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).