We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach.
- Authors
Yang, Jaemo; Astitha, Marina; Delle Monache, Luca; Alessandrini, Stefano
- Abstract
This study presents a new implementation of the analog ensemble method (AnEn) to improve the prediction of wind speed for 146 storms that have impacted the northeast United States in the period 2005–16. The AnEn approach builds an ensemble by using a set of past observations that correspond to the best analogs of numerical weather prediction (NWP). Unlike previous studies, dual-predictor combinations are used to generate AnEn members, which include wind speed, wind direction, and 2-m temperature, simulated by two state-of-the-science atmospheric models [the Weather Research and Forecasting (WRF) Model and the Regional Atmospheric Modeling System–Integrated Community Limited Area Modeling System (RAMS–ICLAMS)]. Bias correction is also applied to each analog to gain additional benefits in predicting wind speed. Both AnEn and the bias-corrected analog ensemble (BCAnEn) are tested with a weighting strategy, which optimizes the predictor combination with root-mean-square error (RMSE) minimization. A leave-one-out cross validation is implemented, that is, each storm is predicted using the remaining 145 as the training dataset, with modeled and observed values over 80 stations in the northeast United States. The results show improvements of 9%–42% and 1%–29% with respect to original WRF and ICLAMS simulations, as measured by the RMSE of individual storms. Moreover, for two high-impact tropical storms (Irene and Sandy), BCAnEn significantly reduces the error of raw prediction (average RMSE reduction of 22% for Irene and 26% for Sandy). The AnEn and BCAnEn techniques demonstrate their potential to combine different NWP models to improve storm wind speed prediction, compared to the use of a single NWP.
- Subjects
UNITED States; STORM winds; WIND speed; NUMERICAL weather forecasting; METEOROLOGICAL research; BIAS correction (Topology)
- Publication
Monthly Weather Review, 2018, Vol 146, Issue 12, p4057
- ISSN
0027-0644
- Publication type
Article
- DOI
10.1175/MWR-D-17-0198.1