Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Home » MAPP Webinar Series: The North American Multi-Model Ensemble Seasonal Prediction System: Research, Operations, and Applications

MAPP Webinar Series: The North American Multi-Model Ensemble Seasonal Prediction System: Research, Operations, and Applications


The NOAA CPO Modeling, Analysis, Predictions, and Projections (MAPP) program hosted a webinar on the topic of The North American Multi-Model Ensemble (NMME) Seasonal Prediction System: Research, Operations, and Applications on Thursday, June 2, 2016. The announcement is provided below.

Date/Time Title
June 2, 2016
11:30 AM – 1:00 PM ET
The North American Multi-Model Ensemble (NMME) Seasonal Prediction System: Research, Operations, and Applications
  Speakers and Topics: Ben Kirtman (University of Miami)
The Current Status of the NMME Project

Emily Becker (NOAA Climate Prediction Center)
The Real-time NMME Seasonal Prediction System

Gabriele Villarini (University of Iowa)
A Statistical/Dynamical Framework for Seasonal Streamflow Forecasting in an Agricultural Watershedt

Hyemi Kim (Stony Brook University)
Toward the Seasonal Prediction of Atmospheric Rivers over the Northeast Pacific Ocean and Western North America

Remote Access:   To view the slideshow:
1. Click the link below or copy and paste the link to a browser:
2. Enter your name and e-mail address, and click “Join Now”. If necessary, enter the event passcode: 20910
To hear the audio:
Utilize the on-screen dial-in instructions visible after logging into webex
Webex and the teleconference line can accommodate only 100 attendees on a first-come, first-served basis. Please try to share a connection with colleagues at your institution to preserve space for others.
Watch Webcast:

(Right click and Save Link As) .mp4


Ben Kirtman 
The North American Multi-Model Ensemble (NMME) experiment is an unprecedented effort to improve intraseasonal to interannual (ISI) operational predictions based on the leading North American climate models. As of May 2016, the NMME seasonal prediction system has completed transition to NWS operations. The objectives of the NMME project include: (i) Continued real-time forecasts and incorporating updated models; (ii) coordinated predictability research that identifies the benefit of the multi-model approach; (iii) developing and evaluating an intraseasonal protocol; and (iv) continued and enhanced data distribution to facilitate use of NMME data. This talk highlights the evolution of the project in terms of its goals, the retrospective forecast skill and some of the emerging prediction science. This talk also discusses forecast skill during the real-time phase (i.e., 2010-present) and how this compares with the historical skill assessment. 

Emily Becker
This presentation will cover the various uses of NMME monthly mean and seasonal forecast products and their contribution to Climate Prediction Center operational forecasts. Experimental, developmental, and research projects will also be briefly reviewed.

Gabriele Villarini
The state of Iowa in the agricultural U.S. Midwest is regularly affected by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for such events, we develop a statistical-dynamical prediction framework providing probabilistic seasonal streamflow forecasts ranging from low to high flows for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Statistical model fits for each discharge quantile (from seasonal minimum to maximum; predictands) are based on observed basin-averaged total seasonal precipitation and annual row crop (corn and soybean) production acreage (predictors). Using the most recently-updated relationship between predictand and predictors every year, we produce forecasts from one to ten months ahead of the given season based on annual row crop acreage from the previous year (persistence forecast) and the monthly precipitation forecasts provided by dynamical predictions from eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME). Additionally, observed precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The skill of our forecast discharge is assessed both in deterministic and probabilistic terms for all lead times, flow quantiles, and forecast seasons. Overall, the system produces relatively skillful streamflow forecasts with good prediction even at high flows and long lead times. The seasonal flow forecast accuracy is notably improved by weighting the contribution of individual GCMs in a superensemble, and by the inclusion of antecedent precipitation to characterize initial conditions.

Hyemi Kim
The atmospheric moisture transport plays a significant role in global hydrological cycle. The poleward atmospheric moisture transport is predominantly confined to Atmospheric Rivers (ARs) which form as spatially narrow plumes (500-1000 km wide) that can stretch over thousands of kilometers in the troposphere. ARs transport approximately 90 % of the water vapor from the tropics into the extratropics and often induce heavy wintertime precipitation along the west coast US. Therefore, accurate prediction of ARs on sub-seasonal to seasonal timescales (S2S) is urgent. While prediction skill of ARs beyond 10-days has not been assessed, several recent studies suggest an existence of potential predictability of ARs at S2S timescales by linking the AR activity to ENSO and MJO. To understand the cause of the year-to-year change in AR activity and moisture transport, we have undertaken research to quantify the AR characteristics (frequency, landfall latitude, intensity) and the causes of moisture transport change in different ENSO phases.  NMME hindcasts are used to examine whether the state-of-the-art climate prediction model simulate the relationship between ARs and ENSO, thus have potential to predict the seasonal AR activity.

Scroll to Top