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S2S Prediction Task Force news

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. 

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

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 Program efforts helped advance understanding of tropical cyclone subseasonal variability and predictability Read More »

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.

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

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.

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When noise becomes signal

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. 

When noise becomes signal Read More »

The Experts Weigh In: How To Close the Gap Between Weather and Climate Predictions

Three leaders from the weather and climate research communities share their perspective on how best to address the subseasonal to seasonal prediction challenge in a new open-access paper in Nature Partner Journals – Climate and Atmospheric Science. The authors include Annarita Mariotti, Director of the NOAA MAPP Program, as well as Paolo Ruti and Michel Rixen, who coordinate research for the World Weather Research Program (WWRP) and World Climate Research Program (WCRP), respectively.

The Experts Weigh In: How To Close the Gap Between Weather and Climate Predictions Read More »

Global rainfall pattern could offer prediction skill three weeks out

A new study says that teleconnections with certain phases of a recurring tropical rainfall pattern could extend predictions up to 20-25 days in advance. The authors’ findings provide guidance on which tropical conditions might lead to improved forecasts beyond our current capability – and more time to prepare for extreme events.

Global rainfall pattern could offer prediction skill three weeks out Read More »

NOAA and partners release database for research to bridge weather to climate forecast gap

Two new datasets, funded in part by NOAA Research’s Modeling, Analysis, Predictions, and Projections (MAPP) Program, now provide easy public access to 60 terabytes of climate forecasts containing predictions of rainfall, temperature, winds and other variables at the subseasonal level (two weeks to two months ahead).

NOAA and partners release database for research to bridge weather to climate forecast gap Read More »

NOAA Research leads to a new milestone in improving operational predictions from weeks to seasons

As an important milestone for NOAA’s ongoing efforts, researchers from universities, NOAA OAR research laboratories and the National Weather Service (NWS) recently met to discuss efforts to improve S2S predictions.

NOAA Research leads to a new milestone in improving operational predictions from weeks to seasons Read More »

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