486 Probabilistic Prediction of Hurricane Intensity with an Analog Ensemble

Tuesday, 12 January 2016
Room 344 ( New Orleans Ernest N. Morial Convention Center)
Stefano Alessandrini, NCAR, Boulder, CO; and L. Delle Monache, C. M. Rozoff, and W. E. Lewis

The Analog Ensemble (AnEn) technique (Delle Monache et al. 2011, 2013) has been extensively tested for the probabilistic prediction of meteorological variables (e.g., wind and temperature) and renewable energy applications (both solar and power; Alessandrini et al. 2014, 2015). We will present a novel application of AnEn to tropical cyclone (TC) intensity forecasting. The AnEn technique is used here to create a naturally calibrated ensemble prediction of TC intensity from a training dataset composed of the deterministic predictions from the Hurricane Weather Research and Forecasting (HWRF) model and observed tropical storm intensity. In the AnEn, a set of analog forecasts is created by searching archived HWRF forecasts that share key features with a current forecast from the same configuration of HWRF. The meteorological variables used to identify the past forecast similar to the current one are called analog predictors. The actual 1-min Best Track maximum surface wind speed observations associated with the best analog forecasts are then used to produce an ensemble forecast.

The AnEn is developed here using HWRF reforecast data and corresponding Best Track intensity data from 2008-2013. There are 77 Atlantic TCs in this dataset. A total of 51 predictors (based on environmental and storm inner-core characteristics) were derived from all of the 2024 HWRF runs available (with 0-126 h lead-times, at 3 h increments).

The analogs are selected from a “training period” defined by HWRF reforecast data covering the period May 2008 – July 2012. This period is also used to select a subset of the best predictors and define their weights optimizing the AnEn's performance in term of Mean Absolute Error (MAE). The predictor weights are kept unchanged during the testing period (August 2012-November 2013) used to estimate the AnEn performance. To mimic real-time operations, for each forecast, the training goes from the first TC forecast in the dataset to the last one before which the current forecast is issued.

The AnEn TC intensity deterministic forecasts have been obtained as the mean of the 20 ensemble members at each lead-time. In this presentation, the improvements compared to the raw TC intensity forecast from HWRF will be shown in terms of MAE and other commonly used metrics. An in-depth analysis of important attributes of probabilistic predictions of TC intensity generated with the AnEn will also be shown. These attributes include statistical consistency, reliability, resolution, sharpness, and the spread-skill relationship. The improvements to TC intensity forecasting produced by this inexpensive technique applied to a single deterministic HWRF model, which itself has continually improved over the last decade, are quite promising given NOAA's longstanding goal to improve intensity change forecasting (e.g., Gall et al. 2013).

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