J64.5 Improving CPC's Weeks 3&4 Outlooks via Incorporating Extratropical Predictors and an Objective Guidance Blend

Thursday, 16 January 2020: 11:30 AM
154 (Boston Convention and Exhibition Center)
Daniel S. Harnos, NOAA, College Park, MD; and L. M. Ciasto, J. Gottschalck, M. Halpert, and M. L'Heureux

The National Oceanic and Atmospheric Association Climate Prediction Center (CPC) has been issuing temperature and precipitation outlooks for the Weeks 3 and 4 period since September 2015. Over the past four plus years, the development of these outlooks have come to rely on a core tool suite consisting of multiple dynamical model forecasts and a statistical tool leveraging the background states of the Madden-Julian Oscillation, El Niño-Southern Oscillation, and decadal trends. The resulting official temperature forecasts have proven to be consistently skillful, while precipitation performance is far more modest.

This presentation serves to explore two projects underway at CPC to improve the Weeks 3 and 4 outlooks moving forward. The first project aims to bolster existing statistical guidance by leveraging potential extratropical predictors in the form stratospheric-tropospheric coupling indices or empirical orthogonal functions of stratospheric geopotential height. Initial skill evaluation of incorporating the extratropical predictors into the statistical guidance during both a 30+ year cross-validation period and retrospective real-time runs since 2015 reveals improvement in temperature forecasts relative to the original statistical guidance, particularly during boreal autumn and winter. The second project explores the development of an objective blend of forecast tools among CPC’s available dynamical and statistical guidance to serve as a first guess for the forecaster. This work has also led to demonstrable improvement over the retrospective real-time period for both temperature and precipitation relative to existing guidance and CPC’s official outlooks.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner