RT Journal Article
T1 Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases
A1 Mínguez Solana, Roberto
A1 Reguero, B. C.
A1 Luceño, A.
A1 Méndez, F.J.
AB ThThe development of numerical wave prediction models for hindcast applications allows a detailed descriptionof wave climate in locations where long-term instrumental records are not available. Wave hindcastdatabases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offeringimportant advantages for the statistical characterization of wave climate all over the globe (continuous timeseries, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series).However, WHDBs present several deficiencies reported in the literature. One of these deficiencies isrelated to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The mainreasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficientspatiotemporal resolution used. These difficulties make the data related to these events appear as ‘‘outliers’’when compared with instrumental records. These bad data distort results from calibration and/or correctiontechniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data arepresented, and their automatic outlier identification capabilities are analyzed and compared. All the methodsare applied to a global wave hindcast database and results are compared with existing hurricane and buoydatabases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean.
PB American Meteorological Society
SN 0739-0572
YR 2012
FD 2012-02-01
LK https://hdl.handle.net/10016/34922
UL https://hdl.handle.net/10016/34922
LA eng
DS e-Archivo
RD 1 sept. 2024