Climate Dynamics

, Volume 43, Issue 7–8, pp 1731–1752 | Cite as

Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America

  • Bradford Johnson
  • Vinay Kumar
  • T. N. Krishnamurti


This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric–ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.


Amazon Basin Rainfall Anomaly Statistical Downscaling Precipitation Forecast Anomaly Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag (outside the USA) 2013

Authors and Affiliations

  • Bradford Johnson
    • 1
  • Vinay Kumar
    • 1
  • T. N. Krishnamurti
    • 1
  1. 1.Department of Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

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