Building: Research Hall
Office: Room 266
Mail stop: 6C5
Phone: +1-703-993-5363
E-mail: pdirmeye
gmu.edu
p;
NOAA: NA160AR4310072
Improving Subseasonal to Seasonal Forecast Skill of North American Precipitation and Surface Air Temperature Using A Multi-Model Strategy 2016-2019
P.I.: Zhichang Guo (GMU)
Co-I: Paul A. Dirmeyer (GMU)
Program:
NOAA Modeling, Analysis and Prediction and Projection (MAPP) Program
S2S Prediction Task Force
Project Summary:
Abstract:
This research responds to the 2016 solicitation for CPO's Modeling, Analysis, Prediction and Projection (MAPP)
program Competition 2: "Research to Advance Prediction of Subseasonal to Seasonal Phenomena." The proposed project
focuses on some of MAPP's primary objectives, namely, "improving methodologies for global to regional-scale
analysis, predictions, and projections" and "developing integrated assessment and prediction capabilities
relevant to decision makers based on climate analyses, predictions, and projections."
A large number of forecasts from a suite of models are routinely provided by the Subseasonal to Seasonal (S2S)
Prediction Project and the North American Multi-Model Ensemble (NMME) Project. To develop a reliable and timely
climate product from these datasets, we propose a new methodology to assess an individual model's forecast skill,
generate statistical weights based on the skill of member model forecasts of slowly-varying surface states, and use
aforementioned weights to produce an optimized single forecast. We will compare this methodology to traditional
multi-model combination techniques. The new methodology has unique advantages: a) It provides an ideal framework
for regional analyses and prediction; b) It allows the combined atmospheric forecast to rely more on models with
superior forecast skill of surface anomalies, which are the main drivers of the S2S forecast skill; c) Calculations
of forecast skill and weights for each model are highly flexible, and the methodology has many potential
applications. The weight of each model member can be calculated from the latest evaluation of the model's forecast
performance and may evolve over time. Preliminary results show that the new methodology outperforms individual
models and can increase the one-month lead forecast skill of surface air temperature by 50% over the simple
multi-model average across much of the area of focus. Even though the forecast skill improvement of precipitation
(P) and surface air temperature (T2m) over North America is our primary target, the effects are expected to reach
all forecast variables over the globe. We propose to identify regions where there is significant forecast skill of
North American P and T2m and diagnose the dominant factors influencing such skill. We seek to understand how these
factors contribute to the forecast skill of P and T2m, especially the role of land surface processes in achieving
S2S forecast skill, through crafted numerical experiments with the Climate Forecast System (CFS). The project will
also explore the potential to improve S2S forecast skill by improving the quality of land surface initial states in
CFS and examine impacts of land initialization on S2S forecast skill.
The overall goal of the proposal is to enhance the Nation's capability to predict variability on S2S time scales.
By performing our analysis with the NMME and S2S forecast datasets and adapting it to operational settings, this
proposal directly contributes to the NOAA Next-Generation Strategic Plan objectives of "an improved scientific
understanding of the changing climate system and its impacts" and "mitigation and adaptation choices supported by
sustained, reliable, and timely climate services."
Paul Dirmeyer is co-lead of the NOAA/MAPP Subseasonal-to-Seasonal Prediction Task Force
Reports:
Publications: