Dynamic Stochastic Model of Atmospheric Drought Forecast in Uzbekistan
M. L. Arushanov, G. Sh. Eshmuratova
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).
