  • MAPP Program management oversees Task Force activities, working with the leads.
  • All PIs supported through the MAPP FY16 S2S research competition are expected to participate in the Task Force, as described in their proposals. Otherwise, participation in the Task Force is by invitation.
  • Most of the Task Force work will be conducted remotely via telecons or virtual meetings, or through meetings of opportunity.

MAPP Task Force Concept and Terms of Reference


Highlight

bridging-weather-and-climate.cover

Image: Cover photo for AGU S2S Prediction Task Force Special Collection. Credit: Andrea Lang (U. of Albany)

An AGU special collection

titled “Bridging Weather and Climate: Subseasonal-to-Seasonal (S2S) Prediction” is being published in the Journal of Geophysical Research-Atmospheres and Geophysical Research Letters. This special collection is organized by the S2S Prediction Task Force leads and was open to the entire international S2S community until June 30, 2019. To date, the collection includes 25 published research papers.

 

News & Events

Bridging the Weather-Climate Prediction Skill Gap with a Multi-Model Experiment 18 December 2019

Bridging the Weather-Climate Prediction Skill Gap with a Multi-Model Experiment

Researchers have for years been working to tackle the subseasonal to seasonal (S2S) prediction problem to improve forecasts. The Subseasonal Experiment, SubX, is an interagency research-to-operations project designed to help tackle this problem. The results of the project were recently published in BAMS. 

MAPP Program efforts helped advance understanding of tropical cyclone subseasonal variability and predictability 2 October 2019

MAPP Program efforts helped advance understanding of tropical cyclone subseasonal variability and predictability

A new review paper describes how MAPP-funded and organized work has contributed to recent progress in understanding tropical cyclones.

MAPP’s S2S Prediction Task Force Announces AGU Special Collection on S2S Research 28 June 2019

MAPP’s S2S Prediction Task Force Announces AGU Special Collection on S2S Research

Since 2016, the Subseaonal-to-Seasonal (S2S) Task Force has been focusing efforts on bridging the skill gap between weather and seasonal prediction lead times. Publication of this special collection represents an important milestone for the group.

Bridging the Weather-to-Climate Prediction Gap 27 February 2019

Bridging the Weather-to-Climate Prediction Gap

An EOS project update on the MAPP S2S Prediction Task Force has been published. A task force of researchers set out to bridge the gap between the 2-week weather forecast and long-term climate predictions; their findings could help in forecasting the likelihood of extreme events.
When noise becomes signal 27 July 2018

When noise becomes signal

Unusual California Precipitation Over Last Two Winters Could Have Been Predicted

A new study shows that though seasonal forecasts failed to predict the unusual California preciptation during the winters of 2015-16 and 2016-17, forecasts issued a month ahead -- within the subseasonal timescale and much further ahead than a normal weather forecast -- could have accurately predicted the abnormal winter rain. 

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