Improved one‐month lead‐time forecasting of the SPI over Russia with pressure covariates based on the SL–AV model. (26th September 2017)

• Record Type: Journal Article
• Authors: Willink, Diliara; Khan, Valentina; Donner, Reik V.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 143:Number 707(2017)
• Page Start: 2636
• Page End: 2649
• Publication Date: 2017-09-26
• Subjects: standardized precipitation index -- one‐month lead‐time forecast -- pressure covariates -- teleconnections -- SL–AV model -- statistical postprocessing; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3114
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : 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. Abstract : We address the problem of obtaining reliable long‐range precipitation forecasts using statistical post‐processing methods. Specifically, we introduce a new post‐processing scheme for improving one‐month lead‐time SPI forecasts over Russia based on regression analysis and teleconnections inferred from two different pressure fields as provided by the SL‐AV model. We demonstrate an improved accuracy of both deterministic and probabilistic forecasts in comparison with the model's pure precipitation output for boreal summer.