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Improved one‐month lead‐time forecasting of the SPI over Russia with pressure covariates based on the SL–AV model
Author(s) -
Willink Diliara,
Khan Valentina,
Donner Reik V.
Publication year - 2017
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3114
Subject(s) - hindcast , geopotential height , hydrometeorology , geopotential , lead time , climatology , meteorology , forecast skill , probabilistic logic , lead (geology) , precipitation , environmental science , predictability , covariate , index (typography) , econometrics , mathematics , statistics , computer science , geography , geology , engineering , operations management , geomorphology , world wide web
The standardized precipitation index (SPI) is an important yet easy‐to‐calculate means to describing wet or dry conditions in very different climates. In this work, we develop a new scheme for improved one‐month lead‐time forecasts of this index over Russia. As a basic seasonal forecasting model, we utilize the semi‐implicit semi‐Lagrangian vorticity‐divergence (SL–AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences. Based on hindcast simulations of this model, we demonstrate its relatively poor skills in obtaining direct one‐month lead‐time SPI forecasts in the region of interest. In order to improve the accuracy of these forecasts, we use mean sea‐level pressure and 500 hPa geopotential height fields from model output of the same SL–AV hindcasts to identify informative predictors for the local SPI values, based on the observation that the cross‐correlation structure between the three different fields reveals relevant interdependencies between precipitation, mean sea‐level pressure and 500 hPa geopotential height in different regions. Using this information in terms of regression models for obtaining both, deterministic and probabilistic forecasts provides a significant improvement of the SPI forecast skills, pointing to the potential for implementing the proposed scheme in operational one‐month lead‐time precipitation forecasts.