Abstract
The article considers a predictive model of atmospheric dryness with a one-month lead time, developed at the Scientific Research Hydrometeorological Institute. The model is based on a dynamic stochastic approach to constructing a regression predictive equation. From the set of existing methods for constructing regression equations, a method based on the characteristic roots (eigenvalues) of a correlation matrix, including a column of the predicted variable and predictor columns (extended matrix) is applied. The predicted variable is the standardized drought index SPI, and the predictors are the average monthly precipitation for the 3 months preceding the forecast month, the average monthly value of variations in solar activity (Wolf numbers) and the average monthly value of the Southern Oscillation index for the month preceding the forecast. The predictors were selected on the basis of mutual correlation and applied time series analyses between the aridity index SPI and the indicated heliogeophysical values. The performed estimates of the studied dependence of the aridity index SPI on the state of solar activity, the influence of El Niño (La Niña) and precipitation preceding the forecast date showed their high correlation. Estimates of the accuracy of the SPI forecast with a monthly advance lead time for the territory of Uzbekistan, performed on an independent sample, were quite high, which was the basis for the introduction of this model into the operational work of the hydrometeorological service of Uzbekistan (Uzhydromet).
Published Version
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