DEVELOPMENT PRIORITY AREAS

 

MISSION

To accelerate the transition of scientific advances from the climate research community to improved NOAA climate forecast products and services.

VISION

To significantly increase the accuracy, reliability, and scope of NOAA's suite of operational climate forecast products to meet the needs of a diverse user community.

 

Recent Activities

June 6, 2019   The North American Multi-Model Ensemble (NMME) forecast products have been widely used for decision making since 2011. The quality of the prediction is an important focus for service improvement. Dr. Sarah Strazzo of Climate Prediction Center conducted a study on four types of NMME seasonal forecasts (uncalibrated1, Probability Anomaly Correlation (PAC) calibrated (van den Dool et al. 2017), “Calibration, Bridging, and Merging” (CBaM) post-processed (Strazzo et al. 2018), and “Bridged”2 NMME forecasts) over the period of 2012-2018 and presented her findings in the June teleconference. Her results showed overall more hits and correct negatives than false positives and misses from contingency table of verification. The observation of more above than below normal temperatures was captured by uncalibrated, PAC calibrated, and CBaM post-processed forecasts, among which uncalibrated and PAC calibrated forecasts over-predicted above normal temperatures while CBaM post-processed and “Bridged” over-predicted below normal temperatures. Moreover, PAC calibrated precipitation forecasts achieved impressive Heidke skill scores, and NMME forecasts, particularly the post-processed versions, tended to outperform ENSO-derived forecasts in general. Her results further showed “hits” also increased as predicted probabilities increased, a good indication of “forecast opportunity” potential. In her summary, Dr. Strazzo shared with audience her promising thoughts to leverage other predictability sources, specifically to improve the prediction of winter below normal temperatures.

1) NMME includes CFSv2, CanCM3, CanCM4, GFDL-CM2.1, GFDL-FLOR, NASA-GEOS, and NCAR-RSMAS-CCSM4 participant models. Probabilities are calculated as ensemble frequencies relative to model mean terciles.

2) Uses bridged forecasts (1-month lead) as a stand-in for empirical ENSO-derived forecasts.

References

Strazzo, S., D. Collins, A. Schepen, Q. J. Wang, E. Becker, and L. Jia, 2018: Seasonal prediction of North American temperature and precipitation using the Calibration, Bridging, and Merging (CBaM) method. NWS Sci. Technol. Infusion Clim. Bull., 42nd NOAA Annu. Clim. Diagn. Predict. Workshop, Norman, OK, National Oceanic and Atmospheric Administration, 177-180, doi:10.7289/V5/CDPW-NWS-42nd-2018.

van den Dool, H., E. Becker, L.-C. Chen, and Q. Zhang, 2017: The probability anomaly correlation and calibration of probabilistic forecasts. Wea. Forecasting, 199-206.