Open AccessA statistical method for predictions of mean monthly precipitation in Cuba
Author(s) -
NaranjoDíaz Lino R,
Centella Abel,
Cardenas Pedro
Publication year - 1995
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.5060020407
Subject(s) - anomaly (physics) , climatology , precipitation , linear regression , statistics , variable (mathematics) , standard deviation , environmental science , regression , mathematics , meteorology , geography , geology , mathematical analysis , physics , condensed matter physics
Statistical methods for predicting the mean monthly precipitation in Cuba have been developed. Two different approaches have been used: multiple linear regression (MLR) and an adaptive scheme using a LVQ algorithm. Three main climatic divisions were defined over the Cuban territory using 180 observation points with a nearly homogeneous geographical distribution. A 50‐year (1941–1990) period was considered. Three types of variable were used as predictors: The Southern Oscillation Index and the Sea Surface Temperature anomaly in the central equatorial Pacific as the ENSO related variables, the Wolf Sunspot Index as a solar activity related variable and variables related to seasonality and persistence. The last of these was found to be the most important in producing high skill. A cross‐validation approach was used to test the prediction quality. This indicated that the LVQ algorithm provided a greater number of correct forecasts but the MLR method showed a slightly higher level of skill. The feasibility of using a LVQ algorithm for predicting mean monthly precipitation is established. However, the high skills found for the MLR method suggest that it should be used as a complementary tool in the decision‐making process.