Dataset associated with "Skillful all-season S2S prediction of U.S. precipitation using the MJO and QBO"
Date
2019
Authors
Nardi, Kyle M.
Baggett, Cory F.
Barnes, Elizabeth A.
Maloney, Eric D.
Harnos, Daniel S.
Ciasto, Laura M.
Journal Title
Journal ISSN
Volume Title
Abstract
Although useful at short and medium-ranges, current dynamical models provide little additional skill for precipitation forecasts beyond Week 2 (14 days). However, recent studies have demonstrated that downstream forcing by the Madden-Julian oscillation (MJO) and quasi-biennial oscillation (QBO) influences subseasonal variability, and predictability, of sensible weather across North America. Building on prior studies evaluating the influence of the MJO and QBO on the subseasonal prediction of North American weather, we apply an empirical model that uses the MJO and QBO as predictors to forecast anomalous (i.e., categorical above or below-normal) pentadal precipitation at Weeks 3 through 6 (15-42 days). A novel aspect of our study is the application and evaluation of the model for subseasonal prediction of precipitation across the entire contiguous U.S. and Alaska during all seasons. In almost all regions and seasons, the model provides "skillful forecasts of opportunity" for 20-50% of all forecasts valid Weeks 3 through 6. We also find that this model skill is correlated with historical responses of precipitation, and related synoptic quantities, to the MJO and QBO. Finally, we show that the inclusion of the QBO as a predictor increases the frequency of skillful forecasts of opportunity over most of the contiguous U.S. and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks.
Description
This repository contains files depicting the model’s skill in each region and season for different combinations of MJO phase, QBO phase, and forecast lead time. The files also show which phase and lead combinations are "skillful forecasts of opportunity", situations in which the model is significantly more skillful than a random forecast. Together, the contents of this repository allow users to better assess the utility of the empirical model in particular regions and seasons of interest. Please refer to the README file for additional details.
Department of Atmospheric Science
Department of Atmospheric Science
Rights Access
Subject
Weather
Prediction
Precipitation
Climate
Teleconnections
Citation
Associated Publications
Nardi, K., C. Baggett, E. Barnes, E. Maloney, D. Harnos, and L. Ciasto, 2020: Skillful all-season S2S prediction of U.S. precipitation using the MJO and QBO, Wea. Forecasting, https://doi.org/10.1175/WAF-D-19-0232.1
Mundhenk, B., E. A. Barnes, E. Maloney and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden-Julian oscillation and the quasi-biennial oscillation. npj Climate and Atmospheric Science, https://doi.org/10.1038/s41612-017-0008-2
Johnson, N. C., D. C. Collins, S. B. Feldstein, M. L. L'Heureux, and E. E. Riddle, 2014: Skillful Wintertime North American Temperature Forecasts out to 4 Weeks Based on the State of ENSO and the MJO. Weather Forecast., 29, 23–38, https://doi.org/10.1175/WAF-D-13-00102.1
Mundhenk, B., E. A. Barnes, E. Maloney and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden-Julian oscillation and the quasi-biennial oscillation. npj Climate and Atmospheric Science, https://doi.org/10.1038/s41612-017-0008-2
Johnson, N. C., D. C. Collins, S. B. Feldstein, M. L. L'Heureux, and E. E. Riddle, 2014: Skillful Wintertime North American Temperature Forecasts out to 4 Weeks Based on the State of ENSO and the MJO. Weather Forecast., 29, 23–38, https://doi.org/10.1175/WAF-D-13-00102.1