A statistical method for predictions of mean monthly precipitation in Cuba

✍ Scribed by Lino R Naranjo-Díaz; Abel Centella; Pedro Cardenas

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 502 KB
• Volume: 2
• Category: Article
• ISSN: 1350-4827
• DOI: 10.1002/met.5060020407

✦ Synopsis

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 L V Q algorithm. Three main climatic divisions were defined over the Cuban territory using 180 observation points with a nearly homogeneous geographical distribution. A JO-year (1941JO-year ( -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 E N S 0 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 L V Q algorithm provided a greater number of correct forecasts but the M L R method showed a slightly higher level of skill. The feasibility of using a L V Q algorithm for predicting mean monthly precipitation is established. However, the high skills found for the M L R method suggest that it should be used as a complementary tool in the decision-making process.