More Like this

1. Skillful All-Season S2S Prediction of U.S. Precipitation Using the MJO and QBO

   https://doi.org/10.1175/WAF-D-19-0232.1  

   Nardi, Kyle M.; Baggett, Cory F.; Barnes, Elizabeth A.; Maloney, Eric D.; Harnos, Daniel S.; Ciasto, Laura M. (October 2020, Weather and Forecasting)

   null (Ed.)

   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 3–6 (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 United States 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 3–6. 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 United States and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks. 

   more » « less
2. Mapping Large-Scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal Prediction

   https://doi.org/10.1175/JCLI-D-20-0502.1  

   Tseng, Kai-Chih; Johnson, Nathaniel C.; Maloney, Eric D.; Barnes, Elizabeth A.; Kapnick, Sarah B. (June 2021, Journal of Climate)

   null (Ed.)

   Abstract The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads. 

   more » « less
3. Subseasonal Prediction of Impactful California Winter Weather in a Hybrid Dynamical‐Statistical Framework

   https://doi.org/10.1029/2023GL105360  

   Guirguis, Kristen; Gershunov, Alexander; Hatchett, Benjamin_J; DeFlorio, Michael_J; Subramanian, Aneesh_C; Clemesha, Rachel; Delle_Monache, Luca; Ralph, F_Martin (November 2023, Geophysical Research Letters)

   Abstract Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. 

   more » « less
4. Subseasonal prediction of springtime Pacific–North American transport using upper-level wind forecasts

   https://doi.org/10.5194/wcd-2-433-2021  

   Albers, John R.; Butler, Amy H.; Breeden, Melissa L.; Langford, Andrew O.; Kiladis, George N. (January 2021, Weather and Climate Dynamics)

   Abstract. Forecasts of Pacific jet variability are used to predictstratosphere-to-troposphere transport (STT) and tropical-to-extratropicalmoisture export (TME) during boreal spring over the Pacific–North Americanregion. A retrospective analysis first documents the regionality of STT andTME for different Pacific jet patterns. Using these results as a guide,Pacific jet hindcasts, based on zonal-wind forecasts from the European Centrefor Medium-Range Weather Forecasting Integrated Forecasting System, areutilized to test whether STT and TME over specific geographic regions may bepredictable for subseasonal forecast leads (3–6 weeks ahead of time). Largeanomalies in STT to the mid-troposphere over the North Pacific, TME to thewest coast of the United States, and TME over Japan are found to have the bestpotential for subseasonal predictability using upper-level wind forecasts. STTto the planetary boundary layer over the intermountain west of the UnitedStates is also potentially predictable for subseasonal leads but likely onlyin the context of shifts in the probability of extreme events. While STT andTME forecasts match verifications quite well in terms of spatial structure andanomaly sign, the number of anomalous transport days is underestimatedcompared to observations. The underestimation of the number of anomaloustransport days exhibits a strong seasonal cycle, which becomes steadily worseas spring progresses into summer. 

   more » « less
5. Deep learning for bias correction of MJO prediction

   https://doi.org/10.1038/s41467-021-23406-3  

   Kim, H.; Ham, Y. G.; Joo, Y. S.; Son, S. W. (December 2021, Nature Communications)

   Abstract Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent. 

   more » « less