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Climate Test Bed

Completion of MAPP-CTB NMME Seasonal Forecast System Transition to NWS Operations

NOAA's OAR and NWS jointly announced that the North American Multi-Model Ensemble (NMME), a seasonal prediction system that combines forecasts from the leading North American climate models, had completed transition to NWS operations. NMME real-time prediction has been used as part of the NWS operational product suite. The next round of development is going to explore the system more deeply with more focused tests. The R2O research projects funded by CPO/MAPP have multiple focuses, e.g. identifying and assessing gaps in prediction skill, evaluating and developing applications, improving drought prediction and water management, detecting predictability of weather statistics, predicting tropical cyclones, evaluating sudden stratospheric warming and North American Monsoon predictability, predicting atmospheric rivers, forecasting risk of seasonal temperature extremes, making week-2 to week-4 excessive heat outlooks etc.

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“The NMME system exemplifies the long-standing partnership between NCEP and CPO towards the mutual goal of transferring cutting-edge OAR-sponsored research into NWS operations.”

Dr. William Lapenta, Director of NWS National Centers for Environmental Prediction (NCEP)

“The NMME is accelerating improvements in drought prediction in support of the National Integrated Drought Information System, can be applied to the prediction of other types of extreme events on seasonal timescales (such as heat and cold waves and tropical cyclones), and is a concrete step towards the development of a National Earth System Prediction Capability.”

Dr. Wayne Higgins, Director of OAR Climate Program Office (CPO)

NOAA CTB Seminar

The CBaM Seasonal Climate Forecast Post-processing Method: Development and Applications for Water Resources Management in Australia

June 2, 2016 Drs. Q. J. Wang and Andrew Schepen of CSIRO Land and Water, Australia were invited to give a CTB seminar on CBaM for post-processing seasonal climate forecasts. CBaM stands for calibration, bridging and merging. Calibration uses a Bayesian joint probability (BJP) model to post-process a target variable directly. Bridging produces additional forecasts of a target variable by using BJP models to link the GCM’s SSTs to the target variable. Merging combines the two approaches using Bayesian model averaging.  Extensive evaluations show that calibration is an essential step to improve skill and reliability of forecasts. Bridging can augment skill in many regions and seasons. It can be used for combining multiple GCMs for both large scale and catchment scale applications. Having in-depth discussions with forecasters in audience, potential benefits to short-term climate forecast in operation were explored.

(More information: Abstract,  (More information: Abstract,  Presentation pptx) Presentation pptx)

Reference

Wang, Q. J., A. Schepen, and D. E. Robertson, 2012: Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging. J. Climate, 25, 5524-5537.