Analog-Based Methods

FY2018 Accomplishments

Figure 1. Atlantic Basin Skill Assessment for the period 27 JUL – 31 OCT 2018. AnEn is compared in terms of Brier Skill Score with other operational models showing the overall best performances for 0-24 h and 0-48 h time increments.
Figure 1. Atlantic Basin Skill Assessment for the period 27 JUL – 31 OCT 2018. AnEn is compared in terms of Brier Skill Score with other operational models showing the overall best performances for 0-24 h and 0-48 h time increments. 

The Analog Ensemble (AnEn)

The analog ensemble has been applied with success to the following applications:

  • Generating 0-240 hour ahead wind and solar power probabilistic forecast for a site in Kuwait.
  • The prediction of tropical cyclones rapid intensification, structure, and track on both East Pacific and Atlantic basins on the new dataset including storms from 2018. In Figure 1, a comparison between AnEn and other predictions systems is presented for the period 27 JUL – 31 OCT 2018 in terms of Brier Skill Score.
  • The bias correction technique for rare events has been included in the official version of the code distributed through the Github platform. In Figure 2, a comparison between AnEn and AnEn with the bias correction is presented in terms of bias as a function of the predicted wind speed.

FY2019 PLANS

Figure 2. AnEn (analog ensemble in its original formulation) and AnEnBc (Analog Ensemble with the bias correction algorithm) ensemble mean bias as a function of the wind speed from Global Environmental Multiscale (GEM) model averaged over equally populated bins. 1-year training period and the shorter 9-month training period are used for both AnEn (analog ensemble in its original formulation) and AnEnBc. The error bars indicate the 95% bootstrap confidence intervals.
Figure 2. AnEn (analog ensemble in its original formulation) and AnEnBc (Analog Ensemble with the bias correction algorithm) ensemble mean bias as a function of the wind speed from Global Environmental Multiscale (GEM) model averaged over equally populated bins. 1-year training period and the shorter 9-month training period are used for both AnEn (analog ensemble in its original formulation) and AnEnBc. The error bars indicate the 95% bootstrap confidence intervals.

n FY19 the potential of the analog ensemble technique will be further explored for several applications: 1) Improving WRF-CHEM operational air quality predictions over New Delhi (India). 2) Consolidating real-time operational forecasting of tropical cyclones rapid intensification. 3) Exploring the use of AnEn to post-process predictions from Neural Networks systems for tropical cyclone intensity predictions.

REFERENCES

Alessandrini, S., Delle Monache, L., Rozoff, C.M. and Lewis, W.E., 2018. Probabilistic Prediction of Tropical Cyclone Intensity with an Analog Ensemble. Monthly Weather Review, 146(6), pp.1723-1744

Sperati, S., Alessandrini, S. and Delle Monache, L., 2017. Gridded probabilistic weather forecasts with an analog ensemble. Quarterly Journal of the Royal Meteorological Society, 143(708), pp.2874-2885.